Volume 9 Issue 5
Oct.  2020
Turn off MathJax
Article Contents
Abstract
References
Article Navigation >  Journal of Radars  > 2020  >  9(5): 916-924
WANG Junjie, FENG Dejun, WANG Zhisong, et al. Synthetic aperture rader imaging characteristics of electronically controlled time-varying electromagnetic materials[J]. Journal of Radars, 2021, 10(6): 865–873. doi: 10.12000/JR21104
Citation: GAO Xiangying, ZHAO Yongjun, LIU Zhixin, et al. Robust source localization using TDOA and FDOA with receiver location errors[J]. Journal of Radars, 2020, 9(5): 916–924. doi: 10.12000/JR20039
shu

Robust Source Localization Using TDOA and FDOA with Receiver Location Errors 

DOI: 10.12000/JR20039

  • School of Data and Target Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China

Funds:  The National Natural Science Foundation of China (61703433)
More Information
    Abstract

  • To address the low location accuracy and poor robustness of existing methods, error correction to improve the Stage 2 of the original Two-Stage Weighted Least Squares (TSWLS)-based methods is proposed, which involves a robust moving source localization method with high accuracy based on Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival (FDOA) in the presence of receiver location errors. This newly proposed Stage 2 performs Taylor expansion on the nuisance variables introduced in Stage 1 to construct the error correction equation, thereby avoiding the rank deficiency problem and nonlinear mathematical operations in the original TSWLS-based methods; and improving the robustness and location accuracy of the method. Theoretical analysis indicates that the proposed method can attain the Cramer-Rao Lower Bound (CRLB) under small noise condition. Simulation results show the proposed method has stronger localization robustness and better anti-noise performance over the existing methods under the common level of receiver location and measurement error.

     

    • FullText(HTML)
    • References(15)
  • [1]
    田中成, 刘聪锋. 无源定位技术[M]. 北京: 国防工业出版社, 2015: 8–12.

    TIAN Zhongcheng and LIU Congfeng. Passive Locating Technology[M]. Beijing: National Defense Industry Press, 2015: 8–12.
    [2]
    孙霆, 董春曦. 传感器参数误差下的运动目标TDOA/FDOA无源定位算法[J]. 航空学报, 2020, 41(2): 323317. doi: 10.7527/S1000-7527.2019.23317

    SUN Ting and DONG Chunxi. TDOA/FDOA passive localization algorithm for moving target with sensor parameter errors[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(2): 323317. doi: 10.7527/S1000-7527.2019.23317
    [3]
    LIU Congfeng, YANG Jie, and WANG Fengshuai. Joint TDOA and AOA location algorithm[J]. Journal of Systems Engineering and Electronics, 2013, 24(2): 183–188. doi: 10.1109/JSEE.2013.00023
    [4]
    CHEN Xin, WANG Ding, LIU Ruirui, et al. Structural total least squares algorithm for locating multiple disjoint sources based on AOA/TOA/FOA in the presence of system error[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 917–936. doi: 10.1631/FITEE.&1700735
    [5]
    LIU Zhixin, HU Dexiu, ZHAO Yongjun, et al. Computationally efficient TDOA, FDOA and differential Doppler rate estimation algorithm for passive emitter localization[J]. Digital Signal Processing, 2020, 96: 102598. doi: 10.1016/j.dsp.2019.102598
    [6]
    LIU Zhixin, WANG Rui, and ZHAO Yongjun. Computationally efficient TDOA and FDOA estimation algorithm in passive emitter localisation[J]. IET Radar, Sonar & Navigation, 2019, 13(10): 1731–1740. doi: 10.1049/&iet-rsn.2019.0101
    [7]
    KIM D G, PARK G H, KIM H N, et al. Computationally efficient TDOA/FDOA estimation for unknown communication signals in electronic warfare systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 77–89. doi: 10.1109/TAES.2017.2735118
    [8]
    YU H, HYANG G, and GAO J. Constrained total least-squares localisation algorithm using time difference of arrival and frequency difference of arrival measurements with sensor location uncertainties[J]. IET Radar, Sonar & Navigation, 2012, 6(9): 891–899.
    [9]
    HO K C, LU Xiaoning, and KOVAVISARUCH L. Source localization using TDOA and FDOA measurements in the presence of receiver location errors: Analysis and solution[J]. IEEE Transactions on Signal Processing, 2007, 55(2): 684–696. doi: 10.1109/TSP.2006.885744
    [10]
    NOROOZI A, OVEIS A H, HOSSEINI S M, et al. Improved algebraic solution for source localization from TDOA and FDOA measurements[J]. IEEE Wireless Communications Letters, 2018, 7(3): 352–355. doi: 10.1109/LWC.2017.2777995
    [11]
    LIU Yang, GUO Fucheng, YANG Le, et al. An improved algebraic solution for TDOA localization with sensor position errors[J]. IEEE Communications Letters, 2015, 19(12): 2218–2221. doi: 10.1109/LCOMM.2015.2486769
    [12]
    刘洋, 杨乐, 郭福成, 等. 基于定位误差修正的运动目标TDOA/FDOA无源定位方法[J]. 航空学报, 2015, 36(5): 1617–1626.

