{"title":"The improved constant false alarm rate detector based on multi-frame integration for fluctuating target detection in heavy-tailed clutter","authors":"Chenghu Cao, Yongbo Zhao","doi":"10.1049/sil2.12145","DOIUrl":null,"url":null,"abstract":"<p>In this paper, attention is devoted to the analysis of the detection threshold <i>based on the multi-frame integration</i> in heavy-tailed clutter for the radar with high resolution and even smaller grazing angle. The closed-form expressions of both the probability of the detection and the probability of false alarm for the heavy-tailed clutter background, which can be used for the theoretical analysis of constant false alarm rate (CFAR) detectors, are derived with the multi-frame integration technique. Accordingly, an improved CFAR detector is designed to work well with the presence of target-like outliers in the heavy-tailed clutter. In addition, the proposed CFAR detector is capable to alleviate the masking-effect resorting to the additive feedback operation when a target is large enough to cross several cells in multi-target case. The theoretical analysis and numerical simulations demonstrate that the proposed CFAR detector based on multi-frame integration can improve the signal-to-clutter rate of the targets exhibiting better performance than ones based on single frame in heavy-tailed clutter background. It is validated from the simulations that the proposed CFAR detector with additive feedback operation can deal with masking-effect for large target occupying several cells.</p>","PeriodicalId":56301,"journal":{"name":"IET Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/sil2.12145","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/sil2.12145","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, attention is devoted to the analysis of the detection threshold based on the multi-frame integration in heavy-tailed clutter for the radar with high resolution and even smaller grazing angle. The closed-form expressions of both the probability of the detection and the probability of false alarm for the heavy-tailed clutter background, which can be used for the theoretical analysis of constant false alarm rate (CFAR) detectors, are derived with the multi-frame integration technique. Accordingly, an improved CFAR detector is designed to work well with the presence of target-like outliers in the heavy-tailed clutter. In addition, the proposed CFAR detector is capable to alleviate the masking-effect resorting to the additive feedback operation when a target is large enough to cross several cells in multi-target case. The theoretical analysis and numerical simulations demonstrate that the proposed CFAR detector based on multi-frame integration can improve the signal-to-clutter rate of the targets exhibiting better performance than ones based on single frame in heavy-tailed clutter background. It is validated from the simulations that the proposed CFAR detector with additive feedback operation can deal with masking-effect for large target occupying several cells.
期刊介绍:
IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more.
Topics covered by scope include, but are not limited to:
advances in single and multi-dimensional filter design and implementation
linear and nonlinear, fixed and adaptive digital filters and multirate filter banks
statistical signal processing techniques and analysis
classical, parametric and higher order spectral analysis
signal transformation and compression techniques, including time-frequency analysis
system modelling and adaptive identification techniques
machine learning based approaches to signal processing
Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques
theory and application of blind and semi-blind signal separation techniques
signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals
direction-finding and beamforming techniques for audio and electromagnetic signals
analysis techniques for biomedical signals
baseband signal processing techniques for transmission and reception of communication signals
signal processing techniques for data hiding and audio watermarking
sparse signal processing and compressive sensing
Special Issue Call for Papers:
Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf