scispace - formally typeset
Proceedings ArticleDOI

Co-channel interference between WiFi and through-wall micro-Doppler radar

Reads0
Chats0
TLDR
This work shows, through experiments, how the radar degrades the WiFi throughput by lowering the signal to noise and interference ratio at the WiFi receiver, and WiFi interference causes deterioration in the radar performance by increasing the probability of false alarms.
Abstract
Narrowband through-wall radars have been researched for detecting and classifying indoor movers on the basis of their micro-Doppler signatures. These radars usually operate in the unlicensed 2.4GHz ISM band and are therefore susceptible to interference from WiFi networks operating with the IEEE 802.11g protocol. In this work, we show, through experiments, how the radar degrades the WiFi throughput by lowering the signal to noise and interference ratio at the WiFi receiver. Similarly, WiFi interference causes deterioration in the radar performance by increasing the probability of false alarms.

read more

Citations
More filters
Journal ArticleDOI

Dictionary Learning With Low Computational Complexity for Classification of Human Micro-Dopplers Across Multiple Carrier Frequencies

TL;DR: This paper examines the performances of three sparsity driven dictionary learning algorithms—synthesis, deep, and analysis—for learning unique features extracted from training data gathered across multiple carrier frequencies and shows that they are capable of extracting meaningful representations of the micro-Dopplers.
Journal ArticleDOI

LoRadar: Enabling Concurrent Radar Sensing and LoRa Communication

TL;DR: In this article , the authors proposed LoRadar, which enables an FMCW (Frequency-Modulated Continuous Wave) radar to carry LoRa signals in sensing waves.
Journal ArticleDOI

Review on IoT Based Precision Irrigation System in Agriculture

TL;DR: A review of the scope of smart irrigation using IoT has been discussed, and the scarcity of agricultural workers in irrigation can be compensated by the Internet of Things (IoT) platform.
Book ChapterDOI

Ultra-Low Power Localization System Using Mobile Cloud Computing

TL;DR: An Ultra-Low power indoor localization system using mobile cloud computing that reduces the signal interference of the positioning device, improves the positioning accuracy and reduces the system energy consumption by controlling the working mode ofThe positioning device.
Posted Content

Neural Style Transfer Enhanced Training Support For Human Activity Recognition

TL;DR: In this article, a style-transfer neural network was proposed to extract environmental effects such as noise, multipath, and occlusions effects directly from the measurement data and transfer these features to the clean simulated signatures.
References
More filters
Journal ArticleDOI

Sparse Approximate Solutions to Linear Systems

TL;DR: It is shown that the problem is NP-hard, but that the well-known greedy heuristic is good in that it computes a solution with at most at most $\left\lceil 18 \mbox{ Opt} ({\bf \epsilon}/2) \|{\bf A}^+\|^2_2 \ln(\|b\|_2/{\bf
Book

High Resolution Radar

TL;DR: In this paper, the authors apply the radar range equation to high-resolution radar high resolution radar design high range resolution waveforms and processing synthetic high-range resolution radar synthetic aperture radar inverse synthetic aperture Radar (ISAR) three dimensional imaging with monopulse radar target imaging with non-coherent radar systems applications for surveillance.
Journal ArticleDOI

Micro-Doppler effect in radar: phenomenon, model, and simulation study

TL;DR: In this paper, the micro-Doppler effect was introduced in radar data, and a model of Doppler modulations was developed to derive formulas of micro-doppler induced by targets with vibration, rotation, tumbling and coning motions.
Proceedings ArticleDOI

Discriminative K-SVD for dictionary learning in face recognition

TL;DR: The proposed method to learn an over-complete dictionary is based on extending the K-SVD algorithm by incorporating the classification error into the objective function, thus allowing the performance of a linear classifier and the representational power of the dictionary being considered at the same time by the same optimization procedure.
Proceedings Article

Supervised Dictionary Learning

TL;DR: A novel sparse representation for signals belonging to different classes in terms of a shared dictionary and discriminative class models is proposed, with results on standard handwritten digit and texture classification tasks.
Related Papers (5)
Trending Questions (1)
How can I boost my wifi signal on my smartphone?

In this work, we show, through experiments, how the radar degrades the WiFi throughput by lowering the signal to noise and interference ratio at the WiFi receiver.