    LIU Yang, YANG Le, GUO Fucheng, et al. Moving targets TDOA/FDOA passive localization algorithm based on localization error refinement[J]. Acta Aeronauticaet Astronautica Sinica, 2015, 36(5): 1617–1626.
    [13]
    SORENSON H W. Parameter Estimation: Principles and Problems[M]. New York: Marcel Dekker Inc., 1980.
    [14]
    LIU Zhixin, HU Dexiu, ZHAO Yongsheng, et al. An algebraic method for moving source localization using TDOA, FDOA, and differential Doppler rate measurements with receiver location errors[J]. EURASIP Journal on Advances in Signal Processing, 2019, 2019(1): 25. doi: 10.1186/s13634-019-0621-9
    [15]
    LIU Zhixin, HU Dexiu, ZHAO Yongsheng, et al. An improved closed-form method for moving source localization using TDOA, FDOAs, differential Doppler rate measurements[J]. IEICE Transactions on Communications, 2019, E102. B(6): 1219–1228. doi: 10.1587/transcom.&2018ebp3249
    • Relative Articles

  • Relative Articles

    [1]LIAO Zhipeng, DUAN Keqing, HE Jinjun, QIU Zizhou, WANG Yongliang. Interpretable STAP Algorithm Based on Deep Convolutional Neural Network[J]. Journal of Radars, 2024, 13(4): 917-928. doi: 10.12000/JR24024
    [2]LI Zhongyu, PI Haozhuo, LI Jun’ao, YANG Qing, WU Junjie, YANG Jianyu. Clutter Suppression Technology Based Space-time Adaptive ANM-ADMM-Net for Bistatic SAR[J]. Journal of Radars. doi: 10.12000/JR24032
    [3]QUAN Yinghui, WU Yaojun, DUAN Lining, XU Gang, XUE Min, LIU Zhixing, XING Mengdao. A Review of Radar Signal Processing Based on Sparse Recovery[J]. Journal of Radars, 2024, 13(1): 46-67. doi: 10.12000/JR23211
    [4]HU Xueyao, LIANG Can, LU Shanshan, WANG Zaiyang, ZHENG Le, LI Yang. Matrix Completion-based Range-Doppler Spectrum Estimation for Random Stepped-frequency Radars[J]. Journal of Radars, 2024, 13(1): 200-214. doi: 10.12000/JR23176
    [5]MA Yuxin, HAI Yu, LI Zhongyu, HUANG Peng, WANG Chaodong, WU Junjie, YANG Jianyu. 3D High-resolution Imaging Algorithm with Sparse Trajectory for Millimeter-wave Radar[J]. Journal of Radars, 2023, 12(5): 1000-1013. doi: 10.12000/JR23001
    [6]LIN Yun, ZHANG Lin, WEI Lideng, ZHANG Hanqing, FENG Shanshan, WANG Yanping, HONG Wen. Research on Full-aspect Three-dimensional SAR Imaging Method for Complex Structural Facilities without Prior Model[J]. Journal of Radars, 2022, 11(5): 909-919. doi: 10.12000/JR22148
