基于证据理论的雷达信号融合识别算法改进研究

Study on improvement of radar signal fusion recognition algorithm based on evidence theory

针对利用证据理论不能高效识别和区分复杂多变的雷达信号的问题,对证据融合识别算法进行了改进。首先引入分类策略和运用一致性排序法分别对待识别信号进行证据分类和求各参数的权重值;然后用改进的证据融合规则对信号进行参数加权融合和验证,再对各信号集中的信号进行融合和判决识别。仿真表明,改进后的算法可对待识别信号进行正确分类识别,识别率高,消耗时间少,适合实际信号的识别应用。

As evidence theory algorithms fail to identify and distinguish the complex and unstable radar signals efficiently, the improvement have be presented for the algorithm of fusion recognition based on evidence theory. Firstly, add the classification strategy to classifying the signals and use the consistency of ranking method to measuring the weight value of each parameters. Then, the improved evidence fusion rules is subsequently applied to exercise fusion processing on the similarity of parameters concerning each signal and the trust value concerning target framework of each signal in order. Finally, finish the recognition according to the final trust value. The results of simulation reveal that the improved algorithm can exert favorable effect on the distinction of unknown complex radar signals, with higher recognition rate and less consumption time, which is more fit for the actual signal fusion recognition.