Co-Prime Sampling and Cross-Correlation Estimation
|
|
- Lawrence Goodman
- 5 years ago
- Views:
Transcription
1 Twenty Fourth National Conference on Communications (NCC) Co-Prime Sampling and Estimation Usham V. Dias and Seshan Srirangarajan Department of Electrical Engineering Bharti School of Telecommunication Technology and Management Indian Institute of Technology Delhi, New Delhi, India Abstract Research in the field of co-prime arrays and samplers has been mainly focused on reconstructing the autocorrelation and the spectral content of a signal at the rate from sub- data. This has found applications in power spectrum estimation, beamforming, direction-of-arrival estimation, and system identification. However, the use of coprime samplers for cross-correlation estimation has not received much attention. We describe cross-correlation estimation using co-prime samplers and consider two scenarios. In the first, both signals are acquired using co-prime samplers, while in the second scenario we assume one of the signals to be a known signal and thus available at the rate, and the second signal is acquired using a co-prime sampler. We determine the number of contributors available for cross-correlation estimation at each difference value as this is a key parameter in determining the estimation accuracy. The work presented in this paper will have applications in time-delay, range, velocity, acceleration, and crossspectrum estimation, which require cross-correlation estimation. I. INTRODUCTION The co-prime array and its variants were introduced as efficient structures to estimate the second order statistics at the rate from sub- data [] [3]. Most of the work in this field has focused on autocorrelation estimation with applications to power spectrum estimation [], [5], system identification [], beamforming [7], and direction-of-arrival estimation []. However, application of these structures for cross-correlation and cross-spectral estimation has not received much attention. Cross-correlation estimation is of importance in many applications including radars for time-delay, velocity and acceleration estimation, and underwater monitoring. Underwater acoustic sensor networks (UASN) play an important role in communication and data collection in applications such as tsunami warning, environmental pollution and oil spill monitoring, as well as underwater surveillance and assisted navigation. In these applications it is vital to estimate the number of active nodes required to guarantee proper network operation, also known as cardinality estimation. In the underwater environment the nodes tend to drift from their equally spaced geometry and hence the work in [9] analyzes the effect of unequal sensor separation on cardinality estimation using a cross-correlation based method for sensorin-line (SL) geometry. Chirp signals are a popular class of signals that have been used in automotive radar ranging [], multifunctional communication and radar systems [], measurement of sea ice thickness [], and multiple-input multiple-output (MIMO) radars [3]. The work in [] describes a practical approach for Usham Dias is supported through the Visvesvaraya PhD Scheme fellowship from the Ministry of Electronics and Inf. Tech. (MEITY), Govt. of India. implementing a receiver that can handle high linear chirp rates and a time-of-arrival (ToA) estimator that can detect and measure stationary radio frequency pulses and linear chirp rates of up to. GHz in ns. [5] describes a cross-spectral singular value decomposition (SVD) method for time-delay estimation of a chirp signal and compares it with the correlation method. Time-delay estimation of wideband signals using the cross-correlation method is analyzed and a generalized maximum-likelihood estimate based on the main peak and spill over cells of the cross correlation is presented in []. It relates the estimation accuracy to the sampling period, signal-to-noise ratio, and the number of samples in each observation interval. Motion estimation of targets using a stepped frequency chirp signal and an extended cross-correlation method to estimate the radial velocity and acceleration by using echoes of the sub-pulses within a burst is presented in [7]. The difference set for co-prime arrays and samplers was studied in []. The co-prime array-based acquisition models can also be employed for applications that rely on the cross-correlation between signals. We consider this for two scenarios. The first is referred to as fully sub- with both signals being acquired using co-prime samplers. The second scenario is referred to as partially sub- with one co-prime sampled signal and the other being a rate signal. The partial sub- scenario is encountered in applications where a pilot or known template signal is employed. Consider for example the estimation of range and velocity by transmitting predefined pulses and listening to the echoes. Since a known signal is transmitted we can generate this rate signal at the receiver. The remainder of this paper is organized as follows. In Section II we describe the fully sub- cross-correlation estimation. Section III analyzes the partial sub- scenario and a comparison of the two schemes is presented in Section IV. Co-prime based cross power spectral density is discussed in Section V while applications of cross-correlation estimation to time-delay, range, velocity, and acceleration estimation, are considered in Section VI. Section VII concludes the paper. II. FULLY SUB-NYQUIST CO-PRIME CROSS-CORRELATION In this section we analyze the cross-correlation estimation process for signals that are independently acquired using sub- co-prime samplers operating at rates M and N times the rate. Let and represent the two signals whose crosscorrelation needs to be estimated. In the first case, we assume and are of duration or length equal to one co //$3. IEEE
2 Twenty Fourth National Conference on Communications (NCC) MN (a) Case MN (b) Case 3 3MN (c) Case 3 Fig.. Signal for fully sub- and partial sub- scenarios..... MN.... (a) Case and Case MN (b) Case 3 Fig.. Signal for the fully sub- scenario. prime period, i.e. MNT s where T s is the sampling period. In the second case, is assumed to have a duration equal to one co-prime period, while is a multiple of the co-prime period. Finally, we describe the case where both signals have lengths that are an integer multiple (greater than one) of the co-prime period. We refer to these as Cases,, and 3, respectively, and (M, N) = (, 3) for all the examples and illustrations in this paper. A. Case Consider the signals shown in Fig. (a) and Fig. (a), where each signal has a duration equal to the co-prime period and is sampled using co-prime samplers with (M, N) = (, 3). Let i denote the sample index which lies in the range [, MN ]. When two signals of the same length are sampled at the rate, the cross-correlation length will be twice the signal length minus one and the number of samples that contribute to the estimate at each difference value (popularly known as lag) has a maximum value equal to the signal length at difference value zero and decreases linearly for both positive and negative difference values. The co-prime sampled signals do not exhibit such uniform behavior. Cross-correlation operation for the co-prime sampled signals is described in Fig. 3 where represents the number of samples that contribute to the estimate at difference value l. is also known as the weight function and is shown in Fig. (a) for the co-prime and traditional sampling schemes. A larger value of can improve the estimation accuracy. = for l {,, 7, 7,, } for the co-prime scheme and thus the cross-correlation cannot be estimated for these difference values. The weights for the sub- cross-correlation estimation equal the weights for the autocorrelation estimation, presented as Proposition-III in [], which also contains missing values. For real signals, the autocorrelation estimate at difference value l is equal to the estimate at difference value l. However, this does not hold for the cross-correlation estimation since the cross-correlation function is not symmetric. In Fig. (a), the normalized cross-correlation estimate for Case is compared with the scheme estimate. The root mean square error (RMSE) between the two crosscorrelation estimates without normalization is.33. It may also be noted that the co-prime based cross-correlation has a maximum at l =, while the framework estimates it at l =, i.e. an erroneous delay of two samples. B. Case In practice two signals to be correlated may not have the same length. For example, an incoming radar echo signal is typically a long signal in which a short length transmitted pulse is to be detected. Consider the signals shown in Fig. (b) and Fig. (a) which are sampled using co-prime samplers. Signal is of length MN and has length equal to the co-prime period MN. The number of contributors for the cross-correlation estimation at each difference value is shown in Fig. (b) along with the weight function. A comparison between the and sub- normalized cross-correlation estimates is also shown in Fig. (b) and both schemes result in maximum correlation at difference value l =. In this case, the sub- technique accurately estimates the time delay and this is mainly due to the fact that at the true cross-correlation peak (l = ) the co-prime scheme has a large number of contributors. Thus the weight function is an important parameter in the cross-correlation estimation. The overall RMSE compared to the scheme is C. Case 3 Now consider the case where both signals have lengths that are greater than the co-prime period. The co-prime sampled signals and are shown in Fig. (c) and Fig. (b), where is of length 3MN while is of length M N. The weight function in this case for cross-correlation estimation is shown in Fig. (c) for the sub- and schemes. The RMSE for cross-correlation estimation is 5.37 and the location of maximum correlation is l = for both schemes.
