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International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC)

作者： Sato, Kotone Wandale, Steven Ichige, Koichi Kimura, Kazuya Sugiura, Ryo Yokohama Natl Univ Dept Elect & Comp Engn Yokohama Kanagawa Japan Univ Malawi Sch Nat & Appl Sci Zomba Malawi Murata Mfg Co Ltd Kyoto Japan

ISBN: (纸本)9798350379068;9798350379051

This paper describes a method for detecting infants in a passenger vehicle by constructing a concise decision tree model from several proposed features. The decision tree model is constructed by approximating the model to overcome the black box structure of machine learning. A type of millimeter-wave radar, Frequency Modulated Continuous Wave - Multiple Input Multiple Output (FMCW-MIMO) radar, is used for detection in a passenger vehicle because it is low cost, non-contact, and insensitive to external factors. The data acquired from the radar is preprocessed to extract features. A previous method proposed to detect the presence/absence of infants at each seat using an approximate decision tree model with about 70 features as input. This method is simple and fast by constructing an approximate decision tree model, but it is still difficult to interpret the large number of features. Therefore, this paper outputs the detection points that can be visualized by preprocessing using the Constant False Alarm Rate (cfar) algorithm. Approximate decision tree models are constructed using these features, and the accuracy is compared with conventional methods to further improve explainability.

关键词： Occupant detection FMCW-MIMO radar improved machine learning explainability cfar algorithm

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Extraction of Small Objects from Ground-based Multi-static SAR Images using cfar algorithm with Generalized Gamma Distribution

Extraction of Small Objects from Ground-based Multi-static S...

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IEEE Radar Conference (RadarConf)

作者： Heinzel, Andreas Schreiber, Eric Peichl, Markus Dill, Stephan DLR EV Microwaves & Radar Inst Oberpfaffenhofen Germany

ISBN: (纸本)9781728189420

Ground-based multi-static synthetic aperture radar is able to produce high-resolution images with sufficient clutter suppression. It is therefore capable of detecting even small and weakly reflecting objects. The automatic extraction of these targets is of great interest for many applications like efficient landmine detection, for instance. Furthermore the automatic extraction of targets is the first useful stage for automatic target recognition. A Constant False Alarm Rate (cfar) algorithm is a suitable approach for this task by analyzing the distribution of the surrounding clutter and calculating a reasonable threshold for each pixel. The Generalized Gamma distribution forms a large class of distributions and is therefore suitable to model different kind of clutter. In this paper the selected and implemented algorithm is validated using near-range high-resolution data from different kind of objects.

关键词： synthetic aperture radar SAR ground-based radar multi-static radar cfar algorithm

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A New Method Based on cfar and Poisson Distribution Process (PDP_cfar) for Naval Vessels Detection Using Sentinel-1 Radar Images

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2025年 1-14页

作者： Jafarzadeh, Jafar Attarchi, Sara Univ Tehran Fac Geog Dept Remote Sensing & Geog Informat Syst Tehran Iran

Continuous monitoring in maritime areas using the latest remote sensing technologies is vital for the security, environmental protection, and supervision of these areas. SAR space technology has been used since 1985 to detect ships. However, the high cost, poor accessaibility, and complicated processing of SAR images limited their application. This research sought to extract the vessels and ships on the surface of international waters by processing the Sentinel-1 radar images. This research aims to detect marine targets using high-resolution radar images based on the constant false alarm rate (cfar) method. The proposed algorithm is a combination of the cfar algorithm and the Poisson distribution process (PDP) (i.e., PDP-cfar). To this end, naval vessels were extracted through cfar and the results were improved by the developed Poisson algorithm. Four different values of the k constant in the Poisson distribution were examined, where k = 4 gave the best result with an ENL index value of 6.52, a coefficient of variation of 0.3917, and a maximum error of 0.1306. The results have shown that the proposed algorithm is very effective in objects extraction from the Sentinel-1 radar images.

关键词： Naval vessel detection SAR Sentinel-1 cfar algorithm Poisson distribution

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Contribution of SAR Radar Imagery in the Detection of Suspicious Vessels in the Ivorian EEZ

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World Journal of Engineering and Technology 2024年第1期12卷 200-212页

作者： Koffi Cyprien Yoboue Jean-Baptiste Kassi Eric Valère Djagoua Tiemelé Jacque-André Laboratory of Remote Sensing and GIS University Center for Research and Application in Remote Sensing (CURAT) Félix Houphouet-Boigny University of Abidjan Cocody Ivory Coast

The present work proposed a method for match SAR and AIS data to detect vessels carrying out suspicious activity in the Ivorian EEZ. Two superposition methods, detected AIS and SAR data, based on point-to-point association on the one hand and point-to-line on the other hand, were used to detect suspicious vessels in Ivorian marine waters. The results showed that most vessels detected in the Ivorian EEZ do not declare their positions to avoid being spotted. These funds are likely to practice illegal, undeclared and unregulated fishing (INN fishing). This clandestine activity is very recurrent in Ivorian waters. This is illustrated by the number of suspicious vessels detected by SAR radar imagery which is greater compared to declared or authorized vessels.

关键词： INN Fishing cfar algorithm AIS Sentinel-1A Ivory Coast

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Radar Signal Processing Based on Sparse Fast Fourier Transform 2

Radar Signal Processing Based on Sparse Fast Fourier Transfo...

