Microwave-Photonic Sensor for Remote Water-Level Monitoring Based on Chaotic Laser

Transcription

1 International Journal of Bifurcation and Chaos, Vol. 24, No. 3 (2014) (7 pages) c World Scientific Publishing Company DOI: /S Microwave-Photonic Sensor for Remote Water-Level Monitoring Based on Chaotic Laser Yongning Ji,, Mingjiang Zhang,,,, Yuncai Wang,, Peng Wang,, Anbang Wang,,YuanWu,, Hang Xu, and Yongning Zhang, Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan , P. R. China Institute of Optoelectronic Engineering, College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan , P. R. China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, Shenzhen University, Shenzhen , P. R. China College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin , P. R. China zhangmingjiang@tyut.edu.cn Received September 6, 2013; Revised November 13, 2013 A microwave-photonic sensor for remote water-level monitoring based on chaotic laser is proposed and demonstrated. The probe chaotic signal with bandwidth of 18 GHz is generated in the central office and then transmitted at the remote antenna unit after a 24 km single-mode fiber transmission. And the monitoring data from the remote antenna unit is sent back to the central office over fiber. The water-level measuring is accomplished by cross-correlation between reference signal and probe signal at the central office. So the remote water-level monitoring system with a high spatial resolution of 2 cm is achieved and the height of water surface can be displayed in real time. Keywords: Chaos; microwave-photonic; laser; remote water-level sensor. 1. Introduction Chaotic laser generated by the laser diodes has attracted widespread attention because of its excellent nonlinear dynamics and important applications in encrypted communications [Argyris et al., 2005], high speed physical random number generation [Uchida et al., 2008] and high-precision ranging (e.g. [Wang et al., 2008; Lin & Liu, 2004]). As for the correlation theory of ranging, the range resolution of the system is determined by the bandwidth of the probe signal. Chaotic signals can realize high accuracy performances easily due to their high bandwidth characteristics. As a consequence of the broadband and noise-like properties of chaotic signals, the performances of some systems can be improved, for example, correlation optical timedomain reflectometer system [Wang et al., 2008] and sensor networks [Cimatti et al., 2007]. The water-level sensor system may go the same way. The technology for water-level sensor is not only widely utilized in head water tanks of high-rise buildings, urban sewage pipe network, underground

2 SMF3 Y. Ji et al. cisterns or groundwater wells, but also widely used in the large-scale water conservancy projects, the observation of ocean waves and tides, monitoring of water quality, and the water level measurement of industry and agriculture (e.g. [Akyildiz et al., 2002; Dinh et al., 2007; Glasgow et al., 2004]). Different kinds of water-level sensing techniques based on mechanical, electrical, magneticmicrowave and optical methods have been reported. Electrical water-level sensors have been widely applied, but their applicability is limited when the water to be monitored is erosive or not pure enough. In addition, they are not suitable for remote control and monitoring due to high-loss of electrical signal. Microwave water-level sensors offer numerous advantages such as high reflectivity from the target medium (water), low sensitivity to ambient temperature and humidity, and noncontact between the device and the water [Boon & Brubaker, 2008]. However, similarly to electricity sensors, it is hard for microwave water-level sensors to realize remote control. Several microwave techniques based on flood monitoring were developed, and the flood extents were monitored using satellites to realize global monitoring (e.g. [De Groeve, 2010; Hall et al., 2011]). But the temporal and spatial resolutions are not precise enough. Since the satellite passes most locations on earth once per day, the delay between flood and observation is between 0 h and 24 h. Optical sensors offer many benefits under these rigorous conditions owing to their Chaotic Generator DAS Central Office A D PD2 PD3 VOA EDFA high sensitivity, dielectric, nonconducting or antierosion, large distance between signal generation and detection, and they are also immune to electromagnetic interference (e.g. [Antonio-Lopez et al., 2011; Yun et al., 2007; Guo et al., 2005]). But they are sensitive to impurities in the water and limited in the heavy foggy weather and rainy days. Flooding could not be located because of the poor temporal and spatial resolutions of previous sensors for remote water-level monitoring. In this paper, we propose and demonstrate a microwave-photonic sensor for remote water-level monitoring based on chaotic lasers. In order to realize a high resolution sensor for remote waterlevel monitoring, the radio-over-fiber technology and ranging technology of chaotic signals are combined. In the experiment, a fiber link of 24 km for remote transmission is realized. Different heights of water level are measured and a high spatial resolution of 2 cm is obtained experimentally. And the system has a high temporal resolution too. The system has advantages in remote real-time water-level measuring and high range resolution. 2. Experimental Setup An experimental setup of microwave-photonic sensor for remote water-level monitoring based on chaotic lasers is shown in Fig. 1. The remote waterlevel sensor system consists of three parts: the central office, the remote antenna unit and the fiber Downstream SMF1(24km) SMF2(24km) Upstream PD1 LD Remote Antenna C AMP1 AMP2 RA B TA Water Fig. 1. Experimental setup of microwave-photonic sensor for remote water-level monitoring based on chaotic laser. SMF: single mode fiber, VOA: variable optical attenuator, PD: photo detector, AMP: low noise amplifier, TA: transmitting antenna, RA: receiving antenna, LD: laser diode, EDFA: erbium doped fiber amplifier, OSC: oscilloscope