3 Twenty Fourth National Conference on Communications (NCC) = l= = l= = l= = l= = l= = l= = l= = l= =3 l= = l= = l= = l= = l= = l= =3 l= = l= = l= = l= = l= = l= = l= = l= = l= Fig. 3. Cross-correlation process for the fully sub- scenario with and of length equal to one co-prime period (a) Case (b) Case.... (c) Case 3 RMSE= RMSE= RMSE= Fig.. Contributors per difference value or weight function (left) and crosscorrelation estimate (right) for the fully sub- scenario. III. PARTIAL SUB-NYQUIST CROSS-CORRELATION In many applications where cross-correlation estimation is required, one of the signals is predefined and the system does not have to acquire it via sampling. Therefore, only the incoming signal is sampled using the co-prime scheme, denoted by (Fig. ), while the rate signal (or pattern) is known (Fig. 7). A classic example of this is a radar system that tracks a target by estimating the timedelay profile. The transmitted pulse could be a sinusoid or a mixture of frequencies similar to a chirp signal, which can be synthetically generated at the rate. A. Case The co-prime sampled signal is shown in Fig. (a), while the known rate signal is shown in Fig. 7(a). In this case we consider signals having the same length equal to the co-prime period. The cross-correlation process between these two signals is described in Fig. 5. The weight function and cross-correlation estimates are shown in Fig. (a). Here the number of contributors at each difference value is higher compared to the fully sub- scenario resulting in cross-correlation estimation with higher accuracy (RMSE of.3 as compared to.33 for the fully sub- Case ). In addition, the maximum correlation is correctly detected at l =, while this detection was erroneous in the fully sub- scenario. B. Case Now consider signal of length MN as shown in Fig. 7(a), while is acquired over two co-prime periods MN and is shown in Fig. (b). The weight function and the cross-correlation estimates are shown in Fig. (b). The crosscorrelation peak occurs at l = which matches the result in the case, and the RMSE is.7 as compared to 3.33 for the fully sub- scenario. C. Case 3 Next we consider the case where both the acquired signal and the synthetically generated rate signal
4 Twenty Fourth National Conference on Communications (NCC) = l= = l= = l= = l= =3 l= =3 l= = l= = l= =5 l= = l= = l= = l= =5 l= =5 l= =5 l= = l= =3 l= =3 l= = l= = l= = l= = l= = l= Fig. 5. Cross-correlation process for the partial sub- scenario with and of length equal to one co-prime period (a) Case (b) Case.... (c) Case 3 RMSE= RMSE= RMSE= Fig.. Contributors per difference value or weight function (left) and crosscorrelation estimate (right) for the partial sub- scenario. have lengths that are an integer multiple of the coprime period MN. is shown in Fig. (c) with length 3MN and with length MN is shown in Fig. 7(b). The weight function and the cross-correlation estimates are shown in Fig. (c). The RMSE is 3.5 which is significantly lower.... MN.... (a) Case and Case MN (b) Case 3 Fig. 7. Signal for the partial sub- scenario. than the RMSE of 5.37 obtained when both the signals were acquired using co-prime samplers. The cross-correlation peak occurs at l = matching the result from the case. IV. COMPARISON BETWEEN SUB-NYQUIST AND NYQUIST SCENARIOS In this section we compare the proposed sub- schemes for cross-correlation estimation with the traditional scheme. We begin with a set of definitions to aid our discussion. The cross-correlation of signals and
5 Twenty Fourth National Conference on Communications (NCC) is defined as: y cc (l) = ( i) = i= (i + l) () where represents the convolution operation. Set containing the possible difference values for cross-correlation estimation when and have lengths equal to r A MN and r B MN, respectively, can be defined as: R l = {l l [ r B MN +, r A MN ]} () The maximum cross-correlation value (m cc ) and the corresponding difference value (l cc ) are defined as: m cc = max l R l y cc (l) and l cc = {l y cc (l) = m cc } (3) The mean square error of the cross-correlation estimation using the sub- and schemes is computed as: RMSE = (y cc (s) (l) y cc (n) (l)) R l () l R l where y (n) cc (l) and y (s) cc (l) represent the cross-correlation estimate for the and sub- schemes respectively, and R l represents the cardinality of the set R l. Table I compares the and the proposed sub- schemes. The fully sub- scenario results in a relatively higher RMSE but is able to estimate the cross-correlation peak location correctly except in Case. RMSE increases from Case through Case 3 since the number of contributors for estimation do not increase proportionately to the increase in the number of difference values in R l. For the partial sub- scenario, a lower RMSE is observed and the crosscorrelation peak location l cc is estimated correctly in all the cases. The peak value of the cross-correlation estimate m cc is generally not of significance in applications that are trying to estimate the time-delay. However, it could be of significance in applications where different signals or patterns have to be classified. V. CROSS POWER SPECTRAL DENSITY ESTIMATION Cross power spectral density is the Fourier transform of the cross-correlation function and is expected to have spectral peaks at frequencies present in the cross-correlation signal. A relatively large peak will occur at frequencies that are common to both signals. Consider signals (t) and (t), where (t) has frequencies of 5 Hz and Hz, and (t) contains Hz and 35 Hz. Both signals are corrupted by additive white Gaussian noise with signal-to-noise ratio (SNR) of db. Consider these signals of s duration sampled at a rate of