Bearing-Only Target Tracking Based on Big Bang – Big Crunch Algorithm

Abstract

Target tracking based on passive sensor data is of great importance in practical applications. In bearing only target tracking, the basic parameters defining the target motion is estimated through noise corrupted measurement data. Depending on the noise characteristics, the search space has many local minima. Obtaining the global minimum -that is the optimal solution - is an active area of research over the past few decades. In this work, a new optimization algorithm, namely Big Bang - Big Crunch algorithm is shown to fit this problem. The results are superior relative to classical genetic algorithm approach both in terms of speed and accuracy.