3 Microwave-Photonic Sensor for Remote Water-Level Monitoring link. Firstly, the chaotic laser is generated in the central office by using an optically injected semiconductor laser with optical feedback [Zhang et al., 2011], and this is our earlier work. And the generated chaotic laser is split into two branches by a single mode fiber coupler with a coupling ratio of 80:20. The light in one path is transmitted over a single mode fiber1 (SMF1, 24 km) towards the remote antenna unit as a detection signal. A chaotic laser in the other branch is transmitted over single mode fiber (SMF3, 48 km) which is compensating fiber, and detected by photo detector2 (PD2, 12.5 GHz) within the central office as a reference signal. The intensity of the detection light is controlled by adjusting a variable optical attenuator (VOA), and the chaotic laser with proper power is converted into electrical signals by the photo detector1 (PD1, 12.5 GHz). Secondly, the resulting electrical chaotic signal is amplified by a low noise amplifier (AMP1, 1 18 GHz) and then transmitted by a transmitting antenna (TA, 1 18 GHz) towards potential water surface. Due to the strong reflecting characteristics of water, the detection signal will be reflected when it touches the water surface. And the reflected signal is collected by a receiving antenna (RA, 1 18 GHz), amplified by another low noise amplifier (AMP2, 1 18 GHz) and converted into optical signal via a high-speed modulated LD with a modulation rate of up to 10 GHz, in turn. After that the transformed optical signal is amplified by an erbium doped fiber amplifier (EDFA), transmitted back towards the central office over single mode fiber (SMF2, 24 km) and then detected by photo detector3 (PD3, 40 GHz). Finally, the resulting electrical waveform is sampled and stored by a real time oscilloscope (OSC). The height of the water level is obtained based on the cross-correlation peaks of detecting signal and reference signal. A signal analyzer is used to observe the power spectrum of the signal. At the beginning of the experiment, the system is calibrated by measuring the water level at a known height. 3. Experimental Results 3.1. Characteristic of the detection signal In the experiments, the slave LD is biased at 1.4 times of its threshold current, and its output wavelength is stabilized at nm by a precise temperature controller. Figure 2 shows the detection signal features of remote water-level sensor system. The time series of the transmit signal is shown in Fig. 2(a). The waveform has a noise-like feature and no obvious periodicity is shown in the figure. In Fig. 2(b), the blue curve is the power spectra of the chaotic detection signal obtained experimentally when the output power of chaotic laser is 0.79 dbm. The gray curve is the noise floor of the signal analyzer. As seen in the figure, the power spectral density is rather flat and smooth in the range from 1 GHz to near 19 GHz. The signal below 1 GHz is filtered by AMP1. Figure 2(c) is the autocorrelation trace of the time series with a correlation length of 20 µs. No apparent side-lobes are seen except a narrow and sharp peak at the distance of 0 m. With its very low side-lobe level, target location can be done without ambiguity. From (a) (b) Fig. 2. Characteristics of transmit signal. (a) Time series, (b) power spectra of the chaotic detection signal obtained experimentally with detuning frequency f =7.1GHz, optical power of injection P =1.76dBm, and feedback strength dbm. The gray curve is the noise floor. (c) Autocorrelation trace of the chaotic state and (d) amplitude histogram of time series