f s = Hz. The cross power spectral density is estimated using the cpsd function in Matlab for the scheme. A Bartlett window of size 5 with an overlap of samples is used for estimation with -point FFT. We employ a pair of co-prime samplers with M = and N = 3 to sample both (t) and (t), i.e. the fully sub- scenario. Next, we consider the partial sub- scenario where (t) is sampled using the co-prime sampler while (t) is sampled at the rate. The simulation results are shown in Fig.. The peak at Hz shows the ability of the proposed scheme to detect the frequencies common to both the signals. It may be noted that the results are based on a single realization of the signals (t) and (t), and the Power (db) Power (db) Frequency (Hz) (a) Fully sub- scenario Frequency (Hz) (b) Partial sub- scenario. Fig.. Cross power spectral density estimation. fully sub- scenario does not result in a strong peak at Hz which needs further investigation. VI. TIME-DELAY, RANGE, VELOCITY, AND ACCELERATION ESTIMATION Consider an underwater sonar system that is used for tracking targets by transmitting chirp signal pulses with frequencies in the range [.5MHz,.5MHz] and a pulse width of µs. The echoes received are sampled at a rate f s = MHz, which is then compared with the template transmit pulse. For the sub- co-prime sampling scheme, we use (M, N) = (, 3). The difference value or lag at which the cross-correlation peak occurs gives the time-delay estimate. This delay depends on the distance or the range of the target or object. We consider an object moving away from the transmitter at a constant velocity along the axis of the transmitted signal. The received echo is assumed to be corrupted by additive white Gaussian noise with an SNR of db. The velocity and acceleration estimate is obtained by computing the change in the range and velocity estimates, respectively. Fig. 9 compares the fully sub- and partial sub- schemes with the traditional framework. Since the object is assumed to be moving away from the transmitter at a constant velocity, the time-delay and range profiles are linear. The velocity is constant and the acceleration is zero. Both the fully sub- and partial sub- schemes are found to work well. The partial sub- estimates the acceleration with a higher accuracy. In these simulations chirp pulses were transmitted and their received echo indices are denoted along the x-axis. VII. CONCLUSION The cross-correlation estimation procedure using sub- co-prime samplers is presented under two scenarios. In the first, both signals are sampled using co-prime samplers which is referred to as the fully sub- scenario. This relates to situations where both signals have to be physically acquired. The second scenario is referred to as the partial sub- scenario in which one signal is available at the rate while the other is acquired using a co-prime sampler. This
6 Twenty Fourth National Conference on Communications (NCC) TABLE I COMPARISON OF THE PROPOSED SUB-NYQUIST SCHEMES WITH THE TRADITIONAL NYQUIST SCHEME Fully sub- Partial sub- m cc l cc RMSE m cc l cc RMSE m cc l cc Case Case Case Time-Delay (sec) Range (m) Velocity (m/sec)57.5 (a) Fully sub- scenario. Acceleration (m/sec ) Time-Delay (sec) Range (m) Velocity (m/sec) 57.5 (b) Partial sub- scenario. Fig. 9. Application of cross-correlation for time-delay, range, velocity, and acceleration estimation. Acceleration (m/sec ) occurs in applications where a predefined signal is transmitted and thus can be generated at the rate at the receiver for cross-correlation estimation. We also describe how the signal can be acquired over multiple co-prime periods, and its effect on the number of contributors for estimation and hence the estimation accuracy. We present some results for cross power spectral estimation to detect common frequencies between signals. Finally, we demonstrate the application of co-prime samplers for time-delay, range, velocity, and acceleration estimation using the fully sub- and partial sub- schemes. REFERENCES [] P. P. Vaidyanathan and P. Pal, Sparse sensing with co-prime samplers and arrays, IEEE Trans. Signal Process., vol. 59, no., pp , Feb.. [] Q. Si, Y. D. Zhang, and M. G. Amin, Generalized coprime array configurations, in IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM),, pp [3], Generalized coprime array configurations for direction-of-arrival estimation, IEEE Trans. Signal Process., vol. 3, no., pp , Mar. 5. [] S. Ren, Z. Zeng, C. Guo, and X. Sun, Wideband spectrum sensing based on coprime sampling, in nd Int. Conf. Telecommunications (ICT), 5, pp [5] P. Pal and P. P. Vaidyanathan, Soft-thresholding for spectrum sensing with coprime samplers, in IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM),, pp [] P. P. Vaidyanathan and P. Pal, System identification with sparse coprime sensing, IEEE Signal Processing Letters, vol. 7, no., pp. 3, Oct.. [7] Y. Gu, C. Zhou, N. A. Goodman, W. Z. Song, and Z. Shi, Coprime array adaptive beamforming based on compressive sensing virtual array signal, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar., pp [] Y. D. Zhang, M. G. Amin, and B. Himed, Sparsity-based doa estimation using co-prime arrays, in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 3, pp [9] B. K. Dash, S. A. H. Chowdhury, A. H. M. M. Kamal, M. S. Anower, and A. Halder, Underwater network cardinality estimation using crosscorrelation: Effect of unequal sensor spacing, in International Workshop on Computational Intelligence (IWCI), Dec., pp.. [] M. Kronauge and H. Rohling, New chirp sequence radar waveform, IEEE Transactions on Aerospace and Electronic Systems, vol. 5, no., pp. 7 77, Oct.. [] G. N. Saddik, R. S. Singh, and