Subspace SNR Maximization: The Constrained Stochastic Matched Filter

This paper introduces a new fast algorithm named CSMFST which estimates the p-dimensional optimal subspace, i.e. where the signal-to-noise ratio is maximized in the case of n-dimensional nonstationary signals. We assume that we treat both signal and noise which are characterized by their samples. This algorithm is an SP-type algorithm and uses the same principles as the Yet Another Subspace Tracking (YAST) algorithm when estimating the covariance matrices. At each step, it estimates a matrix which spans the optimal subspace.

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“…To estimate ( ) which generates the p-dimensional optimal subspace, in which ( ) is maximized [12], we introduce the (n x n) following matrix:…”

Section: Principle Of the Csmfst Algorithmmentioning

confidence: 99%

“…Besides, receiving new signal and noise realizations at each time step, requires the reestimating of the correlation matrices A(t) and B(t) in addition to the weighting matrix ( ). In order to do this, we implemented an algorithm that computes ( ) related to the matrix presented in (11), it's named optSNR [12,13,14].…”

Section: Principle Of the Csmfst Algorithmmentioning

confidence: 99%

The constrained stochastic matched filter subspace tracking

Chagmani

Xerri

Borloz

et al. 2017

Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis

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“…The adaptive MF has better matching performance than the model-based MF in slow time-varying coherent-multipath channels. The third technique is the stochastic MF [ 11 , 12 , 13 ], which can be applied to frequency time-varying signals such as the wide band modulated sonar signal propagated in shallow water. The stochastic MF is able to take into account uncertainties and variations of the multipath channel, thus enhancing the detection efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Frequency-Domain Adaptive Matched Filter for Active Sonar Detection

Zhao

Jiang

et al. 2017

Sensors

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The most classical detector of active sonar and radar is the matched filter (MF), which is the optimal processor under ideal conditions. Aiming at the problem of active sonar detection, we propose a frequency-domain adaptive matched filter (FDAMF) with the use of a frequency-domain adaptive line enhancer (ALE). The FDAMF is an improved MF. In the simulations in this paper, the signal to noise ratio (SNR) gain of the FDAMF is about 18.6 dB higher than that of the classical MF when the input SNR is −10 dB. In order to improve the performance of the FDAMF with a low input SNR, we propose a pre-processing method, which is called frequency-domain time reversal convolution and interference suppression (TRC-IS). Compared with the classical MF, the FDAMF combined with the TRC-IS method obtains higher SNR gain, a lower detection threshold, and a better receiver operating characteristic (ROC) in the simulations in this paper. The simulation results show that the FDAMF has higher processing gain and better detection performance than the classical MF under ideal conditions. The experimental results indicate that the FDAMF does improve the performance of the MF, and can adapt to actual interference in a way. In addition, the TRC-IS preprocessing method works well in an actual noisy ocean environment.

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Adaptive control and signal processing literature survey (No. 25)

2011

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized, these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please email Erfu Yang at erfu.yang@gmail.com.

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Subspace SNR Maximization: The Constrained Stochastic Matched Filter

Cited by 7 publications

References 20 publications

The constrained stochastic matched filter subspace tracking

The constrained stochastic matched filter subspace tracking

A Frequency-Domain Adaptive Matched Filter for Active Sonar Detection

Adaptive control and signal processing literature survey (No. 25)

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