Abstract：To solve the nonlinear equation problems of Time-Difference-Of-Arrival (TDOA) passive location, a new swarm intelligence optimization algorithm called Salp-Swarm-Algorithm (SSA) is used. Firstly, a new renewal model of salps is proposed to balance exploration and exploitation properly during iteration in SSA. SSA not only ensures the wholeness of searching and the diversity of individuals, but also improves the problem that other intelligent optimization algorithms fall into local optima easily. Besides, there are few parameters to be adjusted, therefor, the computation speed is obviously improved. Moreover, the convergence performance of the proposed algorithm is very stable and the accuracy of location is higher. Simulation results show that the proposed algorithm can converge to the position of emitters fast and stably in 3D TDOA location. Comparing with Particle-Swarm- Optimization (PSO) and Improved-Particle-Swarm-Optimization (IPSO), the proposed algorithm has lower mean square error.
FANG Jiaqi, FENG Dazheng, and LI Jin. Research on modified Newton and Taylor-series methods in TDOA[J]. Journal of Xidian University, 2016, 43(6): 27-33. doi: 10.396/ j.issn.1001-2400.2016.03.005.
DENG Bing, SUN Zhengbo, YANG Le, et al. Geolocation of a known altitude object using TOA measurements[J]. Journal of Xidian University, 2017, 44(3): 133-137. doi: 10.3969/j.issn. 1001-2400.2017.03.023.
FENG Qi, QU Changwen, and LI Tingjun. Closed-form solution for passive location based on constrained weighted lease squares[J]. System Engineering and Electronics, 2017, 39(2): 263-268. doi: 10.3969/j.issn.1001-506X.2017.02.05.
ZHAO Yongjun, ZHAO Yongsheng, and ZHAO Chuang. Single-observer passive DOA-TDOA location based on regularized constrained total least squares[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2336-2343. doi: 10.11999/JEIT151379.
[7]
QU Xiaomei and XIE Lihua. An efficient convex constrained weighted least squares source localization algorithm based on TDOA measurements[J]. Signal Processing, 2016, 119(C): 142-152. doi: 10.1016/j.sigpro.2015.08.001.
QU Fuyong and MENG Xiangwei. Source localization using TDOA and FDOA measurements based on constrained total least squares algorithm[J]. Journal of Electronics & Information Technology, 2014, 36(5): 1075-1081. doi: 10.3724 /SP.J.1146.2013.01019.
[9]
YIN Jihao, WAN Qun, YANG Shiwen, et al. A simple and accurate TDOA-AOA localization method using two stations [J]. IEEE Signal Processing Letters, 2015, 23(1): 144-148. doi: 10.1109/LSP.2015.2505138.
[10]
WEI Yuanyuan and YAO Jinjie. Application on target localization based on adaptive particle swarm optimization algorithm[C]. 2010 6th International Conference on Wireless Communications Networking and Mobile Computing. Chengdu, China, 2010: 1245-1254.
[11]
KENNETH W K, ZHENG Jun, and So HC. Particle swarm optimization for time-difference-of-arrival based localization [C]. Signal Processing Conference, Wielkopolskie, Poland, 2007: 414-417.
[12]
MAJA Rosi, MIRJANA Simie, and PETAR Luki. TDOA approach for target localization based on improved genetic algorithm[C]. Telecommunications Forum, 2016 24th, Serbia, 2017: 1-4.
[13]
GAO Lipeng, SUN Heng, and LIU Mengnan. TDOA collaborative localization algorithm based on PSO and Newton iteration in WGS-84 coordinate system[C]. International Conference on Signal Processing, Chengdu, China, 2017: 1571-1575.
[14]
DAVID H W and WILLIAM G M. No free lunch theorems for optimization[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67-82. doi: 10.1109/4235.585893.
[15]
SEYEDALI M, AMIR H G, SEYEDEH Z M, et al. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 2017, 114(1): 163-191. doi: 10.1016/j.advengsoft.2017.07.002.