E. R. Brown, Ultra-wideband multifunctional communications/radar system, IEEE Transactions on Microwave Theory and Techniques, vol. 55, no. 7, pp. 3 37, Jul. 7. [] P. Kanagaratnam, T. Markus, V. Lytle, B. Heavey, P. Jansen, G. Prescott, and S. P. Gogineni, Ultrawideband radar measurements of thickness of snow over sea ice, IEEE Transactions on Geoscience and Remote Sensing, vol. 5, no. 9, pp. 75 7, Sep. 7. [3] W. Q. Wang, Large time-bandwidth product mimo radar waveform design based on chirp rate diversity, IEEE Sensors Journal, vol. 5, no., pp. 7 3, Feb. 5. [] S. Benson, C. i. H. Chen, D. M. Lin, and L. L. Liou, Digital linear chirp receiver for high chirp rates with high resolution time-of-arrival and time-of-departure estimation, IEEE Transactions on Aerospace and Electronic Systems, vol. 5, no. 3, pp. 5, Jun.. [5] X. Yu, Y. Shi, and Y. Zhang, A cross-spectral svd method for chirp time delay estimation, in IEEE International Symposium on Communications and Information Technology (ISCIT), vol., Oct. 5, pp [] Y. Bar-Shalom, F. Palimieri, A. Kumar, and H. M. Shertukde, Analysis of wide-band cross correlation for time-delay estimation, IEEE Transactions on Signal Processing, vol., no., pp. 35, Jan [7] W. Zhai and Y. Zhang, Motion parameters estimation of high-speed moving target for stepped frequency chirp signal, in International Radar Conference, Oct., pp. 5. [] U. V. Dias and S. Srirangarajan, Co-prime arrays and difference set analysis, in 5th European Signal Processing Conference (EUSIPCO), 7, pp
Waveform Multiplexing using Chirp Rate Diversity for Chirp-Sequence based MIMO Radar Systems
Waveform Multiplexing using Chirp Rate Diversity for Chirp-Sequence based MIMO Radar Systems Fabian Roos, Nils Appenrodt, Jürgen Dickmann, and Christian Waldschmidt c 218 IEEE. Personal use of this material
More informationUnderwater communication implementation with OFDM
Indian Journal of Geo-Marine Sciences Vol. 44(2), February 2015, pp. 259-266 Underwater communication implementation with OFDM K. Chithra*, N. Sireesha, C. Thangavel, V. Gowthaman, S. Sathya Narayanan,
More informationA New Data Conjugate ICI Self Cancellation for OFDM System
A New Data Conjugate ICI Self Cancellation for OFDM System Abhijeet Bishnu Anjana Jain Anurag Shrivastava Department of Electronics and Telecommunication SGSITS Indore-452003 India abhijeet.bishnu87@gmail.com
More informationTime Delay Estimation: Applications and Algorithms
Time Delay Estimation: Applications and Algorithms Hing Cheung So http://www.ee.cityu.edu.hk/~hcso Department of Electronic Engineering City University of Hong Kong H. C. So Page 1 Outline Introduction
More informationMulti-Doppler Resolution Automotive Radar
217 2th European Signal Processing Conference (EUSIPCO) Multi-Doppler Resolution Automotive Radar Oded Bialer and Sammy Kolpinizki General Motors - Advanced Technical Center Israel Abstract Automotive
More informationInterference of Chirp Sequence Radars by OFDM Radars at 77 GHz
Interference of Chirp Sequence Radars by OFDM Radars at 77 GHz Christina Knill, Jonathan Bechter, and Christian Waldschmidt 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must
More informationAnalysis and Improvements of Linear Multi-user user MIMO Precoding Techniques
1 Analysis and Improvements of Linear Multi-user user MIMO Precoding Techniques Bin Song and Martin Haardt Outline 2 Multi-user user MIMO System (main topic in phase I and phase II) critical problem Downlink
More informationCarrier Frequency Offset Estimation in WCDMA Systems Using a Modified FFT-Based Algorithm
Carrier Frequency Offset Estimation in WCDMA Systems Using a Modified FFT-Based Algorithm Seare H. Rezenom and Anthony D. Broadhurst, Member, IEEE Abstract-- Wideband Code Division Multiple Access (WCDMA)
More informationarxiv: v1 [cs.sd] 4 Dec 2018
LOCALIZATION AND TRACKING OF AN ACOUSTIC SOURCE USING A DIAGONAL UNLOADING BEAMFORMING AND A KALMAN FILTER Daniele Salvati, Carlo Drioli, Gian Luca Foresti Department of Mathematics, Computer Science and
More informationStudy on Imaging Algorithm for Stepped-frequency Chirp Train waveform Wang Liang, Shang Chaoxuan, He Qiang, Han Zhuangzhi, Ren Hongwei
Applied Mechanics and Materials Online: 3-8-8 ISSN: 66-748, Vols. 347-35, pp -5 doi:.48/www.scientific.net/amm.347-35. 3 Trans Tech Publications, Switzerland Study on Imaging Algorithm for Stepped-frequency
More informationSIGNAL MODEL AND PARAMETER ESTIMATION FOR COLOCATED MIMO RADAR
SIGNAL MODEL AND PARAMETER ESTIMATION FOR COLOCATED MIMO RADAR Moein Ahmadi*, Kamal Mohamed-pour K.N. Toosi University of Technology, Iran.*moein@ee.kntu.ac.ir, kmpour@kntu.ac.ir Keywords: Multiple-input
More informationMultiple Sound Sources Localization Using Energetic Analysis Method
VOL.3, NO.4, DECEMBER 1 Multiple Sound Sources Localization Using Energetic Analysis Method Hasan Khaddour, Jiří Schimmel Department of Telecommunications FEEC, Brno University of Technology Purkyňova
More informationSOURCE LOCALIZATION USING TIME DIFFERENCE OF ARRIVAL WITHIN A SPARSE REPRESENTATION FRAMEWORK
SOURCE LOCALIZATION USING TIME DIFFERENCE OF ARRIVAL WITHIN A SPARSE REPRESENTATION FRAMEWORK Ciprian R. Comsa *, Alexander M. Haimovich *, Stuart Schwartz, York Dobyns, and Jason A. Dabin * CWCSPR Lab,
More informationAn Improved DBF Processor with a Large Receiving Antenna for Echoes Separation in Spaceborne SAR
Progress In Electromagnetics Research C, Vol. 67, 49 57, 216 An Improved DBF Processor a Large Receiving Antenna for Echoes Separation in Spaceborne SAR Hongbo Mo 1, *,WeiXu 2, and Zhimin Zeng 1 Abstract
More informationS PG Course in Radio Communications. Orthogonal Frequency Division Multiplexing Yu, Chia-Hao. Yu, Chia-Hao 7.2.
