Optimal Array Processing with Companion Code

Optimal array processing represents a crucial direction in modern signal processing, with extensive applications particularly in radar, sonar, and wireless communication systems. Such textbooks typically combine theoretical derivations with practical programming implementations to help students deeply understand core algorithms like beamforming and spatial filtering.

The companion programs generally include basic simulation modules and exercise implementations covering: - Classical beamforming algorithms (such as delay-and-sum and MVDR beamformers) - Adaptive array processing techniques (like LMS and RLS adaptive filtering) - Spatial spectrum estimation methods (subspace algorithms including MUSIC and ESPRIT) - Robust processing and anti-interference schemes

Complete versions of these programs are typically implemented in MATLAB or Python, demonstrating array response variations through parametric designs. The exercise code intentionally preserves the mapping relationship between theoretical formulas and programming implementations - for example, showing how covariance matrix calculations translate into matrix operations in code. These resources provide significant reference value for researchers and engineers, serving both to validate theoretical results and as starting templates for actual system development.