One-Step Persymmetric GLRT for Subspace Signals

Li, Jun; Sun, Siyu; Liu, Weijian

doi:10.1109/tsp.2019.2918994

Cited by 53 publications

(9 citation statements)

References 45 publications

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“…The above studies exploiting persymmetry primarily focused on rank-one signal detection. In contrast, persymmetric detection of subspace signals was considered in [268][269][270][271][272][273]. In addition, persymmetry can be used in non-Gaussian noise [71,252,[274][275][276][277] or MIMO radar [278][279][280][281].…”

Section: Priori Information-based Methodsmentioning

confidence: 99%

Multichannel adaptive signal detection: basic theory and literature review

Liu

Chen

et al. 2022

Sci. China Inf. Sci.

Self Cite

149

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Multichannel adaptive signal detection uses test and training data jointly to form an adaptive detector to determine whether a target exists. The resulting adaptive detectors typically possess constant false alarm rate (CFAR) properties; thus, no additional CFAR processing is required. In addition, a filtering process is also not required because the filtering function is embedded in the adaptive detector. Adaptive detection typically exhibits better detection performance than the filtering-then-CFAR detection technique. It has been approximately 35 years since the first multichannel adaptive detector was proposed by Kelly in 1986. However, there are few overview articles on this topic. Thus, in this study, we present a tutorial overview of multichannel adaptive signal detection with an emphasis on the Gaussian background. We discuss the main design criteria for adaptive detectors, investigate the relationship between adaptive detection and filtering-then-CFAR detection techniques, investigate the relationship between adaptive detectors and adaptive filters, summarize typical adaptive detectors, present numerical examples, provide a comprehensive literature review, and discuss potential future research tracks.

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Section: Priori Information-based Methodsmentioning

confidence: 99%

Multichannel adaptive signal detection: basic theory and literature review

Liu

Chen

et al. 2022

Sci. China Inf. Sci.

Self Cite

149

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“…After evaluating several strategies for assigning weights, the most popular scheme was adopted: the weight of an onset may be compared to the note length, to melody characteristics, or all onsets are assigned the same weight. To evaluate a piece’s time signature, all pairwise dissimilarities between songs were computed using either the scale-free auto correlation function (ACF) or the STM vectors, and a cosine distance; a similar method was used in [ 87 ]. The same method used by Brown in [ 88 ] since it is a count of the number of events that occur during an occurrence at time zero if events are clustered from measure to measure, with a higher occurrence of an event happening with the measure’s time isolation, therefore peaks in the auto-correlation function should show the periods when measurements begin [ 89 ].…”

Section: Classical Methodsmentioning

confidence: 99%

Time Signature Detection: A Survey

Abimbola

Kostrzewa

Kasprowski

2021

Sensors

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This paper presents a thorough review of methods used in various research articles published in the field of time signature estimation and detection from 2003 to the present. The purpose of this review is to investigate the effectiveness of these methods and how they perform on different types of input signals (audio and MIDI). The results of the research have been divided into two categories: classical and deep learning techniques, and are summarized in order to make suggestions for future study. More than 110 publications from top journals and conferences written in English were reviewed, and each of the research selected was fully examined to demonstrate the feasibility of the approach used, the dataset, and accuracy obtained. Results of the studies analyzed show that, in general, the process of time signature estimation is a difficult one. However, the success of this research area could be an added advantage in a broader area of music genre classification using deep learning techniques. Suggestions for improved estimates and future research projects are also discussed.

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“…However, this assumption is usually inappropriate for practical implementations, owing to environmental and instrumental factors. To overcome this difficulty, the a priori information about the noise covariance matrix is often utilized, such as low-rank structure [30][31][32], knowledge-aided information [33], Bayesian methods [25,26,[34][35][36], and persymmetric structure [37][38][39][40][41]. However, the above detectors may suffer from significant performance loss, if the a priori information imposed on the covariance matrix considerably departs from the actual one.…”

Section: Introductionmentioning

confidence: 99%

Detection of a rank-one signal with limited training data

Liu

Zhang

et al. 2021

Preprint

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In this paper, we reconsider the problem of detecting a matrix-valued rank-one signal in unknown Gaussian noise, which was previously addressed for the case of sufficient training data. We relax the above assumption to the case of limited training data. We re-derive the corresponding generalized likelihood ratio test (GLRT) and two-step GLRT (2S-GLRT) based on certain unitary transformation on the test data. It is shown that the re-derived detectors can work with low sample support. Moreover, in sample-abundant environments the re-derived GLRT is the same as the previously proposed GLRT and the re-derived 2S-GLRT has better detection performance than the previously proposed 2S-GLRT. Numerical examples are provided to demonstrate the effectiveness of the re-derived detectors.

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One-Step Persymmetric GLRT for Subspace Signals

Cited by 53 publications

References 45 publications

Multichannel adaptive signal detection: basic theory and literature review

Multichannel adaptive signal detection: basic theory and literature review

Time Signature Detection: A Survey

Detection of a rank-one signal with limited training data

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