Time-frequency DOA estimation of chirp signals based on multi-subarray

Qi, Bingbing; Zhang, Huansheng; Zhang, Xiaobo

doi:10.1016/j.dsp.2021.103031

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…Subsequently, the recovery ratios from the schemes are investigated along with the frequency component numbers at the lowest SNR of −15 dB as shown in Figure 3 a. As expected, a lesser frequency component number is beneficial for accurate frequency detections, and the recovery ratios decrease with the increment of the component number except for the ESPRIT with poor performance at low SNR as in [ 7 , 8 , 9 , 10 ]. As shown in Figure 3 b, where 25 tonal components are included, the contaminated result from the FFT with the low resolution cannot accurately resolve the true components owing to the closer components.…”

Section: Frequency Analysis Using Synthetic Datamentioning

confidence: 80%

“…Signal subspace schemes such as RMUSIC and ESPRIT recover the frequency components with higher resolution. However, their applications are limited by the prerequisite condition of signal frequency component number as well as the intensive noise [ 7 , 8 , 9 , 10 ].…”

Section: Discussionmentioning

confidence: 99%

“…The noise in signals directly contaminates the frequency detections from the FFT, which deteriorate with the decrement of SNRs. While the RMUSIC and ESPRIT ameliorate the FFT detection results, their performances (in particular, ESPRIT) are significantly diminished by the intensive noise such as the beamforming [ 7 , 8 , 9 , 10 ]. However, the SBL has an acceptable recovery ratio of 0.88 even at the lowest SNR of −15 dB by exploiting the commonality over the multiple measurements (i.e., sharing the frequency components across the measurements).…”

Section: Frequency Analysis Using Synthetic Datamentioning

confidence: 99%

“…However, to obtain the signal subspace reliably, a large number of observations are required. Furthermore, these algorithms have the prerequisite condition of the signal component number in the observations for accurate frequency estimation and their performances significantly deteriorate at low signal to noise ratio (SNR) [ 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning

Shin

Hong

Lee

et al. 2021

Sensors

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Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components.

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Section: Frequency Analysis Using Synthetic Datamentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Frequency Analysis Using Synthetic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning

Shin

Hong

Lee

et al. 2021

Sensors

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“…The RMSE and POR are respectively defined aswhere W represents the number of Monte Carlo trials. P is the source number, which is known (Qi, 2022; Qi et al. , 2021) or correctly estimated (Wan et al.…”

Section: Numerical Resultsmentioning

confidence: 99%

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

Qi,

Xu,

Liu

2024

Self Cite

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PurposeThe purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the direction-of-arrival (DOA) estimation performance of coherent signals at low signal-to-noise ratio (SNRs).Design/methodology/approachAn improved multiple-Toeplitz matrices reconstruction method is proposed via quadratic spatial smoothing processing. Our proposed method takes advantage of the available information contained in the auto-covariance matrices of individual Toeplitz matrices and the cross-covariance matrices of different Toeplitz matrices, which results in a higher noise suppression ability.FindingsTheoretical analysis and simulation results show that, compared with the existing Toeplitz matrix processing methods, the proposed method improves the DOA estimation performance in cases with a low SNR. Especially for the cases with a low SNR and small snapshot number as well as with closely spaced sources, the proposed method can achieve much better performance on estimation accuracy and resolution probability.Research limitations/implicationsThe study investigates the possibility of reusing pre-existing designs for the DOA estimation of the coherent signals. The proposed technique enables achieve good estimation performance at low SNRs.Practical implicationsThe paper includes implications for the DOA problem at low SNRs in communication systems.Originality/valueThe proposed method proved to be useful for the DOA estimation at low SNR.

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Frequency Offset Estimation of X-band Marine Radar Sampling Signal Based on Phase Difference

Wang

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Time-frequency DOA estimation of chirp signals based on multi-subarray

Cited by 14 publications

References 38 publications

Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning

Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning

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

Frequency Offset Estimation of X-band Marine Radar Sampling Signal Based on Phase Difference

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