Jian-Feng Gu

Joint 2-D DOA Estimation via Sparse L-shaped Array

In this paper, we address the problem of estimating the two-dimensional (2-D) directions of arrival (DOA) of multiple signals, by means of a sparse L-shaped array. The array consists of one uniform linear array (ULA) and one sparse linear array (SLA). The shift-invariance property of the ULA is used to estimate the elevation angles with low computational burden. The signal subspace is constructed by the cross-covariance matrix (CCM) of the received data without implementing eigendecomposition. The source waveforms are then obtained by the estimated elevation angles, which together with each sensor of the SLA, considered as a linear regression model, is used to estimate the azimuth angle by the modified total least squares (MTLS) technique. Our new algorithm yields correct parameter pairs without requiring the computationally expensive pairing operation, and therefore, has at least two advantages over the previous L-shaped array based algorithms: less computational load and better performance due to the use of SLA and CCM. Expressions for the asymptotic mean-squared error (MSE) of the 2-D DOA estimates are derived. Simulation results show that our method provides accurate and consistent 2-D DOA estimation results that could not be obtained by the existing methods with comparable computational complexity.

Compressed sensing for DOA estimation with fewer receivers than sensors

This paper addresses the problem of the direction-ofarrival (DOA) estimation using fewer receivers than sensors. Inspired by the Compressed Sensing (CS) theory developed in recent years, we present a new preprocessing scheme for a large array using a small size receiver. Unlike the traditional ℓ2 -norm-based algorithms by judicious selection of the preprocessing matrix, the proposed scheme uses a random weight generator as a measurement of the compressed sensing to form the output data for each time interval. The formulated CS problem for DOA estimation is then solved based on the convex programming via ℓ1 -norm approximation such as Dantzig Selector. We consider two different scenarios in the CS domain, i.e., the angle domain and the angle-frequency domain. It is shown that the number of receivers can be reduced significantly for a given number of sensors by using the proposed CS-based DOA estimation approach.

Minimum redundancy linear sparse subarrays for direction of arrival estimation without ambiguity

This paper presents a new method of estimating the direction-of-arrival (DOA) for multiple signals using minimum redundancy linear sparse subarrays (MRLSS). The proposed method makes use of the array structure to obtain the extended correlation matrix that is constructed by Kronecker Steering Vectors (KSVs) of which each contains the ambiguous and unambiguous angle with a one-to-one relationship. Our method enjoys two advantages in comparison to the existing methods. First, the cyclic ambiguity can be resolved by the one-to-one mapping of unambiguous angle without requiring additional algorithms such as MUSIC and MODE. Second, the proposed method can deal with different unambiguous angles with the same ambiguous angles, which could not have been possible by using the traditional schemes due to the fact that our method obtains the ambiguous and unambiguous angles simultaneously.

Joint DOA Estimation and Source Signal Tracking With Kalman Filtering and Regularized QRD RLS Algorithm

In this brief, we present a nontraditional approach for estimating and tracking signal direction-of-arrival (DOA) using an array of sensors. The proposed method consists of two stages: in the first stage, the sources modeled by autoregressive (AR) processes are estimated by the celebrated Kalman filter, and in the second stage, the efficient QR-decomposition-based recursive least square (QRD-RLS) technique is employed to estimate the DOAs and AR coefficients in each observed time interval. The AR-modeled sources can provide useful temporal information to handle cases such as the number of sources being larger than the number of antennas. In addition, the symmetric array enables one to transfer a complex-valued nonlinear problem to a real-valued linear one, which can reduce the computational complexity. Simulation results demonstrate the superior performance of the algorithm for estimating and tracking DOA under different scenarios.

Fast and efficient DOA estimation method for signals with known waveforms using nonuniform linear arrays

