Detection of Multiple Noise-like Jammers for Radar Applications

被引:0
|
作者
Carotenuto, Vincenzo [1 ]
Hao, Chengpeng [2 ]
Orlando, Danilo [3 ]
De Maio, Antonio [4 ]
Iommelli, Salvatore [5 ]
机构
[1] Univ Napoli Federico II, CNIT Udr, Via Claudio 21, I-80125 Naples, Italy
[2] Chinese Acad Sci, Inst Acoust, Beijing 100190, Peoples R China
[3] Univ Niccolo Cusano, Via Don Carlo Gnocchi 3, I-00166 Rome, Italy
[4] Univ Napoli Federico II, DIETI, Via Claudio 21, I-80125 Naples, Italy
[5] Ente Formaz Profess Maxwell, Via GA Campano 103-105, I-80145 Naples, Italy
关键词
Noise-like jammer; Model Order Selection; Interference Covariance Matrix; Detection; Metrology in Radar; EXPONENTIALLY EMBEDDED FAMILY; LIKELIHOOD RATIO TEST; SIDELOBE BLANKING; MODEL; CLASSIFICATION; PROBABILITY;
D O I
暂无
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Model Order Selection (MOS) rules are exploited to devise two adaptive architectures for multiple noise-like jammer detection which process clutter-free data. Specifically, the former contains a MOS-based stage, that makes inference on the number of jammers, and a detection stage, that confirms the presence of the jammers suitably exploiting the estimates provided by the first stage. On the other hand, the latter architecture, which relies on the Modified Likelihood Ratio Test proposed in [1], jointly performs detection and estimation. Remarkably, both guarantee the constant false alarm rate property with respect to the thermal noise power. At the analysis stage, the performance of the proposed architectures are investigated highlighting the interplay among the different parameters.
引用
收藏
页码:328 / 333
页数:6
相关论文
共 50 条
  • [1] A Sparse Learning Approach to Multiple Noise-like Jammers Detection
    Yan, L.
    Addabbo, P.
    Hao, C.
    Orlando, D.
    Liu, J.
    [J]. 2019 PHOTONICS & ELECTROMAGNETICS RESEARCH SYMPOSIUM - FALL (PIERS - FALL), 2019, : 155 - 161
  • [2] A Sparse Learning Approach to the Detection of Multiple Noise-Like Jammers
    Yan, Linjie
    Addabbo, Pia
    Zhang, Yuxuan
    Hao, Chengpeng
    Liu, Jun
    Li, Jian
    Orlando, Danilo
    [J]. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2020, 56 (06) : 4367 - 4383
  • [3] A sparse learning approach to the detection of multiple noise-like jammers
    Yan, Linjie
    Addabbo, Pia
    Zhang, Yuxuan
    Hao, Chengpeng
    Liu, Jun
    Li, Jian
    Orlando, Danilo
    [J]. arXiv, 2020,
  • [4] ECCM Strategies for Radar Systems Against Smart Noise-Like Jammers
    Benvenuti, Dario
    Addabbo, Pia
    Giunta, Gaetano
    Foglia, Goffredo
    Orlando, Danilo
    [J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2024, 72 : 3912 - 3926
  • [5] CFAR detection algorithm of noise-like radar signals
    Grigorevich, Prokopenko Igor
    Andreevich, Martynchuk Igor
    [J]. 2014 IEEE MICROWAVES, RADAR AND REMOTE SENSING SYMPOSIUM (MRRS), 2014, : 71 - 74
  • [6] ON THE PROBABILITY OF DETECTION OF NOISE-LIKE SIGNALS
    STONE, WM
    HAMMERLE, KJ
    [J]. ANNALS OF MATHEMATICAL STATISTICS, 1960, 31 (01): : 243 - 243
  • [7] Correlation processing of noise-like signals from coherent radar
    Knoechel, Reinhard
    Tepliuk, Alexander L.
    Khlopov, Grigory I.
    Shuenemann, Klaus
    [J]. 2008 PROCEEDINGS INTERNATIONAL RADAR SYMPOSIUM, 2008, : 136 - +
  • [8] NOISE IMMUNITY OF A POWER RECEIVER IN DETECTION OF A NOISE-LIKE PULSE SIGNAL
    FIRSOV, VL
    CHAPOVSK.MZ
    [J]. RADIO ENGINEERING AND ELECTRONIC PHYSICS-USSR, 1969, 14 (01): : 138 - &
  • [9] DISCRIMINATION OF NOISE-LIKE SOUNDS INVOLVING MULTIPLE INTERACTING FEATURES
    MARTIN, DW
    [J]. JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 1979, 66 (06): : 1901 - 1901
  • [10] Multi-target detection using noise-like signals
    Raout, Jacques
    Preaux, Jean-Philippe
    [J]. 2008 IEEE RADAR CONFERENCE, VOLS. 1-4, 2008, : 590 - 594