融合隐马尔科夫模型的雷达工作状态跟踪

针对电子侦察系统对雷达工作状态的跟踪问题,提出了一种基于融合隐马尔科夫模型的雷达工作状态跟踪方法。该算法首先将雷达工作过程建模成隐马尔科夫模型,其次通过对侦察的雷达短语序列识别,完成单个平台下雷达工作状态的跟踪;最后再运用DS证据理论将多平台的识别结果进行融合,实现多平台融合跟踪。对算法的识别率进行仿真,仿真结果表明,提出的算法提高了错误观测下对雷达工作状态跟踪的准确率,当观测值错误率为20%时跟踪正确率高达93%。

Radar working state recognition based on the fusion hidden Markov model

For the tracking problem of radar working state by electronic reconnaissance system, a radar working state tracking method based on fused hidden Markov model is proposed. First, the radar working process is modeled as a hidden Markov model by this algorithm. Second, by recognizing the reconnaissance radar phrase sequence, the tracking of radar working state is realized on a single platform. Finally, the DS evidence theory is used to fuse the recognition results of multi platform to realize the multi platform fusion tracking. The recognition rate of the algorithm is simulated, and the simulation result shows that the proposed algorithm can improve the tracking accuracy of radar working state under error observation. When the error rate of observation is 20%, the tracking accuracy reaches 93%.