DOA Estimation for Monostatic MIMO Radar

DOA (Direction of Arrival) estimation for monostatic MIMO radar represents a significant research focus, primarily employed for accurately determining target azimuth information. MIMO (Multiple-Input Multiple-Output) radar leverages its multi-antenna configuration to substantially enhance spatial resolution and direction-finding precision. In code implementation, this typically involves designing orthogonal waveforms for transmit antennas and applying signal separation techniques at the receiver.

In DOA estimation, the MUSIC (Multiple Signal Classification) algorithm serves as a classic high-resolution technique. This algorithm utilizes the orthogonality between signal subspace and noise subspace, performing eigenvalue decomposition on the covariance matrix to estimate signal directions. The MUSIC algorithm's key advantage lies in its super-resolution capability, achieving near-theoretical-limit estimation accuracy under high SNR conditions. Code implementation typically involves calculating the sample covariance matrix from received data, performing eigenvalue decomposition using functions like numpy.linalg.eig in Python or eig in MATLAB, and constructing the MUSIC spectrum through peak search algorithms.

The CRB (Cramer-Rao Bound) provides the theoretical lower bound for evaluating DOA estimation algorithm performance, representing the minimum variance achievable by unbiased estimators under given signal models. Comparing MUSIC algorithm simulation results with CRB values offers intuitive assessment of algorithm effectiveness. When MUSIC's estimation error approaches CRB, it indicates near-optimal performance in that scenario. Code implementation often includes CRB calculation based on signal model parameters and Monte Carlo simulations for MSE comparison.

Simulation results are typically presented graphically, including MUSIC spectrum peak plots and MSE comparison curves between MUSIC algorithm and CRB under varying SNR conditions or snapshot numbers. These visualizations clearly demonstrate algorithm performance across different operational conditions, providing valuable references for engineering applications. Code implementations commonly use visualization libraries like matplotlib in Python for generating these comparative performance charts.