Direction Of Arrival Method Research Articles

Direction of Arrival and Polarization Estimation for Nested Polarization Sensitive Array

This paper presents a new polarization sensitive array composed of a two-level nested subarray and an optimally nested subarray with orthogonal oriented antennas, and then proposes a correlation matrix reconstruction based estimation method for direction of arrival (DOA) and polarization state. The optimally nested array with N antennas can provide maximum degrees of freedom (DOF) of difference co-array (i.e., (N - 1)N + 1), which increases the aperture of the proposed array. However, both the difference co-arrays of the optimally nested subarray and between subarrays are nonuniform linear arrays (i.e., holes appear), and hence most existing methods fall to use all information received from the proposed array, resulting in estimation performance loss. Depending on the oblique projection (OP) operator constructed by initial DOAs from the two-level nested subarray, the proposed method first fills the holes to generate the virtual correlation matrix with increased DOF. Then the DOA and polarization state are estimated efficiently. The resulting DOAs can be regarded as the new initial angles for OP operator construction, to iterate aforesaid steps for estimation performance enhancement. The Cramer-Rao bound (CRB) and the computational complexity of the proposed method are provided. Simulation results are given to validate the effectiveness of the proposed array and the proposed method.

An Improved Squirrel Search Algorithm for Maximum Likelihood DOA Estimation and Application for MEMS Vector Hydrophone Array

Maximum likelihood (ML) method for direction of arrival (DOA) estimation achieves an excellent performance in array signal processing, but the complexity and computational load of searching the multidimensional nonlinear function prevented it from practical application. Based on squirrel search algorithm (SSA), an improved SSA (ISSA) for ML DOA estimation is proposed in this paper, which can reduces the computational complexity. The idea of spatial variation and diffuse inspired by the invasive weed optimization(IWO) algorithm is applied to ISSA. The simulation experiments compared ISSA with SSA, IWO, seeker optimization algorithm(SOA), sine cosine algorithm (SCA), genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) method for ML DOA estimator show that the proposed algorithm has faster convergence speed, fewer iterations and lower root mean square error(RMSE) under different number of signal sources, different signal to noise ratio(SNR) and different population size. Therefor the proposed algorithm does not only ensure the estimation accuracy, but also greatly reduce the computation complexity of multidimensional nonlinear optimization for the ML method. Finally, the test experiment using Micro Electronic Mechanical Systems(MEMS) vector hydrophone array in Fenhe lake show the engineering practicability of proposed ML DOA estimator with ISSA.The results obtained will be valuable in the application of engineering.

Direction of arrival estimation using deep neural network for hearing aid applications using smartphone.

Deep neural network (DNN) techniques are gaining popularity due to performance boost in many applications. In this work we propose a DNN-based method for finding the direction of arrival (DOA) of speech source for hearing study improvement and hearing aid applications using popular smartphone with no external components as a cost-effective stand-alone platform. We consider the DOA estimation as a classification problem and use the magnitude and phase of speech signal as a feature set for DNN training stage and obtaining appropriate model. The model is trained and derived using real speech and real noisy speech data recorded on smartphone in different noisy environments under low signal to noise ratios (SNRs). The DNN-based DOA method with the pre-trained model is implemented and run on Android smartphone in real time. The performance of proposed method is evaluated objectively and subjectively in the both training and unseen environments. The test results are presented showing the superior performance of proposed method over conventional methods.

Selective Range Iterative Adaptive Approach for High-Resolution DOA Estimation

In this paper, the problem of direction-of-arrival (DOA) estimation for a uniform linear array with single-snapshot observations is addressed. Two non-parametric DOA estimators are developed, which can be applied in any azimuth range with one snapshot. Their main idea is iteratively updating the DOA estimates using the weighted least squares and covariance matrix. Two criteria for implementing the covariance matrix are devised, which guarantee high resolution of the proposed methods. The simulation results are included to demonstrate the superiority of our algorithms over several conventional DOA methods in terms of both estimation performance and computational complexity.

