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联合概率数据关联算法(JPDA)是一种在杂波环境下能对多目标进行数据互联的良好算法。它的计算量随着回波数目和杂波数目的增加会呈指数上升。虽然有一些类JPDA的方法可以减少其计算量,但在杂波环境下其准确性仍不能保证。本文将JPDA中关联矩阵的求解等效为多维分配问题,通过霍普菲尔德神经网络CHNN及混沌算法等智能算法的应用来提高算法的准确性。
The Joint Probabilistic Data Association Algorithm (JPDA) is a good algorithm for multi-objective data interconnection in a clutter environment. Its computational complexity increases exponentially with increasing number of echoes and clutter. Although some JPDA-like methods can reduce the computational complexity, their accuracy in clutter environments can not be guaranteed. In this paper, the solution of the correlation matrix in JPDA is equivalent to the problem of multidimensional distribution. The application of intelligent algorithms such as Hopfield neural network CHNN and chaos algorithm to improve the accuracy of the algorithm.