基于级数展开的位置误差加权定位方法

随着对海洋探索的愈加深入,对水下目标探测能力的要求也愈发严格。针对噪声干扰严重时算法性能大幅度下降的问题,文章主要研究仅利用分布式矢量水听器的波达方向信息对未知目标进行融合定位。在已有的基于位置误差加权方法的基础上,利用几何二次约束减小波达方位角量测误差,并结合泰勒级数展开,将一阶权值推导至高阶,解决在低信噪比情况下定位精度差的问题。仿真结果表明:相对于传统的基于方差倒数的自适应加权算法,该方法在定位精度上有很大提高,尤其在低信噪比的情况下效果显著。

A Position Error Weighted Localization Method Based on Series Expansion

With the deepening of ocean exploration, the requirements for underwater target detection capability are be-coming increasingly strict. In response to the problem of significant decline in algorithm performance under severe noise interference, the fusion localization of unknown targets using only the direction of arrival information of distributed vec-tor hydrophones is studied. On the basis of existing position error weighting methods, geometric quadratic constraints are used to reduce the measurement error of wave arrival azimuth, and the first-order weight is derived to a higher order by combining Taylor series expansion, solving the problem of poor positioning accuracy in low signal-to-noise ratio situa-tions. The simulation results show that compared to traditional adaptive weighting algorithms based on reciprocal vari-ance, the proposed method has a significant improvement in positioning accuracy, especially in low signal-to-noise ratio situations.