Matrix analysis
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
SIAM Review
Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Space-Time Block Coding for Wireless Communications
Space-Time Block Coding for Wireless Communications
Linear minimax regret estimation of deterministic parameters with bounded data uncertainties
IEEE Transactions on Signal Processing
Spatial diversity in radars-models and detection performance
IEEE Transactions on Signal Processing
Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals
IEEE Transactions on Signal Processing
Covariance shaping least-squares estimation
IEEE Transactions on Signal Processing
Range Compression and Waveform Optimization for MIMO Radar: A CramÉr–Rao Bound Based Study
IEEE Transactions on Signal Processing
Optimal design and placement of pilot symbols for channel estimation
IEEE Transactions on Signal Processing
Evaluation of Transmit Diversity in MIMO-Radar Direction Finding
IEEE Transactions on Signal Processing
Optimal placement of training for frequency-selective block-fading channels
IEEE Transactions on Information Theory
Signal design for a class of clutter channels (Corresp.)
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Information theory and radar waveform design
IEEE Transactions on Information Theory
MIMO radar waveform design in colored noise based on information theory
IEEE Transactions on Signal Processing
On parameter identifiability of MIMO radar with waveform diversity
Signal Processing
Optimization of radar phase-coded signals for multiple target detection
Signal Processing
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Waveform design for target identification and classification in MIMO radar systems has been studied in several recent works. While the previous works assumed that the noise was independent of the transmission signals, here we extend the results to the signal dependent noise (clutter). We consider two scenarios. In the first scenario it is assumed that different transmit antennas see uncorrelated aspects of the target. In the second scenario, we consider the correlated target. As clutter is dependent to signal, target estimation error cannot vanish only by increasing the transmission power. It can be shown that in the second scenario, MIMO radar receiver can nullify the clutter subspace. Thus, in the second scenario, target estimation error tends to zero if the transmission power tends to infinity. We consider waveform design problem for these scenarios based on MMSE and MI criteria. Like previous works, we find that these criteria lead to the same solution. Our problems lead to the convex optimization problems, which can be efficiently solved through tractable numerical methods. Closed-form solutions are also developed for this SDP problem in two cases. In the first case, target and clutter covariance matrices are jointly diagonalizable and in the second, signal to noise ratio (SNR) is assumed to be sufficiently high. We also present two suboptimal formulations which require less knowledge of the statistical model of the target. In the first one the robust waveforms are computed by minimizing the estimation error of the worst-case target realization and in the second, target estimation error of the scaled least square (SLS) estimator is minimized.