基于樽海鞘群算法的無源時差定位

陳濤; 王夢馨; 黃湘松

doi:10.11999/JEIT170979

基于樽海鞘群算法的無源時差定位

doi: 10.11999/JEIT170979 cstr: 32379.14.JEIT170979

(哈爾濱工程大學(xué)信息與通信工程學(xué)院哈爾濱 150001)

基金項目:

國家自然科學(xué)基金項目(61571146)，中央高校基本科研業(yè)務(wù)費(fèi)專項基金(HEUCFP201769)

詳細(xì)信息

作者簡介:
陳濤：陳濤：男，1974年生，教授，博士生導(dǎo)師，研究方向為寬帶信號檢測、處理與識別. 王夢馨：女，1994年生，碩士生，研究方向為寬帶信號檢測、無源定位. 黃湘松：女，1980年生，講師，研究方向為無源定位，語音、圖像信號處理.

中圖分類號: TN971
計量
- 文章訪問數(shù): 2555
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-10-20
- 修回日期: 2018-03-30
- 刊出日期: 2018-07-19

Time Difference of Arrival Passive Location Based on Salp Swarm Algorithm

CHEN Tao WANG Mengxin HUANG Xiangsong

Funds:

The National Natural Science Foundation of China (61571146), The Fundamental Research Funds for the Central Universities (HEUCFP201769)

摘要

摘要: 針對無源時差(TDOA)定位的非線性方程解算問題，論文使用一種名為樽海鞘群算法(SSA)的新的群體智能優(yōu)化算法。首先，該算法采用一種新的群體更新模型，充分平衡迭代過程中的探索行為與開發(fā)行為，在保證搜索的全局性與個體的多樣性的同時，改善了其他智能優(yōu)化算法容易陷入局部極值的問題。其次，該算法控制參數(shù)很少，運(yùn)算速度明顯提高。該算法的收斂速度十分穩(wěn)定，定位精度更高。仿真結(jié)果表明，樽海鞘群算法在3維時差定位中能夠快速、穩(wěn)定地收斂至目標(biāo)位置，對傳統(tǒng)粒子群算法(PSO)、改進(jìn)的線性權(quán)重粒子群算法(IPSO)與SSA的定位精度進(jìn)行比較，SSA精度明顯高于PSO與IPSO。
- 無源定位 /
- 到達(dá)時差 /
- 智能優(yōu)化算法 /
- 樽海鞘群算法
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.
- Passive location、Time-Difference-Of-Arrival (TDOA)、Intelligence optimization algorithm、Salp- Swarm-Algorithm (SSA) /

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參考文獻(xiàn)(16)

田中成, 劉聰鋒. 無源定位技術(shù)[M]. 北京: 國防工業(yè)出版社, 2015: 264-265.

TIAN Zhongcheng and LIU Congfeng. Passive locating technology[M]. Beijing: National Defence Industry Press, 2015: 264-265.

[2] FOY W H. Position-location solutions by Taylor-series estimation[J]. IEEE Transactions on Aerospace & Electronic Systems, 2007, AES-12(2): 187-194. doi: 10.1109/TAES.1976. 308294.

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.

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