UAV Object Tracking for Battlefield Reconnaissance Based on a Chaotic-mutated Bat Algorithm

To solve the particle dilution problem of particle filter during the resampling process, we propose a novel unmanned aerial vehicle (UAV) object tracking algorithm based on a new heuristic algorithm called chaotic mutated bat algorithm. The bat algorithm (BA) is inspired by the echolocation mechanism of bats in accordance with pulse rates variance of emission and loudness. It has been proven effective in a wide range of optimization problems. We adopt the BA in UAV object tracking by using particle filter, and a chaotic-mutated improvement is proposed by using chaotic theory and a mutated operator for BA. We were able to resolve the particle dilution problem in the particle filter. To justify the effectiveness of the proposed method, comparative tracking simulations are conducted by employing the standard particle filter. Experimental results show that the proposed method is superior to other methods, which achieves a higher fitness value than standard optimization algorithms.