Hao Tianduo, Cui Chen, Gong Yang, Sun Congyi. Waveform Design for Cognitive Radar Under Low PAR Constraints by Convex Optimization[J]. Journal of Radars, 2018, 7(4): 498-506. doi: 10.12000/JR18002
Citation: LAN Xiaoyu, HU Jiyan, LIANG Mingshen, et al. Sparse DOA estimation method based on Riemann averaging under strong intermittent jamming[J]. Journal of Radars, 2025, 14(2): 280–292. doi: 10.12000/JR24175

Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming

DOI: 10.12000/JR24175 CSTR: 32380.14.JR24175
Funds:  National Science Foundation for Young Scientists of China (61801308), Aeronautical Science Foundation (2020Z017054001), General Program of the Education Department of Liaoning Province (LJKMZ20220535), Natural Science Foundation of Liaoning Province of China (2024-MS-135)
More Information
  • Corresponding author: HU Jiyan, 1534018950@qq.com
  • Received Date: 2024-08-31
  • Rev Recd Date: 2024-11-08
  • Available Online: 2024-11-14
  • Publish Date: 2024-12-11
  • Aiming to address the problem of increased radar jamming in complex electromagnetic environments and the difficulty of accurately estimating the target signal close to a strong jamming signal, this paper proposes a sparse Direction of Arrival (DOA) estimation method based on Riemann averaging under strong intermittent jamming. First, under the extended coprime array data model, the Riemann averaging is introduced to suppress the jamming signal by leveraging the property that the target signal is continuously active while the strong jamming signal is intermittently active. Then, the covariance matrix of the processed data is vectorized to obtain virtual array reception data. Finally, the sparse iterative covariance-based estimation method, which is used for estimating the DOA under strong intermittent interference, is employed in the virtual domain to reconstruct the sparse signal and estimate the DOA of the target signal. The simulation results show that the method can provide highly accurate DOA estimation for weak target signals whose angles are closely adjacent to strong interference signals when the number of signal sources is unknown. Compared with existing subspace algorithms and sparse reconstruction class algorithms, the proposed algorithm has higher estimation accuracy and angular resolution at a smaller number of snapshots, as well as a lower signal-to-noise ratio.

     