S-72.4210 PG Course in Radio Communications Orthogonal Frequency Division Multiplexing Yu, Chia-Hao chyu@cc.hut.fi 7.2.2006 Outline OFDM History OFDM Applications OFDM Principles Spectral shaping Synchronization
More informationGeneral MIMO Framework for Multipath Exploitation in Through-the-Wall Radar Imaging
General MIMO Framework for Multipath Exploitation in Through-the-Wall Radar Imaging Michael Leigsnering, Technische Universität Darmstadt Fauzia Ahmad, Villanova University Moeness G. Amin, Villanova University
More informationAdaptive Multi-Coset Sampler
Adaptive Multi-Coset Sampler Samba TRAORÉ, Babar AZIZ and Daniel LE GUENNEC IETR - SCEE/SUPELEC, Rennes campus, Avenue de la Boulaie, 35576 Cesson - Sevigné, France samba.traore@supelec.fr The 4th Workshop
More informationEnhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis
Enhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis Mohini Avatade & S.L. Sahare Electronics & Telecommunication Department, Cummins
More informationAnalyzing Pulse Position Modulation Time Hopping UWB in IEEE UWB Channel
Analyzing Pulse Position Modulation Time Hopping UWB in IEEE UWB Channel Vikas Goyal 1, B.S. Dhaliwal 2 1 Dept. of Electronics & Communication Engineering, Guru Kashi University, Talwandi Sabo, Bathinda,
More informationImplementation of OFDM Modulated Digital Communication Using Software Defined Radio Unit For Radar Applications
Volume 118 No. 18 2018, 4009-4018 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Implementation of OFDM Modulated Digital Communication Using Software
More informationLow Power LFM Pulse Compression RADAR with Sidelobe suppression
Low Power LFM Pulse Compression RADAR with Sidelobe suppression M. Archana 1, M. Gnana priya 2 PG Student [DECS], Dept. of ECE, Gokula Krishna College of Engineering, Sullurpeta, Andhra Pradesh, India
More informationPassive Emitter Geolocation using Agent-based Data Fusion of AOA, TDOA and FDOA Measurements
Passive Emitter Geolocation using Agent-based Data Fusion of AOA, TDOA and FDOA Measurements Alex Mikhalev and Richard Ormondroyd Department of Aerospace Power and Sensors Cranfield University The Defence
More informationSimulation and Implementation of Pulse Compression Techniques using Ad6654 for Atmospheric Radar Applications
Simulation and Implementation of Pulse Compression Techniques using Ad6654 for Atmospheric Radar Applications Shaik Benarjee 1, K.Prasanthi 2, Jeldi Kamal Kumar 3, M.Durga Rao 4 1 M.Tech (DECS), 2 Assistant
More informationPerformance of Wideband Mobile Channel with Perfect Synchronism BPSK vs QPSK DS-CDMA
Performance of Wideband Mobile Channel with Perfect Synchronism BPSK vs QPSK DS-CDMA By Hamed D. AlSharari College of Engineering, Aljouf University, Sakaka, Aljouf 2014, Kingdom of Saudi Arabia, hamed_100@hotmail.com
More informationInternational Research Journal of Engineering and Technology (IRJET) e-issn: Volume: 03 Issue: 12 Dec p-issn:
Performance comparison analysis between Multi-FFT detection techniques in OFDM signal using 16-QAM Modulation for compensation of large Doppler shift 1 Surya Bazal 2 Pankaj Sahu 3 Shailesh Khaparkar 1
More information15 th Asia Pacific Conference for Non-Destructive Testing (APCNDT2017), Singapore.
Time of flight computation with sub-sample accuracy using digital signal processing techniques in Ultrasound NDT Nimmy Mathew, Byju Chambalon and Subodh Prasanna Sudhakaran More info about this article:
More informationORTHOGONAL frequency division multiplexing (OFDM)
144 IEEE TRANSACTIONS ON BROADCASTING, VOL. 51, NO. 1, MARCH 2005 Performance Analysis for OFDM-CDMA With Joint Frequency-Time Spreading Kan Zheng, Student Member, IEEE, Guoyan Zeng, and Wenbo Wang, Member,
More informationEVALUATION OF BINARY PHASE CODED PULSE COMPRESSION SCHEMES USING AND TIME-SERIES WEATHER RADAR SIMULATOR
7.7 1 EVALUATION OF BINARY PHASE CODED PULSE COMPRESSION SCHEMES USING AND TIMESERIES WEATHER RADAR SIMULATOR T. A. Alberts 1,, P. B. Chilson 1, B. L. Cheong 1, R. D. Palmer 1, M. Xue 1,2 1 School of Meteorology,
More informationTracking of Moving Targets with MIMO Radar
Tracking of Moving Targets with MIMO Radar Peter W. Moo, Zhen Ding Radar Sensing & Exploitation Section DRDC Ottawa Research Centre Presentation to 2017 NATO Military Sensing Symposium 31 May 2017 waveform
More informationNoise-robust compressed sensing method for superresolution
Noise-robust compressed sensing method for superresolution TOA estimation Masanari Noto, Akira Moro, Fang Shang, Shouhei Kidera a), and Tetsuo Kirimoto Graduate School of Informatics and Engineering, University
More informationInterference Mitigation in Automotive Radars
Interference Mitigation in Automotive Radars Shunqiao Sun Department of Electrical & Computer Engineering Rutgers, The State University of New Jersey Email: shunq.sun@rutgers.edu 1 Abstract We study the
More informationOrthogonal Radiation Field Construction for Microwave Staring Correlated Imaging
Progress In Electromagnetics Research M, Vol. 7, 39 9, 7 Orthogonal Radiation Field Construction for Microwave Staring Correlated Imaging Bo Liu * and Dongjin Wang Abstract Microwave staring correlated
More informationAdaptive Beamforming Applied for Signals Estimated with MUSIC Algorithm
Buletinul Ştiinţific al Universităţii "Politehnica" din Timişoara Seria ELECTRONICĂ şi TELECOMUNICAŢII TRANSACTIONS on ELECTRONICS and COMMUNICATIONS Tom 57(71), Fascicola 2, 2012 Adaptive Beamforming
More informationSelf Localization Using A Modulated Acoustic Chirp
Self Localization Using A Modulated Acoustic Chirp Brian P. Flanagan The MITRE Corporation, 7515 Colshire Dr., McLean, VA 2212, USA; bflan@mitre.org ABSTRACT This paper describes a robust self localization
More informationAccurate Delay Measurement of Coded Speech Signals with Subsample Resolution
PAGE 433 Accurate Delay Measurement of Coded Speech Signals with Subsample Resolution Wenliang Lu, D. Sen, and Shuai Wang School of Electrical Engineering & Telecommunications University of New South Wales,
More informationDigital Modulation Recognition Based on Feature, Spectrum and Phase Analysis and its Testing with Disturbed Signals
