提出一种基于 L1-加权工具变量方法的纯方位目标运动分析(Bearing-only Target Motion Analysis,BOTMA)算法,以解决传统BOTMA算法在Laplace噪声下的性能下降问题.首先用伪线性估计算法对目标状态进行初步估计,其次采用 Maj orization-Minimization伪线性估计实现 L1范数下的BOTMA,最后采用 L1-加权工具变量法减少估计偏差.仿真实验给出了所提算法在不同噪声标准差、采样次数和传感器观测位置下的估计性能.结果表明,所提出算法相比传统的BOTMA算法具有更高的估计精度.
BOTMA algorithm based on L1-WIV under Laplace measurement noise
A bearing-only target motion analysis(BOTMA)algorithm based on the L1-weighted instrumental variable method is proposed for solving the performance degradation problem of traditional BOTMA algorithms under Laplace noise.Firstly,a pseudo linear estimation algorithm is used to preliminarily estimate the target state.Secondly,a Majorization-Minimization pseudo linear estimation is used to achieve BOTMA under the L1 norm.Finally,the L1-weighted instrumental variable method is used to reduce the estimation bias.Simulation experiments show the better estimation performance of the proposed algorithm under different noise standard deviations,sampling times,and sensor observation positions.The results show that the proposed algorithm has higher estimation accuracy than the traditional BOTMA algorithm.
Bearing-onlyMajorization-Minimization methodweighted instrumental variable algorithmpseudo linear estimation
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