Volume 8 Issue 3
Jun.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(3): 344-354
SONG Jiaqi and TAO Haihong. A fast parameter estimation algorithm for near-field non-circular signals[J]. Journal of Radars, 2020, 9(4): 632–639. doi: 10.12000/JR20053
Citation: CHEN Shichao, LUO Feng, HU Chong, et al. Small target detection in sea clutter background based on Tsallis entropy of Doppler spectrum[J]. Journal of Radars, 2019, 8(3): 344–354. doi: 10.12000/JR19012
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Small Target Detection in Sea Clutter Background Based on Tsallis Entropy of Doppler Spectrum

DOI: 10.12000/JR19012
  • ①.

    National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

  • ②.

    Southwest China Institute of Electronic Technology, Chengdu 610036, China

Funds:  The National Key Scientific Instrument and Equipment Development (2013YQ20060705)
More Information
  • Corresponding author: LUO Feng, luofeng@xidian.edu.cn
  • Received Date: 2019-01-24
  • Rev Recd Date: 2019-02-09
    • Available Online: 2019-03-22
  • Publish Date: 2019-06-01
    Abstract
  • According to the different concentration levels of Doppler spectrum between sea clutter and target, small target in sea clutter background can be detected using Shannon entropy. However, Shannon entropy is merely a special case of Tsallis entropy and cannot reflect the multifractality of sea clutter. In this paper, the relation between Tsallis entropy and the generalized fractal dimension is first presented, and then the Doppler spectrum’s concentrative level and multifractality of sea clutter are combined; finally an algorithm for detecting small target in sea clutter background based on Tsallis entropy of Doppler spectrum rather than of Shannon entropy is proposed. By comparison via IPIX dataset, the detection’s performance of Tsallis entropy is better than that of Shannon entropy and Hurst exponent as per short observations.

     

