Research On Ground Target Hierarchical Association Algorithm Based On Radar And Communication Reconnaissance

In the field of electronic reconnaissance,passive detection is gaining more and more attention due to its unique advantages such as high concealment and long distance of action.Radar reconnaissance and communication reconnaissance are the two most widely used reconnaissance methods in passive detection.Radar reconnaissance has a long detection range and can work all-weather,which can accurately determine the target orientation and identify the target attributes.At the same time,communication reconnaissance has the advantages of wide frequency coverage and reconnaissance area.The same platform may carry both radar radiation source and communication radiation source,while radar and communication reconnaissance can obtain different kinds of feature information of the same platform,can detect more comprehensive and complete target information,and can obtain more effective and accurate target information through target association technology.However,there are some difficulties in target association due to the large difference of data information between radar and communication reconnaissance and the mismatch of measurement dimensions.In response to the above problems,this paper conducts the target correlation study of similar information sources from radar radiation source and communication radiation source respectively,and carries out the target correlation study of dissimilar information sources of radar and communication radiation source based on the correlation between the two.The details of the research are as follows:（1）A multi-target data correlation algorithm based on orientation and attributes is proposed for the problem of false targets easily appearing in the radar source target association localization under radar reconnaissance.First,the D-S evidence synthesis rule is used to calculate the attribute feature parameters of radar targets to obtain the overall support degree to reject some candidate association combinations;then,the angular measurements of each sensor are used to calculate the line-of-sight distances between all lines of sight of the same target and construct the distance statistical test volume to reject the false candidate association combinations again;the final association combinations are obtained to provide the basis for the next positioning step.The results of experiment demonstrate that the algorithm is effective in accomplishing target correlation between radar targets.（2）A communication target association method based on adaptive entropyweighted gray correlation is proposed for the problem that the large amount of characteristic parameters and data of communication radiation sources make association difficult.First,the original data is analyzed and processed by the gray correlation algorithm to calculate the gray correlation coefficients of communication target attribute features;then,the adaptive entropy weights are constructed for different attribute feature parameters,and the gray correlation degrees are obtained by using the gray correlation coefficients and adaptive entropy weights;finally,the gray correlation is used as the distance between two classes to reduce the operational complexity,and the final association results are obtained by k-means and DBSCAN clustering algorithms,respectively,and their effectiveness is verified by simulation experiments.（3）To address the problem that the association accuracy of traditional target association algorithms based on location information decreases in ground target detection scenarios when the predicted positions of targets obtained by radar and communication reconnaissance may be inaccurate,a target association algorithm based on point pair topology is proposed to solve the problem of ground radiation source.First,this paper describes the topology between the targets in the local topology,reducing the impact of noise;then use adaptive weight similarity calculation method to obtain similarity between the respective points,thereby forming a similarity matrix;finally this point patterner matching problem is converted to a problem with the maximum perfect match in the weighted bipartite graph,and the Hungarian algorithm is used to perform the best match,and ultimately the association.The experimental results show that the algorithm in this paper can still maintain a good association effect in the case of low localization accuracy,high noise interference and the existence of target omission detection.