scholarly journals Narrowband Interference Cancellation using Set-membership Adaptive Predictor for GPS Receiver

2021 ◽  
Vol 15 ◽  
pp. 59-64
Author(s):  
Wei-Lung Mao ◽  
Chorng-Sii Hwang ◽  
Chung-Wen Hung ◽  
Jyh Sheen
Keyword(s):  
Interference Cancellation ◽  
Continuous Wave ◽  
Signal To Noise Ratio ◽  
Least Mean Square ◽  
Gps Receiver ◽  
Wave Interference ◽  
Set Membership ◽  
Military Applications ◽  
Squared Prediction Error ◽  
Narrowband Interference Cancellation

The global positioning system (GPS) provides accurate positioning and timing information that is useful in various civil and military applications. The adaptive filtering predictor for GPS jamming suppression applications is proposed. This research uses the gLab-G software to substitute for the hardware receiver to record the GPS signal waveform. The normalized least-mean-square (NLMS) and set-membership NLMS (SM-NLMS) filtering methods are employed for continuous wave interference suppression. Simulation results reveal that our proposed methods can provide the better performances when the interference-to-noise ratios (INR) are varied from 20 to 50 dB. The anti-jamming performances are evaluated via extensive simulation by computing mean squared prediction error (MSPE) and signal-to-noise ratio (SNR) improvements.

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

scholarly journals Phase Stability under Thermal Drifts in Photodiode-Conditioning Transimpedance Amplifiers for Distance Metrology

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3455
Author(s):  
Francisco Javier Meca Meca ◽  
Ernesto Martín-Gorostiza ◽  
Miguel Ángel García-Garrido ◽  
David Salido-Monzú
Keyword(s):  
Delay Time ◽  
Measurement Errors ◽  
Continuous Wave ◽  
Signal To Noise Ratio ◽  
Circuit Board ◽  
Transimpedance Amplifiers ◽  
Optical Distance ◽  
High Measurement ◽  
Analytic Expressions ◽  
Measured Distance

Transimpedance amplifiers (TIA) are widely used for front-end signal conditioning in many optical distance measuring applications in which high accuracy is often required. Small effects due to the real characteristics of the components and the parasitic elements in the circuit board may cause the error to rise to unacceptable levels. In this work we study these effects on the TIA delay time error and deduce analytic expressions, taking into account the trade-off between the uncertainties caused by the delay time instability and by the signal-to-noise ratio. A specific continuous-wave phase-shift case study is shown to illustrate the analysis, and further compared with real measurements. General strategies and conclusions, useful for designers of this kind of system, are extracted too. The study and results show that the delay time thermal stability is a key determinant factor in the measured distance accuracy and, without an adequate design, moderate temperature variations of the TIA can cause extremely high measurement errors.

scholarly journals High-speed high-resolution laser diode-based photoacoustic microscopy for in vivo microvasculature imaging

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Xiufeng Li ◽  
Victor T C Tsang ◽  
Lei Kang ◽  
Yan Zhang ◽  
Terence T W Wong
Keyword(s):  
High Resolution ◽  
High Speed ◽  
High Performance ◽  
Continuous Wave ◽  
Signal To Noise Ratio ◽  
Cost Effective ◽  
High Signal ◽  
Photoacoustic Microscopy ◽  
Mouse Ear

AbstractLaser diodes (LDs) have been considered as cost-effective and compact excitation sources to overcome the requirement of costly and bulky pulsed laser sources that are commonly used in photoacoustic microscopy (PAM). However, the spatial resolution and/or imaging speed of previously reported LD-based PAM systems have not been optimized simultaneously. In this paper, we developed a high-speed and high-resolution LD-based PAM system using a continuous wave LD, operating at a pulsed mode, with a repetition rate of 30 kHz, as an excitation source. A hybrid scanning mechanism that synchronizes a one-dimensional galvanometer mirror and a two-dimensional motorized stage is applied to achieve a fast imaging capability without signal averaging due to the high signal-to-noise ratio. By optimizing the optical system, a high lateral resolution of 4.8 μm has been achieved. In vivo microvasculature imaging of a mouse ear has been demonstrated to show the high performance of our LD-based PAM system.

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