Guoping Hu

Sparsity-Based Two-Dimensional DOA Estimation for Coprime Array: From Sum–Difference Coarray Viewpoint

This paper addresses the issue of two-dimensional (2-D) direction of arrival (DOA) estimation with coprime planar arrays (CPPAs) via sparse representation. Our work differs from the partial spectral search approach [25], which suppresses the phase ambiguity by searching the common peaks of two subarrays. We focus on the coprime property of CPPA, where the sparse array extension model with sum–difference coarray (SDCA) is derived for larger degrees of freedom (DOFs). Besides, to optimize the selection of regularization parameter, we also construct a new sparse representation algorithm by estimating the errors between the signal and noise parts. Further, an iterative scheme is presented to transform the 2-D grids searching to several times of 1-D searching, where the initial values are obtained by extracting one difference coarray from SDCA. So the proposed method can achieve aperture extension, high estimation performance, and low computational complexity. Besides, the sparse array extension model for multiple-input multiple-output radars is discussed and the Cramér–Rao bound for 2-D DOA estimation with CPPA is also derived in detail. Finally, simulation results demonstrate the effectiveness of proposed method compared to the state-of-the-art methods.

DOA Estimation Using Multipath Echo Power for MIMO Radar in Low-Grazing Angle

In this paper, we propose a novel direction of arrival estimation algorithm for multi-input multi-output radar in a low-grazing angle (LGA) condition. Considering the effect of earth curvature and atmosphere, we derive the received signal model based on the assumption that the waveforms are transmitted by widely spaced sensors and received by some separated uniform linear arrays (ULAs). In order to use the multipath echo power, the proposed algorithm can select the best transmitter multipath reflected coefficients by optimizing the virtual transmitter signal, remove the coherency by improved spatial smoothing method and select the best receiver multipath reflected coefficients by peaks searching method. As a result, the algorithm can avoid the attenuation caused by different multipath reflected coefficients and targets glint, resulting from the spatial diversity for transmitter sensors and receiver subarrays. Simulation results show that, in LGA, the proposed algorithm can achieve performance improvements for the realistic LGA model, and also with the increase of the transmit sensors and ULAs or compared with other recently developed methods.

Direction of Arrival Estimation in Low-Grazing Angle: A Partial Spatial-Differencing Approach

This paper addresses a partial spatial-differencing (PSD) approach for the direction of arrival estimation in a low-grazing angle (LGA) condition. By dividing the sample covariance matrix into several column subvectors, we first form the corresponding reconstructed subarray covariance matrices (RSCMs). We then calculate the spatial differencing matrix for the noise parts of RSCMs, while the non-noise parts are kept completely. That is, we build a PSD matrix. Compared with the existing spatial smoothing and full spatial-differencing methods, the PSD approach can use all the data information of the sample covariance matrix and also suppress the effect of additive white or colored noise more effectively. Simulation results show that our method provides a higher estimation accuracy and resolution than the state-of-the-art methods.

Improved Spatial Differencing Scheme for 2-D DOA Estimation of Coherent Signals with Uniform Rectangular Arrays

This paper proposes an improved spatial differencing (ISD) scheme for two-dimensional direction of arrival (2-D DOA) estimation of coherent signals with uniform rectangular arrays (URAs). We first divide the URA into a number of row rectangular subarrays. Then, by extracting all the data information of each subarray, we only perform difference-operation on the auto-correlations, while the cross-correlations are kept unchanged. Using the reconstructed submatrices, both the forward only ISD (FO-ISD) and forward backward ISD (FB-ISD) methods are developed under the proposed scheme. Compared with the existing spatial smoothing techniques, the proposed scheme can use more data information of the sample covariance matrix and also suppress the effect of additive noise more effectively. Simulation results show that both FO-ISD and FB-ISD can improve the estimation performance largely as compared to the others, in white or colored noise conditions.

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.

