多雷达协同组网进行目标探测识别时,受复杂战场环境影响,获取的数据富含大量杂波和不确定信息,传统雷达点迹识别算法在处理此类数据时具有一定局限.为了解决上述问题,提出一种基于自更新置信分类网络的雷达点迹识别算法(radar plots recognition algorithm based on self-updating confidence classification network,RPR-SCCN).首先,构建置信分类网络,获取各轮迭代下雷达点迹隶属目标、杂波和不确定的初始置信度.然后,基于点迹的空间分布特性构造决策证据并进行修正融合,融合结果进行点迹类别更新,更新点迹则再次驱动置信分类网络训练学习.优化后的置信分类网络继续进行下一轮次的点迹置信更新、决策证据构建以及类别标签更新等,此过程一直循环迭代,直至前后两轮次的雷达点迹类别标签不再变化时停止.实测雷达点迹的实验验证结果显示,点云分形网络(point fractal network,PF-Net)、基于全连接神经网络的雷达点迹分类算法(radar plot classification based on fully connected neural network,RPC-FNN)、粒子群优化概率神经神经网络(particle swarm optimization probabilistic neural network,PSO-PNN)和基于卷积神经网络的雷达点迹分类算法(radar plot classification based on convolutional neural networks,RPC-CNN)典型雷达点迹智能识别算法的识别正确率为82％～90％,所提算法则可以达到93％,提升3％～10％.此外,所提算法对训练样本数目依赖较小,便于推广应用.
A Radar Plots Recognition Algorithm Based on Self-updating Confidence Classification Network
When multiple radars collaborate for target detection and recognition,the obtained data is rich in clutter and uncertain information due to the complex battlefield environment.Traditional radar plots recognition algorithms have certain limitations in process-ing such data.Therefore,a radar plots recognition algorithm based on self-updating confidence classification network(RPR-SCCN)was proposed.Firstly,a confidence classification network was constructed to obtain the belief of each radar plots belonging to target,clutter,and uncertainty that under each iteration.Then,based on the spatial distribution characteristics of the dots,decision evidence was constructed and corrected for fusion.The fusion result updated the dot category,and the updated dot drove the training and learn-ing of the confidence classification network again.The optimized confidence classification network continued to perform the next round of trajectory confidence updates,decision evidence construction,and category label updated.This process iterated continuously until the radar trajectory category labels in the previous and subsequent rounds no longer changed.The experimental verification results of actual radar tracking show that the recognition accuracy of typical radar tracking intelligent recognition algorithms such as point fractal network(PF-Net),radar plot classification based on fully connected neural network(RPC-FNN),particle swarm optimization probabi-listic neural network(PSO-PNN)and radar plot classification based on convolutional neural networks(RPC-CNN)is 82％～90％,and the proposed algorithm can reach 93％,an improvement of 3％～10％.In addition,the proposed algorithm has a small dependence on the number of training samples,making it easy to promote and apply.
万方数据
维普
