An enhanced spatial smoothing algorithm for coherent signals DOA estimation

Abstract

PurposeThe existing dimensionality reduction algorithms suffer serious performance degradation under low signal-to-noise ratio (SNR) owing to the presence of noise. To address these problems, an enhanced spatial smoothing scheme is proposed that exploits the subarray time-space correlation matrices to reconstruct the data matrix to overcome this weakness. This method uses the strong correlation of signal and the weak correlation of noise in time and space domains, which improves the noise suppression ability.Design/methodology/approachIn this paper, an enhanced spatial smoothing method is proposed. By using the strong correlation of signal and the weak correlation of noise, the time-space smoothed array covariance matrix based on the subarray time-space correlation matrices is constructed to improve the noise suppression ability. Compared with the existing Toeplitz matrix reconstruction and spatial smoothing methods, the proposed method improves the DOA estimation performance at low SNR.FindingsTheoretical analysis and simulation results show that compared with the existing dimensionality reduction processing algorithms, the proposed method improves the DOA estimation performance in cases with a low SNR. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation performance.Originality/valueThe proposed method improves the DOA estimation performance at low SNR. In particular, for the cases with a low SNR, the proposed method provides a better RMSE. The convergence of the proposed method is also faster than other methods for the low number of snapshots. Our analysis also confirms that in cases where the DOAs between the coherent sources are closely spaced, the proposed method achieves a much higher angular resolution than that of the other methods.

Improved multiple-Toeplitz matrices reconstruction method using quadratic spatial smoothing for coherent signals DOA estimation

“…2000 independent trials are performed to obtain the statistical results. The mean value of the ratio of the signal subspace eigenvalues to the noise subspace eigenvalues as the eigenvalue SNR is employed to reflect the noise suppression ability (Qi, 2022; Qi and Liu, 2021). Furthermore, there is a random phase delay from [0,2π] between these signals at each trial.…”

“…Furthermore, similar to the spatial smoothing, because of the increased spatial smoothing number, the MTOPE method will achieve better DOA estimation performance when compared to the FB-PTMR method. However, the estimation accuracy of the reconstructed matrix for the FB-PTMR and MTOEP methods may become poor under a low SNR, which further results in degradation of the DOA estimation performance (Qi, 2022; Qi and Li, 2021; Qi and Liu, 2021, 2022).…”

“…However, these methods suffer serious performance degradation in the case of coherent signals owing to the rank loss of the source covariance matrix. Thus, the rank restoration for the source covariance matrix is the key to the decoherence methods (Qi, 2022; Qi and Li, 2021; Qi and Liu, 2021).…”

Underwater wideband coherent signals DOA estimation using sparse representation and deconvolution

“…can be classified into two categories: subspace-based [9,10], and beamforming-based [11]. Representative subspace-based algorithms, such as MUSIC [9] and ESPRIT [10], can be combined with various decoherence techniques [12][13][14][15] to obtain high-resolution DOA estimation. However, it is necessary to know the number of coherent sources and the covariance matrix of the signals.…”

The DOA Estimation Method for Low-Altitude Targets under the Background of Impulse Noise

“…In the literature [12][13][14], the spatial differencing algorithm was used for decoherence, where the accuracy was improved to some extent but the DOA estimation effect was not satisfactory under the condition of low signal-to-noise ratio (SNR). In the literature [15][16][17][18][19], a new method with a combination of time reversal technology and a generalized MUSIC was proposed for the DOA estimation of low-altitude targets, which improved the estimation accuracy of the algorithm to some extent.…”