Digital Modulation Recognition Based on Feature, Spectrum and Phase Analysis and its Testing with Disturbed Signals A. KUBANKOVA AND D. KUBANEK Department of Telecommunications Brno University of Technology
More informationSPACE TIME coding for multiple transmit antennas has attracted
486 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 3, MARCH 2004 An Orthogonal Space Time Coded CPM System With Fast Decoding for Two Transmit Antennas Genyuan Wang Xiang-Gen Xia, Senior Member,
More informationExperimental Study on Super-resolution Techniques for High-speed UWB Radar Imaging of Human Bodies
PIERS ONLINE, VOL. 5, NO. 6, 29 596 Experimental Study on Super-resolution Techniques for High-speed UWB Radar Imaging of Human Bodies T. Sakamoto, H. Taki, and T. Sato Graduate School of Informatics,
More informationPassive Beamforming with Coprime Arrays
This paper is a postprint of a paper submitted to and accepted for publication in IET Radar, Sonar & Navigation and is subject to Institution of Engineering and Technology Copyright. The copy of record
More informationAnalysis of Ternary and Binary High Resolution Codes Using MATLAB
Analysis of Ternary and Binary High Resolution Codes Using MATLAB Annepu.Venkata NagaVamsi Dept of E.I.E, AITAM, Tekkali -532201, India. Dr.D.Elizabeth Rani Dept of E.I.E,Gitam university, Vishakapatnam-45,
More informationDesign and Application of Triple-Band Planar Dipole Antennas
Journal of Information Hiding and Multimedia Signal Processing c 2015 ISSN 2073-4212 Ubiquitous International Volume 6, Number 4, July 2015 Design and Application of Triple-Band Planar Dipole Antennas
More informationSound Parameter Estimation in a Security System
INSTITUTE OF INFORMATION AND COMMUNICATION TECHNOLOGIES BULGARIAN ACADEMY OF SCIENCE Sound Parameter Estimation in a Security System I. Garvanov 1, Chr. Kabakchiev 2, V. Behar 3 1 University of Library
More informationChapter 5. Signal Analysis. 5.1 Denoising fiber optic sensor signal
Chapter 5 Signal Analysis 5.1 Denoising fiber optic sensor signal We first perform wavelet-based denoising on fiber optic sensor signals. Examine the fiber optic signal data (see Appendix B). Across all
More informationG.Raviprakash 1, Prashant Tripathi 2, B.Ravi 3. Page 835
International Journal of Scientific Engineering and Technology (ISS : 2277-1581) Volume o.2, Issue o.9, pp : 835-839 1 Sept. 2013 Generation of Low Probability of Intercept Signals G.Raviprakash 1, Prashant
More informationMillimeter Wave Small-Scale Spatial Statistics in an Urban Microcell Scenario
Millimeter Wave Small-Scale Spatial Statistics in an Urban Microcell Scenario Shu Sun, Hangsong Yan, George R. MacCartney, Jr., and Theodore S. Rappaport {ss7152,hy942,gmac,tsr}@nyu.edu IEEE International
More informationMel Spectrum Analysis of Speech Recognition using Single Microphone
International Journal of Engineering Research in Electronics and Communication Mel Spectrum Analysis of Speech Recognition using Single Microphone [1] Lakshmi S.A, [2] Cholavendan M [1] PG Scholar, Sree
More informationCognitive Ultra Wideband Radio
Cognitive Ultra Wideband Radio Soodeh Amiri M.S student of the communication engineering The Electrical & Computer Department of Isfahan University of Technology, IUT E-Mail : s.amiridoomari@ec.iut.ac.ir
More informationPerformance Evaluation of STBC-OFDM System for Wireless Communication
Performance Evaluation of STBC-OFDM System for Wireless Communication Apeksha Deshmukh, Prof. Dr. M. D. Kokate Department of E&TC, K.K.W.I.E.R. College, Nasik, apeksha19may@gmail.com Abstract In this paper
More informationMaximum-Likelihood Co-Channel Interference Cancellation with Power Control for Cellular OFDM Networks
Maximum-Likelihood Co-Channel Interference Cancellation with Power Control for Cellular OFDM Networks Manar Mohaisen and KyungHi Chang The Graduate School of Information Technology and Telecommunications
More informationAnalysis of Processing Parameters of GPS Signal Acquisition Scheme
Analysis of Processing Parameters of GPS Signal Acquisition Scheme Prof. Vrushali Bhatt, Nithin Krishnan Department of Electronics and Telecommunication Thakur College of Engineering and Technology Mumbai-400101,
More informationECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading
ECE 476/ECE 501C/CS 513 - Wireless Communication Systems Winter 2005 Lecture 6: Fading Last lecture: Large scale propagation properties of wireless systems - slowly varying properties that depend primarily
More informationECE 476/ECE 501C/CS Wireless Communication Systems Winter Lecture 6: Fading
ECE 476/ECE 501C/CS 513 - Wireless Communication Systems Winter 2004 Lecture 6: Fading Last lecture: Large scale propagation properties of wireless systems - slowly varying properties that depend primarily
More informationSIGNAL PROCESSING FOR COMMUNICATIONS
Introduction ME SIGNAL PROCESSING FOR COMMUNICATIONS Alle-Jan van der Veen and Geert Leus Delft University of Technology Dept. EEMCS Delft, The Netherlands 1 Topics Multiple-antenna processing Radio astronomy
More informationAN ACCURATE ULTRA WIDEBAND (UWB) RANGING FOR PRECISION ASSET LOCATION
AN ACCURATE ULTRA WIDEBAND (UWB) RANGING FOR PRECISION ASSET LOCATION Woo Cheol Chung and Dong Sam Ha VTVT (Virginia Tech VLSI for Telecommunications) Laboratory, Bradley Department of Electrical and Computer
More informationPerformance analysis of MISO-OFDM & MIMO-OFDM Systems
Performance analysis of MISO-OFDM & MIMO-OFDM Systems Kavitha K V N #1, Abhishek Jaiswal *2, Sibaram Khara #3 1-2 School of Electronics Engineering, VIT University Vellore, Tamil Nadu, India 3 Galgotias
More informationA High Resolution Ultrawideband Wall Penetrating Radar
A High Resolution Ultrawideband Wall Penetrating Radar Erman Engin, Berkehan Çiftçioğlu, Meriç Özcan and İbrahim Tekin Faculty of Engineering and Natural Sciences Sabanci University, Tuzla, 34956 Istanbul,
More informationSPARSE CHANNEL ESTIMATION BY PILOT ALLOCATION IN MIMO-OFDM SYSTEMS
SPARSE CHANNEL ESTIMATION BY PILOT ALLOCATION IN MIMO-OFDM SYSTEMS Puneetha R 1, Dr.S.Akhila 2 1 M. Tech in Digital Communication B M S College Of Engineering Karnataka, India 2 Professor Department of
More informationNarrow Band Interference (NBI) Mitigation Technique for TH-PPM UWB Systems in IEEE a Channel Using Wavelet Packet Transform
