Statistical Performance Analysis of the Adaptive Orthogonal Rejection Detector

Liu, Weijian; Li, Jun; Hu, Xiaoqin; Zhikai, Tang; Wang, Yongliang

doi:10.1109/lsp.2016.2550495

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…Another distinctive feature of the AORD is that it can provide significant performance improvement compared to the KGLRT and AMF in the absence of interference. This was demonstrated in [179], where the statistical performance of the AORD was presented.…”

Section: Spectral Symmetrymentioning

confidence: 87%

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: Spectral Symmetrymentioning

confidence: 87%

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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“…is the oblique projection onto H along J , and in (80) we have used P ⊥ BH = 0 N ×p . Note that the Wald test in (80) is a generalization of the AOPD in [35], for which the signal subspace is constrained to have a dimension of one and no nonhomogeneity is considered.…”

Section: Wald Test-based Detectormentioning

confidence: 99%

“…Liu et al [34] investigated the detection problem in completely unknown directional interference and two detectors are proposed, namely, the adaptive orthogonal rejection detector (AORD) and adaptive oblique projection detector (AOPD). The statistical performance of the AORD was exploited in [35].…”

Section: Introductionmentioning

confidence: 99%

Multichannel radar adaptive signal detection in interference and structure nonhomogeneity

Liu¹,

Han

Liu

et al. 2017

Sci. China Inf. Sci.

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In this paper, we consider the problem of multichannel radar signal detection in interference and structure nonhomogeneity. The interference is often caused by electromagnetic countermeasure (ECM) systems or industrial activity, while the nonhomogeneity usually arises because of rapid variations in terrain or radar antenna structure. We propose three adaptive detectors according to three common criteria of detector design, namely, the generalized likelihood ratio test (GLRT), Rao test, and Wald test. Extensive performance comparisons are conducted under different scenarios. It is shown that when the nonhomogeneity is severe, the detector devised according to the GLRT achieves the best detection performance. In other scenarios, the detector designed according to the Wald test may be the best choice, which has the highest probability of detection.

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“…Lots of adaptive detection algorithms have been proposed, including the generalised likelihood ratio test (GLRT) [1], the two‐step design procedure GLRT [2], and variants of many detectors based on the GLRT decision rule, e.g. [3–5]. All above solutions are devised for rank‐one signal model that the actual signal steering vector is exactly matched to the assumed one.…”

Section: Introductionmentioning

confidence: 99%