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Incoherent backscattering of mm-waves from natural rough surfaces is considered. A novel method is proposed to determine the statistical properties of surface scattering from range profile measurements. The method is based on modeling the road surface as a grid of uncorrelated scattering elements, described by normalized scattering matrices. Using this model, expressions are derived to estimate the mean value and covariance matrix of surface scattering from measurement data. This procedure is then applied to measurement data of four road surface types, namely: 1) dry asphalt, 2) wet asphalt, 3) asphalt covered by basalt gravel, and 4) old asphalt. Using the derived statistical normalized radar cross-section models, two novel applications are proposed. First, a procedure for synthesizing/simulating surface clutter is proposed. This procedure is subsequently used to simulate received power from surfaces comprising patches of one or multiple road surface conditions. Excellent agreement between simulation and measurement results is demonstrated. Second, a method for determining the optimal polarization of the electromagnetic sensing waves used in a single-polarized radar system is proposed. This method is based on factorizing the antenna polarization vector into two bounded parameters, allowing for numerical evaluation of the minima and maxima for targets with a specified scattering matrix. This method is further extended to work with statistical descriptions of scattering matrices by means of Monte Carlo simulations.

Abstract This paper presents a radar‐based algorithm for autonomous estimation of drone intention. The algorithm is based on radar's kinematic measurements, providing fast and robust intention estimation for multiple targets. The core idea of the proposed algorithm is to build intention‐specific features for each intention in advance and use them in actual drone surveillance situations. To effectively depict the trajectory characteristics of various intentions, the trajectory frequency is computed on multiple phase planes through Monte Carlo Simulations. Finally, a naive Bayes classifier is applied to integrate the trajectory likelihood in different phases and ultimately compute the intention likelihood of all intentions concerned. Numerical simulations for the three candidate intentions of image acquisition, smuggling, and kamikaze attack demonstrated the performance of the presented method. The simulation results show that one can estimate the true intention of a specific drone by comparing the value of each intention likelihood.

Abstract Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are however often complex, focusing on a single domain of radar, and requiring significant computational resources that prevent their deployment in embedded platforms which often have limited memory and computational resources. To address this issue, we present an adaptive magnitude thresholding approach for highlighting the region of interest in the multi-domain micro-Doppler signatures. The region of interest is beneficial to extract salient features, meanwhile it ensures the simplicity of calculations with less computational cost. The results for the proposed approach show an accuracy of up to 93.1% for six activities, outperforming state-of-the-art deep learning methods on the same dataset with an over tenfold reduction in both training time and memory footprint, and a twofold reduction in inference time compared to a series of deep learning implementations. These results can help bridge the gap toward embedded platform deployment.

Radar-based human motion and activity recognition is currently a topic of great research interest, as the aging population increases and older individuals prefer an independent lifestyle. This technology has a wide range of applications, such as fall detection in assisted living, gesture recognition for human-machine interfaces, and many more. Numerous studies exist on various approaches for radar-based activity capture and classification. However, most of these employ rather artificial data, often obtained in laboratory environments, and typically collected under particular conditions. Specifically, most research so far has aimed at distinguishing a predefined set of single activities with a defined start, stop and duration. This paper aims at drawing the attention to a so far less researched issue, one that will be of vital importance for future real-world application of radar-based human activity recognition: continuous activity recognition, i.e. recognizing specific activities in a stream of several sequential activities with unknown duration and arbitrary transitions between different classes of activities. A review on the current state of the art in this relatively new topic is given, followed by a discussion on future research directions.

Abstract Continuous Human Activity Recognition (HAR) in arbitrary directions is investigated in this paper using a network of five spatially distributed pulsed Ultra‐Wideband radars. While activities performed continuously and in unconstrained trajectories provide a more realistic and natural scenario for HAR, the network of radar sensors is proposed to address the issue of unfavourable or occluded perspectives when using only a single sensor. Different techniques to combine the relevant information from the multiple radars in the network are investigated, focussing on signal level fusion directly applied on Range‐Time maps, and the selection of radar nodes based on location and velocity of the target derived from multilateration processing and tracking. Recurrent Neural Networks with and without bidirectionality are used to classify the activities based on the micro‐Doppler (μD) spectrograms obtained for sensor fusion techniques. To assess classification performances, novel evaluation metrics accounting for the continuous nature of the sequence of activities and inherent imbalances in the dataset are proposed and compared with existing metrics. It is shown that the conventional accuracy metric may not capture all the important aspects for continuous HAR, and the proposed metrics can be considered for a more comprehensive evaluation.

