Frequency Diverse Array Target Localization Based on IPSO-BP

Abstract

For the traditional target localization algorithms of frequency diverse array (FDA), there are some problems such as angle and distance coupling in single-frequency receiving FDA mode, large amount of calculation, and weak adaptability. This paper introduces a good learning and predictive method of target localization by using BP neural network on FDA, and FDA-IPSO-BP neural network algorithm is formed. The improved particle swarm optimization (IPSO) algorithm with nonlinear weights is developed to optimize the neural network weights and biases to prevent BP neural network from easily falling into local minimum points. In addition, the decoupling of angle and distance with single frequency increment is well solved. The simulation experiments show that the proposed algorithm has better target localization effect and convergence speed, compared with FDA-BP and FDA-MUSIC algorithms.

Complex Multisnapshot Sparse Bayesian Learning for Offgrid DOA Estimation

“…rough the above analysis, the performance of the twolayer hierarchical prior p(x i |α i ) and p(α i ) is better than p(x i |α i ) and p(c i ), and the IG prior model is better than Gamma prior model. e proposed algorithm is further applied to the angledistance positioning in the FDA (frequency diverse array) [34]. e complex multisnapshot SBL combined with FDA radar characteristics is used for target angle-distance twodimensional localization.…”

Radar Working State Recognition Based on Improved HPSO-BP

“…e PSO method finds the optimal solution through collaboration and information sharing between the individuals in the group and has the characteristics of simple principles, fewer parameters, and strong global search capabilities. erefore, this method has been widely used in function optimisation [20], neural network training [21], fuzzy control systems [22], and other fields, and the algorithm is considered to be relatively mature. However, PSO may easily converge prematurely.…”