Passive Localization by Multiple Observers Based on the Phase Difference of the Arrival of a Long Baseline Interferometer
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摘要: 针对常用多站无源定位技术存在时差(TDOA)/频差(FDOA)定位对超低旁瓣辐射源适应性差、测向(DOA)定位的造价成本和系统复杂度高等缺点，该文提出了一种基于相位差(PDOA)的多站无源定位新体制，利用每个观测站上至少两个接收天线和通道构成的长基线干涉仪(LBI)，通过测量辐射源信号到达长基线干涉仪天线的相位差实现定位。针对相位差的2π模糊引入的非线性和非连续性，提出了一种基于多假设迭代优化的定位方法，首先利用一组相位差确定多个可能的辐射源位置初始值，然后采用高斯-牛顿(GN)方法对每个位置初始值进行迭代优化并计算代价函数，最后选择具有最小代价函数的估计值作为最终的定位结果。该方法可获取稳健的迭代初始值，算法运算量适中。仿真结果表明该定位方法的均方根误差(RMSE)在高斯观测噪声条件下可达到克拉美罗下限(CRLB)。
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关键词:
- 无源定位 /
- 长基线干涉仪 /
- 相位差(PDOA) /
- 高斯-牛顿法 /
- 克拉美罗下限(CRLB)
Abstract: The deficiencies in the commonly used passive localization technologies using multiple observers are the failure of the emitter with a very low sidelobe based on the Time Difference of Arrival (TDOA)/ Frequency Difference of Arrival (FDOA) and the high cost and system complexity based on the Direction of Arrival (DOA), a novel passive localization system based on the Phase Difference of Arrival (PDOA) is presented. Herein, a Long Baseline Interferometer (LBI) comprising at least two sets of the receiving antenna and channel on each observer is used to measure the PDOA and locate the emitter. Moreover,to solve the nonlinearity and discontinuity caused by the 2π ambiguity, an iterative optimization method based on multiple hypotheses is proposed. First, a pair of PDOA is selected to obtain multiple initial values; Second, the Gauss-Newton (GN) method is applied to update each initial value; Finally, the cost function corresponding to the updated estimate is calculated. The result with the minimum cost function is selected as the final estimate. The initial values of the emitter position can be robustly obtained with moderate computational complexity. Simulation results show that the Root Mean Square Error (RMSE) of the proposed method can reach the Cramer-Rao Lower Bound (CRLB) at moderate Gaussian measurement noise. -
[1] ATIF M, AHMAD R, AHMAD W, et al. UAV-assisted wireless localization for search and rescue[J]. IEEE Systems Journal, 2021, 15(3): 3261–3272. doi: 10.1109/JSYST.2020.3041573 [2] ARAFAT M Y and MOH S. Localization and clustering based on swarm intelligence in UAV networks for emergency communications[J]. IEEE Internet of Things Journal, 2019, 6(5): 8958–8976. doi: 10.1109/JIOT.2019.2925567 [3] BABJAK M, OCHODNICKÝ J, and MATOUŠEK Z. Wideband electronic reconnaissance and localization in jamming environment[C]. Proceedings of 2017 Communication and Information Technologies (KIT), Vysoke Tatry, Slovakia, 2017: 1–5. [4] SUN Yimao, HO K C, and WAN Qun. Solution and analysis of TDOA localization of a near or distant source in closed form[J]. IEEE Transactions on Signal Processing, 2019, 67(2): 320–335. doi: 10.1109/TSP.2018.2879622 [5] 孙霆, 董春曦, 毛昱. 一种基于半定松弛技术的TDOA-FDOA无源定位算法[J]. 电子与信息学报, 2020, 42(7): 1599–1605. doi: 10.11999/JEIT190435SUN Ting, DONG Chunxi, and MAO Yu. A TDOA-FDOA passive positioning algorithm based on the semi-definite relaxation technique[J]. Journal of Electronics &Information Technology, 2020, 42(7): 1599–1605. doi: 10.11999/JEIT190435 [6] JIA Tianyi, WANG Haiyan, SHEN Xiaohong, et al. Target localization based on structured total least squares with hybrid TDOA-AOA measurements[J]. Signal Processing, 2018, 143: 211–221. doi: 10.1016/j.sigpro.2017.09.011 [7] CELENTANO S, FARINA A, TIMMONERI L, et al. Co-existence of AESA (active electronically scanned array) radar and electronic warfare (EW) systems on board of a military ship[C]. Proceedings of 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020: 1–5. [8] DOĞANÇAY K. Passive emitter localization using weighted instrumental variables[J]. Signal Processing, 2004, 84(3): 487–497. doi: 10.1016/j.sigpro.2003.11.014 [9] ZHANG Min, GUO Fucheng, and ZHOU Yiyu. A closed-form solution for moving source localization using LBI changing rate of phase difference only[J]. Chinese Journal of Aeronautics, 2014, 27(2): 365–374. doi: 10.1016/j.cja.2014.02.013 [10] HMAM H and DOGANCAY K. Passive localization of scanning emitters[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(2): 944–951. doi: 10.1109/TAES.2010.5461671 [11] GROVES P D. Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems[M]. 2nd ed. Boston: Artech House, 2013: 72–78. [12] ZHANG Tienan, MAO Xingpeng, HOU Yuguan, et al. An improved grid-search method for the identity-test of ionosphere-layer virtual heights via TDOA measurements[J]. IEEE Access, 2019, 7: 92861–92870. doi: 10.1109/ACCESS.2019.2927656 [13] CHEN Hui, BALLAL T, SAEED N, et al. A joint TDOA-PDOA localization approach using particle swarm optimization[J]. IEEE Wireless Communications Letters, 2020, 9(8): 1240–1244. doi: 10.1109/LWC.2020.2986756 [14] MARDIA K V and JUPP P E. Directional Statistics[M]. Chichester: John Wiley & Sons Ltd., 1999: 13–23. [15] BAHLMANN C. Directional features in online handwriting recognition[J]. Pattern Recognition, 2006, 39(1): 115–125. doi: 10.1016/j.patcog.2005.05.012 [16] GIBBS B P. Advanced Kalman Filtering, Least-Squares and Modeling[M]. Hoboken: John Wiley & Sons Inc. , 2011: 259–282. [17] 李腾, 郭福成, 姜文利. 基于旋转干涉仪模糊相位差的多假设NLS定位算法[J]. 电子与信息学报, 2012, 34(4): 956–962. doi: 10.3724/SP.J.1146.2011.00699LI Teng, GUO Fucheng, and JIANG Wenli. Multiple hypothesis NLS location algorithm based on ambiguous phase difference measured by a rotating interferometer[J]. Journal of Electronics &Information Technology, 2012, 34(4): 956–962. doi: 10.3724/SP.J.1146.2011.00699 [18] 潘玉剑, 宋慧娜. 基于混合基线的任意平面阵列干涉仪测向方法[J]. 电子与信息学报, 2021, 43(12): 3703–3709. doi: 10.11999/JEIT210406PAN Yujian and SONG Huina. Direction finding method with arbitrary planar array based on mixed baselines[J]. Journal of Electronics &Information Technology, 2021, 43(12): 3703–3709. doi: 10.11999/JEIT210406 [19] KAY S M. Fundamentals of Statistical Signal Processing: Estimation Theory[M]. Englewood Cliffs: PTR Prentice-Hall, 1993: 39–50. -
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