Abstract This article presents the results of a series of measurements of multistatic radar signatures of small UAVs at L‐ and X‐bands. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter‐receiver and two bistatic receivers. NeXtRAD is capable of recording simultaneous bistatic and monostatic data with baselines and two‐way bistatic range of the order of a few kilometres. The paper presents an empirical analysis with range‐time plots and micro‐Doppler signatures of UAVs and birds of opportunity recorded at several hundred metres of distance. A quantitative analysis of the overall signal‐to‐noise ratio is presented along with a comparison between the power of the signal scattered from the drone body and blades. A simple study with empirically obtained features and four supervised‐learning classifiers for binary drone versus non‐drone separation is also presented. The results are encouraging with classification accuracy consistently above 90% using very simple features and classification algorithms.

Abstract The latest progress of the multiple‐input multiple‐output (MIMO) radar system developed for small drones detection at Beijing Institute of Technology is presented herein. A low‐cost S‐band MIMO scanning radar system is designed for the detection of small drones. A practical design optimisation and implementation of a sparse array covering the whole airspace, and a target‐based MIMO radar array calibration method to improve the signal‐to‐noise ratio of the target are proposed. An experiment in the microwave chamber was conducted to verify the designed sparse‐array beam‐pattern, and measurement campaigns for small drones were performed to test the system performance where the radar achieved detection and tracking of a small DJI‐Phantom 4 drone at a range of up to 5 km. Target features, including micro‐Doppler and radar cross‐section signatures were extracted to assist target classification. The experimental results indicate that the system has promising capability in small drones detection and tracking, despite the challenges of low dwell time on targets due to the scanning nature of the system.

Abstract Radar micro‐Doppler signatures have been proposed for human monitoring and activity classification for surveillance and outdoor security, as well as for ambient assisted living in healthcare‐related applications. A known issue is the performance reduction when the target is moving tangentially to the line of sight of the radar. Multiple techniques have been proposed to address this, such as multistatic radar and to some extent, interferometric (IF) radar. A simulator is presented to generate synthetic data representative of eight radar systems (monostatic, circular multistatic and in‐line multistatic [IM] and IF) to quantify classification performances as a function of aspect angles and deployment geometries. This simulator allows an unbiased performance evaluation of different radar systems. Six human activities are considered with signatures originating from motion‐captured data of 14 different subjects. The classification performances are analysed as a function of aspect angles ranging from 0° to 90° per activity and overall. It demonstrates that IF configurations are more robust than IM configurations. However, IM performs better at angles below 55° before IF configurations take over.

COVID-19, caused by SARS-CoV-2, is now a global pandemic disease. This outbreak has affected every aspect of life including work, leisure, and interaction with technology. Governments around the world have issued orders for travel bans, social distancing, and lockdown to control the spread of the virus and prevent strain on hospitals. This paper explores potential applications for radar-based non-contact remote respiration sensing technology that may help to combat the COVID-19 pandemic, and outlines potential advantages that may also help to reduce the spread of the virus. Applications arising from recent developments in the state of the art for transceiver and signal processing technologies will be discussed along associated technical implications. These applications include remote breathing rate monitoring, continuous identity authentication, occupancy detection, and hand gesture recognition. This paper also highlights future research directions that must be explored further to bring this innovative non-contact sensor technology into real-world implementation.

Suppressing the main-beam deceptive jamming in traditional radar systems is challenging. Furthermore, the observations corrupted by false targets generated by smart deceptive jammers, which are not independent and identically distributed because of the pseudo-random time delay. This in turn complicates the task of jamming suppression. In this paper, a new main-beam deceptive jamming suppression approach is proposed, using nonhomogeneous sample detection in the frequency diverse array-multiple-input and multiple-output radar with non-perfectly orthogonal waveforms. First, according to the time delay or range difference, the true and false targets are discriminated in the joint transmit-receive spatial frequency domain. Subsequently, due to the range mismatch, the false targets are suppressed through a transmit-receive 2-D matched filter. In particular, in order to obtain the jamming-plus-noise covariance matrix with high accuracy, a nonhomogeneous sample detection method is developed. Simulation results are provided to demonstrate the detection performance of the proposed approach.

