scholarly journals A Novel 2-D Coherent DOA Estimation Method Based on Dimension Reduction Sparse Reconstruction for Orthogonal Arrays

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1496 ◽  
Author(s):  
Xiuhong Wang ◽  
Xingpeng Mao ◽  
Yiming Wang ◽  
Naitong Zhang ◽  
Bo Li
Keyword(s):  
Dimension Reduction ◽  
Estimation Method ◽  
Orthogonal Arrays ◽  
Doa Estimation ◽  
Sparse Reconstruction

scholarly journals Underdetermined DOA Estimation of Wideband LFM Signals Based on Gridless Sparse Reconstruction in the FRF Domain

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2383 ◽  
Author(s):  
Yue Cui ◽  
Junfeng Wang ◽  
Jie Qi ◽  
Zhanying Zhang ◽  
Jinqi Zhu
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Sparse Matrix ◽  
Estimation Method ◽  
Doa Estimation ◽  
Sparse Reconstruction ◽  
Virtual Sensors ◽  
Steering Vector ◽  
Wideband Signals ◽  
Variant Frequency

An underdetermined direction of arrival (DOA) estimation method of wideband linear frequency modulated (LFM) signals is proposed without grid mismatch. According to the concentration property of LFM signal in the fractional Fourier (FRF) domain, the received sparse model of wideband signals with time-variant steering vector is firstly derived based on a coprime array. Afterwards, by interpolating virtual sensors, a virtual extended uniform linear array (ULA) is constructed with more degrees of freedom, and its covariance matrix in the FRF domain is recovered by employing sparse matrix reconstruction. Meanwhile, in order to avoid the grid mismatch problem, the modified atomic norm minimization is used to retrieve the covariance matrix with the consecutive basis. Different from the existing methods that approximately assume the frequency and the steering vector of the wideband signals are time-invariant in every narrowband frequency bin, the proposed method not only can directly solve more DOAs of LFM signals than the number of physical sensors with time-variant frequency and steering vector, but also obtain higher resolution and more accurate DOA estimation performance by the gridless sparse reconstruction. Simulation results demonstrate the effectiveness of the proposed method.

An Improved Direction-of-Arrival Estimation Method Based on Sparse Reconstruction

2012 ◽  
Vol 263-266 ◽  
pp. 135-138
Author(s):  
Xue Bing Han ◽  
Zhao Jun Jiang
Keyword(s):  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Sparse Reconstruction ◽  
Direction Of Arrival Estimation ◽  
Sensing Matrix ◽  
Signal Model ◽  
Multiple Measurement Vectors ◽  
Underdetermined System ◽  
Simulation Results

In this paper, we account for efficient approach of direction-of-arrival estimation based on sparse reconstruction of sensor measurements with an overcomplete basis. MSD-FOCUSS ( MMV Synchronous Descending FOCal Underdetermined System Solver) algorithm is developed against to sparse reconstruction in multiple-measurement-vectors (MMV) system where noise perturbations exist in both the measurements and sensing matrix. The paper shows how sparse-signal model of DOA estimation is established and MSD-FOCUSS is derived, then the simulation results illustrate the advantage of MSD-FOCUSS when it is used to solve the problem of DOA estimation.

A Novel Sparse Reconstruction Based DOA Estimation Method Under Grid Mismatch

2021 ◽  
Author(s):  
Dingke Yu ◽  
Xin Wang ◽  
Wenwei Fang ◽  
Yuzhang Xi ◽  
Min Li ◽  
...  
Keyword(s):  
Estimation Method ◽  
Doa Estimation ◽  
Sparse Reconstruction

scholarly journals An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4403
Author(s):  
Ji Woong Paik ◽  
Joon-Ho Lee ◽  
Wooyoung Hong
Keyword(s):  
Covariance Matrix ◽  
Phased Array ◽  
Null Space ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Direction Of Arrival Estimation ◽  
Improve Performance ◽  
Space Projection

An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

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