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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 4.4 %其他: 4.4 %其他: 0.4 %其他: 0.4 %Central District: 0.0 %Central District: 0.0 %China: 0.5 %China: 0.5 %Falls Church: 0.0 %Falls Church: 0.0 %India: 0.0 %India: 0.0 %Jonquiere: 0.1 %Jonquiere: 0.1 %Rochester: 0.0 %Rochester: 0.0 %Taiwan, China: 0.1 %Taiwan, China: 0.1 %Tiruchi: 0.1 %Tiruchi: 0.1 %United States: 0.0 %United States: 0.0 %[]: 0.7 %[]: 0.7 %三亚: 0.1 %三亚: 0.1 %上海: 1.1 %上海: 1.1 %东莞: 0.1 %东莞: 0.1 %临汾: 0.0 %临汾: 0.0 %临沂: 0.3 %临沂: 0.3 %丹东: 0.0 %丹东: 0.0 %丽江: 0.0 %丽江: 0.0 %亳州: 0.1 %亳州: 0.1 %佛山: 0.1 %佛山: 0.1 %保定: 0.1 %保定: 0.1 %信阳: 0.0 %信阳: 0.0 %六安: 0.0 %六安: 0.0 %兰州: 0.0 %兰州: 0.0 %兰辛: 0.0 %兰辛: 0.0 %内蒙古自治区包头: 0.1 %内蒙古自治区包头: 0.1 %加利福尼亚: 0.1 %加利福尼亚: 0.1 %包头: 0.0 %包头: 0.0 %北京: 15.6 %北京: 15.6 %北京市: 0.2 %北京市: 0.2 %北海: 0.1 %北海: 0.1 %十堰: 0.0 %十堰: 0.0 %南京: 1.3 %南京: 1.3 %南京市: 0.0 %南京市: 0.0 %南充: 0.0 %南充: 0.0 %南宁: 0.1 %南宁: 0.1 %南昌: 0.1 %南昌: 0.1 %南通: 0.1 %南通: 0.1 %南阳: 0.1 %南阳: 0.1 %厦门: 0.2 %厦门: 0.2 %台北: 0.1 %台北: 0.1 %台州: 0.1 %台州: 0.1 %台湾省: 0.0 %台湾省: 0.0 %合肥: 0.2 %合肥: 0.2 %合肥市: 0.0 %合肥市: 0.0 %呼和浩特: 0.3 %呼和浩特: 0.3 %咸阳: 0.1 %咸阳: 0.1 %哈尔滨: 0.3 %哈尔滨: 0.3 %哈尔滨市: 0.0 %哈尔滨市: 0.0 %哥伦布: 0.0 %哥伦布: 0.0 %嘉兴: 0.0 %嘉兴: 0.0 %四平: 0.2 %四平: 0.2 %大同: 0.1 %大同: 0.1 %大连: 0.2 %大连: 0.2 %天津: 0.4 %天津: 0.4 %威海: 0.0 %威海: 0.0 %孟买: 0.2 %孟买: 0.2 %宁夏回族自治区中卫: 0.0 %宁夏回族自治区中卫: 0.0 %宁波: 0.0 %宁波: 0.0 %安康: 0.1 %安康: 0.1 %宜春: 0.0 %宜春: 0.0 %宝鸡: 0.0 %宝鸡: 0.0 %宣城: 0.1 %宣城: 0.1 %宿迁: 0.1 %宿迁: 0.1 %巴彦淖尔: 0.0 %巴彦淖尔: 0.0 %常州: 0.1 %常州: 0.1 %平顶山: 0.1 %平顶山: 0.1 %广州: 0.9 %广州: 0.9 %庆阳: 0.0 %庆阳: 0.0 %库比蒂诺: 0.1 %库比蒂诺: 0.1 %延安: 0.0 %延安: 0.0 %开封: 0.1 %开封: 0.1 %张家口: 0.9 %张家口: 0.9 %张家界: 0.0 %张家界: 0.0 %德罕: 0.2 %德罕: 0.2 %德里: 0.1 %德里: 0.1 %成都: 0.4 %成都: 0.4 %成都市: 0.0 %成都市: 0.0 %扬州: 0.1 %扬州: 0.1 %新乡: 0.5 %新乡: 0.5 %无锡: 0.1 %无锡: 0.1 %昆明: 0.2 %昆明: 0.2 %昌吉: 0.0 %昌吉: 0.0 %晋城: 0.0 %晋城: 0.0 %普洱: 0.1 %普洱: 0.1 %杭州: 0.9 %杭州: 0.9 %枣庄: 0.1 %枣庄: 0.1 %株洲: 0.2 %株洲: 0.2 %格兰特县: 0.1 %格兰特县: 0.1 %榆林: 0.0 %榆林: 0.0 %武汉: 0.5 %武汉: 0.5 %沈阳: 0.2 %沈阳: 0.2 %泰米尔纳德: 0.1 %泰米尔纳德: 0.1 %洛阳: 0.1 %洛阳: 0.1 %济南: 0.2 %济南: 0.2 %济宁: 0.0 %济宁: 0.0 %海口: 0.0 %海口: 0.0 %淮南: 0.0 %淮南: 0.0 %淮安: 0.0 %淮安: 0.0 %深圳: 0.7 %深圳: 0.7 %温州: 0.1 %温州: 0.1 %渭南: 0.0 %渭南: 0.0 %湖州: 0.2 %湖州: 0.2 %湘潭: 0.0 %湘潭: 0.0 %滨州: 0.0 %滨州: 0.0 %漯河: 0.3 %漯河: 0.3 %潍坊: 0.1 %潍坊: 0.1 %潍坊市寿光: 0.1 %潍坊市寿光: 0.1 %烟台: 0.2 %烟台: 0.2 %玉林: 0.1 %玉林: 0.1 %珠海: 4.0 %珠海: 4.0 %白银: 0.0 %白银: 0.0 %益阳: 0.0 %益阳: 0.0 %石家庄: 0.2 %石家庄: 0.2 %石家庄市: 0.0 %石家庄市: 0.0 %秦皇岛: 0.1 %秦皇岛: 0.1 %纽约: 0.2 %纽约: 0.2 %绵阳: 0.1 %绵阳: 0.1 %美国伊利诺斯芝加哥: 0.0 %美国伊利诺斯芝加哥: 0.0 %舟山: 0.0 %舟山: 0.0 %芒廷维尤: 16.0 %芒廷维尤: 16.0 %芜湖: 0.0 %芜湖: 0.0 %芝加哥: 0.4 %芝加哥: 0.4 %苏州: 0.3 %苏州: 0.3 %莆田: 0.1 %莆田: 0.1 %莫斯科: 0.0 %莫斯科: 0.0 %菏泽: 0.0 %菏泽: 0.0 %营口: 0.1 %营口: 0.1 %衡水: 0.1 %衡水: 0.1 %衡阳: 0.1 %衡阳: 0.1 %衢州: 0.1 %衢州: 0.1 %西宁: 35.0 %西宁: 35.0 %西安: 1.0 %西安: 1.0 %诺沃克: 0.0 %诺沃克: 0.0 %贵港: 0.3 %贵港: 0.3 %贵阳: 0.1 %贵阳: 0.1 %赤峰: 0.1 %赤峰: 0.1 %运城: 0.4 %运城: 0.4 %连云港: 0.1 %连云港: 0.1 %邢台: 0.1 %邢台: 0.1 %邯郸: 0.1 %邯郸: 0.1 %郑州: 1.4 %郑州: 1.4 %郴州: 0.0 %郴州: 0.0 %重庆: 0.2 %重庆: 0.2 %重庆市: 0.0 %重庆市: 0.0 %钱德勒: 0.1 %钱德勒: 0.1 %长春: 0.2 %长春: 0.2 %长沙: 0.8 %长沙: 0.8 %长治: 0.1 %长治: 0.1 %阳泉: 0.0 %阳泉: 0.0 %雅加达: 0.1 %雅加达: 0.1 %青岛: 1.5 %青岛: 1.5 %香港特别行政区: 0.0 %香港特别行政区: 0.0 %齐齐哈尔: 0.0 %齐齐哈尔: 0.0 %其他其他Central DistrictChinaFalls ChurchIndiaJonquiereRochesterTaiwan, ChinaTiruchiUnited States[]三亚上海东莞临汾临沂丹东丽江亳州佛山保定信阳六安兰州兰辛内蒙古自治区包头加利福尼亚包头北京北京市北海十堰南京南京市南充南宁南昌南通南阳厦门台北台州台湾省合肥合肥市呼和浩特咸阳哈尔滨哈尔滨市哥伦布嘉兴四平大同大连天津威海孟买宁夏回族自治区中卫宁波安康宜春宝鸡宣城宿迁巴彦淖尔常州平顶山广州庆阳库比蒂诺延安开封张家口张家界德罕德里成都成都市扬州新乡无锡昆明昌吉晋城普洱杭州枣庄株洲格兰特县榆林武汉沈阳泰米尔纳德洛阳济南济宁海口淮南淮安深圳温州渭南湖州湘潭滨州漯河潍坊潍坊市寿光烟台玉林珠海白银益阳石家庄石家庄市秦皇岛纽约绵阳美国伊利诺斯芝加哥舟山芒廷维尤芜湖芝加哥苏州莆田莫斯科菏泽营口衡水衡阳衢州西宁西安诺沃克贵港贵阳赤峰运城连云港邢台邯郸郑州郴州重庆重庆市钱德勒长春长沙长治阳泉雅加达青岛香港特别行政区齐齐哈尔

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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