Abstract
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) and Estimation Signal Parameter via Rotational Invariance Technique (ESPRIT), which are well-known for their high-resolution capability for detecting DOA, are two examples of an eigen-subspace algorithm. However, missed detection and estimation accuracy reduction often occur due to the low signal-to-noise ratio (SNR) and snapshot deficiency (small time-domain samples of the observed signal), especially for sources with different SNRs. To avoid the above problems, in this study, we develop a DOA detection approach through signal subspace reconstruction using Quantum-Behaved Particle Swarm Optimization (QPSO). In the developed scheme, according to received data, a noise subspace is established through performing an eigen-decomposition operation on a sampling covariance matrix. Then, a collection of angles randomly selected from the observation space are used to build a potential signal subspace on the basis of the steering matrix of the array. Afterwards, making use of the fact that the signal space is orthogonal to the noise subspace, a cost function, which contains the desired DOA information, is designed. Thus, the problem of capturing the DOA information can be transformed into the optimization of the already constructed cost function. In this respect, the DOA finding of multiple signal sources—that is, the multi-objective optimization problem—can be regarded as a single objective optimization problem, which can effectively reduce the probability of missed detection of the signals. Subsequently, the QPSO is employed to determine an optimal signal subspace by minimizing the orthogonality error so as to obtain the DOA. Ultimately, the performance of DOA detection is improved. An explicit analysis and derivation of the developed scheme are provided. The results of computer simulation show that the proposed scheme has superior estimation performance when detecting signals with very different SNR levels and small snapshots.
Highlights
Array signal processing is a crucial branch of signal processing and is extensively applied in remote sensing [1,2,3], radar [4,5,6,7,8,9,10,11], wireless communication, sonar detection, and navigation and positioning systems
MUltiple SIgnal Classification (MUSIC) [12,13,14,15,16] and the Estimation Signal Parameter via Rotational Invariance Technique (ESPRIT) [17,18,19,20,21] are the most representative methods encountered in direction of arrival (DOA) detection
The cost function is a possible multimodality problem, and the Quantum-Behaved Particle Swarm Optimization (QPSO) will be employed for its good astringency and fast convergence speed to obtain the true DOA information of signal sources
Summary
Array signal processing is a crucial branch of signal processing and is extensively applied in remote sensing [1,2,3], radar [4,5,6,7,8,9,10,11], wireless communication, sonar detection, and navigation and positioning systems. ESPRIT exploits the subarray partition and rotational invariant of different subarrays to obtain high-resolution DOA information It suffers from a loss of array aperture to a certain extent, which results in performance degradation for DOA detection, especially for low SNR thresholds. Some additional preprocessing operation is indispensable when applying it to arbitrary antenna configurations, such as array transformation [23] or special array geometry’s high-order cumulant calculation [24] Another kind of DOA detection algorithm is the subspace fitting algorithm [25,26,27,28,29];. The subspace-class algorithm based on covariance matrix decomposition has poor adaptability to a low SNR and fewer snapshots, and the high computational complexity of a subspace fitting algorithm is often unacceptable in practical application.
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