Sea Clutter Covariance Matrix Estimation and Its Application to Whitening Filter

Choi, Sang-Hyun; Yang, Hoon‐Gee; Song, Ji-Min; Jeon, Haemin; Kim, Jong-Mann; Chung, Yong-Seek

doi:10.26866/jees.2021.21.2.134

Cited by 5 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A whitening filter is usually used for data pre-processing. The aim is to make the pulses of the same range bin not correlated with each other, and it is conducive to the extraction of subsequent feature components [27]. The principle of the whitening filter is to find a linear transformation so that the original data vector can become a whitening vector after being projected into a new subspace.…”

Section: Selective Whitening Filter Based On Correlation Estimationmentioning

confidence: 99%

Low Observable Radar Target Detection Method within Sea Clutter Based on Correlation Estimation

Luo

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

The long-time coherent integration can effectively improve the detection ability of radar targets. However, this strategy usually shows poor effect in resisting the sea clutter, which produces difficulties for accurate estimation of sea clutter characteristics and results in the inability to differentiate between the target and sea clutter. To solve this problem, a two-stage method is proposed, which consists of the sea clutter suppression stage and target decision stage. In the sea clutter suppression stage, the correlation time differences in the time and the space domains are adopted to estimate the correlation of sea clutter. Then, a selective whitening filter is proposed, which is performed more adaptively according to the estimation results. In the decision stage, the peak average ratio in the fractional Fourier domain (FRFT-PAR) is presented, which can make better use of the energy accumulation characteristics and further suppress the interference of sea clutter. Experiments on the IPIX datasets with various observation times and polarization modes are included. The results indicate that the proposed method could not only effectively suppress sea clutter but also achieve better target detection performance than baseline algorithms.

show abstract

Section: Selective Whitening Filter Based On Correlation Estimationmentioning

confidence: 99%

Low Observable Radar Target Detection Method within Sea Clutter Based on Correlation Estimation

Luo

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Subspace decomposition method mainly includes using eigenvalue decomposition [ 31 , 32 , 33 , 34 ] and singular value decomposition (SVD) [ 35 , 36 , 37 , 38 ] to suppress sea clutter. Among them, the SVD method is a typical research content and a method based on the characteristics of the first-order Bragg peak of sea clutter in the Doppler domain.…”

Section: Introductionmentioning

confidence: 99%

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

Wen

Wei

2022

Entropy

View full text Add to dashboard Cite

In marine radar target detection, sea clutter will cause a large number of missed alarms and false alarms, which will affect the accuracy of target detection. In order to suppress sea clutter effectively, a sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering is proposed in this paper. The proposed method is to add a sea clutter suppression link before detecting the target. Firstly, the marine radar image sequence is transformed into three-dimensional frequency wavenumber domain by three-dimensional fast Fourier transform (3D-FFT), and then the three-dimensional image spectrum is obtained. According to the fact that the sea clutter spectrum obtained from the image spectrum satisfies the dispersion relation of linear wave theory in the three-dimensional frequency wavenumber domain, a sea clutter model is established. Then, through the established sea clutter model, a spatio-temporal domain joint sea clutter suppressor is designed to filter the image spectrum. After that, the filtered image spectrum is transformed by three-dimensional inverse fast Fourier transform (3D-IFFT) to obtain the image sequence in which sea clutter is suppressed. Finally, target detection is carried out for sea clutter suppressed image sequence. The method is validated by using the real data of X-band marine radar. Compared with the classical Empirical mode decomposition (EMD) method, the improvement of signal-to-noise ratio (SNR) is more obvious, and SNR can be increased by 15.3 db at most. In addition, compared with target detection on original images directly, the proposed method has excellent detection rate and can increase detection rates by at least 8%.

show abstract

“…The covariance matrix, which is a fundamental quantity for quantifying the variance and covariance among the variables, plays an essential role in both multivariate statistics and practical data processing 1 , 2 . In general, people can hardly know the exact value of the covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

“…, p , w ii is the sum of squares of 2n i.i.d. Gaussian random variables with variance1 2 . Hence, we can obtain that 2w ii ∼ χ 2 (2n) .…”

mentioning

confidence: 99%

Shrinkage estimators of large covariance matrices with Toeplitz targets in array signal processing

Zhang

Yuan

2022

Sci Rep

View full text Add to dashboard Cite

The problem of estimating a large covariance matrix arises in various statistical applications. This paper develops new covariance matrix estimators based on shrinkage regularization. Individually, we consider two kinds of Toeplitz-structured target matrices as the data come from the complex Gaussian distribution. We derive the optimal tuning parameter under the mean squared error criterion in closed form by discovering the mathematical properties of the two target matrices. We get some vital moment properties of the complex Wishart distribution, then simplify the optimal tuning parameter. By unbiasedly estimating the unknown scalar quantities involved in the optimal tuning parameter, we propose two shrinkage estimators available in the large-dimensional setting. For verifying the performance of the proposed covariance matrix estimators, we provide some numerical simulations and applications to array signal processing compared to some existing estimators.

show abstract

Sea Clutter Covariance Matrix Estimation and Its Application to Whitening Filter

Cited by 5 publications

References 17 publications

Low Observable Radar Target Detection Method within Sea Clutter Based on Correlation Estimation

Low Observable Radar Target Detection Method within Sea Clutter Based on Correlation Estimation

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

Shrinkage estimators of large covariance matrices with Toeplitz targets in array signal processing

Contact Info

Product

Resources

About