    [7]DUAN Keqing, LI Xiang, XING Kun, WANG Yongliang. Clutter Mitigation in Space-based Early Warning Radar Using a Convolutional Neural Network[J]. Journal of Radars, 2022, 11(3): 386-398. doi: 10.12000/JR21161
    [8]CUI Guolong, FAN Tao, KONG Yukai, YU Xianxiang, SHA Minghui, KONG Lingjiang. Pseudo-random Agility Technology for Interpulse Waveform Parameters in Airborne Radar[J]. Journal of Radars, 2022, 11(2): 213-226. doi: 10.12000/JR21189
    [9]LI Wenna, ZHANG Shunsheng, WANG Wenqin. Multitarget-tracking Method for Airborne Radar Based on a Transformer Network[J]. Journal of Radars, 2022, 11(3): 469-478. doi: 10.12000/JR22009
    [10]ZHU Hangui, FENG Weike, FENG Cunqian, ZOU Bo, LU Fuyu. Deep Unfolding Based Space-Time Adaptive Processing Method for Airborne Radar[J]. Journal of Radars, 2022, 11(4): 676-691. doi: 10.12000/JR22051
    [11]QU Haiyou, CHENG Di, CHEN Chang, CHEN Weidong. High-resolution Sparse Self-calibration Imaging for Vortex Radar with Phase Error[J]. Journal of Radars, 2021, 10(5): 699-717. doi: 10.12000/JR21094
    [12]Wang Yuzhuo, Zhu Shengqi, Xu Jingwei. A Range-ambiguous Clutter Suppression Method for MIMO Bistatic Airborne Radar[J]. Journal of Radars, 2018, 7(2): 202-211. doi: 10.12000/JR18016
    [13]Wang Yong, Chen Xuefei. Three-dimensional Geometry Reconstruction of Ship Targets with Complex Motion for Interferometric ISAR with Sparse Aperture[J]. Journal of Radars, 2018, 7(3): 320-334. doi: 10.12000/JR18019
    [14]Xie Wenchong, Duan Keqing, Wang Yongliang. Space Time Adaptive Processing Technique for Airborne Radar: An Overview of Its Development and Prospects[J]. Journal of Radars, 2017, 6(6): 575-586. doi: 10.12000/JR17073
    [15]Xu Jing-wei, Liao Gui-sheng. Range-ambiguous Clutter Suppression for Forward-looking Frequency Diverse Array Space-time Adaptive Processing Radar[J]. Journal of Radars, 2015, 4(4): 386-392. doi: 10.12000/JR15101
    [16]Wang Ting, Zhao Yong-jun, Hu Tao. Overview of Space-Time Adaptive Processing for Airborne Multiple-Input Multiple-Output Radar[J]. Journal of Radars, 2015, 4(2): 136-148. doi: 10.12000/JR14091
    [17]Wang Yong-liang, Liu Wei-jian, Xie Wen-chong, Duan Ke-qing, Gao Fei, Wang Ze-tao. Research Progress of Space-Time Adaptive Detection for Airborne Radar[J]. Journal of Radars, 2014, 3(2): 201-207. doi: 10.3724/SP.J.1300.2014.13081
    [18]Wang Fu-you, Luo Ding, Liu Hong-wei. Low-resolution Airborne Radar Aircraft Target Classification[J]. Journal of Radars, 2014, 3(4): 444-449. doi: 10.3724/SP.J.1300.2014.14075
