Abstract
In the context of multi-array multi-target bearing-only localization, due to the existence of direction-finding errors, the crossing results of bearing lines cannot accurately determine correspondence with targets. Under conditions that clutter interference and missing of detection in direction-finding, the traditional method will produce false alarm targets and miss some targets. To address this issue, this paper draws on the idea of a spatial likelihood map which calculates the likelihood of target presence at each grid point within the observation area by partitioning the observation area into grids and utilizing bearing data from each array, yielding the distribution of targets in the observation area. Then, a multi-target cyclic peak extraction algorithm based on a statistical dual-threshold is proposed, which eliminates false peaks by cyclic extraction of target positions, so as to reduce false targets. Simulation results demonstrate that the spatial likelihood mapping-based localization exhibits good performance. Furthermore, when the multi-target cyclic peak extraction algorithm based on statistical dual-thresholds is applied, it outperforms direct target extraction from the spatial likelihood map, showcasing enhanced multi-target localization capabilities. Moreover, compared to the position non-linear least squares multi-target localization method, the proposed method has lower optimal sub-pattern assignment distance and lower localization error under the condition of interference and missing detection.
Published Version
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