Narrow Band Interference (NBI) Mitigation Technique for TH-PPM UWB Systems in IEEE 82.15.3a Channel Using Wavelet Pacet Transform Brijesh Kumbhani, K. Sanara Sastry, T. Sujit Reddy and Rahesh Singh Kshetrimayum
More informationWAVELET AND S-TRANSFORM BASED SPECTRUM SENSING IN COGNITIVE RADIO
WAVELET AND S-TRANSFORM BASED SPECTRUM SENSING IN COGNITIVE RADIO S.Raghave #1, R.Saravanan *2, R.Muthaiah #3 School of Computing, SASTRA University, Thanjavur-613402, India #1 raga.vanaj@gmail.com *2
More informationPROGRESSIVE CHANNEL ESTIMATION FOR ULTRA LOW LATENCY MILLIMETER WAVE COMMUNICATIONS
PROGRESSIVECHANNELESTIMATIONFOR ULTRA LOWLATENCYMILLIMETER WAVECOMMUNICATIONS Hung YiCheng,Ching ChunLiao,andAn Yeu(Andy)Wu,Fellow,IEEE Graduate Institute of Electronics Engineering, National Taiwan University
More informationOrthogonal Cyclic Prefix for Time Synchronization in MIMO-OFDM
Orthogonal Cyclic Prefix for Time Synchronization in MIMO-OFDM Gajanan R. Gaurshetti & Sanjay V. Khobragade Dr. Babasaheb Ambedkar Technological University, Lonere E-mail : gaurshetty@gmail.com, svk2305@gmail.com
More informationTHE EFFECT of multipath fading in wireless systems can
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 47, NO. 1, FEBRUARY 1998 119 The Diversity Gain of Transmit Diversity in Wireless Systems with Rayleigh Fading Jack H. Winters, Fellow, IEEE Abstract In
More informationEffective Collision Avoidance System Using Modified Kalman Filter
Effective Collision Avoidance System Using Modified Kalman Filter Dnyaneshwar V. Avatirak, S. L. Nalbalwar & N. S. Jadhav DBATU Lonere E-mail : dvavatirak@dbatu.ac.in, nalbalwar_sanjayan@yahoo.com, nsjadhav@dbatu.ac.in
More informationPerformance of Impulse-Train-Modulated Ultra- Wideband Systems
University of Wollongong Research Online Faculty of Infmatics - Papers (Archive) Faculty of Engineering and Infmation Sciences 2006 Perfmance of Impulse-Train-Modulated Ultra- Wideband Systems Xiaojing
More informationAnalysis of LFM and NLFM Radar Waveforms and their Performance Analysis
Analysis of LFM and NLFM Radar Waveforms and their Performance Analysis Shruti Parwana 1, Dr. Sanjay Kumar 2 1 Post Graduate Student, Department of ECE,Thapar University Patiala, Punjab, India 2 Assistant
More information16QAM Symbol Timing Recovery in the Upstream Transmission of DOCSIS Standard
IEEE TRANSACTIONS ON BROADCASTING, VOL. 49, NO. 2, JUNE 2003 211 16QAM Symbol Timing Recovery in the Upstream Transmission of DOCSIS Standard Jianxin Wang and Joachim Speidel Abstract This paper investigates
More informationThis is a repository copy of Robust DOA estimation for a mimo array using two calibrated transmit sensors.
This is a repository copy of Robust DOA estimation for a mimo array using two calibrated transmit sensors. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/76522/ Proceedings
More informationComparison of Two Detection Combination Algorithms for Phased Array Radars
Comparison of Two Detection Combination Algorithms for Phased Array Radars Zhen Ding and Peter Moo Wide Area Surveillance Radar Group Radar Sensing and Exploitation Section Defence R&D Canada Ottawa, Canada
More informationDecision Feedback Equalization for Filter Bank Multicarrier Systems
Decision Feedback Equalization for Filter Bank Multicarrier Systems Abhishek B G, Dr. K Sreelakshmi, Desanna M M.Tech Student, Department of Telecommunication, R. V. College of Engineering, Bengaluru,
More informationBroadband Microphone Arrays for Speech Acquisition
Broadband Microphone Arrays for Speech Acquisition Darren B. Ward Acoustics and Speech Research Dept. Bell Labs, Lucent Technologies Murray Hill, NJ 07974, USA Robert C. Williamson Dept. of Engineering,
More informationPARAMETER ESTIMATION OF CHIRP SIGNAL USING STFT
PARAMETER ESTIMATION OF CHIRP SIGNAL USING STFT Mary Deepthi Joseph 1, Gnana Sheela 2 1 PG Scholar, 2 Professor, Toc H Institute of Science & Technology, Cochin, India Abstract This paper suggested a technique
More informationWind profile detection of atmospheric radar signals using wavelets and harmonic decomposition techniques
ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. : () Published online 7 January in Wiley InterScience (www.interscience.wiley.com). DOI:./asl.7 Wind profile detection of atmospheric radar signals using wavelets
More informationA Multicarrier CDMA Based Low Probability of Intercept Network
A Multicarrier CDMA Based Low Probability of Intercept Network Sayan Ghosal Email: sayanghosal@yahoo.co.uk Devendra Jalihal Email: dj@ee.iitm.ac.in Giridhar K. Email: giri@ee.iitm.ac.in Abstract The need
More informationThe Acoustic Channel and Delay: A Tale of Capacity and Loss
The Acoustic Channel and Delay: A Tale of Capacity and Loss Yashar Aval, Sarah Kate Wilson and Milica Stojanovic Northeastern University, Boston, MA, USA Santa Clara University, Santa Clara, CA, USA Abstract
More informationSonar imaging of structured sparse scene using template compressed sensing
Sonar imaging of structured sparse scene using template compressed sensing Huichen Yan, Xudong Zhang, Shibao Peng Tsinghua University, Beijing, China Jia Xu Beijing Institute of Technology, Beijing, China
More informationSignal Processing and Time Delay Resolution of Noise Radar System Based on Retrodirective Antennas
PIERS ONLINE, VOL. 5, NO. 8, 2009 741 Signal Processing and Time Delay Resolution of Noise Radar System Based on Retrodirective Antennas V. V. Chapursky 1, V. A. Cherepenin 2, and V. I. Kalinin 2 1 Bauman
More informationOn Comparison of DFT-Based and DCT-Based Channel Estimation for OFDM System
www.ijcsi.org 353 On Comparison of -Based and DCT-Based Channel Estimation for OFDM System Saqib Saleem 1, Qamar-ul-Islam Department of Communication System Engineering Institute of Space Technology Islamabad,
More informationLow-Complexity Spectral Partitioning Based MUSIC Algorithm for Automotive Radar
http://dx.doi.org/.5755/j.eie.23.4.879 Low-Complexity Spectral Partitioning Based Algorithm for Automotive Radar Sangdong Kim, Bong-Seok Kim, Yeonghwan Ju, Jonghun Lee Advanced Radar Technology Laboratory,
More informationImplementation and Comparative analysis of Orthogonal Frequency Division Multiplexing (OFDM) Signaling Rashmi Choudhary