Numerical Analysis of CNC Milling Chatter Using Embedded Miniature MEMS Microphone Array System

With the increasingly common use of industrial automation for mass production, there are many computer numerical control (CNC) machine tools that require the collection of data from intelligent sensors in order to analyze their processing quality. In general, for high speed rotating machines, an accelerometer can be attached on the spindle to collect the data from the detected vibration of the CNC. However, due to their cost, accelerometers have not been widely adopted for use with typical CNC machine tools. This study sought to develop an embedded miniature MEMS microphone array system (Radius 5.25 cm, 8 channels) to discover the vibration source of the CNC from spatial phase array processing. The proposed method utilizes voice activity detection (VAD) to distinguish between the presence and absence of abnormal noise in the pre-stage, and utilizes the traditional direction of arrival method (DOA) via multiple signal classification (MUSIC) to isolate the spatial orientation of the noise source in post-processing. In the numerical simulation, the non-interfering noise source location is calibrated in the anechoic chamber, and is tested with real milling processing in the milling machine. As this results in a high background noise level, the vibration sound source is more accurate in the presented energy gradation graphs as compared to the traditional MUSIC method.

Direction of Arrival Estimation by Convex Optimization Methods With Unknown Sensor Gain and Phase

Direction of arrival (DOA) estimation performance severely degrades for the reasons that the perturbation from their assumed nominal complex gain values in practical sensor system. In this paper, to mitigate the effects of the uncertainty perturbation, a two-step method for joint DOAs and sensor errors estimation is proposed. In the first step, convex optimization and $l_{1}$ norm penalties are used to conduct a second-order cone framework to estimate DOAs roughly under the assumption that all sensor gains are identity. Second, we calibrate the perturbed gains with the DOAs estimated by the first step. The iterative process of the DOAs and gains estimation is taken until convergence. Furthermore, a refinement procedure is addressed to alleviate the effects of the grid limitations to spatial locations. In the present method, no auxiliary calibrated sensors or co-operation signal source is required. And the two-step method can handle both uncorrelated signals and coherent signals, it is also a high resolution method and robust in the condition of low signal-to-noise ratio (SNR). Several aspects are taken into consideration, and numerical experimental results are given to show the advantages of the new method for the DOA estimation.

Phased-Array-Based Sub-Nyquist Sampling for Joint Wideband Spectrum Sensing and Direction-of-Arrival Estimation

In this paper, we study the problem of joint wideband spectrum sensing and direction-of-arrival (DoA) estimation in a sub-Nyquist sampling framework. Specifically, considering a scenario where a few uncorrelated narrowband signals spread over a wide (say, several GHz) frequency band, our objective is to estimate the carrier frequencies and the DoAs associated with the narrowband sources, as well as reconstruct the power spectra of these narrowband signals. To overcome the sampling rate bottleneck for wideband spectrum sensing, we propose a new phased-array based sub-Nyquist sampling architecture with variable time delays, where a uniform linear array (ULA) is employed and the received signal at each antenna is delayed by a variable amount of time and then sampled by a synchronized low-rate analog-digital converter (ADC). Based on the collected sub-Nyquist samples, we calculate a set of cross-correlation matrices with different time lags, and develop a CANDECOMP/PARAFAC (CP) decomposition-based method for joint DoA, carrier frequency and power spectrum recovery. Perfect recovery conditions for the associated parameters and the power spectrum are analyzed. Our analysis reveals that our proposed method does not require to place any sparse constraint on the wideband spectrum, only needs the sampling rate to be greater than the bandwidth of the narrowband source signal with the largest bandwidth among all sources. Simulation results show that our proposed method can achieve an estimation accuracy close to the associated Cram\'{e}r-Rao bounds (CRBs) using only a small number of data samples.

