闪烁噪声条件下基于交互多模框架的雷达目标跟踪算法

针对传统雷达目标跟踪算法在处理闪烁噪声时面临的性能下降问题，提出一种将容积卡尔曼估计器与交互多模框架相结合的高性能滤波算法。该算法将目标状态建模为高斯分布，将闪烁噪声建模为混合高斯分布，同时将其发生概率建模为一阶马尔可夫过程；在此基础上，利用交互多模框架实现对不同高斯噪声分量的匹配滤波处理。为了减轻非线性观测条件对目标跟踪精度的影响，进一步采用容积卡尔曼估计器作为高斯近似滤波器，对目标状态进行递推预测和更新。仿真结果表明：所提算法较传统高斯混合滤波器和粒子滤波器具有更高的跟踪精度和更好的实时性能，同时还能对闪烁噪声出现时刻进行有效的估计。

Radar target tracking algorithm in the framework of interacting multiple model with glint noise

Aiming at the problem of the performance degradation of the traditional tracking algorithm when confronted with glint noise, a high performance filtering method named as IMM-CKF was proposed by integrating the CKF(cubature Kalman filter) into the framework of IMM(interacting multiple model). In the proposed algorithm, the target state was modeled as Gaussian distribution, the glint noise was modeled as Gaussian mixture distribution, and the occurrence probability of the glint noise was modeled as the first-order Markov process. An IMM framework was then used to implement model-matched filtering for each Gaussian component. To further mitigate the impact of nonlinear observation condition on tracking accuracy, the CKF was utilized as Gaussian approximation filter to realize recursive prediction and update of the target state. Simulation results show that the proposed method not only has higher tracking accuracy than traditional algorithms such as Gaussian sum filter and particle filter, but also has better real time ability. Additionally, the IMM-CKF can effectively estimate the existence of glint noise.