GPS Anti-jamming Performance Simulation Based on LCMV Algorithm Jian WANG and Rui QIN

Transcription

1 2017 2nd International Conference on Software, Multimedia and Communication Engineering (SMCE 2017) ISBN: GPS Anti-jamming Performance Simulation Based on LCMV Algorithm Jian WANG and Rui QIN School of Electronics & Information Engineering and Automation Civil Aviation University of China, Tianjin, China Keywords: GPS, Array antenna, LCMV algorithm, Anti-interference, DOA. Abstract. The most efficient and effective adaptive control of the GPS anti-jamming technology is the adaptive zero adjusting antenna. Aiming at anti-jamming problem of GPS receiver under interference condition, the LCMV algorithm is used to simulate and analyze the number of different interference sources and different dry to noise ratio in uniform linear array. The results show that the method can suppress the interference signal effectively, and can realize the high gain reception of the desired signal direction, and the higher the interference the better signal power. Finally, using satellite ephemeris and inertial navigation parameters, to calculate the satellite signal solution of antenna array of DOA, then the LCMV algorithm is proposed in this paper can form the main beam to the desired signal to receive the desired signal on high gain. Introduction The global satellite navigation system has been developed as an important infrastructure for many countries. It can provide global sea, road, air carriers with high accuracy position, speed and time information. However, due to the low power of the navigation satellite transmit signal, the signal reaches the ground when the power is about -160dBW, so the GPS receive can to work in a low signal to noise ratio of the environment. At the same time, in the complex electromagnetic environment, the GPS receiver is vulnerable to interference signal, which can lead to the receiver not working properly. So how to improve the anti-jamming capability of the receiver is always an important task for the researchers. At present, more effective GPS anti-jamming methods are anti-interference technique in time domain, airspace anti-jamming technology and frequency domain anti-jamming technology, etc. In many anti-jamming techniques, the spatial domain anti-jamming technology is mainly controlled by the adaptive algorithm. An adaptive beam forming algorithm based on LCMV criterion is proposed in this paper, the optimal weight vector is adjusted constantly by the adaptive algorithm, which makes its convergence and the current optimal solution. And the optimal weight of this algorithm is derived in detail [1]. By using this algorithm, the simulation and performance analysis of different interference sources and different jamming and noise ratio (JNR) under uniform linear array are carried out to verify the feasibility of the proposed algorithm. The Adaptive Zero Adjusting Antenna Receiving Signal Model The anti-interference technology of the adaptive modulation antenna is composed of the adaptive modulation antenna and the signal processing module, with the principle block diagram of the adaptive modulation antenna for receiving signal shown in Figure 1. The antenna elements are arranged according to certain requirements of formation, and weighted signals from different array elements, can generate beams with different directions, with the main beam formed at the desired signal, high gain from the desired signal receiver is achieved, while nulling in the direction of interference signal, thus restraining the interference signal, and achieving the interference effect. 423