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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 14.9 %其他: 14.9 %其他: 0.1 %其他: 0.1 %China: 0.8 %China: 0.8 %India: 0.1 %India: 0.1 %United States: 0.0 %United States: 0.0 %[]: 0.3 %[]: 0.3 %三明: 0.0 %三明: 0.0 %上海: 1.3 %上海: 1.3 %东京: 0.0 %东京: 0.0 %东莞: 0.0 %东莞: 0.0 %中卫: 0.1 %中卫: 0.1 %临沂: 0.0 %临沂: 0.0 %乌海: 0.0 %乌海: 0.0 %亳州: 0.0 %亳州: 0.0 %佛山: 0.1 %佛山: 0.1 %佳木斯: 0.0 %佳木斯: 0.0 %兰州: 0.1 %兰州: 0.1 %兰辛: 0.0 %兰辛: 0.0 %加利福尼亚州: 0.1 %加利福尼亚州: 0.1 %包头: 0.0 %包头: 0.0 %北京: 17.3 %北京: 17.3 %北京市: 0.2 %北京市: 0.2 %北海: 0.0 %北海: 0.0 %十堰: 0.1 %十堰: 0.1 %南京: 0.4 %南京: 0.4 %南宁: 0.1 %南宁: 0.1 %南昌: 0.0 %南昌: 0.0 %厦门: 0.0 %厦门: 0.0 %台北: 0.0 %台北: 0.0 %台州: 0.1 %台州: 0.1 %合肥: 0.5 %合肥: 0.5 %呼和浩特: 0.1 %呼和浩特: 0.1 %哥伦布: 0.0 %哥伦布: 0.0 %嘉兴: 0.1 %嘉兴: 0.1 %圣地亚哥: 0.0 %圣地亚哥: 0.0 %大连: 0.0 %大连: 0.0 %天津: 0.4 %天津: 0.4 %太原: 0.0 %太原: 0.0 %宁波: 0.1 %宁波: 0.1 %安康: 0.1 %安康: 0.1 %安阳: 0.2 %安阳: 0.2 %宣城: 0.1 %宣城: 0.1 %巴中: 0.0 %巴中: 0.0 %常州: 0.1 %常州: 0.1 %广州: 0.3 %广州: 0.3 %库比蒂诺: 0.1 %库比蒂诺: 0.1 %张家口: 1.2 %张家口: 1.2 %张家口市: 0.1 %张家口市: 0.1 %怒江: 0.0 %怒江: 0.0 %成都: 0.3 %成都: 0.3 %扬州: 0.1 %扬州: 0.1 %新乡: 0.4 %新乡: 0.4 %无锡: 0.1 %无锡: 0.1 %旧金山: 0.0 %旧金山: 0.0 %昆明: 0.0 %昆明: 0.0 %昌吉: 0.0 %昌吉: 0.0 %朝阳: 0.0 %朝阳: 0.0 %杭州: 1.3 %杭州: 1.3 %格兰特县: 0.1 %格兰特县: 0.1 %武汉: 0.2 %武汉: 0.2 %沈阳: 0.1 %沈阳: 0.1 %沧州: 0.0 %沧州: 0.0 %洛阳: 0.0 %洛阳: 0.0 %济南: 0.1 %济南: 0.1 %淄博: 0.0 %淄博: 0.0 %淮南: 0.0 %淮南: 0.0 %深圳: 0.4 %深圳: 0.4 %湖州: 0.0 %湖州: 0.0 %湘潭: 0.0 %湘潭: 0.0 %滨州: 0.0 %滨州: 0.0 %漯河: 0.2 %漯河: 0.2 %潍坊: 0.0 %潍坊: 0.0 %烟台: 0.0 %烟台: 0.0 %玉林: 0.1 %玉林: 0.1 %珠海: 0.0 %珠海: 0.0 %白城: 0.0 %白城: 0.0 %白银: 0.3 %白银: 0.3 %盐城: 0.1 %盐城: 0.1 %石家庄: 0.3 %石家庄: 0.3 %福州: 0.1 %福州: 0.1 %秦皇岛: 0.1 %秦皇岛: 0.1 %纽约: 0.1 %纽约: 0.1 %美国伊利诺斯芝加哥: 0.0 %美国伊利诺斯芝加哥: 0.0 %芒廷维尤: 12.6 %芒廷维尤: 12.6 %芝加哥: 0.2 %芝加哥: 0.2 %苏州: 0.1 %苏州: 0.1 %衡水: 0.0 %衡水: 0.0 %衡阳: 0.1 %衡阳: 0.1 %衢州: 0.0 %衢州: 0.0 %西宁: 37.8 %西宁: 37.8 %西安: 0.4 %西安: 0.4 %贵港: 0.2 %贵港: 0.2 %赤峰: 0.0 %赤峰: 0.0 %运城: 0.1 %运城: 0.1 %连云港: 0.0 %连云港: 0.0 %邯郸: 0.1 %邯郸: 0.1 %郑州: 1.3 %郑州: 1.3 %鄂州: 0.1 %鄂州: 0.1 %重庆: 0.1 %重庆: 0.1 %银川: 0.1 %银川: 0.1 %镇江: 0.1 %镇江: 0.1 %长春: 0.0 %长春: 0.0 %长沙: 0.7 %长沙: 0.7 %长治: 0.0 %长治: 0.0 %防城港: 0.0 %防城港: 0.0 %青岛: 0.4 %青岛: 0.4 %鞍山: 0.0 %鞍山: 0.0 %黄冈: 0.1 %黄冈: 0.1 %龙岩: 0.0 %龙岩: 0.0 %其他其他ChinaIndiaUnited States[]三明上海东京东莞中卫临沂乌海亳州佛山佳木斯兰州兰辛加利福尼亚州包头北京北京市北海十堰南京南宁南昌厦门台北台州合肥呼和浩特哥伦布嘉兴圣地亚哥大连天津太原宁波安康安阳宣城巴中常州广州库比蒂诺张家口张家口市怒江成都扬州新乡无锡旧金山昆明昌吉朝阳杭州格兰特县武汉沈阳沧州洛阳济南淄博淮南深圳湖州湘潭滨州漯河潍坊烟台玉林珠海白城白银盐城石家庄福州秦皇岛纽约美国伊利诺斯芝加哥芒廷维尤芝加哥苏州衡水衡阳衢州西宁西安贵港赤峰运城连云港邯郸郑州鄂州重庆银川镇江长春长沙长治防城港青岛鞍山黄冈龙岩

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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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