Implementation and Comparative analysis of Orthogonal Frequency Division Multiplexing (OFDM) Signaling Rashmi Choudhary M.Tech Scholar, ECE Department,SKIT, Jaipur, Abstract Orthogonal Frequency Division
More informationDESIGN AND ANALYSIS OF MULTIBAND OFDM SYSTEM OVER ULTRA WIDE BAND CHANNELS
DESIGN AND ANALYSIS OF MULTIBAND OFDM SYSTEM OVER ULTRA WIDE BAND CHANNELS G.Joselin Retna Kumar Research Scholar, Sathyabama University, Chennai, Tamil Nadu, India joselin_su@yahoo.com K.S.Shaji Principal,
More informationPerformance Evaluation of OFDM System with Rayleigh, Rician and AWGN Channels
Performance Evaluation of OFDM System with Rayleigh, Rician and AWGN Channels Abstract A Orthogonal Frequency Division Multiplexing (OFDM) scheme offers high spectral efficiency and better resistance to
More informationJournal Papers. No. Title
Journal Papers No. Title 1 2 3 4 5 6 7 8 M.-L. Wang, C.-P. Li*, and W.-J. Huang, Semi-blind channel estimation and precoding scheme in two-way multi-relay networks, IEEE Trans. on Signal Processing, Accepted,
More informationDetecting the Position and Number of Sharks in the Sea Using Active Sound Navigation and Ranging (SONAR) Technique
WCE 015, July 1-3, 015, London, U.K. Detecting the Position and Number of Sharks in the Sea Using Active Sound Navigation and Ranging (SONAR) Technique Hauwa T. Abdulkarim, Member, IAENG Abstract SONAR
More informationUltra-Wideband Radars for Measurements Over Land and Sea Ice
Ultra-Wideband Radars for Measurements Over Land and Sea Ice R. Hale, H. Miller, S. Gogineni, J.-B. Yan, F. Rodriguez-Morales, C. Leuschen, Z. Wang, J. Paden, D. Gomez-Garcia, T. Binder, D. Steinhage,
More informationFractional Fourier Transform Based Co-Radar Waveform: Experimental Validation
Fractional Fourier Transform Based Co-Radar Waveform: Experimental Validation D. Gaglione 1, C. Clemente 1, A. R. Persico 1, C. V. Ilioudis 1, I. K. Proudler 2, J. J. Soraghan 1 1 University of Strathclyde
More informationApproach of Pulse Parameters Measurement Using Digital IQ Method
International Journal of Information and Electronics Engineering, Vol. 4, o., January 4 Approach of Pulse Parameters Measurement Using Digital IQ Method R. K. iranjan and B. Rajendra aik Abstract Electronic
More informationSpeech Enhancement Using Beamforming Dr. G. Ramesh Babu 1, D. Lavanya 2, B. Yamuna 2, H. Divya 2, B. Shiva Kumar 2, B.
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 4 Issue 4 April 2015, Page No. 11143-11147 Speech Enhancement Using Beamforming Dr. G. Ramesh Babu 1, D. Lavanya
More informationNoise Plus Interference Power Estimation in Adaptive OFDM Systems
Noise Plus Interference Power Estimation in Adaptive OFDM Systems Tevfik Yücek and Hüseyin Arslan Department of Electrical Engineering, University of South Florida 4202 E. Fowler Avenue, ENB-118, Tampa,
More informationLocal Oscillators Phase Noise Cancellation Methods
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834, p- ISSN: 2278-8735. Volume 5, Issue 1 (Jan. - Feb. 2013), PP 19-24 Local Oscillators Phase Noise Cancellation Methods
More informationSIGNAL PROCESSING ALGORITHMS FOR HIGH-PRECISION NAVIGATION AND GUIDANCE FOR UNDERWATER AUTONOMOUS SENSING SYSTEMS
SIGNAL PROCESSING ALGORITHMS FOR HIGH-PRECISION NAVIGATION AND GUIDANCE FOR UNDERWATER AUTONOMOUS SENSING SYSTEMS Daniel Doonan, Chris Utley, and Hua Lee Imaging Systems Laboratory Department of Electrical
More informationCORRELATION BASED SNR ESTIMATION IN OFDM SYSTEM
CORRELATION BASED SNR ESTIMATION IN OFDM SYSTEM Suneetha Kokkirigadda 1 & Asst.Prof.K.Vasu Babu 2 1.ECE, Vasireddy Venkatadri Institute of Technology,Namburu,A.P,India 2.ECE, Vasireddy Venkatadri Institute
More informationForward-Backward Block-wise Channel Tracking in High-speed Underwater Acoustic Communication
Forward-Backward Block-wise Channel Tracking in High-speed Underwater Acoustic Communication Peng Chen, Yue Rong, Sven Nordholm Department of Electrical and Computer Engineering Curtin University Zhiqiang
More informationTIMIT LMS LMS. NoisyNA
TIMIT NoisyNA Shi NoisyNA Shi (NoisyNA) shi A ICA PI SNIR [1]. S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction, Second Edition, John Wiley & Sons Ltd, 2000. [2]. M. Moonen, and A.
More informationNOISE ESTIMATION IN A SINGLE CHANNEL
SPEECH ENHANCEMENT FOR CROSS-TALK INTERFERENCE by Levent M. Arslan and John H.L. Hansen Robust Speech Processing Laboratory Department of Electrical Engineering Box 99 Duke University Durham, North Carolina
More informationOn the performance of Turbo Codes over UWB channels at low SNR
On the performance of Turbo Codes over UWB channels at low SNR Ranjan Bose Department of Electrical Engineering, IIT Delhi, Hauz Khas, New Delhi, 110016, INDIA Abstract - In this paper we propose the use
More informationMultipath Effect on Covariance Based MIMO Radar Beampattern Design
IOSR Journal of Engineering (IOSRJE) ISS (e): 225-32, ISS (p): 2278-879 Vol. 4, Issue 9 (September. 24), V2 PP 43-52 www.iosrjen.org Multipath Effect on Covariance Based MIMO Radar Beampattern Design Amirsadegh
More informationAn Adaptive Adjacent Channel Interference Cancellation Technique
SJSU ScholarWorks Faculty Publications Electrical Engineering 2009 An Adaptive Adjacent Channel Interference Cancellation Technique Robert H. Morelos-Zaragoza, robert.morelos-zaragoza@sjsu.edu Shobha Kuruba
More informationAdvanced Radar Signal Processing & Information Extraction
Advanced Radar Signal Processing & Information Extraction John Soraghan Professor of Signal Processing, CeSIP, University of Strathclyde & Deputy Director of LSSC Consortium j.soraghan@strath.ac.uk Sensor
More informationDevelopment of a MATLAB Toolbox for Mobile Radio Channel Simulators
J.Univ.Ruhuna 14 :4-45 Volume, December 14 ISSN 345-9387 RESEARCH ARTICLE Development of a MATLAB Toolbox for Mobile Radio Channel Simulators D. S. De Silva Department of Electrical and Information Engineering,
More informationEfficient utilization of Spectral Mask in OFDM based Cognitive Radio Networks
IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) e-issn: 2278-2834,p- ISSN: 2278-8735.Volume 9, Issue 6, Ver. III (Nov - Dec. 2014), PP 94-99 Efficient utilization of Spectral Mask
More information