Volume 5 Issue 1
Feb.  2016
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Article Navigation >  Journal of Radars  > 2016  >  5(1): 90-98
Li Wan-chun, Huang Cheng-feng. Optimal Trajectory Analysis for the Receiver of Passive Location Systems Using Direction Of Arrival and Doppler Measurements[J]. Journal of Radars, 2014, 3(6): 660-665. doi: 10.12000/JR14118
Citation: Yang Jun, Zhang Qun, Luo Ying, Deng Donghu. Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing[J]. Journal of Radars, 2016, 5(1): 90-98. doi: 10.12000/JR14107
shu

Method for Multiple Targets Tracking in Cognitive Radar Based on Compressed Sensing

DOI: 10.12000/JR14107
  • 1.

    (Information and Navigation Institute, Air Force Engineering University, Xi'an 710077, China)

  • 2.

    (Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), Fudan University, Shanghai 200433, China)

Funds:

The National Natural Science Foundation of China (61172169, 61201369), The Natural Science Foundation Research Project of Shaanxi Province (2013JQ8008)

  • Received Date: 2014-08-25
  • Rev Recd Date: 2014-10-31
  • Publish Date: 2016-02-28
    Abstract
  • A multiple targets cognitive radar tracking method based on Compressed Sensing (CS) is proposed. In this method, the theory of CS is introduced to the case of cognitive radar tracking process in multiple targets scenario. The echo signal is sparsely expressed. The designs of sparse matrix and measurement matrix are accomplished by expressing the echo signal sparsely, and subsequently, the restruction of measurement signal under the down-sampling condition is realized. On the receiving end, after considering that the problems that traditional particle filter suffers from degeneracy, and require a large number of particles, the particle swarm optimization particle filter is used to track the targets. On the transmitting end, the Posterior Cramr-Rao Bounds (PCRB) of the tracking accuracy is deduced, and the radar waveform parameters are further cognitively designed using PCRB. Simulation results show that the proposed method can not only reduce the data quantity, but also provide a better tracking performance compared with traditional method.

     

