A DTO and DFO estimation algorithm of broadband frequency-hopping pulse signal

2012 ◽  
Author(s):  
Hui-chuan Zhang ◽  
Zheng-bo Sun ◽  
Hua-feng Peng ◽  
Yu-xiang Yang
Keyword(s):  
Pulse Signal ◽  
Estimation Algorithm ◽  
Frequency Hopping ◽  
Broadband Frequency

TDOA/FDOA estimation algorithm of frequency-hopping signals based on CAF coherent integration

2020 ◽  
Vol 14 (2) ◽  
pp. 331-336
Author(s):  
Xinxin Ouyang ◽  
Qing He ◽  
Yuxiang Yang ◽  
Qun Wan
Keyword(s):  
Estimation Algorithm ◽  
Frequency Hopping ◽  
Coherent Integration

scholarly journals A Parameter Estimation Algorithm for Multiple Frequency-Hopping Signals Based on Sparse Bayesian Method

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Kun-feng Zhang ◽  
Ying Guo ◽  
Zisen Qi
Keyword(s):  
Parameter Estimation ◽  
Spatial Frequency ◽  
Prior Information ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Frequency Hopping ◽  
Blind Estimation ◽  
Filter Methods ◽  
Challenging Tasks ◽  
Parameter Estimation Algorithm

Parameter estimation and network sorting for noncooperative wideband frequency-hopping (FH) signals have been essential and challenging tasks, especially in the case with little or even no prior information at all. In this paper, we propose a nearly blind estimation approach to estimate signal parameters based on sparse Bayesian reconstruction. Taking the sparsity in the spatial frequency domain of multiple FH signals into account, we propose a sparse Bayesian algorithm to estimate the spatial frequency parameters. As a result, the frequency and direction of arrival (DOA) parameters can be obtained. In order to improve the accuracy of the estimation parameters, we employ morphological filter methods to further clean the data poisoned by the noise. Moreover, our method is applicable to the wideband signal models with little prior information. We also conduct extensive numerical simulations to verify the performance of our method. Notably, the proposed method works well even in low signal-to-noise ratio (SNR) environment.

scholarly journals Long-Time Coherent Integration for Frequency Hopping Pulse Signal via Phase Compensation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 30458-30466
Author(s):  
Gang Chen ◽  
Jun Wang ◽  
Luo Zuo ◽  
Dawei Zhao
Keyword(s):  
Pulse Signal ◽  
Frequency Hopping ◽  
Phase Compensation ◽  
Coherent Integration ◽  
Long Time

scholarly journals Continuous Distant Measurement of the User’s Heart Rate in Human-Computer Interaction Applications

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4205 ◽  
Author(s):  
Przybyło
Keyword(s):  
Heart Rate ◽  
Human Computer Interaction ◽  
Pulse Signal ◽  
Estimation Algorithm ◽  
Time Domain Analysis ◽  
Extraction Methods ◽  
Signal Extraction ◽  
Power Spectral ◽  
The Impact ◽  
Computer Interaction

In real world scenarios, the task of estimating heart rate (HR) using video plethysmography (VPG) methods is difficult because many factors could contaminate the pulse signal (i.e. a subjects’ movement, illumination changes). This article presents the evaluation of a VPG system designed for continuous monitoring of the user's heart rate during typical human-computer interaction scenarios. The impact of human activities while working at the computer (i.e. reading and writing text, playing a game) on the accuracy of HR VPG measurements was examined. Three commonly used signal extraction methods were evaluated: green (G), green-red difference (GRD), blind source separation (ICA). A new method based on an excess green (ExG) image representation was proposed. Three algorithms for estimating pulse rate were used: power spectral density (PSD), autoregressive modeling (AR) and time domain analysis (TIME). In summary, depending on the scenario being studied, different combinations of signal extraction methods and the pulse estimation algorithm ensure optimal heart rate detection results. The best results were obtained for the ICA method: average RMSE = 6.1 bpm (beats per minute). The proposed ExG signal representation outperforms other methods except ICA (RMSE = 11.2 bpm compared to 14.4 bpm for G and 13.0 bmp for GRD). ExG also is the best method in terms of proposed success rate metric (sRate).

Sign in / Sign up

Export Citation Format

Share Document