4 Y. Ji et al. (c) Fig. 2. (Continued) (d) the full-width at half-maximum (FWHM) of the peak showed in the illustration of Fig. 2(c), a range resolution of 1.6 cm is derived within the limitation of the bandwidth of the real-time oscilloscope. Figure 2(d) shows an amplitude histogram of the chaotic signal and that the chaotic signal generated in this way makes a good randomicity Transmission characteristics of the signal power spectrum Figure 3 demonstrates the transmission characteristics of power spectra at different points in the system showed in Fig. 1. The gray curve is the noise floor of the signal analyzer. The red line corresponds to the power spectrum of point A. As can been seen, the signal has a very wide, flat and smooth power spectrum. The black curve is the power spectra of point B, in which the chaotic signal is amplified by AMP1 and the power spectral density is rather flat and smooth in the range from 1 GHz to near 19 GHz. The power spectrum of point C is shown by the blue curve. As we all know, there is an extreme limitation for electromagnetic signal transmission in the free space because of attenuation, and the higher the frequency, the greater the loss. In addition, due to the noise interference, not only the detection signal, which carries the water-level information, but also the jamming signals such as the cell phone signals are received by RA. The power spectrum of point D is illustrated by the green curve Measuring results of remote water-level sensor Figure 4 shows the measuring results of microwavephotonic sensor for remote water-level monitoring based on chaotic laser. The illustration in the upper right corner of the figure shows a simple schematic of water-level measuring. With the water-level of 0.99 m, 1.12 m and 1.44 m away from the radar antennas respectively, the height of the water surface is indicated clearly by the peaks of crosscorrelation traces. As can be seen, the side-lobes are quite low relative to the main peaks, and the distance between the water surface and antennas can be identified unambiguously. Fig. 3. The power spectra feature of different points in the system. Points A, B, C and D are marked in Fig. 1 by four dots Spatial resolution For the correlation theory of ranging, the resolution of chaotic signal is determined by the FWHM of the autocorrelation curve. The narrower the FWHM of the main peak, the higher the spatial resolution of

5 Microwave-Photonic Sensor for Remote Water-Level Monitoring remote water-level monitoring sensor system. That is very close to the limit of 1.6 cm. Fig. 4. Measuring results of remote water-level sensor. the system. According to the Wiener Khinchin theorem, the signal s autocorrelation function and its power spectrum are a pair of Fourier transform, so the FWHM of the autocorrelation function depends primarily on the bandwidth of chaotic signal. The wider the bandwidth of the chaotic signal is, the higher the resolution becomes. But in the experiment, due to the effect of interfering signals and the limitation of devices, the actual resolution will be larger than 1.6 cm. Figure 5 shows the resolution of remote water-level sensor. The red and blue curves are the correlation traces with the water surface 0.96 m and 0.98 m away from the antennas, respectively. It is 2 cm between the two main peaks, which is the minimum distance to be distinguished. Therefore, we achieve a spatial resolution of 2 cm for the Fig. 5. The spatial resolution of remote water-level sensor. 4. Discussion Actually, the wider the bandwidth of the chaotic signal is, the higher the spatial resolution will be. According to the Wiener Khinchin theorem, the signals autocorrelation function and its power spectra are a pair of Fourier transform, so the FWHM of the autocorrelation function depends on the bandwidth of chaotic signal, the wider the bandwidth of the chaotic signal is, the narrower the FWHM of autocorrelation function becomes. And for the correlation theory of ranging, the spatial resolution of chaotic signal is determined by the FWHM of the autocorrelation curve (e.g. [Lin & Liu, 2004]). The narrower the FWHM of the main peak is, the higher the spatial resolution of the chaotic radar system becomes. So, we will achieve a higher spatial resolution through widening the bandwidth of the chaotic signal. When it comes to the effects of the distance to the results, the larger the distance is, the more laser power is required. On condition that the power is constant, the farther the distance is, the worse the experimental results that we get. In this situation, because of the wideband signals attenuation in the free space, the peak of the cross-correlation decreases with the increase of the distance from the water surface to the antenna. And the water surface cannot be distinguished clearly when the value of decreasing correlation peak is equal to the maximum side-lobe. As a result, longer detection range can be achieved with greater power detection signal. For cross-correlation method of ranging, the more complex the chaotic signal is, the better the autocorrelation characteristic will be. In other words, the better the peak noise level and signalto-noise ratio of the system we will achieve. So, we can improve the performance of the system through producing more complex chaos. For example, if the much more complex multiscroll chaotic signals (e.g. [Lu & Chen, 2006]) can be generated in optical domain, the performance of the system can be improved using the light multiscroll chaotic signals. In the previous microwave monitoring systems (e.g. [De Groeve, 2010; Hall et al., 2011]), a temporal resolution of 1 day was obtained and a spatial resolution of several meters. The temporal and

