scholarly journals A Robust Direction of Arrival Estimation Method for Uniform Circular Array

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4427
Author(s):  
Xu ◽  
Wu ◽  
Yu ◽  
Guang
Keyword(s):  
Array Signal Processing ◽  
Linear Array ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Circular Array ◽  
Source Signal ◽  
Random Sample Consensus ◽  
Esprit Algorithm ◽  
Source Signals

Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (RANSAC) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified RANSAC method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).

scholarly journals High-Resolution Direction-of-Arrival Estimation via Concentric Circular Arrays

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Serdar Ozgur Ata ◽  
Cevdet Isik
Keyword(s):  
Antenna Arrays ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Sensor Arrays ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Circular Array ◽  
Multiple Signal Classification ◽  
Circular Arrays ◽  
Source Signals

Estimating the direction of arrival (DOA) of source signals is an important research interest in application areas including radar, sonar, and wireless communications. In this paper, the problem of DOA estimation is addressed on concentric circular antenna arrays (CCA) in detail as an alternative to the well-known geometries of the uniform linear array (ULA) and uniform circular array (UCA). We define the steering matrix of the CCA geometry and investigate the performance analysis of the array in the DOA-estimation problem by simulations that are realized through varying the parameters of signal-to-noise ratio, number of sensors, and resolution angle of sensor arrays by using the MUSIC (Multiple Signal Classification) algorithm. The results present that CCA geometries provide higher angle resolutions compared to UCA geometries and require less physical area for the same number of sensor elements. However, as a cost-increasing effect, higher computational power is needed to estimate the DOA of source signals in CCAs compared to ULAs.

scholarly journals Direction of Arrival Estimation of GPS Narrowband Jammers Using High-Resolution Techniques

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5532 ◽  
Author(s):  
Mohamed Moussa ◽  
Abdalla Osman ◽  
Mohamed Tamazin ◽  
Michael J. Korenberg ◽  
Aboelmagd Noureldin
Keyword(s):  
Linear Array ◽  
Classical Method ◽  
Spatial Separation ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Circular Array ◽  
Gps Receivers ◽  
Fast Orthogonal Search ◽  
Jamming Detection ◽  
Ultimate Objective

GPS jamming is a considerable threat to applications that rely on GPS position, velocity, and time. Jamming detection is the first step in the mitigation process. The direction of arrival (DOA) estimation of jamming signals is affected by resolution. In the presence of multiple jamming sources whose spatial separation is very narrow, an incorrect number of jammers can be detected. Consequently, mitigation will be affected. The ultimate objective of this research is to enhance GPS receivers’ anti-jamming abilities. This research proposes an enhancement to the anti-jamming detection ability of GPS receivers that are equipped with a uniform linear array (ULA) and uniform circular array (UCA). The proposed array processing method utilizes fast orthogonal search (FOS) to target the accurate detection of the DOA of both single and multiple in-band CW jammers. Its performance is compared to the classical method and MUSIC. GPS signals obtained from a Spirent GSS6700 simulator and CW jamming signals were used. The proposed method produces a threefold advantage, higher accuracy DOA estimates, amplitudes, and a correct number of jammers. Therefore, the anti-jamming process can be significantly improved by limiting the erroneous spatial attenuation of GPS signals arriving from an angle close to the jammer.

scholarly journals Direction-of-Arrival Estimation for Coprime Array Using Compressive Sensing Based Array Interpolation

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Aihua Liu ◽  
Qiang Yang ◽  
Xin Zhang ◽  
Weibo Deng
Keyword(s):  
Covariance Matrix ◽  
Degrees Of Freedom ◽  
Linear Array ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Interpolation Algorithm ◽  
Estimation Performance ◽  
Sample Covariance ◽  
Coprime Array

A method of direction-of-arrival (DOA) estimation using array interpolation is proposed in this paper to increase the number of resolvable sources and improve the DOA estimation performance for coprime array configuration with holes in its virtual array. The virtual symmetric nonuniform linear array (VSNLA) of coprime array signal model is introduced, with the conventional MUSIC with spatial smoothing algorithm (SS-MUSIC) applied on the continuous lags in the VSNLA; the degrees of freedom (DoFs) for DOA estimation are obviously not fully exploited. To effectively utilize the extent of DoFs offered by the coarray configuration, a compressing sensing based array interpolation algorithm is proposed. The compressing sensing technique is used to obtain the coarse initial DOA estimation, and a modified iterative initial DOA estimation based interpolation algorithm (IMCA-AI) is then utilized to obtain the final DOA estimation, which maps the sample covariance matrix of the VSNLA to the covariance matrix of a filled virtual symmetric uniform linear array (VSULA) with the same aperture size. The proposed DOA estimation method can efficiently improve the DOA estimation performance. The numerical simulations are provided to demonstrate the effectiveness of the proposed method.

Interference Localization via Direction of Arrival Estimation for Satellite with Planar Array

2014 ◽  
Vol 1049-1050 ◽  
pp. 1788-1791
Author(s):  
Xiao Feng Qiu ◽  
Xiao Fei Zhang
Keyword(s):  
Computational Cost ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Music Algorithm ◽  
Angle Estimation ◽  
Multiple Signal Classification ◽  
Estimation Performance ◽  
Planar Array ◽  
Esprit Algorithm

This paper presents the model of satellite planar array, and interference localization via direction of arrival (DOA) estimation. We derive a dimension reduction DOA estimaton algorithm therein. The proposed algorithm, which only requires a one-dimensional local searching, can avoid the high computational cost within two-dimensional multiple signal classification (2D-MUSIC) algorithm. We illustrate that the proposed algorithm has better angle estimation performance than estimation method of signal parameters via rotational invariance technique (ESPRIT) algorithm, and has very close angle estimation performance to 2D-MUSIC algorithm. Furthermore, our algorithm requires no extra pairing. Simulation results present the usefulness of our algorithm.

