基于先验门限优化准则的探测阈值自适应选择

 针对 2维测量和 4 -sigma确认门 ,把先验检测门限优化准则和修正 Riccati方程的解析近似表示相结合 ,得到了在瑞利起伏环境下使跟踪性能优化的信号探测阈值解析表示式 ,从而使在线求解自适应信号探测阈值能比较容易地实现。通过研究和仿真发现 :在滤波稳定阶段 ,本文给出的自适应信号检测门限方法的跟踪性能优于固定虚警率方法的跟踪性能 ;基于先验检测门限优化准则实现检测 -跟踪的联合优化要求信噪比要大于一定的门限 ,在瑞利起伏环境下 ,对 2维测量和 4 -sigma确认门 ,该门限为 1 .57

ADAPTIVE DETECTION THRESHOLD SELECTION BASED ON A PRIORI THRESHOLD OPTIMIZATION

The adaptive optimization of detection thresholds for tracking in clutter is an important issue in tracking. The probabilistic data association filter (PDAF) algorithm for tracking in clutter contains a stochastic (data\|dependent) Riccati equation for updating the estimation error covariance matrix. The adaptive optimization of detection thresholds for tracking in Rayleigh clutter environment is obtained by combining the prior detection threshold optimization criterion and the analytic approximation to the (deterministic) modified Riccati equation. From the simulation results and the research, it is found that (1) the performance of the presented method is superior to that of the fixed false alarm probability method; (2) only when the signal to noise ratio is greater than a certain value (in Rayleigh clutter environment, the value is 1.57 for 2 dimensional measurement and four\|sigma validation gate), the adaptive optimization of detection thresholds for tracking in Rayleigh fading clutter environment can be obtained.