Coherent Direction-of-arrival Research Articles

Simplified Spatial Smoothing for DOA Estimation of Coherent Signals

In coherent direction of arrival (DOA) estimation, subspace-based methods suffer from performance deterioration because of the rank loss of the signal covariance matrix. A variety of spatial smoothing preprocessing techniques have been proposed for decorrelation, among which the enhanced spatial smoothing preprocessing (ESS) technique shows outstanding performance by exploiting the signal subspace. However, ESS is applied by squaring the matrix whose columns span the signal subspace (called signal matrix), which involves unnecessary computational loads. Besides, the smoothed covariance matrix after ESS is a linear combination of the sub-matrices of signal matrix, where the coefficients of the combination may undermine the decorrelation performance. In this context, a simplified spatial smoothing (SSS) technique is proposed for decorrelation by averaging the sub-matrices of the signal matrix directly, and avoids redundant operations in squared signal matrix. The proposed method is tested numerically in terms of the signal-to-noise ratio (SNR), the number of snapshots, angle separation, and the execution time. Simulation results show the improvement of the decorrelation performance, efficiency, and robustness with the proposed method in coherent scenarios, compared with the other spatial smoothing preprocessing based methods.

Super-resolution method for coherent DOA estimation of multiple wideband sources

We focus on coherent Direction Of Arrival (DOA) estimation of wideband sources based on spatial sparsity. By development of super-resolution algorithms, DOA estimation of narrowband sources has already been addressed based on gridless sparse algorithms and atomic norm minimization. In this paper, a super-resolution based method is proposed for coherent DOA estimation of multiple wideband sources. To do so, we first develop a new focusing method to map the subband with the largest center frequency to the other ones with no requirement for any initial estimates. Then, using the latter focusing method, we introduce an atomic norm problem by defining a new set of atoms and exploiting the signal joint sparsity of different frequency subbands in a continuous spatial domain. This problem is then cast as a semidefinite program, which leads to implementing a new coherent DOA estimation method with higher resolution and more robustness to noise. Our method only needs one single snapshot for each frequency subband, although it can also be used for multiple snapshots. Numerical simulations show the outperformance of the proposed method compared to the conventional ones.

Two-Dimensional DOA Estimation of MIMO Radar Coherent Source Based on Toeplitz Matrix Set Reconstruction

In order to realize the high-precision direction of arrival (DOA) estimation of the coherent source of two-dimensional multiple-input and multiple-output (MIMO) radar, a solution is given by combining Toeplitz matrix set reconstruction. MIMO radar obtains a larger aperture with fewer arrays. Traditional two-dimensional reconstruction Toeplitz-like algorithms use part of the information in the construction of two correlation matrices or covariance matrices to construct the Toeplitz matrix when performing two-dimensional coherent source DOA estimation, which makes the information utilization incomplete and requires additional denoising processing. To solve the above problems, this paper proposes an improved Toeplitz matrix set reconstruction algorithm based on the two-dimensional reconstruction Toeplitz class algorithm. The complete array element receiving signal vector is used to construct two Toeplitz matrix sets containing complete information, and then their conjugate transposes. Multiply and sum to correct the matrix to obtain a full-rank matrix, so as to achieve the purpose of decoherence and combine the traditional ESPRIT algorithm to perform two one-dimensional reconstruction processing through rotation invariance and then perform angle matching to achieve two-dimensional coherent signal angle estimation, while avoiding additional denoising processing. Finally, the simulation results of the cross array and the L-shaped array verify the effectiveness of the algorithm in this paper and further extend it to the two-dimensional MIMO radar array model and compare it with the traditional ESPRIT-like algorithm and the REC-FBSS-ESPRIT algorithm. In comparison, the algorithm in this paper has better performance under the conditions of successful resolution, DOA estimation accuracy, and low signal-to-noise ratio.

Efficient Coherent Direction-of-Arrival Estimation and Realization Using Digital Signal Processor

A novel efficient coherent direction-of-arrival (DOA) estimation method is devised in this article. First, a new cost function without the knowledge of source number is developed exploiting the Toeplitz matrices’ joint diagonalization structure. Then, the revised steering vectors are used in the place of projection weights of the steering vectors to reconstruct the power spectrum in both noise and signal subspaces. The coherent DOAs are estimated using the 1-D search. Furthermore, the computational complexity of the proposed method is significantly reduced using the Nystrom approximation. Finally, the developed theoretical model is implemented on the TMS320C6678 digital signal processor (DSP) to exemplify the efficacy of the novel method.

Two‐dimensional DOA estimation for multi‐path environments by accurate separation of signals using k ‐medoids clustering

The authors propose a two-dimensional direction-of-arrival (DOA) estimation for multi-path environments, in which there are uncorrelated, partially correlated and coherent signals. The inability to identify non-coherent signals from coherent ones results in a considerable waste of sensors. In this work, an adaptive and automated threshold is considered for the efficient separation of noise, non-coherent signals, and coherent groups. First, non-coherent signals, coherent groups, and noise subspace are separated using k-medoids clustering. After determining the number of sources, non-coherent signals and coherent groups, non-coherent DOAs are estimated separately. The number of coherent signals in each coherence group is determined by the minimum descriptive length criterion. Finally, coherent DOAs are estimated in each group by constructing a coherent estimation matrix. The proposed method does not require any prior information such as knowing the number of signals or the covariance matrix of uncorrelated signals. The simulation results show that the proposed method is able to distinguish between the non-coherent and coherent signals, even at low signal-to-noise ratios and a small number of snapshots. Also, in terms of detection probability and estimation accuracy, it shows an improvement of over 1.2 and 83%, respectively, compared with the conventional forward−backward spatial smoothing scheme.

A coherent direction of arrival estimation method using a single pulse

This paper addresses the problem of coherent direction of arrival (DOA) estimation in monostatic multi-input multi-output (MIMO) radar using a single pulse, and links the trilinear model to derive a coherent DOA estimation method. We use the received data to construct a set of Toeplitz matrices through which a trilinear model is formed, and then the trilinear decomposition is used to attain the DOAs of sources. The proposed algorithm is effective for a single pulse. Compared to the forward backward spatial smoothing estimation method of signal parameters via rotational invariance techniques (ESPRIT), the ESPRIT-like of Han, and the ESPRIT-like of Li algorithms, our method has better angle estimation performance. Numerical simulations present the effectiveness and improvement of our approach.

A RD-ESPRIT algorithm for coherent DOA estimation in monostatic MIMO radar using a single pulse

This paper discusses the problem of coherent direction of arrival (DOA) estimation in a monostatic multi-input multi-output (MIMO) radar using a single pulse, and proposes a reduced dimension (RD)-estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. We reconstruct the received data and then utilise it to construct a set of Toeplitz matrices. After that, we use RD-ESPRIT to obtain the DOAs of the sources. The proposed algorithm is effective for coherent angle estimation based on a single pulse, and it has much better angle estimation performance than the forward backward spatial smoothing (FBSS)-ESPRIT algorithm and the ESPRIT-like of Li, as well as very close angle estimation performance to the ESPRIT-like of Han. For complexity comparison, our algorithm has very close complexity to the FBSS-ESPRIT algorithm, and lower complexity than the ESPRIT-like of Han and the ESPRIT-like of Li. Simulation results present the effectiveness and improvement of our approach.