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

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.

Dynamic Modeling of Nox Emission from Power Plants Considering Input Variable Time-Delay and Feature Extraction

“…In order to obtain the optimal dynamic model, an improved quantum particle swarm optimization algorithm [9,10] is used to optimize the delay parameters of the dynamic model. The number of particle dimensions is optimized to be 7.…”

Joint Estimation of Azimuth and Distance for Far-Field Multi Targets Based on Graph Signal Processing

“…The eigenvalue matrix of the eigenvector Λ = {λ 1 , λ 2 , • • • , λ m } and the covariance matrix Q = [q 1 , q 2 , • • • , q m ] can be decomposed, but there is no direct description of the azimuth and distance information in the eigenvector Q, but the orthogonality of the eigenvector Q can provide information about the signal subspace and the noise subspace [17]. By traversing the parameter θ and arranging the pairs in descending order of eigenvalues R xx , the signal and noise subspace decomposition form can be obtained [18]:…”

“…The radar step frequency array joint estimation model is shown in Figure 2, and the corresponding graph signal structure is constructed through the radar signal array structure of Figure 2, as shown in Figure 3. The m-th element of the radar array corresponds to the m-th node in the graph signal, which constitute the point set V = {v 1 , • • • , v m }; inspired from the previous work of nofull connected graph signal algorithm [18], the expression of the phase difference of the m-th element relative to the n-th element in the array shown below according to Equation (4):…”

“…2022, 14, x FOR PEER REVIEW 7 of 30 structure of Figure 2, as shown in Figure 3. The -th element of the radar array corresponds to the -th node in the graph signal, which constitute the point set ; inspired from the previous work of nofull connected graph signal algorithm [18], the expression of the phase difference of the -th element relative to the th element in the array shown below according to Formula (4):…”

Guided Lamb Wave Array Time-Delay-Based MUSIC Algorithm for Impact Imaging

“…Moreover, due to its superior performance in resolving closely spaced or overlapping signal sources, the MUSIC algorithm has been widely applied in various fields [ 24 , 25 , 26 ]. In traditional applications, such as radar signal processing, sonar detection, and seismic wave analysis, the MUSIC algorithm has demonstrated significant effectiveness in locating and identifying signal sources [ 27 , 28 , 29 ].…”