Multistatic Single Data Set Target Detection in Unknown Coloured Gaussian Interference

This paper proposes a solution to the problem of searching for a moving airborne target in unknown coloured Gaussian interference. Traditional space-time adaptive processing (STAP) approaches to this problem rely on the existence of a training data set that helps build an estimate to the covariance matrix of the background noise and interference. These algorithms are thus labelled as two data set (TDS). In contrast, the work in this paper focuses on single data set (SDS) STAP detection that forms an estimate to the noise covariance matrix without access to training data and without any assumptions on the shape of the noise spectrum. Two such algorithms for target detection are presented that operate in a multiple-input multiple-output (MIMO) system of widely-spaced transmit and receive antennas. These algorithms are proven to possess the highly desirable constant false alarm rate (CFAR) property. It is shown through simulations that the proposed SDS solutions are comparable in their performance to the existing TDS solutions to the same problem.