Maneuvering Air Target Tracking with Evolutionary Particle Filter

Maneuvering air targets Tracking has many applications in defensive and non-defensive areas. Target tracking requires the estimation of position, velocity, and acceleration, simultaneously. Common approaches for air targets tracking measures the distance to target and target heading angle which is a nonlinear function of system states. Since these measurements are noisy, using estimating and filtering methods for assessing the speed and acceleration of target is essential. Although, extended Kalman filter works well with nonlinear systems and Gaussian noises; in practice we encounter with non-Gaussian noises such as Glint which particle filters show better performance in them. In addition, due to the high computational load of particle filters, implementing and applying them is impossible. In this paper, the evolutionary algorithm of particle swarm optimization has been used in the sampling step to reduce computational load and improve the real-time performance of particle filter in solving air target tracking problems. The proposed method is simulated and evaluated in a scenario involving all possible motions of the target with high maneuvering. In addition, performance of an evolutionary particle filter with some particle filters and extended Kalman filter is compared. The simulation results indicate that the evolutionary particle filter has the capability of real time air targets tracking with maneuvering in comparison with the particle filter and extended Kalman filter while having high precision.