基于统计概率的接收机网络多目标定位

Multi-Target Positioning Based on Statistical Probability by Receiver Network

传统基于测距定位法应用于接收机网络多目标定位时需要解决数据关联问题,计算量大、效率低,而且可能出现解算不收敛等问题。基于成像策略的目标定位方法可以有效解决上述问题,然而在成像方法中,回波相参法相参性难以保证,定位精度低;幅度积累法旁瓣高,弱目标定位困难。针对上述问题,提出了一种新的基于统计概率的多目标定位方法。首先计算目标在空间中的存在概率密度函数,设置某一概率门限,滤除低概率值的数据以减小数据处理量,然后结合后向投影算法,将概率信息作为成像的积累值,计算目标相对不同接收机在空间中的联合概率密度函数,很好地改善了多目标中弱目标的定位性能。通过仿真,验证了该方法的有效性。

Traditional ranging based multi-target positioning methods by receiver network need to re- solve the data association problem and may result in large amount of computation, low efficiency, and convergence problems. These problems can be effectively solved by the use of imaging strategy. However, in the imaging methods, the echo coherence method is difficult to ensure the coherence of the echo and the positioning accuracy is poor. As for amplitude accumulation method, its side lobe is so high that it is difficult to position the weak targets. To solve the above problems, a novel method is proposed based on statistical prob- ability. Firstly, the existence probability of the targets in the surveillance region is calculated, the data whose existence probability is greater than a certain threshold is selected to reduce the amount of data processing, and then the target joint probability density of different receivers is calculated by the use of back projection algorithm. The ability of weak target detection can be improved by the use of this method effectively and it is verified by simulation.