Estimation of Accuracy of Algorithm for Measuring Radiofrequency Pulse Parameters

Zanina, M. A.; Belov, Andrei A.; Volvenko, Sergey V.

doi:10.1109/eexpolytech.2018.8564382

Cited by 7 publications

(1 citation statement)

References 5 publications

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“…Fortunately, the estimation accuracy of the parameters (i.e., regarding the inter-pulse and intra-pulse of the radar signals) could be well managed by existing techniques. For example, the standard deviation of the estimation about the time parameters (i.e., pulse width, pulse rise time and pulse fall time) and pulse amplitude can be lower than 0.2 for signals with SNRs from 5 dB to 25 dB, see [29]. Moreover, an even lower standard deviation (less than 0.1) can be achieved for calculating instantaneous phase, frequency and amplitude for signals in low SNR conditions of order 0 dB using the advanced pulse train measurement technique based on the moving autocorrelation for parameter extraction proposed in [30].…”

Section: Proposed Radar Signal Detection and Synthetic Proceduresmentioning

confidence: 99%

DNCNet: Deep Radar Signal Denoising and Recognition

Zhong

Cai

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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Deep learning with its rapid development and advancement has achieved unparalleled performance in many areas like computer vision as well as cognitive radio and signal recognition. However, the performance of most deep neural networks would suffer from degradation in the data mismatch scenario, e.g., the test dataset has a related but nonidentical distribution with the training dataset. Considering the noise corruption, a classifier's accuracy might drop sharply when it is tested on a dataset with much lower signal-to-noise ratio compared to its training dataset. To address this dilemma, in this work we propose an efficient DeNoising and Classification Network (DNCNet) for radar signals. DNCNet consists of denoising and classification subnetworks. Firstly, a radar signal detection and synthetic mechanism is designed to generate pairwise clean data and noisy data for the DNCNet to train its denoising subnetwork. Then a two-phase training procedure is proposed to train the denoising subnetwork in the first phase and strengthen the mapping between the denoising results and perceptual representation in the second. Experiments on synthetic and benchmark datasets validate the excellent performance of the proposed DNCNet against state-of-the-art methods in terms of both signal restoration quality and classification accuracy.

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Section: Proposed Radar Signal Detection and Synthetic Proceduresmentioning

confidence: 99%