Abstract:

With the vigorous development of mobile communication,the detection of long-distance targets in modern warfare pays more attention to concealment and security.In this context,the research on non-cooperative multi-target positioning by passive receiving signals has attracted wide attention.However,passive localization of multiple non-cooperative targets is greatly affected by the matching relationship between signal data,which influences the accuracy of target position estimation.To address the complexity and diversity of data matching relationships,this paper proposes a distributed data matching localization algorithm for non-cooperative multi-targets.The algorithm obtains angle and intensity measurements from signal calculations,constructs measurement data pairs,and selects candidate reference points to reduce measurement data matching complexity.It then constructs a cost function through the probability analysis of measurement data pair matching,selects the measurement information of the minimum cost point as the best match for the target,and calculates the position of each target by the clustering analysis of final proxy point sets.Simulation results show that the proposed algorithm can increase the target matching success rate by over 26.3% and the computational speed by 16.8% in the same scenario,and can achieve fast and accurate multi-target localization with an average positioning error of about 17.41 m in a region of 6 km×4 km.

Key words: non-cooperative multi-target, distributed localization, data association, cost functions, cluster analysis