Bionic target localization method based on range-azimuth joint dictionary

The traditional underwater sonar system usually achieve high angle resolution by increasing array aperture and the number of array elements, but this method will inevitably lead to complex system and high cost. Given that big brown bats have obtained surprisingly high resolution using a simple system, this paper proposes a bionic target localization method. First, a range-azimuth joint dictionary was constructed based on the bionic system of multi-harmonic emission and double random array reception. Then, the coherence characteristic of the dictionary was analyzed and the range and azimuth of the target were estimated, and at last the experimental verification was completed. The results show that the bionic range-azimuth joint estimation based on sparse signal representation can achieve high-precision target localization under the condition of echo high aliasing.

Computationally Efficient Compressed Sensing-Based Method via FG Nyström in Bistatic MIMO Radar with Array Gain-Phase Error Effect

In this paper, a robust angle estimator for uncorrelated targets that employs a compressed sense (CS) scheme following a fast greedy (FG) computation is proposed to achieve improved computational efficiency and performance for the bistatic MIMO radar with unknown gain-phase errors. The algorithm initially avoids the wholly computation of the received signal by compiling a lower approximation through a greedy Nyström approach. Then, the approximated signal is transformed into a sparse signal representation where the sparsity of the target is exploited in the spatial domain. Finally, a CS method, Simultaneous Orthogonal Matching Pursuit with an inherent gradient descent method, is utilized to reconstruct the signal and estimate the angles and the unknown gain-phase errors. The proposed algorithm, aside achieving closed-form resolution for automatically paired angle estimation, offers attractive computational competitiveness, specifically in large array scenarios. Additionally, the analyses of the computational complexity and the Cramér–Rao bounds for angle estimation are derived theoretically. Numerical experiments demonstrate the improvement and effectiveness of the proposed method against existing methods.

Iterative Sparse Covariance Matrix Fitting Direction of Arrival Estimation Method Based on Vector Hydrophone Array

Aiming at the direction of arrival (DOA) estimation of coherent signals in vector hydrophone array, an iterative sparse covariance matrix fitting algorithm is proposed. Based on the fitting criterion of weighted covariance matrix, the objective function of sparse signal power is constructed, and the recursive formula of sparse signal power iteration updating is deduced by using the properties of Frobenius norm. The present algorithm uses the idea of iterative reconstruction to calculate the power of signals on discrete grids, so that the estimated power is more accurate, and thus more accurate DOA estimation can be obtained. The theoretical analysis shows that the power of the signal at the grid point solved by the present algorithm is preprocessed by a filter, which allows signals in specified directions to pass through and attenuate signals in other directions, and has low sensitivity to the correlation of signals. The simulation results show that the average error estimated by the present method is 39.4% of the multi-signal classification high resolution method and 73.7% of the iterative adaptive sparse signal representation method when the signal-to-noise ratio is 15 dB and the non-coherent signal. Moreover, the average error estimated by the present method is 12.9% of the iterative adaptive sparse signal representation method in the case of coherent signal. Therefore, the present algorithm effectively improves the accuracy of target DOA estimation when applying to DOA estimation with highly correlated targets.

Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization

With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system.

A measurement method of batch solution concentration based on normalized compressed sensing

Measuring large batches of solution concentrations is a cumbersome task that is time consuming and involves many reagents. Determining how to improve the measurement efficiency of batch concentrations is an urgent problem to be solved. This paper introduces an efficient method for the measurement of batch solution concentrations based on normalized compressed sensing. The method is based on the sparsity of natural signals and can reconstruct the original batch concentration signals with a high level of accuracy while taking fewer measurements. The proposed method extracts subsamples from the original samples according to a sampling matrix; the number of subsamples can be much smaller than the original number of samples. Then the solution concentration of the original samples can be reconstructed by measuring the subsamples. The specific process includes sparse signal representation, non-related observation, and nonlinear optimization reconstruction. Compared with the traditional measurement method, the proposed method is demonstrably superior for the measurement of batch solution concentrations; satisfactory batch solution concentration distribution results can be obtained with a number of measurements that is much smaller than the number of samples. The proposed method will greatly reduce the time and cost of measurement.

On-grid Adaptive Compressive Sensing Framework for Underdetermined DOA Estimation by Employing Singular Value Decomposition

In the field of Array Signal Processing, the problem of Direction of Arrival (DOA) estimation has attracted colossal attention of researchers in the past few years. The problem refers to estimating the angle of arrival of the incoming signals at the receiver end, from the knowledge of the received signal itself. Generally, an array of antenna/sensors is employed at the receiver for this purpose. In over-determined DOA estimation, the number of signal sources, whose direction needs to be estimated are usually lesser than half the number of antenna array elements, whereas the challenge is to estimate the DOAs in under-determined case, where the signal source number is quiet larger than the number of antenna array elements. This paper tackles such a problem by the application of multiple level nested array. Instead of subspace-based techniques for the estimation, sparse signal representation for Compressive Sensing (CS) framework is used, which eliminates the requirement of prior information about the source number and also the tedious task of computing the inverse of the covariance matrices. In this paper, we propose an adaptive approach for Least Absolute Shrinkage and Selection Operator (LASSO) with reduced number of computations by singular value decomposing of the received signal vector. The outcomes of this paper showcase that the presented algorithm achieves high degree of freedom (DOF), good resolution, minimum root mean square error and less computational complexity with increased speed of estimation.

Modeling of temperature dependency of structural waves in an ultrasonic flow measurement system

Structural waves transmitted solely through the pipe wall influence the accuracy in a clamp-on ultrasonic flow measurement system because of the superposition with the signals of interest. To improve the measurement against temperature variations, an algorithmic compensation of the structural waves using a temperature model is required. This paper proposes a temperature model for structural waves, using the Matching Pursuit method. In the first section, a sparse signal representation is presented to approximate the structural wave signals. The resulting signal coefficients are used to describe the temperature dependency in a linear model. The method is validated using measurements of structural waves in a circular pipe over a temperature range between 20\hspace{0.1667em}^\circ \text{C} and 80\hspace{0.1667em}^\circ \text{C}. Based on these measurements, the accuracy of the approximated temperature model is evaluated and compared against the baseline signal-stretch method.

Interpolation and denoising of seismic signals using orthogonal matching pursuit algorithm: An aplication in VSP and refraction data

An implementation of the Orthogonal Matching Pursuit (OMP) algorithm was used and the results obtained therefrom are presented for simultaneous interpolation and denoising from seismic signals in the framework of sparse signal representation. OMP is an algorithm for sparse signal representation based on orthogonal projections underlying the signal over an over-complete dictionary. This over-complete dictionary was designed using K-times Singular Values Decomposition (K-SVD). In each iteration, OMP calculates a new signal approximation and the approximation error is used in the next iteration to determine the new element. The new element corresponds to the largest magnitude of the inner products between the current residual and the original elements in the dictionary. The implemented algorithm was applied to VSP seismic data and refraction seismic data; results for the application in restored missing traces and denoise signals are presented.