In this paper, a new approach is proposed to estimate the direction of arrival (DOA) of multiple non-coherent source signals with known waveforms but unknown gains based on a nonuniform linear sensor array. Unlike some previous methods, which estimate the DOA using spatial signatures of the signals with known waveforms, the proposed method first uses the known waveforms and mutually independent sensor measurement noises to establish a maximum likelihood estimation problem corresponding to multiple linear regression models, each containing the DOA and the gain information of all the source signals. Then, regression analysis is performed to estimate the coefficients of each linear regression model, and the well-known generalized least squares is used to obtain the estimates of the angles and gains from the estimated regression coefficients. The proposed method does not require a search over a large region of the parameter space, which is normally needed in ML-based DOA estimation methods. The effect of correlated sources on the performance of the parameter estimation is also studied. It is shown that the DOA and gain estimates are asymptotically optimal as the sources tend to be uncorrelated. Finally, simulation results that demonstrate the estimation performance of the proposed method are given. HighlightsWe propose a new method to estimate the direction of arrival (DOA) of non-coherent source signals with known waveforms.We exploit nonuniform linear array in conjunction with linear regression model for DOA estimation.The effect of correlated sources on the performance of parameter estimation is also investigated.Our method estimates the parameters with linear operation and has lower computational cost compared to existing methods.

Sparse linear arrays for estimating and tracking DOAs of signals with known waveforms

There are two main ways by which antenna arrays can significantly improve the performance of the direction-of-arrival (DOA) estimation. In the first method, one can extend the array aperture by designing a sparse antennas array. The second method makes use of the temporal information of the received signals, such as the signal waveform. A few articles have dealt with DOA estimation by combining the above two approaches. In this paper, we present a DOA estimation and tracking method by employing the known waveform of the signal and the parallel recursive least square (RLS) technique. When the waveform of the signal is known, the output of each sensor in the array can be considered as a linear regression model of which the coefficients contain the whole information for estimating the DOA. Therefore, the RLS filter is used to estimate and track these coefficients and then the relationship among the coefficients of all the sensors is exploited to obtain the final DOA value without ambiguity. Finally, computer simulation of the proposed method with comparison to the previous methods is conducted.

Performance analysis of 2-D DOA estimation via L-shaped array

Signal direction-of-arrival (DOA) estimation using an L-shaped array of sensors configured by two uniform linear arrays (ULA) has been an active research topic in array signal processing. A simple but efficient DOA estimation method has recently been proposed by exploiting the L-shape array geometry and the cross-correlation information of sensor data. In this paper, the asymptotic variance of the estimated azimuth and elevation angles is discussed. Unlike the traditional asymptotic analysis of ESPRIT algorithm where the eigen-decomposition of auto-correlation is used, herein we will focus on the first order perturbation for the cross-correlation matrix where the noise is eliminated. The theoretical analysis is verified through numerical results.

Joint DOA and frequency estimation with sub-Nyquist sampling

In the previous work for joint Direction-Of-Arrival (DOA) and frequency estimation with sub-Nyquist sampling, algorithm JDFTD has a superior estimation performance. However, the computational burden increases with the snapshot for iterative operation. In this paper, singular value decomposition (SVD) is employed to eliminate the effect of snapshot and a new version of algorithm JDFTD based on SVD (SVD-JDFTD) is proposed. Numerical simulations verify that SVD-JDFTD reduces the computational burden without loss the superior estimation performance of JDFTD.

Fast and accurate 2-D DOA estimation via sparse L-shaped array

In this paper, we address the problem of estimating the two-dimensional (2-D) directions of arrival (DOA) of multiple signals, by means of a sparse L-shaped array. The array consists of one uniform linear array (ULA) and one sparse linear array (SLA). The shift-invariance property of the ULA is used to estimate the elevation angles with low computational burden. The source waveforms are then obtained by the estimated elevational angles, which together with each sensor of the SLA, considered as a linear regression model, will be used to estimate the azimuth angle by the modified total least squares (MTLS) technique. The new algorithm yields correct parameter pairs without requiring the computationally expensive pairing operation, and therefore, it has at least two advantages over the previous L-shaped array based algorithms: less computational load and better performance due to using the SLA. Simulation results show that our method provides accurate and consistent 2-D DOA estimation results which could not be achieved by other methods with comparable computational complexity.

Accurate DOA estimation via sparse sensor array

An accurate direction-of-arrival (DOA) estimation algorithm with sparse sensor array is proposed. By dividing the nonuniform linear sparse array (NLSA) into two uniform linear sparse arrays (ULSA), the subarray response vectors yield a property of rotational invariance in the estimation of rough DOA without ambiguity using the so-called generalized ESPRIT. According to the estimated rough DOA, the accurate DOA of each source is then obtained by the proposed alternating null-steering technique (ANST). Simulation results that demonstrate the performance of the algorithm are provided.