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    • References(16)
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0.0 %泸州: 0.0 %洛阳: 0.3 %洛阳: 0.3 %济南: 0.3 %济南: 0.3 %济宁: 0.0 %济宁: 0.0 %济源: 0.1 %济源: 0.1 %海口: 0.0 %海口: 0.0 %海西: 0.0 %海西: 0.0 %淮北: 0.0 %淮北: 0.0 %淮南: 0.1 %淮南: 0.1 %深圳: 1.5 %深圳: 1.5 %清州: 0.0 %清州: 0.0 %清远: 0.0 %清远: 0.0 %渥太华: 0.1 %渥太华: 0.1 %温州: 0.1 %温州: 0.1 %渭南: 0.1 %渭南: 0.1 %湖州: 0.0 %湖州: 0.0 %湘潭: 0.0 %湘潭: 0.0 %湘西: 0.0 %湘西: 0.0 %湛江: 0.0 %湛江: 0.0 %滁州: 0.0 %滁州: 0.0 %漯河: 0.6 %漯河: 0.6 %潍坊: 0.0 %潍坊: 0.0 %潮州: 0.0 %潮州: 0.0 %澳门特别行政区: 0.0 %澳门特别行政区: 0.0 %濮阳: 0.0 %濮阳: 0.0 %烟台: 0.1 %烟台: 0.1 %焦作: 0.1 %焦作: 0.1 %牡丹江: 0.0 %牡丹江: 0.0 %特伦甘地: 0.1 %特伦甘地: 0.1 %珠海: 0.1 %珠海: 0.1 %白山: 0.0 %白山: 0.0 %白银: 0.0 %白银: 0.0 %眉山: 0.0 %眉山: 0.0 %石家庄: 0.6 %石家庄: 0.6 %福州: 0.3 %福州: 0.3 %秦皇岛: 0.3 %秦皇岛: 0.3 %纽敦: 0.0 %纽敦: 0.0 %纽约: 0.1 %纽约: 0.1 %绍兴: 0.1 %绍兴: 0.1 %绥化: 0.0 %绥化: 0.0 %绵阳: 0.2 %绵阳: 0.2 %罗马: 0.0 %罗马: 0.0 %美国加利福尼亚圣地亚哥: 0.0 %美国加利福尼亚圣地亚哥: 0.0 %肇庆: 0.0 %肇庆: 0.0 %胡志明: 0.1 %胡志明: 0.1 %自贡: 0.0 %自贡: 0.0 %舟山: 0.0 %舟山: 0.0 %芒廷维尤: 17.8 %芒廷维尤: 17.8 %芜湖: 0.0 %芜湖: 0.0 %芝加哥: 0.4 %芝加哥: 0.4 %苏州: 0.6 %苏州: 0.6 %莫斯科: 0.0 %莫斯科: 0.0 %莱芜: 0.0 %莱芜: 0.0 %菏泽: 0.0 %菏泽: 0.0 %萨尔州: 0.0 %萨尔州: 0.0 %葫芦岛: 0.0 %葫芦岛: 0.0 %蚌埠: 0.0 %蚌埠: 0.0 %衡水: 0.2 %衡水: 0.2 %衡阳: 0.1 %衡阳: 0.1 %衢州: 0.1 %衢州: 0.1 %西宁: 11.4 %西宁: 11.4 %西安: 2.8 %西安: 2.8 %西班牙: 0.0 %西班牙: 0.0 %西雅图: 0.0 %西雅图: 0.0 %诺伊达: 0.0 %诺伊达: 0.0 %诺沃克: 0.0 %诺沃克: 0.0 %贵阳: 0.3 %贵阳: 0.3 %赣州: 0.0 %赣州: 0.0 %赤峰: 0.0 %赤峰: 0.0 %车士活: 0.0 %车士活: 0.0 %达州: 0.1 %达州: 0.1 %运城: 0.3 %运城: 0.3 %遂宁: 0.0 %遂宁: 0.0 %邢台: 0.1 %邢台: 0.1 %邯郸: 0.1 %邯郸: 0.1 %郑州: 0.7 %郑州: 0.7 %郴州: 0.0 %郴州: 0.0 %鄂州: 0.0 %鄂州: 0.0 %重庆: 0.8 %重庆: 0.8 %金华: 0.1 %金华: 0.1 %金奈: 0.1 %金奈: 0.1 %铁岭: 0.0 %铁岭: 0.0 %银川: 0.0 %银川: 0.0 %锦州: 0.0 %锦州: 0.0 %镇江: 0.2 %镇江: 0.2 %长春: 0.3 %长春: 0.3 %长沙: 1.9 %长沙: 1.9 %长治: 0.0 %长治: 0.0 %阜阳: 0.0 %阜阳: 0.0 %阳泉: 0.0 %阳泉: 0.0 %阿坝: 0.0 %阿坝: 0.0 %阿姆斯特丹: 0.0 %阿姆斯特丹: 0.0 %阿尔泽特河畔埃施: 0.0 %阿尔泽特河畔埃施: 0.0 %阿布扎比: 0.1 %阿布扎比: 0.1 %阿德莱德: 0.1 %阿德莱德: 0.1 %霍德夏沙隆: 0.0 %霍德夏沙隆: 0.0 %青岛: 0.6 %青岛: 0.6 %鞍山: 0.0 %鞍山: 0.0 %韶关: 0.0 %韶关: 0.0 %香港: 0.1 %香港: 0.1 %香港特别行政区: 0.2 %香港特别行政区: 0.2 %马鞍山: 0.0 %马鞍山: 0.0 %黄冈: 0.0 %黄冈: 0.0 %齐齐哈尔: 0.1 %齐齐哈尔: 0.1 %龟尾: 0.0 %龟尾: 0.0 %其他其他ArlingtonAustraliaBadenBangladeshBelgiumCanadaCantonCentral DistrictChinaCzech RepublicEuropean UnionFalls ChurchGermanyGreeceHanoiHerndonIndiaIndonesiaKorea Republic ofMacauMatawanMoroccoNorth PointSaudi ArabiaSeattleSecaucusState CollegeSwedenTaichungTaiwan, ChinaThaneUnited KingdomUnited StatesViet NamWyoming[]三亚三明上海上饶东京东京都东莞中卫中山丹东乌海乐山九江亳州伊斯兰堡休斯敦伦敦佛山佳木斯保定信阳六安兰州内江凤凰城加利福尼亚州包头北京北海十堰南京南充南宁南平南昌南荷兰省南通卡纳塔克邦印度尼西亚北苏门答腊厦门台中台北台州台湾省合肥吉林吉隆坡呼和浩特咸阳哈尔滨哥伦布唐山商丘嘉兴圣地亚哥圣安东尼奥圣彼得堡圣约翰斯埃德蒙顿墨尔本大庆大连天水围天津太原奥卢奥尔巴尼威海娄底孟买宁波安卡拉安康安顺宜兰宜宾宜春宝鸡宣城密蘇里城巴中巴伐利亚州巴音郭楞巴音郭楞蒙古自治州布兰普顿常州常德平顶山广州庆阳库比蒂诺廊坊延安开封张家口徐州德罕德里德阳德黑兰怀化悉尼惠州意法半成都扬州抚州拉斯维加斯拉萨拉贾斯坦邦拉雷多揭阳新伦敦新加坡新北新竹无锡日照昆明晋中晋城朝阳杭州枣庄柳州株洲格兰特县格拉沃利讷桂林梅州榆林橙武汉毕节永州汉中汕头江门池州沈阳沧州泰州泰米尔纳德泸州洛阳济南济宁济源海口海西淮北淮南深圳清州清远渥太华温州渭南湖州湘潭湘西湛江滁州漯河潍坊潮州澳门特别行政区濮阳烟台焦作牡丹江特伦甘地珠海白山白银眉山石家庄福州秦皇岛纽敦纽约绍兴绥化绵阳罗马美国加利福尼亚圣地亚哥肇庆胡志明自贡舟山芒廷维尤芜湖芝加哥苏州莫斯科莱芜菏泽萨尔州葫芦岛蚌埠衡水衡阳衢州西宁西安西班牙西雅图诺伊达诺沃克贵阳赣州赤峰车士活达州运城遂宁邢台邯郸郑州郴州鄂州重庆金华金奈铁岭银川锦州镇江长春长沙长治阜阳阳泉阿坝阿姆斯特丹阿尔泽特河畔埃施阿布扎比阿德莱德霍德夏沙隆青岛鞍山韶关香港香港特别行政区马鞍山黄冈齐齐哈尔龟尾1/2

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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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