    [19]Duan Ke-qing, Wang Ze-tao, Xie Wen-chong, Gao Fei, Wang Yong-liang. A Space-time Adaptive Processing Algorithm Based on Joint Sparse Recovery[J]. Journal of Radars, 2014, 3(2): 229-234. doi: 10.3724/SP.J.1300.2014.13149
    [20]Ma Ze-qiang, Wang Xi-qin, Liu Yi-min, Meng Hua-dong. An Overview on Sparse Recovery-based STAP[J]. Journal of Radars, 2014, 3(2): 217-228. doi: 10.3724/SP.J.1300.2014.14002
    • Supplements(0)
    • Cited By
  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-04020406080
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 34.5 %FULLTEXT: 34.5 %META: 58.2 %META: 58.2 %PDF: 7.3 %PDF: 7.3 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 4.7 %其他: 4.7 %其他: 1.0 %其他: 1.0 %Algeria: 0.1 %Algeria: 0.1 %Australia: 0.0 %Australia: 0.0 %Canton: 0.0 %Canton: 0.0 %China: 1.8 %China: 1.8 %Germany: 0.0 %Germany: 0.0 %India: 0.0 %India: 0.0 %Kennedy Town: 0.0 %Kennedy Town: 0.0 %Malvern: 0.1 %Malvern: 0.1 %Nahant: 0.1 %Nahant: 0.1 %Rochester: 0.1 %Rochester: 0.1 %Seattle: 0.0 %Seattle: 0.0 %Singapore: 0.1 %Singapore: 0.1 %Taichung: 0.0 %Taichung: 0.0 %Turkey: 0.1 %Turkey: 0.1 %United Kingdom: 0.4 %United Kingdom: 0.4 %United States: 0.3 %United States: 0.3 %Viet Nam: 0.1 %Viet Nam: 0.1 %[]: 1.1 %[]: 1.1 %三明: 0.0 %三明: 0.0 %上海: 1.3 %上海: 1.3 %上饶: 0.0 %上饶: 0.0 %东京都: 0.2 %东京都: 0.2 %东莞: 0.3 %东莞: 0.3 %中卫: 0.2 %中卫: 0.2 %临汾: 0.0 %临汾: 0.0 %丹东: 0.1 %丹东: 0.1 %乌海: 0.0 %乌海: 0.0 %乐山: 0.1 %乐山: 0.1 %伊春: 0.0 %伊春: 0.0 %伊犁: 0.0 %伊犁: 0.0 %伦敦: 0.2 %伦敦: 0.2 %佛山: 0.0 %佛山: 0.0 %信阳: 0.0 %信阳: 0.0 %六安: 0.1 %六安: 0.1 %兰州: 0.0 %兰州: 0.0 %兰辛: 0.0 %兰辛: 0.0 %内江: 0.1 %内江: 0.1 %凉山: 0.1 %凉山: 0.1 %加利福尼亚州: 0.2 %加利福尼亚州: 0.2 %北京: 6.3 %北京: 6.3 %南京: 2.2 %南京: 2.2 %南宁: 0.1 %南宁: 0.1 %南平: 0.0 %南平: 0.0 %南昌: 0.1 %南昌: 0.1 %南通: 0.1 %南通: 0.1 %卡拉奇: 0.0 %卡拉奇: 0.0 %厦门: 0.0 %厦门: 0.0 %台北: 0.1 %台北: 0.1 %台州: 0.2 %台州: 0.2 %台湾省: 0.0 %台湾省: 0.0 %合肥: 0.8 %合肥: 0.8 %呼和浩特: 0.1 %呼和浩特: 0.1 %咸阳: 0.0 %咸阳: 0.0 %哈尔滨: 0.3 %哈尔滨: 0.3 %商丘: 0.1 %商丘: 0.1 %嘉兴: 0.1 %嘉兴: 0.1 %大同: 0.1 %大同: 0.1 %大庆: 0.1 %大庆: 0.1 %大连: 0.3 %大连: 0.3 %天津: 0.8 %天津: 0.8 %太原: 0.1 %太原: 0.1 %威海: 0.1 %威海: 0.1 %娄底: 0.1 %娄底: 0.1 %宁波: 0.0 %宁波: 0.0 %安卡拉: 0.1 %安卡拉: 0.1 %安康: 0.1 %安康: 0.1 %安顺: 0.0 %安顺: 0.0 %宜春: 0.0 %宜春: 0.0 %宝鸡: 0.1 %宝鸡: 0.1 %宣城: 0.2 %宣城: 0.2 %巴中: 0.2 %巴中: 0.2 %巴音郭楞蒙古自治州: 0.0 %巴音郭楞蒙古自治州: 0.0 %巴黎: 0.1 %巴黎: 0.1 %常州: 0.1 %常州: 0.1 %常德: 0.2 %常德: 0.2 %广州: 1.1 %广州: 1.1 %库比蒂诺: 0.4 %库比蒂诺: 