A CFAR adaptive subspace detector for second-order Gaussian signals

Jin, Yufeng; Friedlander, B.

doi:10.1109/tsp.2004.842196

Cited by 59 publications

(2 citation statements)

References 28 publications

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“…As seen in figure 2, under same signal-to-noise ratio condition, the highest total detection probability is 5/8 detection, while the lowest total detection probability is 2/3 detection. As seen in figure 1, to guarantee the request of total false alarm probability smaller than 10 -6 and total detection probability bigger than 0.9, signal-to-noise ratio of single detection required by 2/3, 3/5, 5/8 detection should bigger than 10.27dB, 8.65dB and 7.1ldB separately [1,3,4].…”

Section: Total Detection Probability and Total False Alarm Probabilit...mentioning

confidence: 99%

Study on Threshold Detection of Micro-Seismic Signal Based on Constraint Judgment

Yang

2011

AMR

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Double threshold detection based on constraint judgment is proposed for micro-seismic signal detection. The improvement effect on Probability of False Alarm and influence on Probability of Detection are quantitatively analyzed with constraint judgment. The mathematical models of total PFA and PD of double threshold detection based on constraint judgment are built, and the validity of the mathematical model is verified by simulation tests and experiments. The results show that the signal-to-noise ratio under scheduled PFA and PD Call be decreased by introducing constraint judgment to double threshold detection, and improve the identification accuracy of micro-seismic signal.

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Section: Total Detection Probability and Total False Alarm Probabilit...mentioning

confidence: 99%

Study on Threshold Detection of Micro-Seismic Signal Based on Constraint Judgment

Yang

2011

AMR

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“…In this paper, the FOG model for amplitudes of reflection scatterers of the extended target, which is a deterministic but unknown vector, is adopted to realise the extended target detection. For the SOG, Jin (Jin & Friedlander, 2005) derives a closed-form expression of the approximate distribution function of the detection statistic for the false alarm rate and detection probability. However, a set of the secondary data is needed to estimate the noise covariance for those subspace detector, which is supposed to be the same as for the primary data.…”

Section: Introductionmentioning

confidence: 99%

Adaptive subspace detection of extended target in white Gaussian noise using sinc basis

Zhang

Qu³

et al. 2015

International Journal of Electronics

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For the high resolution radar (HRR), the problem of detecting the extended target is considered in this paper. Based on a single observation, a new two-step detection based on sparse representation (TSDSR) method is proposed to detect the extended target in the presence of Gaussian noise with unknown covariance. In the new method, the Sinc dictionary is introduced to sparsely represent the high resolution range profile (HRRP). Meanwhile, adaptive subspace pursuit (ASP) is presented to recover the HRRP embedded in the Gaussian noise and estimate the noise covariance matrix. Based on the Sinc dictionary and the estimated noise covariance matrix, one step subspace detector (OSSD) for the first-order Gaussian (FOG) model without secondary data is adopted to realise the extended target detection. Finally, the proposed TSDSR method is applied to raw HRR data. Experimental results demonstrate that HRRPs of different targets can be sparsely represented very well with the Sinc dictionary. Moreover, the new method can estimate the noise power with tiny errors and have a good detection performance.

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Adaptive Subspace Detectors

Ramírez

Santamarı́a²,

Scharf³

2022

Coherence

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In this paper, we use the theory of generalized likelihood ratio tests (GLRTs) to adapt the matched subspace detectors (MSDs) of [1] and [2] to unknown noise covariance matrices. In so doing, we produce adaptive MSDs that may be applied to signal detection for radar, sonar, and data communication. We call the resulting detectors adaptive subspace detectors (ASDs). These include Kelly's GLRT and the adaptive cosine estimator (ACE) of [6] and [19] for scenarios in which the scaling of the test data may deviate from that of the training data. We then present a unified analysis of the statistical behavior of the entire class of ASDs, obtaining statistically identical decompositions in which each ASD is simply decomposed into the nonadaptive matched filter, the nonadaptive cosine or -statistic, and three other statistically independent random variables that account for the performance-degrading effects of limited training data.

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A CFAR adaptive subspace detector for second-order Gaussian signals

Cited by 59 publications

References 28 publications

Study on Threshold Detection of Micro-Seismic Signal Based on Constraint Judgment

Study on Threshold Detection of Micro-Seismic Signal Based on Constraint Judgment

Adaptive subspace detection of extended target in white Gaussian noise using sinc basis

Adaptive Subspace Detectors

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