基于粒子群差分进化混合算法的水下目标 电场定位方法研究

To achieve long-distance and high-precision positioning of underwater targets in shallow sea environments, a novel underwater target electric field positioning method based on the particle swarm optimization and differential evolution(PSODE) hybrid algorithm was proposed. Starting from the three-layer medium electric field radiation model, the underwater target was equivalent to a constant current electric dipole source. The electric field measurement data were obtained by using the irregularly arranged three-axis electric field sensor array, and a target function based on the dynamic weight of the signal-to-noise ratio and the robust Huber loss was constructed. The positioning problem was transformed into the minimization problem of the target function. To address the premature convergence of the traditional differential evolution(DE) algorithm and the insufficient local search ability of the particle swarm optimization(PSO) algorithm, a collaborative optimization mechanism was proposed. This mechanism generated diverse solution sets through DE mutation and crossover and combined the dynamic weight update strategy of PSO to enhance the local search ability. Meanwhile, an adaptive parameter adjustment and probability selection mechanism was introduced to achieve a better balance between global exploration and local exploitation, thereby effectively reducing the risk of the algorithm getting trapped in local optimal solutions. Simulation experiment results show that the proposed method has the advantages of being insensitive to initial values, strong anti-noise ability, and fast convergence speed. Compared with the traditional PSO and DE algorithms, it has higher positioning accuracy, providing an effective solution for high-precision positioning of underwater targets in shallow sea environments.