Bacterial Foraging Optimization for High-Resolution LIDAR Target Discrimination in Closely Spaced Scenarios

Abstract

In this work, Bacterial Foraging Optimization (BFO) is utilized to correct two close-spaced targets at 1000 m and 1005 m in LIDAR processing. The algorithm has been tuned to achieve minimum mean-square error between simulated and received signal using realistic system parameters like aperture size and pulse width. BFO pursues exploration and exploitation in a balance manner and yields accurate range estimates with an MSE of 1.76×10⁻15. This bio-inspired strategy exhibits outstanding capacity to tackle ambiguous targets and can be extended to multi-scenario LIDAR application.