15 research outputs found
Feature diversity for optimized human micro-doppler classification using multistatic radar
This paper investigates the selection of different combinations of features at different multistatic radar nodes, depending on scenario parameters, such as aspect angle to the target and signal-to-noise ratio, and radar parameters, such as dwell time, polarisation, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analysed for two separate problems, namely the classification of unarmed vs potentially armed multiple personnel, and the personnel recognition of individuals based on walking gait. The results show that the overall classification accuracy can be significantly improved by taking into account feature diversity at each radar node depending on the environmental parameters and target behaviour, in comparison with the conventional approach of selecting the same features for all nodes
Personnel recognition and gait classification based on multistatic micro-doppler signatures using deep convolutional neural networks
In this letter, we propose two methods for personnel recognition and gait classification using deep convolutional neural networks (DCNNs) based on multistatic radar micro-Doppler signatures. Previous DCNN-based schemes have mainly focused on monostatic scenarios, whereas directional diversity offered by multistatic radar is exploited in this letter to improve classification accuracy. We first propose the voted monostatic DCNN (VMo-DCNN) method, which trains DCNNs on each receiver node separately and fuses the results by binary voting. By merging the fusion step into the network architecture, we further propose the multistatic DCNN (Mul-DCNN) method, which performs slightly better than VMo-DCNN. These methods are validated on real data measured with a 2.4-GHz multistatic radar system. Experimental results show that the Mul-DCNN achieves over 99% accuracy in armed/unarmed gait classification using only 20% training data and similar performance in two-class personnel recognition using 50% training data, which are higher than the accuracy obtained by performing DCNN on a single radar node
Activity Classification Using Raw Range and I & Q Radar Data with Long Short Term Memory Layers
This paper presents the first initial results of using
radar raw I & Q data and range profiles combined with Long
Short Term Memory layers to classify human activities. Although
tested only on simple classification problems, this is an innovative
approach that enables to bypass the conventional usage of
Doppler-time patterns (spectrograms) as inputs of the Long Short
Term Memory layers, and adopt instead sequences of range
profiles or even raw complex data as inputs. A maximum 99.56%
accuracy and a mean accuracy of 97.67% was achieved by
treating the radar data as these time sequences, in an effective
scheme using a deep learning approach that did not require the
pre-processing of the radar data to generate spectrograms and
treat them as images. The prediction time needed for a given
input testing sample is also reported, showing a promising path
for real-time implementation once the Long Short Term Memory
layers network is properly trained
Radar Sensing in Assisted Living: An Overview
This paper gives an overview of trends in radar sensing for assisted living. It focuses on signal processing and classification, looking at conventional approaches, deep learning and fusion techniques. The last section shows examples of classification in human activity recognition and medical applications, e.g. breathing disorder and sleep stages recognition
Bistatic Human micro-Doppler Signatures for Classification of Indoor Activities
This paper presents the analysis of human micro- Doppler signatures collected by a bistatic radar system to classify different indoor activities. Tools for automatic classification of different activities will enable the implementation and deployment of systems for monitoring life patterns of people and identifying fall events or anomalies which may be related to early signs of deteriorating physical health or cognitive capabilities. The preliminary results presented here show that the information within the micro-Doppler signatures can be successfully exploited for automatic classification, with accuracy up to 98%, and that the multi-perspective view on the target provided by bistatic data can contribute to enhance the overall system performance
Practical classification of different moving targets using automotive radar and deep neural networks
In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed
Radar for Assisted Living in the Context of Internet of Things for Health and Beyond
This paper discusses the place of radar for assisted living in the context of IoT for Health and beyond. First, the context of assisted living and the urgency to address the problem is described. The second part gives a literature review of existing sensing modalities for assisted living and explains why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that helps improve performances in classification especially with deep learning with a reflection on lessons learned from it. The fourth section introduces recent published work from our research group in the area that shows promise with multimodal sensor fusion for classification and long short-term memory applied to early stages in the radar signal processing chain. Finally, we conclude with open challenges still to be addressed in the area and open to future research directions in animal welfare
Magnetic and radar sensing for multimodal remote health monitoring
With the increased life expectancy and rise in health conditions related to aging, there is a need for new technologies that can routinely monitor vulnerable people, identify their daily pattern of activities and any anomaly or critical events such as falls. This paper aims to evaluate magnetic and radar sensors as suitable technologies for remote health monitoring purpose, both individually and fusing their information. After experiments and collecting data from 20 volunteers, numerical features has been extracted in both time and frequency domains. In order to analyse and verify the validation of fusion method for different classifiers, a Support Vector Machine with a quadratic kernel, and an Artificial Neural Network with one and multiple hidden layers have been implemented. Furthermore, for both classifiers, feature selection has been performed to obtain salient features. Using this technique along with fusion, both classifiers can detect 10 different activities with an accuracy rate of approximately 96%. In cases where the user is unknown to the classifier, an accuracy of approximately 92% is maintained
Performance Evaluation of Two Multistatic Radar Detectors on Real and Simulated Sea-Clutter Data
In this paper we evaluate the performance of two multistatic radar detection schemes on real and synthetically-generated sea clutter data. The analysis utilizes both monostatic and bistatic radar data, simultaneously collected by the nodes of the N etRAD system. The first contribution is a demonstration of how the evolved Doppler spectrum model can accurately simulate multistatic sea-clutter with statistical and spectral properties which match the real data. The second contribution is an analysis of the detection performance obtained from different bistatic angles and a demonstration that the results from both real and simulated data are comparabl