多站联合观测非合作目标的定位批号关联算法

Locational lot number association algorithm for multi-station joint observation of non-cooperative targets

在多站联合对非合作多目标进行定位场景中,提出了一种批号关联算法。该方法利用模糊数学理论描述已关联集和待关联量测中方位信息、连续谱特征信息和线谱特征信息之间的模糊隶属度,构建综合模糊关系矩阵进行二维关联,通过设置隶属度门限消除低隶属度关联的影响。仿真结果表明,所提算法克服了传统算法在目标增多关联成功率下降的问题,在一定的漏报虚警时关联结果召回率可达到0.8以上,关联结果精度也保持在较高水平。仿真结果及试验数据均验证了该方法在复杂水下环境中较好的目标关联定位性能。

A locational lot number association algorithm is proposed for joint multi-station observation of non-cooperative target localization scenarios.The algorithm uses fuzzy theory to describe the fuzzy membership function between the direction information,continuous spectral feature information,and line spectral feature information in the correlated set and the measure to be correlated.A comprehensive membership matrix of fuzzy is constructed for the two-dimensional association,reducing the impact of low membership by setting a membership threshold.Simulation results show that the algorithm overcomes the problem that the success rate of association decreases when the number of targets increases,and the recall rate of association results can reach more than 0.8 when there are certain missed and false alarms,and the accuracy of association results is also maintained at a high level.Both simulation results and experimental data confirm the superior performance of this method for target correlation localization in complex underwater environments.