Aninvestigation oftheeffects ofsystemandmediumperturbations, andthedeviations ofsignal field statistics fromusual assumptions, on theperformance ofadaptive arraysispresented. Theemphasis isplaced on thedetermination ofthesensitivity oftheadaptive processorstoperturbations, rather thanitsoverall performance in arepresentative background. Thenumerical results arelimited to CW signals, andafrequency domainelement spaceadaptive beamformerismodeledforthemajority oftheresults. Representative comparative results including frequency domainbeamspace,time domainelement space,andmatched arrayprocessorsarealsogiven.

https://ieeexplore.ieee.org/iel5/7/4102094/04102106.pdf

Effects ofPerturbations on thePerformance of Optimum/Adaptive Arrays

Robust Adaptive Beamforming via Subspace for Interference Covariance Matrix Reconstruction

Robust Adaptive Beamforming Based on Steering Vector Estimation and Covariance Matrix Reconstruction.

Robust adaptive beamforming based on a method for steering vector estimation and interference covariance matrix reconstruction

To develop an adaptive beamformer against the signal of interest (SOI) steering vector mismatch, a robust Capon beamformer (RCB) like steering vector estimation method based on the interference matrix reduction is proposed. Different from the RCB and its modified versions that optimize the SOI steering vector with the Capon power estimator, this article designs an SOI power estimator to formulate the steering vector optimization problem with an uncertainty set constraint. In terms of that, the unknown SOI covariance matrix is needed to realize the SOI power estimator, an efficient interference matrix reconstruction way via SOI blocking and matrix eigen-transition is exploited to reduce the interference component from the sample covariance matrix. Herein, after solving the given steering vector optimization problem and adding the noise component to the aforesaid interference matrix, the weight vector of the derived algorithm is, thereby, computed using the estimated SOI steering vector and interference covariance matrix. The proposed method only requires the source number and prior direction of the SOI. The numerical simulations show that the proposed approach can outperform the compared ones with reduced complexity in the situation of various steering vector mismatches.

An RCB-Like Steering Vector Estimation Method Based on Interference Matrix Reduction

Recently, a number of robust adaptive beamforming (RAB) methods based on Capon power spectrum estimator integrated over a specific region for covariance matrix reconstruction have been proposed. However, all of these methods ignore the residual noise existing in the Capon spectrum estimator, which results in reconstruction errors. In this paper, we propose a RAB algorithm via residual noise elimination and interference powers estimation to reconstruct covariance matrix. First, the proposed algorithm demonstrates the existence of residual noise and analyze its relationship to actual noise. Then, after eliminating the residual noise, the modified Capon power spectrum estimator is utilized to reconstruct the covariance matrix and desired signal SV. Moreover, to reduce the influence of the desired signal on interference powers estimation, we project the snapshots onto the complementary subspace of the desired signal and estimated interference powers are derived according to the theoretical formulation of the interference covariance matrix (ICM). The simulation results demonstrate that the proposed method is robust against various mismatches and can achieve superior performance.

/pdf/covariance-matrix-reconstruction-via-residual-noise-4jwnzu34ko.pdf

Covariance Matrix Reconstruction via Residual Noise Elimination and Interference Powers Estimation for Robust Adaptive Beamforming

The invention discloses a co-prime array robust adaptive beamforming method based on interference covariance matrix reconstruction. The method comprises the following steps of reconstructing a sampling covariance matrix on the virtual uniform matrix, removing expected signal components in the sampling covariance matrix by using projection, estimating accurate power and direction of each interference according to semi-positive qualitative constraints, reconstructing an interference covariance matrix, estimating noise power in a noise angle area, and obtaining an interference and noise covariance matrix; and taking the main characteristic value of the reconstructed expected signal covariance matrix as an expected signal steering vector, thereby obtaining a weight vector of the robust adaptive beamformer based on the co-prime matrix, and forming the output of the robust adaptive beamformer.

Co-prime array robust adaptive beamforming method based on interference covariance matrix reconstruction

Correlation filtering has been widely used in recent years because of its high real-time performance. But many of them ignore the reliability of the tracking results, so it is possible to find the model drift in the long-term tracking. Moreover, it is unavoidable that the target will be blocked or lost in the long duration of the tracking of the target. Therefore, the research on the tracking algorithm for such complicated problems is extremely important. In order to solve these problems, we present a long term tracking framework which consists of a tracking module and a re-detection module. The tracking module is on ECO, an effective convolution operator. For the detection of missing objects, an approach to estimate the uncertainty of tracking is proposed, which is used to evaluate the performance of each image. On this basis, an adaptive update algorithm is proposed, which can reduce the touch times of the model and improve the robust performance of the tracking algorithm. Examples show that this algorithm uses only CPU to reach more than 30 fPs. Compared with the existing excellent tracking techniques, the proposed approach has better performance.

Visual Long-Term Target Tracking Method Based on Uncertain Estimation Principle

Forward-looking sonar (FLS) is widely applied in underwater operations, among which the search of underwater crash objects and victims is an incredibly challenging task. An efficient detection method based on deep learning can intelligently detect objects in FLS images, which makes it a reliable tool to replace manual recognition. To achieve this aim, we propose a novel Swin Transformer based anchor-free network (STAFNet), which contains a strong backbone Swin Transformer and a lite head with deformable convolution network (DCN). We employ a ROV equipped with a FLS to acquire dataset including victim, boat and plane model objects. A series of experiments are carried out on this dataset to train and verify the performance of STAFNet. Compared with other state-of-the-art methods, STAFNet significantly overcomes complex noise interference, and achieves the best balance between detection accuracy and inference speed.

Xingyu Zhu

Papers

Robust Adaptive Beamforming via Subspace for Interference Covariance Matrix Reconstruction

Covariance Matrix Reconstruction via Residual Noise Elimination and Interference Powers Estimation for Robust Adaptive Beamforming

Co-prime array robust adaptive beamforming method based on interference covariance matrix reconstruction

Visual Long-Term Target Tracking Method Based on Uncertain Estimation Principle

STAFNet: Swin Transformer Based Anchor-Free Network for Detection of Forward-looking Sonar Imagery