2 Antenna 1 Antenna i Antenna M RF RF A/D A/D x ( t 0 ) (t) x i w 1 w i y(t) RF A/D x M (t) w M Adaptive weights are generated Figure 1. Assuming that the number of the adaptive zero adjusting antenna array is M, array spacing is d 0.5 ( is the GPS signal wavelength), q is a signal incident to the adaptive antenna, q<m, the 1, 2,..., is the receiver receives the signal direction, signal received at t time can be k expressed as [2] : y( t) [ x0 ( t), x1 ( t),.., xm 1( t)] w (1) X( t) AS( t) N( t) (2) y(t) is the output of the adaptive antenna. X( t) [ x0( t), x1 ( t),..., xm 1( t)], x0( t) is the received signal of the first element, and so on. w is a weighted vector of antenna array, T w w, w,..., ]. X (t ) is the array received signal vector, A is a signal [ 0 1 w M 1 2d 2 d ( M 1) j sin j sin i i T oriented matrix, a( i ) [1, e,..., e ], (i=0,1,... k) for the direction vector of the i signal. S(t) is the complex envelope vector of the signal, which S( t) [ s0( t), s1( t),..., sk ( t)]. N (t) is noise vector. LCMV Algorithm Linear constrained minimum variance criterion, also called the minimum variance criteria, the principle which is in keeping the desired signal gain certain cases, the interference signal or undesired signal output power is the minimum, which is equivalent to the output signal-to-noise ratio being the largest. The constraint equations for [3] : min P s. t. C H OUT E{ y( n) w g; H 2 }; Where y( n) w x( n), C is the signal steering vector matrix, w is the weighting coefficient, in the direction of g for each limit space filter response. The output power is: (3) P OUT 2 H H H E{ y( n) } E{( w x( n))( w x( n))} w R w where R for input signal autocorrelation matrix. Using Lagrange algorithm can be found out the optimal weight vector [4] : (4) w opt 1 1 H 1 R C( C R C) g (5) If a( i ) is steering vector of desired signal, according to the rule of LCMV algorithm: 424

3 min H H w Rws. t. w a( i) 1 (6) Using the Lagrange multiplier method: w R a( )( a( ) R a( )) (7) 1 H 1 1 opt i i i Assuming a ( 0 ) for the desired signal steering vector, a( 1), a( 2),..., a( k ) for the jamming signal steering vector, the multi-beam formation technologies can be expressed as the following optimization problem: K 2 H min w a( i ) i 0 H w a( 0 ) 1 (8) H w a( 1) 0 H w a( i ) 0 where i 1 2, 3,,..., k ; i for the i in the direction of jamming signal, 0 is the direction of the desired signal. In order to guarantee the minimum output power, the first constraint conditions to ensure the main beam is formed on the direction of the desired signal, other constraints to ensure zero depression effect on jamming signal direction. The Simulation Analysis In order to verify the feasibility of LCMV algorithm, respectively choose different number of interference sources and different interference and noise ratio proceed algorithm simulation. Selection of simulation environment to consider the following 3 points: (1) According to the principle of adaptive antenna, the N array element array, most can produce N - 1 degrees of freedom, most can produce N - 1 zero point. Arrays and the more the number, the more complex array, the higher the cost, the greater the difficulty, the general is inhibition of 1 to 4 jamming signal. Considering simulation environment to select 7 array element. (2) The relative position between the arrays will affect the performance of the whole system. In order to avoid the electromagnetic coupling effect array element, array element spacing is set to half wavelength d 0.5 ( for the GPS signal wavelength). (3) Common formations are homogeneous array, circle array, planar array shape, etc. Circle and plane array suppress interference effect will be better, but also the whole to the manipulation of the zero and beam, not easy the creation of lateral fuzzy, but complex structure and high cost. Uniform linear array can effectively suppress interference signal, simple structure, so this article selects the uniform linear array to the simulation analysis. Number of Different Interference Sources. Simulations take 7 array element uniform line array, array element spacing d 0.5. To reflect LCMV algorithm anti-interference performance, simulation is carried out under the different number of interference sources, respectively, set up three scenario of jamming simulation in table 1. Name Table 1. The direction of the desired signal (c) The direction of the jamming signal (c) The simulation scenario 1-30,0,20 40 The simulation scenario 2-25,0 10,30 The simulation scenario ,30,60 425