Multi-Frequency Based Direction-of-Arrival Estimation for 2q-Level Nested Radar & Sonar Arrays

Direction finding is a hot research area in radar and sonar systems. In the case of q ≥ 2, the 2qth-order cumulant based direction of arrival (DOA) estimation algorithm for the 2q-level nested array can achieve high resolution performance. A virtual 2qth-order difference co-array, which contains O(N2q) virtual sensors in the form of a uniform linear array (ULA), is yielded and the Gaussian noise is eliminated. However, some virtual elements are separated by the holes among the 2qth-order difference co-array and cannot be fully used. Even though the application of the multi-frequency method for minimum frequency separation (MFMFS) can fill the holes with low computation complexity, it requires that the number of frequencies must increase with the number of holes. In addition, the signal spectra have to be proportional for all frequencies, which is hard to satisfy when the number of holes is large. Aiming at this, we further propose a multi-frequency method for a minimum number of frequencies (MFMNF) and discuss the best frequency choice under two specific situations. Simulation results verify that, compared with the MFMFS method, the proposed MFMNF method can use only one frequency to fill all the holes while achieving a longer virtual array and the DOA estimation performance is, therefore, improved.

Novel Diagonal Reloading Based Direction of Arrival Estimation in Unknown Non-Uniform Noise

Nested array can expand the degrees of freedom (DOF) from difference coarray perspective, but suffering from the performance degradation of direction of arrival (DOA) estimation in unknown non-uniform noise. In this paper, a novel diagonal reloading (DR) based DOA estimation algorithm is proposed using a recently developed nested MIMO array. The elements in the main diagonal of the sample covariance matrix are eliminated; next the smallest MN-K eigenvalues of the revised matrix are obtained and averaged to estimate the sum value of the signal power. Further the estimated sum value is filled into the main diagonal of the revised matrix for estimating the signal covariance matrix. In this case, the negative effect of noise is eliminated without losing the useful information of the signal matrix. Besides, the degrees of freedom are expanded obviously, resulting in the performance improvement. Several simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

Smoothing matrix set-based MIMO radar coherent source localisation

ABSTRACT In this paper, we study the problem of coherent source localisation with multi-input multi-output (MIMO) radar via a smoothing matrix set (SMS) scheme. We first perform the transmission diversity smoothing to form some transmission sub-vectors (TSVs) and then perform reception diversity smoothing for each TSV. That is, each TSV consists of some reception sub-vectors (RSVs). By rearranging the elements of the covariance sub-matrix of each TSV along the vertical direction, both the forward-only SMS (FO-SMS) and forward backward SMS (FB-SMS) methods are derived using these RSVs. Compared with the existing smoothing methods, the proposed scheme can extract more data information from each covariance sub-matrix and also be suitable for joint direction of arrival (DOA) and direction of departure (DOD) estimation. Simulation results show that, FB-SMS can improve the estimation performance as compared to the state-of-the-art methods for monostatic or bistatic MIMO radars.

Sum and difference coarrays based 2-D DOA estimation with co-prime parallel arrays

This paper proposes a symmetric co-prime parallel array (S-CPPA) configuration for two-dimensional direction of arrival (2-D DOA) estimation. Specifically, the S-CPPA consists of a co-prime pair of symmetric uniform linear subarrays (ULAs). By vectorizing the cross-correlation matrix of the received signals, the sum and difference coarrays perspective is developed to obtain a large number of degrees of freedom (DOFs). Compared with the existing configurations, S-CPPA can achieve a superior performance. Simulation results verify the effectiveness of the proposed configuration using sparse representation and least square algorithms.

Entropy-based multipath detection model for MIMO radar

An optimized detection model based on weighted entropy for multiple input multiple output (MIMO) radar in multipath environment is presented. After defining the multipath distance difference (MDD), the multipath received signal model with four paths is built systematically. Both the variance and correlation coefficient of multipath scattering coefficient with MDD are analyzed, which indicates that the multipath variable can decrease the detection performance by reducing the echo power. By making use of the likelihood ratio test (LRT), a new method based on weighted entropy is introduced to use the positive multipath echo power and suppress the negative echo power, which results in better performance. Simulation results show that, compared with non-multipath environment or other recently developed methods, the proposed method can achieve detection performance improvement with the increase of sensors.