机动目标的模型与跟踪算法

 <正> 在机动目标的“当前”统计模型中,目标的加速度被描述为修正的瑞利—马尔科夫过程,对应的自适应跟踪算法呈现出较好的跟踪特性。文献2]研究了该模型及其自适应算法在实际的机载雷达跟踪系统的应用;文献3]进一步推广了基于“当前”模型的MPDAF算法。本文提出一个新的机动目标模型,即假定目标加速度为一高斯—马尔

STATISTICAL MODEL AND TRACKING ALGORITHM FOR MANEUVERING TARGET

A new modeland a trackina algorithm for maneuvering taraet are proposed in this paper. The maneuver is modeled as a Gauss-Markov process withour any special assumption for the characteris of the maneuver. The mean value of the Gaussian probability density function is the optimal estimation of the acceleration a(t) at presenty as the one suggested by Zhou in the modified Reyleigh density function However, its variance is a constant at any moment. When we have no any priori knowledge of the maneuver, this model describes the statistical characteristics of an unknown maneuver well. The results oi the Monte Karlo simulations indicate that algorithm presented in this paper has the same good tracking pertormences as the adaptive filter algorithm of the modified Rayleigh-Markov model. Nevertheless, the physical significance of the present model is more clear and, due to the constant variance of the target's acceleration, the computational burden of the present algorithm is lighter and the algorithm is rubuster. Several typical trajectories oi acceleration of maneuvering target are simulated and the results oi the root mean square position errors are shown.