Probabilistic latent component analysis for radar signal detection

2013 ◽  
Author(s):  
Tao Ying ◽  
Gaoming Huang ◽  
Cheng Zhou
Keyword(s):  
Signal Detection ◽  
Component Analysis ◽  
Radar Signal

scholarly journals Field Theoretical Approach for Signal Detection in Nearly Continuous Positive Spectra II: Tensorial Data

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 795
Author(s):  
Vincent Lahoche ◽  
Mohamed Ouerfelli ◽  
Dine Ousmane Samary ◽  
Mohamed Tamaazousti
Keyword(s):  
Principal Component Analysis ◽  
Renormalization Group ◽  
Signal Detection ◽  
Detection Threshold ◽  
Principal Component ◽  
Vacuum Expectation ◽  
Vacuum Expectation Value ◽  
Component Analysis ◽  
Slight Generalization ◽  
Nearly Continuous

The tensorial principal component analysis is a generalization of ordinary principal component analysis focusing on data which are suitably described by tensors rather than matrices. This paper aims at giving the nonperturbative renormalization group formalism, based on a slight generalization of the covariance matrix, to investigate signal detection for the difficult issue of nearly continuous spectra. Renormalization group allows constructing an effective description keeping only relevant features in the low “energy” (i.e., large eigenvalues) limit and thus providing universal descriptions allowing to associate the presence of the signal with objectives and computable quantities. Among them, in this paper, we focus on the vacuum expectation value. We exhibit experimental evidence in favor of a connection between symmetry breaking and the existence of an intrinsic detection threshold, in agreement with our conclusions for matrices, providing a new step in the direction of a universal statement.

Sampling losses in radar signal detection

1986 ◽  
Vol 56 (6-7) ◽  
pp. 237 ◽  
Author(s):  
D. D'Aloisi ◽  
A. Di Vito ◽  
G. Galati
Keyword(s):  
Signal Detection ◽  
Radar Signal

Atmospheric radar signal processing using principal component analysis

2014 ◽  
Vol 32 ◽  
pp. 79-84 ◽  
Author(s):  
D. Uma Maheswara Rao ◽  
T. Sreenivasulu Reddy ◽  
G. Ramachandra Reddy
Keyword(s):  
Principal Component Analysis ◽  
Signal Processing ◽  
Principal Component ◽  
Component Analysis ◽  
Radar Signal ◽  
Radar Signal Processing

scholarly journals A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4751 ◽  
Author(s):  
Xiaoling Li ◽  
Bin Liu ◽  
Yang Liu ◽  
Jiawei Li ◽  
Jiarui Lai ◽  
...  
Keyword(s):  
Signal Detection ◽  
Spectrum Analysis ◽  
Doppler Radar ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Signal Separation ◽  
Radar Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise

Doppler radar for monitoring vital signals is an emerging tool, and how to remove the noise during the detection process and reconstruct the accurate respiration and heartbeat signals are hot issues in current research. In this paper, a novel radar vital signal separation and de-noising technique based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy (SampEn), and wavelet threshold is proposed. First, the noisy radar signal was decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. Then, each IMF was analyzed using SampEn to find out the first few IMFs containing noise, and these IMFs were de-noised using the wavelet threshold. Finally, in order to extract accurate vital signals, spectrum analysis and Kullback–Leible (KL) divergence calculations were performed on all IMFs, and appropriate IMFs were selected to reconstruct respiration and heartbeat signals. Moreover, as far as we know, there is almost no previous research on radar vital signal de-noising based on the proposed technique. The effectiveness of the algorithm was verified using simulated and measured experiments. The results show that the proposed algorithm could effectively reduce the noise and was superior to the existing de-noising technologies, which is beneficial for extracting more accurate vital signals.

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