| 摘要: |
| 为提高基于到达时间差(Time Difference of Arrival,TDOA)的三维无源定位系统的定位精度，提出了一种考虑基站时差测量性能差异的最优布站方法，该方法通过求解目标所在区域定位误差的克拉美罗下界(Cramer Rao Lower Bound，CRLB)，以定位误差CRLB的迹的平均值最小为优化准则，采用粒子群算法对指定区域进行最优布站仿真研究。仿真结果表明，该方法求解的最优布站位置与假设TDOA测量误差为恒定高斯分布时求解的位置相比，提高了目标区域的整体定位精度；与用遗传算法求解最优布站位置相比，其收敛速度更快，更适用于需要快速作出反应的侦察定位场景。 |
| 关键词: TDOA测量误差 最优布站 克拉美罗下界 粒子群算法 |
| DOI：DOI:10.3969/j.issn.1672-2337.2020.01.006 |
| 分类号:TN971 |
| 基金项目: |
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| Study on Optimal Station Distribution and Performance of Passive Time Difference Localization System |
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XIA Wei，LUO Ming，ZHAO Meixia
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Key Lab of Electronic Information Countermeasure and Simulation Technology,Ministry of Education，Xidian University，Xi’an 710071，China
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| Abstract: |
| In order to improve the three dimensional passive localization accuracy of TDOA(time difference of arrival)，an optimal station distribution method considering the different TDOA measure performance is proposed.The adaptive particle swarm optimization is used to study the optimal distributed station simulation in the designated area by minimizing the average of CRLB(Cramer Rao lower bound) trace of location error in target region.The simulation results show that the method improves the overall positioning accuracy of the target area compared with the assumption that the TDOA measurement error is constant Gaussian distribution.And the proposed method has faster convergence speed compared with the optimal station location solved by the genetic algorithm，thus it is more suitable for detection and positioning scenes requiring quick response. |
| Key words: TDOA measurement error optimal station distribution Cramer Rao lower bound (CRLB) particle swarm optimization |