0.4 %廊坊: 0.0 %廊坊: 0.0 %开封: 0.1 %开封: 0.1 %张家口: 2.8 %张家口: 2.8 %张家界: 0.0 %张家界: 0.0 %徐州: 0.1 %徐州: 0.1 %德里: 0.1 %德里: 0.1 %德黑兰: 0.1 %德黑兰: 0.1 %怀化: 0.1 %怀化: 0.1 %惠州: 0.1 %惠州: 0.1 %成都: 3.5 %成都: 3.5 %扬州: 0.3 %扬州: 0.3 %新加坡: 0.3 %新加坡: 0.3 %新泽西: 0.0 %新泽西: 0.0 %无锡: 0.2 %无锡: 0.2 %日喀则: 0.1 %日喀则: 0.1 %日照: 0.0 %日照: 0.0 %昆明: 0.8 %昆明: 0.8 %晋中: 0.1 %晋中: 0.1 %晋城: 0.1 %晋城: 0.1 %朔州: 0.0 %朔州: 0.0 %朝阳: 0.0 %朝阳: 0.0 %杭州: 0.6 %杭州: 0.6 %桂林: 0.1 %桂林: 0.1 %榆林: 0.1 %榆林: 0.1 %武汉: 0.6 %武汉: 0.6 %汉中: 0.1 %汉中: 0.1 %汕头: 0.0 %汕头: 0.0 %沈阳: 0.2 %沈阳: 0.2 %河源: 0.0 %河源: 0.0 %泉州: 0.1 %泉州: 0.1 %泰安: 0.0 %泰安: 0.0 %泰米尔纳德: 0.1 %泰米尔纳德: 0.1 %洛杉矶: 0.1 %洛杉矶: 0.1 %洛阳: 0.3 %洛阳: 0.3 %济南: 0.2 %济南: 0.2 %海口: 0.1 %海口: 0.1 %海得拉巴: 0.0 %海得拉巴: 0.0 %淄博: 0.0 %淄博: 0.0 %淮北: 0.1 %淮北: 0.1 %淮南: 0.1 %淮南: 0.1 %深圳: 0.8 %深圳: 0.8 %清远: 0.1 %清远: 0.1 %温州: 0.1 %温州: 0.1 %渭南: 0.2 %渭南: 0.2 %湖州: 0.1 %湖州: 0.1 %湘潭: 0.0 %湘潭: 0.0 %滁州: 0.1 %滁州: 0.1 %漯河: 0.7 %漯河: 0.7 %濮阳: 0.0 %濮阳: 0.0 %烟台: 0.1 %烟台: 0.1 %玉林: 0.1 %玉林: 0.1 %石家庄: 0.6 %石家庄: 0.6 %福州: 0.0 %福州: 0.0 %秦皇岛: 0.1 %秦皇岛: 0.1 %纽约: 0.1 %纽约: 0.1 %绍兴: 0.1 %绍兴: 0.1 %绵阳: 0.1 %绵阳: 0.1 %罗奥尔凯埃: 0.3 %罗奥尔凯埃: 0.3 %罗马: 0.2 %罗马: 0.2 %芒廷维尤: 16.4 %芒廷维尤: 16.4 %芝加哥: 0.4 %芝加哥: 0.4 %苏州: 1.0 %苏州: 1.0 %莆田: 0.1 %莆田: 0.1 %莫斯科: 0.1 %莫斯科: 0.1 %葫芦岛: 0.1 %葫芦岛: 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: 33.8 %西宁: 33.8 %西安: 1.5 %西安: 1.5 %诺沃克: 0.3 %诺沃克: 0.3 %贵阳: 0.1 %贵阳: 0.1 %赣州: 0.0 %赣州: 0.0 %运城: 0.5 %运城: 0.5 %邢台: 0.1 %邢台: 0.1 %邯郸: 0.2 %邯郸: 0.2 %郑州: 0.5 %郑州: 0.5 %酒泉: 0.1 %酒泉: 0.1 %里奇兰: 0.1 %里奇兰: 0.1 %重庆: 0.2 %重庆: 0.2 %金华: 0.0 %金华: 0.0 %镇江: 0.0 %镇江: 0.0 %长春: 0.1 %长春: 0.1 %长沙: 0.9 %长沙: 0.9 %长治: 0.2 %长治: 0.2 %阳泉: 0.1 %阳泉: 0.1 %阿姆斯特丹: 0.1 %阿姆斯特丹: 0.1 %隆德: 0.0 %隆德: 0.0 %隆格伊: 0.1 %隆格伊: 0.1 %青岛: 0.4 %青岛: 0.4 %香港特别行政区: 0.1 %香港特别行政区: 0.1 %马鞍山: 0.1 %马鞍山: 0.1 %黄冈: 0.1 %黄冈: 0.1 %齐齐哈尔: 0.1 %齐齐哈尔: 0.1 %其他其他AlgeriaAustraliaCantonChinaGermanyIndiaKennedy TownMalvernNahantRochesterSeattleSingaporeTaichungTurkeyUnited KingdomUnited StatesViet Nam[]三明上海上饶东京都东莞中卫临汾丹东乌海乐山伊春伊犁伦敦佛山信阳六安兰州兰辛内江凉山加利福尼亚州北京南京南宁南平南昌南通卡拉奇厦门台北台州台湾省合肥呼和浩特咸阳哈尔滨商丘嘉兴大同大庆大连天津太原威海娄底宁波安卡拉安康安顺宜春宝鸡宣城巴中巴音郭楞蒙古自治州巴黎常州常德广州库比蒂诺廊坊开封张家口张家界徐州德里德黑兰怀化惠州成都扬州新加坡新泽西无锡日喀则日照昆明晋中晋城朔州朝阳杭州桂林榆林武汉汉中汕头沈阳河源泉州泰安泰米尔纳德洛杉矶洛阳济南海口海得拉巴淄博淮北淮南深圳清远温州渭南湖州湘潭滁州漯河濮阳烟台玉林石家庄福州秦皇岛纽约绍兴绵阳罗奥尔凯埃罗马芒廷维尤芝加哥苏州莆田莫斯科葫芦岛衡水衡阳衢州西宁西安诺沃克贵阳赣州运城邢台邯郸郑州酒泉里奇兰重庆金华镇江长春长沙长治阳泉阿姆斯特丹隆德隆格伊青岛香港特别行政区马鞍山黄冈齐齐哈尔

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(2328) PDF downloads(140) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return