Reseach Of Target Detection And Imaging Of MIMO Radar

A Multiple-Input Multiple-Output (MIMO) radar transmits multiple probing signalsvia multiple antennas, and receives the backscattered signals reflected from the targetsusing multiple antennas. In contrast with conventional phased array radar, the waveformdiversity enables the MIMO radar with collocated antennas to have much improvedcapabilities including significantly improved parameter identifiability. Compared withthe conventional phased array radar, MIMO radar can significantly improve parameteridentifiability, better resolution, higher sensitivity.In the background of central college fund, this paper studies the parameteridentifiability of MIMO radar, signal detection and parameter estimation and imagingproblems. The main contributions of this thesis are as follows.1. Research on parameter identification of MIMO Radar. By creating MIMO radarsystem model, the theoretical conclusion is that MIMO radar can improve parameteridentification.2. Research on target detection and data independence of MIMO Radar. In theabsence of array steering vector errors, we discuss the application of several existingdata-dependent beamforming algorithms including Capon, APES (amplitude and phaseestimation) and GLRT. Via several numerical examples, we show that the proposedCapon method can provide excellent estimation accuracy of target locations and badlyestimation of target amplitudes. In the presence of array steering vector errors, weachieve accurate parameter estimation and superior interference and jammingsuppression performance, by applying the robust Capon beamformer (RCB) and doublyconstrained robust Capon beamformer (DCRCB) approaches to the MIMO radarsystem.3. Research on MIMO radar imaging. MIMO radar has a lower resolution andhigher sidelobe based on the delay-sum (DAS), Therefore, stationary and movingtargets imaging was conducted by applying Iterative adaptive algorithm(IAA) intoMIMO radar. In both the negligible and nonnegligible intrapulse doppler cases, weshow how IAA can be extended to MIMO radar imaging, also prove theoretical convergence properties of IAA. In addition, we propose a regularized IAA algorithm,referred to as IAA-R, which can perform better than IAA by accounting forunrepresented additive noise terms in the signal model. Numerical examples arepresented to demonstrate the superior performance of MIMO radar over single-inputmultiple-output (SIMO) radar, and further highlight the improved performance achievedwith the proposed IAA-R method for target imaging.4. Research on MIMO radar sparse imaging. We present a regularized minimizationapproach to sparse signal recovery. We herein compare SLIM, through imagingexamples and examination of computational complexity, to several well-known sparsemethods, including reweightedl1minimization and IAA algorithm. We show thatSLIM provides superior performance for sparse MIMO radar imaging applications at alow computational cost.