4 Figure 2. Figure 3. Figure 4. Figure 2 shows that the simulation results with the condition of an interference sources, the 0 interference signal produce the deeper zero trapped in the direction of 40, the interference signal is 0 0 effectively suppressed, and realizes the high gain in the direction of 30, 0,20, desired signal receiving, improving the output SNR. Figure 3 shows that the simulation results with the two interference sources, the interference signal 0 0 generated the deeper zero trapped in the direction of 10, 30, the interference signal is effectively 0 0 suppressed, in the direction of 25, 0, desired signal to achieve a high gain reception. Figure 4 shows that the simulation results under the three interference sources, the interference signal generated the deeper zero trapped in the direction of 0, 30, 60, the interference signal is 0 effectively suppressed, in the direction of 30 desired signal to achieve a high gain reception. The simulation results analysis: Under different number of interference sources interference, this paper adopts adaptive beamforming algorithm of LCMV criterion can produce - 35dB to -40dB zero trapped in depth, compared with the classical power inversion algorithm, the results are the same, can satisfy the demand of the GPS receiver anti-jamming. Different Jamming and Noise Ratio Simulation uses the 7 array element uniform line array, array element spacing d 0.5. In jamming and noise ratio respectively for 20dB, 30dB, 40dB, simulation experiment is carried out, assuming 0 that the direction of the jamming signal is 0, the simulation results are shown below: Figure 5. Blue represents the simulation results of JNR of 20dB. 426

5 Figure 6. Red represents the simulation results of JNR of 30dB. Figure 7. Green represents the simulation results of JNR of 40dB. As shown in figure 5, when JNR for 20dB, on the direction of jamming signal suppression gian for - 41dB; As shown in figure 6, when JNR for 30dB, on the direction of interference suppression gain for - 53dB; As shown in figure 7, when JNR is 40dB, on the direction of interference suppression gain for 65dB. From the simulation results can draw the following conclusions: When the disturbance signal of power increases, the JNR increases, the interference signal power is higher, the adaptive nulling antenna generate the deeper zero trapped in the direction of interference signal, so strong interference was beneficial to receive the desired signal in the receiver. Conclusion In this paper, the main work is detailed the adaptive nulling antenna receiving signal model, and on the principles of LCMV based adaptive beamforming algorithm in detail. In the simulation experiment shows that this method can produce in the direction of jamming signal deeper zero trapped, gain anti-interference effect, at the same time it can guarantee on the desired signal direction of high-gain receiving; When interference signal power is higher, it generate the deeper zero trapped on direction of jamming signal, anti-interference effect is better. Simulation results verify the feasibility of the method, but the actual environment complexity is far higher than that of the simulation environment complexity, single anti-interference methods cannot completely suppress interference signals, so also need to study more GPS anti-interference methods, further optimize the anti-jamming algorithm in order to achieve better anti-interference effect. References [1] Nan He. LMS and LCMV algorithm based on MATLAB simulation anti-jamming performance comparison study [J]. Electronic measurement technology, 2010, 09: [2] Jiao Pi, Licheng Liu, Feng Wang and Ding Cao. Based on array antenna processing of GPS signal anti-jamming study [J]. Guangdong University of Technology, 2015, 2015:

6 [3] Pengcheng Li, Yukun Tian and Feng Yang. Based on satellite navigation of LCMV beamforming anti-interference technology [J]. Electronic Information Technology, 2016, 2016: [4] Yongzhou Wang, Bing Xia and Hui Ma. Based on the circular antenna array of GPS anti-jamming performance simulation and interference method research [J]. Communications Technology, 2014,01: [5] Rui Wan. GPS receiver antenna anti-jamming algorithm and its FPGA implementation [D]. University of Electronic Science and Technology of China, [6] Power minimization with derivative constraints for high dynamic GPS interference suppression[j]. Science China (Information Sciences), 2012,04: [7] Hongwei Zhao,Baowang Lian,Juan Feng. Adaptive beamforming and phase bias compensation for GNSS receiver[j]. Journal of Systems Engineering and Electronics, 2015,01: [8] Keyuan Yang. GPS receiver anti-jamming algorithm and its implementation research [D]. University of Electronic Science and Technology of China, [9] Yi Guo. GPS receiver space-time anti-jamming theory and realization of key technology research [D]. National University of Defense Technology, [10] Renbiao wu, Qingyu Sun and Tieqiao Hu. Based on the power inversion algorithm of real-time GPS anti-jamming system [J]. China Civil Aviation University, :