A Single Station Passive Location Method Based on Improved ALA

Passive positioning technology is a kind of positioning technology that determines the position of a signal source by passively receiving the signals emitted by the signal source. The direct positioning determination (DPD) based on the reception of signals by array signal antennas has the advantage of higher positioning accuracy and is widely used. However, the DPD suffers from disadvantages such as a large computational burden. In this article, the artificial lemming algorithm (ALA) is introduced into the optimization process of the DPD cost function (i.e., ALA-DPD). Based on the ALA-DPD, the Optimal-Preservation Gradient-Enhanced ALA for DPD (OGALA-DPD) is proposed. The iterative optimization ability of the algorithm is enhanced by introducing the strategies of optimal individual retention and a bidirectional optimization search method. The results of the simulation demonstrate that the 95% simulation convergence generation of OGALA-DPD is approximately 22 % higher than that of ALA-DPD. Furthermore, in the context of the comparative analysis of other four algorithms, the OGALA-DPD algorithm has been demonstrated to exhibit a reduced error loss. Experiments have demonstrated that the OGALA-DPD proposed in this paper provides a more efficient and accurate solution for single-station passive positioning technology.