Target Angle Estimation Using Compressive Sensing Methods (Echo)

In the field of radar signal processing, compressive sensing offers a novel approach to traditional angle measurement techniques. This method is particularly suitable for MIMO radar systems, enabling high-precision target detection with observation data far below the Nyquist sampling rate. Core Principles By constructing a sparse representation model, the target angle estimation problem is transformed into solving an underdetermined system of equations. Due to the inherent sparsity of targets in the spatial domain (limited number of reflective sources), compressive sensing theory allows reconstruction of the original signal from a small number of random linear measurements. The multi-antenna characteristics of MIMO radar further enhance spatial sampling degrees of freedom. Code Implementation Insight: The sparse recovery typically involves solving an L1-norm minimization problem using algorithms like Orthogonal Matching Pursuit (OMP) or Basis Pursuit. In MATLAB, this can be implemented using built-in optimization functions with proper regularization parameters. Implementation Limitations The current method is only applicable to one-dimensional angle measurement (e.g., azimuth) because two-dimensional angles (azimuth + elevation) would compromise the solvability of the sparse basis matrix, leading to reconstruction algorithm failure. In practical engineering, auxiliary techniques like beamforming or subarray partitioning are required for dimensional extension. Algorithm Consideration: For 2D angle estimation, the dictionary matrix size grows exponentially, making computational complexity prohibitive. Code implementation would require specialized sparse recovery algorithms adapted for higher dimensions. Application Advantages Compared to traditional FFT beam scanning, compressive sensing solutions significantly reduce hardware sampling requirements while demonstrating superior noise immunity. Particularly in low SNR environments, angle resolution can be maintained through L1-norm optimization, which is crucial for covert target detection. Key Functionality: The L1-regularized optimization can be implemented using convex optimization packages (e.g., CVX in MATLAB) with proper constraint handling for real-time radar systems.