6 Y. Ji et al. spatial resolutions of the proposed monitoring system both have been improved. We have achieved a spatial resolution of 2 cm as mentioned previously. And the temporal resolution also has been greatly improved to 5 min due to the high speed of the cross-correlation calculation. 5. Conclusions We demonstrate a microwave-photonic sensor for remote water-level monitoring based on chaotic laser. The cross-correlation ranging technology of chaotic signal, in combination with the radio-overfiber communication technology, allows fast remote monitoring of water-levels. And the system incorporates the advantages of microwave water-level sensors with those of the optical sensors. The chaotic signal is generated in the central office, and the waveforms of more than 18 GHz bandwidth are distributed over km fiber to a remote antenna unit. We have accomplished the remote real-time water-level measurements and achieved high spatial resolution of 2 cm, which is very close to the limit of 1.6 cm. The system has advantages in remote real-time water-level measurements and high range resolution. It can provide early warning of events for flood control projects, coal mines production, the flood control stations of alpine valleys, marine or island resources development stations and provide protection of the safety of monitoring personnel under certain hazardous environments. Acknowledgments This work was supported by the National Natural Science Foundation of China under Grant and Grant , the National Basic Research Program of China under Grant 2010CB327806, the China Postdoctoral Science Foundation under Grant 2011M500048, the Program for the Top Young Academic Leaders of Higher Learning Institutions of Shanxi under Grant 2012lfjyt08, and the Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province under Grant GD References Akyildiz, I., Su, W., Sankarasubramaniam, Y. & Cayirci, E. [2002] Wireless sensor networks: A survey, Comput. Netw. 38, Antonio-Lopez, J., May-Arrioja, D. & LiKamWa, P. [2011] Fiber-optic liquid level sensor, IEEE Photon. Technol. Lett. 23, Argyris, A., Syvridis, D., Larger, L., Annovazzi- Lodi, V., Colet, P., Fischer, I., Garcia-Ojalvo, J., Mirasso, C. R., Pesquera, L. & Shore, K. A. [2005] Chaos-based communications at high bit rates using commercial fibre-optic links, Nature 438, Boon, J. & Brubaker, J. [2008] Acoustic-microwave water level sensor comparisons in an estuarine environment, OCEANS 2008, pp Cimatti, G., Rovatti, R. & Setti, G. [2007] Chaos-based spreading in DS-UWB sensor networks increases available bit rate, IEEE Trans. Circuits and Syst.- I : Reg. Papers 54, De Groeve, T. [2010] Flood monitoring and mapping using passive microwave remote sensing in Namibia, Geomat. Nat. Haz. Risk 1, Dinh, T. L., Hu, W., Sikka, P., Corke, P., Overs, L. & Brosnan, S. [2007] Design and deployment of a remote robust sensor network: Experiences from an outdoor water quality monitoring network, 32nd IEEE Conf. Local Computer Networks, LCN 2007, pp Glasgow, H. B., Burkholder, J. M., Reed, R. E., Lewitus, A. J. & Kleinman, J. E. [2004] Real-time remote monitoring of water quality: A review of current applications, and advancements in sensor, telemetry, and computing technologies, J. Experim. Marine Biol. Ecol. 300, Guo, T., Zhao, Q., Dou, Q., Zhang, H., Xue, L., Huang, G. & Dong, X. [2005] Temperature-insensitive fiber Bragg grating liquid-level sensor based on bending cantilever beam, IEEE Photon. Technol. Lett. 17, Hall, A. C., Schumann, G. J.-P., Bamber, J. L. & Bates, P. D. [2011] Tracking water level changes of the amazon basin with space-borne remote sensing and integration with large scale hydrodynamic modelling: A review, Phys. Chem. Earth, Parts A/B/C 36, Lin, F. & Liu, J. [2004] Chaotic radar using nonlinear laser dynamics, IEEE J. Quant. Electron. 40, Lu, J. & Chen, G. [2006] Generating multiscroll chaotic attractors: Theories, methods and applications, Int. J. Bifurcation and Chaos 16, Uchida, A., Amano, K., Inoue, M., Hirano, K., Naito, S., Someya, H., Oowada, I., Kurashige, T., Shiki, M., Yoshimori, S., Yoshimura, K. & Davis, P. [2008] Fast physical random bit generation with chaotic semiconductor lasers, Nat. Photon. 2, Wang, Y., Wang, B. & Wang, A. [2008] Chaotic correlation optical time domain reflectometer utilizing

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