Direction of arrival estimation using local series expansion evaluated on acoustic data

Direction of arrival (DOA) estimation using a local series expansion of the signal is evaluated on acoustic data. It was recently proposed that DOA estimation using a sensor array could be performed using a local temporal/spatial series expansion. That is, to use the local phase of the signal rather than its estimated second order moments or correlation as in classical methods. The approach should provide easier handling of general sensor configurations, ability to deal with very low sampling rates and few samples, and benefits from miniature sensor configurations. Additionally, the signal is not required to be narrow-banded, as sufficient smoothness is enough. Here, the local series expansion method is benchmarked to a standard beam-forming method using experimental data, recorded in an anechoic chamber at the Technical University of Denmark (DTU). The dataset was recorded using 52 high quality microphones distributed on a sphere with radius 50 mm, and contains several recordings from one or two well defined sources from several different directions, with different levels of reverberation. The dataset is used to evaluate important aspects of the new method and its potential to improve on standard DOA methods.

Generalized Coprime Planar Array Geometry for 2-D DOA Estimation

In this letter, we propose a generalized coprime planar array (GCPA) geometry for 2-D direction of arrival (DOA) estimation, where two rectangular uniform planar subarrays are used. The proposed geometry allows a more flexible array layout and extends the array aperture to achieve a great performance improvement. We verify that GCPA can obtain a higher degree of freedom (DOF) than square coprime planar array and we derive the principle for array layout of GCPA to obtain the maximum DOF. The superiority of GCPA are revealed by numerical simulations with the classical DOA methods.

Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle

In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions.

Gridless quadrature compressive sampling with interpolated array technique

Quadrature compressive sampling (QuadCS) is a sub-Nyquist sampling scheme for acquiring in-phase and quadrature (I/Q) components in radar. In this scheme, the received intermediate frequency (IF) signals are expressed as a linear combination of time-delayed and scaled replicas of the transmitted waveforms. For sparse IF signals on discrete grids of time-delay space, the QuadCS can efficiently reconstruct the I/Q components from sub-Nyquist samples. In practice, the signals are characterized by a set of unknown time-delay parameters in a continuous space. Then conventional sparse signal reconstruction will deteriorate the QuadCS reconstruction performance. This paper focuses on the reconstruction of the I/Q components with continuous delay parameters. A parametric spectrum-matched dictionary is defined, which sparsely describes the IF signals in the frequency domain by delay parameters and gain coefficients, and the QuadCS system is reexamined under the new dictionary. With the inherent structure of the QuadCS system, it is found that the estimation of delay parameters can be decoupled from that of sparse gain coefficients, yielding a beamspace direction-of-arrival (DOA) estimation formulation with a time-varying beamforming matrix. Then an interpolated beamspace DOA method is developed to perform the DOA estimation. An optimal interpolated array is established and sufficient conditions to guarantee the successful estimation of the delay parameters are derived. With the estimated delays, the gain coefficients can be conveniently determined by solving a linear least-squares problem. Extensive simulation results evidently demonstrate the superiority of the proposed algorithms in achieving super-resolution time-delay estimation and high-accuracy sparse signal reconstruction.

Joint DOA and polarization estimation for unequal power sources based on reconstructed noise subspace

In most literature about joint direction of arrival (DOA) and polarization estimation, the case that sources possess different power levels is seldom discussed. However, this case exists widely in practical applications, especially in passive radar systems. In this paper, we propose a joint DOA and polarization estimation method for unequal power sources based on the reconstructed noise subspace. The invariance property of noise subspace (IPNS) to power of sources has been proved an effective method to estimate DOA of unequal power sources. We develop the IPNS method for joint DOA and polarization estimation based on a dual polarized array. Moreover, we propose an improved IPNS method based on the reconstructed noise subspace, which has higher resolution probability than the IPNS method. It is theoretically proved that the IPNS to power of sources is still valid when the eigenvalues of the noise subspace are changed artificially. Simulation results show that the resolution probability of the proposed method is enhanced compared with the methods based on the IPNS and the polarimetric multiple signal classification (MUSIC) method. Meanwhile, the proposed method has approximately the same estimation accuracy as the IPNS method for the weak source.

