Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM

Abstract

Cooperative jamming effectiveness evaluation is a key component in completing the cooperative jamming OODA loop. For the problem of evaluating the effectiveness of cooperative jamming to group network radar by formation aircraft, a cooperative jamming effectiveness evaluation method based on Improved Particle Swarm Optimization–Extreme Learning Machine (IPSO-ELM) is proposed. First, based on the working parameters of the group network radar and the information fusion rules, the cooperative jamming effectiveness evaluation function is established. On this basis, the cooperative jamming decision schemes and their corresponding cooperative jamming effectiveness values are solved at different locations in the target space, and the results are detected as outliers using box plots, thus constructing sample data for cooperative jamming effectiveness evaluation. Subsequently, a neural network based on the extreme learning machine methodology is developed, with its initial weights and biases fine-tuned through an improved particle swarm optimization, which is termed IPSO-ELM. This optimization aims to boost the model’s predictive precision. Finally, the IPSO-ELM algorithm is subjected to rigorous assessment via simulation, confirming its performance of accuracy and efficiency. From the simulation results, the advanced performance of the IPSO-ELM algorithm, specifically in the context of assessing the effectiveness of cooperative jamming, is verified.