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.

scholarly journals Compressed Sensing-Based DOA Estimation with Unknown Mutual Coupling Effect

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 424 ◽  
Author(s):  
Peng Chen ◽  
Zhenxin Cao ◽  
Zhimin Chen ◽  
Linxi Liu ◽  
Man Feng
Keyword(s):  
Compressed Sensing ◽  
Mutual Coupling ◽  
Linear Array ◽  
State Of The Art ◽  
Coupling Effect ◽  
Estimation Method ◽  
Doa Estimation ◽  
System Model ◽  
Reconstruction Problem ◽  
Uniform Linear Array

The performance of a direction-finding system is significantly degraded by the imperfection of an array. In this paper, the direction-of-arrival (DOA) estimation problem is investigated in the uniform linear array (ULA) system with the unknown mutual coupling (MC) effect. The system model with MC effect is formulated. Then, by exploiting the signal sparsity in the spatial domain, a compressed-sensing (CS)-based system model is proposed with the MC coefficients, and the problem of DOA estimation is converted into that of a sparse reconstruction. To solve the reconstruction problem efficiently, a novel DOA estimation method, named sparse-based DOA estimation with unknown MC effect (SDMC), is proposed, where both the sparse signal and the MC coefficients are estimated iteratively. Simulation results show that the proposed method can achieve better performance of DOA estimation in the scenario with MC effect than the state-of-the-art methods, and improve the DOA estimation performance about 31.64 % by reducing the MC effect by about 4 dB.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

scholarly journals Effect of varying Number of Spacing between Antenna Elements, Snapshots, SNR on AP ML, AP-SSF and ESPRIT Algorithm

2021 ◽  
Vol 11 ◽  
pp. 143-150
Author(s):  
Vinod Kumar ◽  
Sanjeev Kumar Dhull
Keyword(s):  
Signal Processing ◽  
Maximum Likelihood ◽  
Key Words ◽  
Array Signal Processing ◽  
Direction Of Arrival ◽  
Performance Comparison ◽  
Direction Of Arrival Estimation ◽  
Multiple Sources ◽  
Alternating Projection ◽  
Esprit Algorithm

The direction of arrival estimation is the main key problem in array signal processing. In this paper, the alternating projection maximum Likelihood (AP-ML), Alternating projection sub space framework (APSSF) and ESPRIT algorithm are studied. The simulation is performed in MATLAB for single and multiple sources. The effect of the varying number of spacing between antenna elements, number of snapshots and SNR are studied. The performance comparison shows that ESPRIT algorithm performs better as compared to the AP-ML and AP-SSF. Key-Words: - AP-ML, AP-SSF, Direction of Arrival, ESPRIT, Snapshots, SNR

scholarly journals DOA Estimation for UCAs Based on Virtual Subarray Beamforming Technique

2018 ◽  
Vol 232 ◽  
pp. 01012
Author(s):  
Bo Xu ◽  
Zhigang Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Signal To Noise ◽  
Circular Arrays ◽  
Noise Ratio ◽  
Subarray Beamforming ◽  
Beamforming Technique

Direction-of-arrival (DOA) estimation is always a hotspot research in the fields of radar, sonar, communication and so on. And uniform circular arrays (UCAs) are more attractive in the context of DOA estimation since their symmetrical structures have potential to provide two directions coverage. This paper proposed a new DOA estimation method for UCAs via virtual subarray beamforming technique. The method would provide an acceptable DOA estimate even if the number of sources is great than the number of array elements. Also, the performance of the proposed method would hold good when the snapshot length or the signal-to-noise ratio (SNR) is small. Simulations show that the proposed technique offers significantly improved estimation resolution, capacity, and accuracy relative to the existing techniques.

A Direction of Arrival Estimation Method for Wideband LFM Signals Based on RWT and ESPRIT Algorithm

2014 ◽  
Vol 1044-1045 ◽  
pp. 976-981
Author(s):  
Jian Zhong Xu ◽  
Fu Qiang Yu ◽  
Ping Guang Duan ◽  
Shu Hua Li
Keyword(s):  
Frequency Modulation ◽  
Estimation Method ◽  
Direction Of Arrival ◽  
Wigner Transform ◽  
Signal Parameter ◽  
Time Frequency ◽  
Cross Terms ◽  
Stationary Signals ◽  
Simulation Results ◽  
Esprit Algorithm

In this paper, we proposed a new algorithm to estimate the direction of arrival (DOA) for wideband linear frequency modulation (LFM) signals, using Radon-Wigner transform (RWT) and estimation of signal parameter via rotational invariance techniques (ESPRIT). To eliminate the cross-terms, we first utilize the RWT with its excellent time-frequency concentration performance. Then, through peak searching, the number of targets, the initial interference and the frequency modulation slope are estimated. On the this base, the array signals are reconstructed. Finally, we adopt the ESPRIT algorithm to estimate the DOA of the array signals. The simulation results show that the proposed algorithm can estimate the DOA of non-stationary signals with high precision.

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