Small Target Detection Using Two-Dimensional Least Mean Square (TDLMS) Filter Based on Neighborhood Analysis

TDLMS is a general adaptive filter algorithm and when applied to infrared small target detection, traditional structure and implementation of TDLMS may cause some problems in this field. This paper presents a new TDLMS filter structure and implementation incorporating neighborhood analysis and data fusion, which is capable of acquiring and analyzing more information from the vicinity of the target, leading to a more prominent detection result. This enables TDLMS filter to perform better and become more suitable in the field of small target detection. Experiments showed the efficiency of the proposed algorithm.