2D‐DOA estimation performance using split vertical linear and circular arrays

This paper presents a new approach to reduce the computational complexity in two‐dimensional (2D) matrix pencil (MP) method for direction of arrival (DOA) estimation of plane wave signals using a combination of vertical uniform linear array (VULA) and uniform circular array (UCA). By applying phase mode excitation based beamforming to the UCA, we can apply the matrix pencil (MP) method to the beamspace data using only a single snapshot. The technique is based on the split array, which is composed of two perpendicular arrays. The vertical uniform linear array used to determine the elevation DOA components is located perpendicularly at the center of the uniform circular array in the horizontal plane used to calculate the azimuth angles. Unlike common planar and circular arrays, this antenna array with its particular geometry requires no pair‐matching between the azimuth and the elevation angle estimation and can also remove the drawbacks of estimation failure problems. Using this particular geometry for the 2D MP method leads to an efficient computational methodology for real‐time implementation on a digital signal processor. The obtained simulation results of the MP method applied to both uncorrelated and correlated narrow‐band sources in the presence of white noise show good performance estimation. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar.

In this paper, a real-valued covariance vector sparsity-inducing method for direction of arrival (DOA) estimation is proposed in monostatic multiple-input multiple-output (MIMO) radar. Exploiting the special configuration of monostatic MIMO radar, low-dimensional real-valued received data can be obtained by using the reduced-dimensional transformation and unitary transformation technique. Then, based on the Khatri–Rao product, a real-valued sparse representation framework of the covariance vector is formulated to estimate DOA. Compared to the existing sparsity-inducing DOA estimation methods, the proposed method provides better angle estimation performance and lower computational complexity. Simulation results verify the effectiveness and advantage of the proposed method.

Compressed sensing techniques for altitude estimation in multipath conditions

In very high frequency array radars, the multipath signal and direct signal, which fall within the beamwidth of the receiving antenna, are highly correlated. This correlation degrades the performance of the low-angle direction-of-arrival (DOA) estimation in multipath conditions. By making use of the sparsity of the targets, after filtering out the clutter in the Doppler dimension, two DOA estimation approaches based on compressed sensing (CS) are proposed. The interpolated array- (IA-) CS and beamspace- (BS-) CS perform compressive sampling, respectively, on an IA and in the BS. The proposed methods are different from subspace-based methods in concept and are not subject to the restricting requirements of spatial and temporal stationarities, as well as the correlation between the sources and noise. Both simulated and measured results verify that the proposed methods provide superior performance in resolving the DOAs as compared with the spatial CS method and other conventional DOA methods.

English

Source Direction of arrival (DOA) estimation plays an important role in array signal processing, and has a wide range of application. Array signal processing is an important branch in the field of signal processing. In recent years, it has developed dramatically. It can be applied in fields such as radar, communication, sonar, earthquake, exploration, astronomy and biomedicine. Over the past few years, all kinds of algorithms which can be used in DOA estimation have made great achievements, the most classic algorithms among which are Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT).In this paper, we will give an overview on performance of the DOA estimation based on MUSIC as well as ESPRIT (both TLS and Pro ESPRIT) algorithm on uniform linear array (ULA) and in the presence of white noise. We will describe what DOA estimation is, and give a mathematical model of DOA estimation for subspace based DOA methods such as MUSIC, TLS ESPRIT and Pro ESPRIT. Then estimate DOA based on the MUSIC, TLS ESPRIT as well as Pro ESPRIT algorithm, with simulations with MATLAB to simulate factors that affect accuracy and resolution of DOA estimation. The performance of these DOA algorithms for a set of input parameters such as number of snapshots, number of array elements, signal-to-noise ratio, angular separation, element spacing are investigated.

