Automatic Radar Waveform Recognition

Lundén, Jarmo; Koivunen, Visa

doi:10.1109/jstsp.2007.897055

Cited by 203 publications

(148 citation statements)

References 22 publications

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“…Twenty percent of the labels are utilized for testing and 80% for training. The result is compared with Lundén's system [13] and our previous work [14], both of which are wide systems in waveform classification. Figure 7 plots the experimental results of RSR with different SNR.…”

Section: Experiments With Snrmentioning

confidence: 99%

“…We measure the time of the proposed method and compare it with [13,14] in the same conditions. Three different SNRs, −4 dB, −0 dB and 6 dB are tested, and each test repeats 50 times to calculate the average value.…”

Section: Experiments With Computational Burdenmentioning

confidence: 99%

“…Additionally, the RSR ≥90% at SNR ≥0 dB. Lundén [13] introduces a wide classification system to classify the intercepted pulse compression waveforms. The system achieves overall RSR ≥98% at SNR ≥6 dB.…”

Section: Introductionmentioning

confidence: 99%

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Neural Networks for Radar Waveform Recognition

et al. 2017

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For passive radar detection system, radar waveform recognition is an important research area. In this paper, we explore an automatic radar waveform recognition system to detect, track and locate the low probability of intercept (LPI) radars. The system can classify (but not identify) 12 kinds of signals, including binary phase shift keying (BPSK) (barker codes modulated), linear frequency modulation (LFM), Costas codes, Frank code, P1-P4 codesand T1-T4 codeswith a low signal-to-noise ratio (SNR). It is one of the most extensive classification systems in the open articles. A hybrid classifier is proposed, which includes two relatively independent subsidiary networks, convolutional neural network (CNN) and Elman neural network (ENN). We determine the parameters of the architecture to make networks more effectively. Specifically, we focus on how the networks are designed, what the best set of features for classification is and what the best classified strategy is. Especially, we propose several key features for the classifier based on Choi-Williams time-frequency distribution (CWD). Finally, the recognition system is simulated by experimental data. The experiments show the overall successful recognition ratio of 94.5% at an SNR of −2 dB.

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Section: Experiments With Snrmentioning

confidence: 99%

Section: Experiments With Computational Burdenmentioning

confidence: 99%

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Neural Networks for Radar Waveform Recognition

et al. 2017

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“…Four basic IMOPs including the CF, LFM, BPC, and BFC modulations are considered here as examples. The modulation features are extracted from the instantaneous phase and instantaneous frequency law (IFL) curve which is calculated according to (14) and (17).…”

Section: Accurate Modulation Classification Within Modulation Familiesmentioning

confidence: 99%

“…For example, an atomic decomposition method employing chirplet dictionary was proposed in [13], and it can realize the automatic detection and classification of radar pulses with LFM, PSK, and FSK modulations. An supervised classification system achieving overall correct classification rate of 98 % at signal-to-noise ratio (SNR) of 6 dB based on multilayer perception networks was proposed in [14] and eight classes of radar signals are classified. Algorithms based on some time-frequency distributions such as the ambiguity function (AF) [15], Zhao Altas and Marks (ZAM) representations [16], the Rihacek distribution and the Hough transform [17], were also applied to extract modulation features for five kinds of radar pulses.…”

Section: Introductionmentioning

confidence: 99%

Autonomous radar pulse modulation classification using modulation components analysis

Wang

Qiu

Zhu

et al. 2016

EURASIP J. Adv. Signal Process.

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An autonomous method for recognizing radar pulse modulations based on modulation components analysis is introduced in this paper. Unlike the conventional automatic modulation classification methods which extract modulation features based on a list of known patterns, this proposed method classifies modulations by the existence of basic modulation components including continuous frequency modulations, discrete frequency codes and discrete phase codes in an autonomous way. A feasible way to realize this method is using the features of abrupt changes in the instantaneous frequency rate curve which derived by the short-term general representation of phase derivative. This method is suitable not only for the basic radar modulations but also for complicated and hybrid modulations. The theoretical result and two experiments demonstrate the effectiveness of the proposed method.

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Spectral Estimation in Cognitive Radios

Farhang‐Boroujeny

2010

Handbook on Array Processing and Sensor Networks

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Automatic Radar Waveform Recognition

Cited by 203 publications

References 22 publications

Neural Networks for Radar Waveform Recognition

Neural Networks for Radar Waveform Recognition

Autonomous radar pulse modulation classification using modulation components analysis

Spectral Estimation in Cognitive Radios

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