A Robust Multi Sample Compressive Sensing Technique for DOA Estimation Using Sparse Antenna Array

Abstract

In this paper, a multi sample compressive sensing (CS) technique is presented for the direction of arrival (DOA) estimation using sparse antenna array that has applications in several fields including radars and sonars. Two different types of sparse antenna arrays are considered. One is linear sparse array for DOA estimation in one dimension and other is L shaped sparse array for DOA estimation in two dimensions. To make the algorithm robust against impulsive and Gaussian noise, a preprocessing stage is introduced. First, in the preprocessing stage median difference correntropy is used that combines median difference and the generalized correntropy. This suppresses the amplitude of impulsive noise. Second, the strength of weighted moving average filter is exploited before applying the CS technique to make the algorithm more robust. In the CS techniques, the source energy is distributed among the adjacent grid due to grid mismatch. Therefore, a fitness function based on the difference of the source energy among the adjacent grid is introduced. This provides the best discretization value through iterative grid refinement for the grid. The effectiveness and robustness of the proposed method is verified through exhaustive simulations for different number of sources and noise scenarios using one dimensional and two-dimensional sparse array structures.

Design of Morlet Wavelet Neural Network for Solving a Class of Singular Pantograph Nonlinear Differential Models

“…So MWNN is the best choice to solve such complicated models using the global and local search terminologies GAIPA. In future, MWNN-GAIPA can be implemented to solve the fluid dynamic nonlinear systems, biological nonlinear systems, singular higher order differential systems, fractional processing, direction of arrival estimation, power and eneggy systems [53][54][55][56][57][58][59][60][61][62].…”

Sparse Linear Antenna Arrays: A Review

“…The Khatri Rao (KR-MUSIC) algorithm which is applicable only to quasi-stationary sources (i.e., the sources which can be assumed to be stationary for short time durations) was introduced prior to the co-array MUSIC [57]. Recently, many algorithms based on compressed sensing have been introduced for DOA estimation in sparse arrays [58,59]. In summary, DOA estimation algorithms that operate (i) when the number of sources is unknown, (ii) in the presence of coherent arrivals, (iii) under unknown mutual coupling, (iv) under low signal-to-noise ratio (low SNR) conditions, (v) under low snapshot conditions, (vi) in the presence of non-uniform or random noise, and (vii) in a short computational time; are largely sought-after for practical applications [60,61].…”

“…Many bio-inspired algorithms or compressed sensing techniques have been used in the past either to detect sensor failures or to compensate the pattern of a faulty array or for both [73][74][75][76][77][78][79][80]. An extreme case and new perspective is presented in [81], where a sparse array is said to be formed when one or more elements of an ULA fail at random.…”

Joint Model-Order and Robust DoA Estimation for Underwater Sensor Arrays

“…The DoA estimation problem is extensively studied under the assumption of independent Gaussian distributed sensor noise [ 10 , 11 , 12 ]. However, non-Gaussian distributed impulsive noise may also arise in some scenarios due to factors such as the equipment’s sudden impact noise, and natural or man-made electromagnetic interference and manifests in the sensor’s received noise as sharp spikes and a heavy-tailed distribution [ 41 , 42 ]. The DoA estimation under impulsive noise has been studied through different approaches including sparse Bayesian learning [ 41 ], non-linear similarity measures such as the generalized maximum complex correntropy criterion [ 43 ], and modeling the impulsive noise with other distributions [ 44 ].…”