Target Parameter Estimation in Frequency Diverse Array Radar using Sparse Reconstruction Algorithms

Abstract

The frequency diverse array radar has brought a lot of interest due to the periodicity of the beampattern in angle, time, and range. It is possible to transmit energy over the required coverage and angle with only a small variation in frequency, yielding an array factor from the angle, duration, and range. This newer technique, MIMO FDA (which refers to multiple inputs and multiple Output frequency diversity array), was recently invented. It is intended to improve upon FDA radar in various ways. It is a hybrid of MIMO and FDA, and in the interest of estimating the angle and range simultaneously, a minimum frequency increment is employed in the transmitting antennas, which are positioned near each other. This is done so that the system can calculate both simultaneously. This aims to ensure that the measurements are as accurate as possible. The sparse reconstruction techniques used in this thesis are utilized in a MIMO FDA transmitting array. The one multiple signal classification (MUSIC) technique is utilized for joint angle estimation in FDA-MIMO. As the methodology recommends, estimating angles using an FDA MIMO radar is accomplished by employing sparsity enforced reconstruction technique. When applied to FDA MIMO radar, numerical findings indicate that the FBMP algorithm provides an outstanding Mean Squared Error versus Signal to Noise Ratio performance compared to other algorithms.