Signal Subspace Reconstruction for DOA Detection Using Quantum-Behaved Particle Swarm Optimization

Spatial spectrum estimation, also known as direction of arrival (DOA) detection, is a popular issue in many fields, including remote sensing, radar, communication, sonar, seismic exploration, radio astronomy, and biomedical engineering. MUltiple SIgnal Classification (MUSIC) Estimation Signal Parameter via Rotational Invariance Technique (ESPRIT), which are well-known for their high-resolution capability detecting DOA, two examples an eigen-subspace algorithm. However, missed detection estimation accuracy reduction often occur due to the low signal-to-noise ratio (SNR) snapshot deficiency (small time-domain samples observed signal), especially sources with different SNRs. To avoid above problems, this study, we develop DOA approach through signal subspace reconstruction using Quantum-Behaved Particle Swarm Optimization (QPSO). In developed scheme, according received data, noise established performing eigen-decomposition operation on sampling covariance matrix. Then, collection angles randomly selected from observation space used build potential basis steering matrix array. Afterwards, making use fact that orthogonal subspace, cost function, contains desired information, designed. Thus, problem capturing information can be transformed into optimization already constructed function. respect, finding multiple sources—that is, multi-objective problem—can regarded single objective problem, effectively reduce probability signals. Subsequently, QPSO employed determine optimal by minimizing orthogonality error so obtain DOA. Ultimately, performance improved. An explicit analysis derivation scheme provided. The results computer simulation show proposed has superior when signals very SNR levels small snapshots.