Falls in the elderly represent a serious challenge for the global population. To address it, monitoring of daily living has been suggested, with radar emerging to be a useful platform for it due to its various benefits with acceptance and privacy. Here, we show results from the use of an S band radar for activity detection and the importance of selecting specific frequency bins to improve its suitability for human movement classification. The use of feature selection to improve detection of key activities such as falls has been presented. Initial results of 65% are improved to 85% and further to 90% with the aforementioned methods.

This study presents preliminary results about the multi-sensory recognition of indoor daily activities and fall detection, to monitor the well-being of older people at risk of physical and cognitive chronic health conditions. Five different sensors, continuous wave (CW) radar, frequency-modulated CW (FMCW) radar, and inertial measurement unit comprising an accelerometer, gyroscope, and magnetometer were used to simultaneously collect data from 20 subjects performing 10 activities. Rather than using all of the available sensors, it is more efficient and economical to select part of them to maximise the classification accuracy and avoid unnecessary computation to process information if it is not salient. Each individual sensor and several sensor combinations are trained with a quadratic-kernel support vector machine classifier. In addition, they are validated with an improved statistical approach, which uses data from unknown participants to test model rather than random cross-validation to verify if the model generalises well for unknown subjects. Furthermore, the most suitable sensor combinations are derived for each specific group of tested subjects selected (e.g. the oldest, youngest, tallest, and shortest sub-groups of participants out of the entire group).

In an era where man-machine interaction increasingly uses remote sensing, gesture recognition through use of the micro-Doppler (mD) effect is an emerging application which has attracted great interest. It is a sensible solution and here the authors show its potential for detecting aperiodic human movements. In this study, the authors classify ten hand gestures with a set of handcrafted features using simulated mD signatures generated from Kinect skeleton data. Data augmentation in the form of synthetic minority oversampling technique has been applied to create synthetic samples and classified with the support vector machine and K-nearest neighbour classifier with classification rate of 71.1 and 51% achieved. Finally, using weights generated by an action pair based one vs. one classification layer improves classification accuracy by 24.7 and 28.4%.

Remote health monitoring is a key component in the future of healthcare with predictive and fall risk estimation applications required in great need and with urgency. Radar, through the exploitation of the micro-Doppler effect, is able to generate signatures that can be classified automatically. In this work, features from two different radar systems operating at C band and K band have been used together co-operatively to classify ten indoor human activities with data from 20 subjects with a support vector machine classifier. Feature selection has been applied to remove redundancies and find a set of salient features for the radar systems, individually and in the fused scenario. Using the aforementioned methods, we show improvements in the classification accuracy for the systems from 75 and 70% for the radar systems individually, up to 89% when fused.

This article presents an analysis of three multi-domain transformations applied to radar data of a micro-drone operating in an open field, with a payload (between 200 and 600 g) and without a payload. Inferring the presence of a drone attempting to transport a payload beyond its normal operating conditions is a key enabler in prospective low altitude airspace security systems. Two scenarios of operation were explored, the first with the drone hovering and the second with the drone flying. Both were accomplished through real experimental trials, undertaken with the multistatic radar, NetRAD. The images generated as a result of the domain transformations were fed into a pretrained convolutional neural network (CNN), known as AlexNet and were treated as a six-class classification problem. Very promising accuracies were obtained, with on average 95.1% for the case of the drone hovering and 96.6% for the case of the drone flying. The activations that these variety of images triggered within the CNN were then visualised to better understand the specific features that the network was learning and distinguishing between, in order to successfully achieve classification.

This study presents preliminary results on the signature of a large offshore wind turbine collected using an active S-band radar. The intensity fluctuations of this clutter signal in the range and Doppler domain are characterised, looking at their evolution over time, statistics, and spread in the Doppler domain. The polarisation impact on the signatures is also discussed.

Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in one study such as walking, sitting, drinking and crawling. However, using the same set of features for multiple activities can be suboptimal per activity and not take into account the diversity of kinematic movements that could be captured by diverse features. In this paper, we propose a hierarchical classification approach that uses a large variety of features including but not limited to energy features like entropy and energy curve, physical features like centroid and bandwidth, image-based features like skewness extracted from multiple radar data domains. Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95.4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83.7% accuracy for ten subjects and 68.2% for a significantly larger group of subjects, i.e., 60 people.