Subspace-based DOA with linear phase approximation and frequency bin selection preprocessing for interactive robots in noisy environments

This work develops a method of estimating subspace-based direction of arrival (DOA) that uses two proposed preprocesses. The method can be used in applications that involve interactive robots to calculate the direction to a noise-contaminated signal in noisy environments. The proposed method can be divided into two parts, which are linear phase approximation and frequency bin selection. Linear phase approximation rectifies the phases of the two-channel signals that are affected by noise, and reconstructs the covariance matrix of the received signals according to the compensative phases using phase line regression. To increase the accuracy of DOA result, a method of frequency bin selection that is based on eigenvalue decomposition (EVD) is utilized to detect and filter out the noisy frequency bins of the microphone signals. The proposed techniques are adopted in a method of subspace-based DOA estimation that is called multiple signal classification (MUSIC). Experimental results reveal that the mean estimation error obtained using proposed method can be reduced by 7.61° from the conventional MUSIC method. The proposed method is compared with the covariance-based DOA method that is called the minimum variance distortionless response (MVDR). The DOA improves the mean estimation accuracy by 4.98° relative to the conventional MVDR method. The experimental results demonstrate that both subspace-based and covariance-based DOA algorithms with the proposed preprocessing method outperform the DOA estimation in detecting the direction of signal in a noisy environment.

이동 목표물 추정 정확도를 향상시키기 위한 고 분해능 최적 빔 지향 패턴에 관한 연구

Method a target estimation in spatial are mobile wireless communication using network cell and GPS. It have much error that mobile wireless communication depend on cell size. GPS method can't find a target in shadow and inner area. In this paper, we estimate a target as direction of arrival method using adaptive array antenna system. Adaptive array antenna system can obtain desired signal to remove other signal This paper studied digital beamforming method in order to estimation a target. Proposed method is modified optimum weight and antenna error correction to estimation an optimal receive signal. Digital beamforming method decided a signal phase and amplitude from received signal on array antenna element. But if it is not to do error correction of received signal, system performance have decreased. Firstly, we proposed modified optimum weight in order to finding desired target. Secondly, we are error correction of antenna incident signals by optimal weight before digital beamforming method. Thirdly, throughly simulation, we showed that system performance of proposed method compare proposal method with general method. It have improved resolution of estimation target to good performance more proposed method than general method.

향상된 신호 추정을 위한 안테나 오차 보정 과 수정된 최적 가중치를 이용한 디지털 빔 형성 성능 분석에 관한 연구

Method a target estimation in spatial are mobile wireless communication using network cell and GPS. It have much error that mobile wireless communication depend on cell size. GPS method can`t find a target in shadow and inner area. In this paper, we estimate a target as direction of arrival method using adaptive array antenna system. Adaptive array antenna system can obtain desired signal to remove other signal This paper studied digital beamforming method in order to estimation a target. Proposed method is modified optimum weight and antenna error correction to estimation an optimal receive signal. Digital beamforming method decided a signal phase and amplitude from received signal on array antenna element. But if it is not to do error correction of received signal, system performance have decreased. Firstly, we proposed modified optimum weight in order to finding desired target. Secondly, we are error correction of antenna incident signals by optimal weight before digital beamforming method. Thirdly, throughly simulation, we showed that system performance of proposed method compare proposal method with general method. It have improved resolution of estimation target to good performance more proposed method than general method.

A Two-Antenna Single RF Front-End DOA Estimation System for Wireless Communications Signals

Both adaptive beamforming methods and interference management systems improve the capacity of wireless communications systems, however they may require direction of arrival (DOA) information of signals. On the other hand, most of the DOA estimation systems suffer from computational complexity, high costs, and bulky size which prevents mobility. Antenna array inflexibility of DOA estimation systems also prevents prevalence over the whole wireless spectrum. In this paper, a solution to these problems which benefits from the advantages of switched antenna systems along with the time-variant sequential sampling process is proposed: a two-antenna, single radio-frequency (RF) front-end DOA estimation system which extends the measurements to different locations by shifting the antenna tuple together as a single entity. The system adjusts the antenna spacing for adaptive spectrum coverage. An extension to total measurement time is introduced, however, when compared to DOA estimation systems which have separate RF front-end for each antenna, the system has moderate complexity and size. A measurement setup is developed and performance of the proposed system is provided for extensive set of wireless standards and measurement parameters. Multiple signal classification is selected as DOA method for the proposed system and the DOA estimation performance is compared with the pseudo-Doppler system.