Direction of Arrival (DOA) Estimation Algorithms

Direction of Arrival (DOA) estimation is a crucial technique in array signal processing, aiming to determine the arrival direction of signal sources. Common DOA estimation algorithms include:

Conventional Beamforming (CBF): Achieves directional selectivity by adjusting array weights through phase shifting and amplitude weighting. Simple to implement with basic array manipulation code, but offers limited resolution. Suitable for high signal-to-noise ratio scenarios.

Capon Beamforming (MVDR): Minimum Variance Distortionless Response beamforming maximizes gain in the target direction while suppressing interference. Requires matrix inversion operations, making it computationally more intensive than CBF. Performance is sensitive to coherent signals and requires proper covariance matrix estimation.

Linear Prediction (LP): Based on linear prediction models using signal autocorrelation matrices. Assumes stationary random processes and works best for narrowband signals. Implementation involves solving Yule-Walker equations, but performance is constrained by model assumptions.

MUSIC Algorithm: Utilizes orthogonality between signal and noise subspaces through eigenvalue decomposition of the covariance matrix. Achieves super-resolution estimation via spectral peak search. Effective for incoherent signals but requires prior knowledge of source number and involves substantial computational load for subspace decomposition.

These algorithms each have advantages and limitations in different scenarios. Practical implementation requires consideration of computational resources, signal environment characteristics, and accuracy requirements when selecting the appropriate approach.