摘要
基于认知雷达波形捷变的思想,以提高雷达跟踪精度为目的,提出了一种基于自适应Kalman滤波的PSO优化算法的认知雷达的波形选择方法。通过发射波形与测量噪声之间的关系,建立了发射波形与雷达跟踪性能之间的关系模型,利用粒子群算法优化雷达发射波形参数,在卡尔曼跟踪滤波算法中增加了波形选择模块,实现对发射波形的自适应选择,以获取更好的目标跟踪性能。仿真结果表明,该方法使雷达对目标的跟踪性能在速度误差和距离误差分别降低50%和60%。
This paper based on the idea of cognitive radar waveform agility,proposes a new method of PSO optimization algorithm based on adaptive Kalman filtering. The relationship between the transmitted waveform and measurement noise and the relationship model was established between the transmitted waveform and tracking performance,using particle swarm algorithm to optimize the radar waveform parameters,in the framework of kalman tracking filtering algorithm increases the waveform selection module,to achieve the adaptive regulation of waveform tracking,to obtain better tracking performance.Simulation results show that the proposed method can significantly improve the tracking performance of radar.
出处
《电子设计工程》
2017年第24期46-49,共4页
Electronic Design Engineering
关键词
波形选择
粒子群优化
卡尔曼滤波
认知雷达
waveform selection
particle swarm optimization
Kalman filter
cognitive radar
