A coarse to accurate noise-tolerant positioning evaluation for mobile target based on modified genetic algorithm

In the process of locating unknown targets within a target area using multiple cruise munition nodes, it is crucial to achieve precise and rapid target localization through sensor data from multiple munitions. This paper presents a target positioning algorithm based on the Particle Swarm Optimization (PSO) algorithm, which enables cooperative positioning by multiple cruise munitions through the establishment of an appropriate objective function. Simulation results show that while the PSO algorithm offers high positioning accuracy, it can sometimes get trapped in local optima. To address this, the PSO algorithm is optimized by introducing dynamic adaptive weights and integrating Differential Evolution (DE) and Simulated Annealing (SA) algorithms. Comparative simulations demonstrate that the improved PSO algorithm achieves greater accuracy and stability than other optimization algorithms.

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A coarse to accurate noise-tolerant positioning evaluation for mobile target based on modified genetic algorithm

Cited by 4 publications

References 37 publications

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Multiple cruise munitions target localisation algorithm based on optimised particle swarm algorithm

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