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2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering

作者： Bai, Xiaojuan Tian, Hao Guan, Lu Northwest Normal Univ Sch Phys & Elect Engn Lanzhou 730000 Gansu Peoples R China

ISBN: (数字)9781510650336

ISBN: (纸本)9781510650336;9781510650329

The target detection capability of radar has always been one of the key contents of radar research. The rapid development of radar and other fields in recent years has made it difficult for the FFT algorithm to meet the real-time requirements of data processing. Sparse Fast Fourier Transform (SFFT) is a feature based on sparse signal frequency domain For some sparse signals, SFFT algorithm has shorter operation time and faster speed than FFT algorithm. This paper uses the constant false alarm algorithm for target detection, and compares and analyzes the time complexity and signal processing efficiency of the SFFT algorithm and the traditional MTD method. The analysis of the detection results shows that the algorithm in this paper has better detection performance and higher computing efficiency.

关键词： echo pulse pressure signal Sparse Fast Fourier Transform cfar algorithm Signal processing

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On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on cfar and Lightweight Deep Learning

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REMOTE SENSING 2021年第10期13卷 1995页

作者： Xu, Pan Li, Qingyang Zhang, Bo Wu, Fan Zhao, Ke Du, Xin Yang, Cankun Zhong, Ruofei Capital Normal Univ Key Lab 3D Informat Acquisit & Applicat Minist Educ Beijing 100048 Peoples R China Capital Normal Univ Coll Resource Environm & Tourism Beijing 100048 Peoples R China Capital Normal Univ Beijing Adv Innovat Ctr Imaging Theory & Technol Beijing 100048 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China

Synthetic aperture radar (SAR) satellites produce large quantities of remote sensing images that are unaffected by weather conditions and, therefore, widely used in marine surveillance. However, because of the hysteresis of satellite-ground communication and the massive quantity of remote sensing images, rapid analysis is not possible and real-time information for emergency situations is restricted. To solve this problem, this paper proposes an on-board ship detection scheme that is based on the traditional constant false alarm rate (cfar) method and lightweight deep learning. This scheme can be used by the SAR satellite on-board computing platform to achieve near real-time image processing and data transmission. First, we use cfar to conduct the initial ship detection and then apply the You Only Look Once version 4 (YOLOv4) method to obtain more accurate final results. We built a ground verification system to assess the feasibility of our scheme. With the help of the embedded Graphic Processing Unit (GPU) with high integration, our method achieved 85.9% precision for the experimental data, and the experimental results showed that the processing time was nearly half that required by traditional methods.

关键词： ship detection lightweight deep learning cfar algorithm on-board processing

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Crashed Airplane Detection Based on Feature Fusion in POLSAR Image

Crashed Airplane Detection Based on Feature Fusion in POLSAR...

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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Ping Han Xinshuai Zhang Peng Ge Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China China Airport Construction Group Corporation of CAAC

ISBN: (纸本)9781467321969

A new crashed airplane detection algorithm based on feature fusion is proposed in this paper. Considering the dihedral characteristic of the airplane's tail and the strong scattering intensity of airplane's body, airplane target detection feature is constructed by combining the above two polarimetric properties. Then Constant False Alarm Rate (cfar) is used for target detection. Experimental results with real polarimetric data from NASA/JPL UAVASR system demonstrate the effectiveness of the proposed method.

关键词： crashed airplane detection dehidral structure feature fusion cfar algorithm

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Hardware architecture for adaptive filtering based on energy-cfar processor for radar target detection

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IEICE ELECTRONICS EXPRESS 2010年第9期7卷 628-633页

作者： Lopez-Estrada, Santos Cumplido, Rene Natl Inst Astrophys Opt & Elect Dept Comp Sci Puebla 72000 Mexico

A hardware architecture that implements an adaptive filter based on energy analysis of radar echoes to improve the detection of the Constant False Alarm Rate (cfar) algorithm is presented. Signal processing based on energy analysis emphasizes the edge of the echoes improving the performance of the detection process. The energy filter coefficients and cfar parameters are calculated adaptively by the architecture, reconfiguring the block of coefficient weights according to environment conditions. The architecture accelerates the data processing by a pipeline structure and sliding window for the coefficients convolution with data, resulting in high performance operation. Results of implementing the architecture in a FPGA device are presented and discussed.

关键词： adaptive filtering target detection cfar algorithm hardware architecture

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A hybrid approach for target detection using cfar algorithm and image processing

A hybrid approach for target detection using CFAR algorithm ...

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5th Mexican International Conference in Computer Science (ENC 2004)

作者： López-Estrada, S Cumplido-Parra, R Torres-Huitzil, C Natl Inst Astrophys Opt & Elect Dept Comp Sci Puebla 72000 Mexico

ISBN: (纸本)0769521606

In ship detection, a key aspect is to keep a low level constant false alarm rate combined with a high detection probability in presence of clutter background, caused by reflections from wave tops on the sea, rain, snow or fog. Generally, the Constant False Alarm Rate (cfar) algorithm is applied, which is based on the assumption that clutter background can be modeled using a Gaussian distribution, generating a high level of false alarms in presence of non Gaussian clutter. This problem has been addressed under two independent approaches: Modeling the environment noise (sea clutter) with independent non Gaussian models or using variations of cfar detection algorithm. Both approaches provide good results only for specific characteristics of clutter. In this paper we discuss a hybrid approach for target detection that use three probabilistic models of clutter associated to sea state (Gauss, Weibull and K distributions), detection algorithms with adaptive threshold for cfar, classification algorithms that associate a noise model with a specific cfar algorithm according to the sea state, and low level morphological operations to generate an image of targets. The goal of this approach is provide an automatic mechanism to associate a clutter model with a specific cfar algorithm according to sea state in order to obtain radar images without clutter. The proposed detection approach is evaluated by high level simulation. Results are presented and discussed.

关键词： cfar algorithm sea clutter target detections

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