An Efficient Two-Dimensional Least Mean Square (TDLMS) Based on Block Statistics for Small Target Detection

In this paper, we introduce an efficient TDLMS filter, using the new weight structure and nonlinear step size for small target detection within infra-red (IR) imagery. A new TDLMS filter that can efficiently detect a small target in IR imagery is proposed. The concept of the proposed filter is to utilize the new weight matrix having the structure reducing effects of the target pixels in order to predict exactly the background. The nonlinear step size utilizing the block statistics is used and background estimation is calculated finally by using the Gaussian distance map. Experimental results show that the proposed method exhibits higher detection rates and lower false alarm rates in comparison to the conventional TDLMS filter.