Modeling Micro-Doppler Signature of Multi-Propeller Drones in Distributed ISAC

Integrated Sensing and Communication (ISAC) will be one key feature of future 6G networks, enabling simultaneous communication and radar sensing. The radar sensing geometry of ISAC will be multistatic since that corresponds to the common distributed structure of a mobile communication network. Within this framework, micro-Doppler analysis plays a vital role in classifying targets based on their micromotions, such as rotating propellers, vibration, or moving limbs. However, research on bistatic micro-Doppler effects, particularly in ISAC systems utilizing OFDM waveforms, remains limited. Existing methods, including electromagnetic simulations often lack scalability for generating the large datasets required to train machine learning algorithms. To address this gap, this work introduces an OFDM-based bistatic micro-Doppler model for multi-propeller drones. The proposed model adapts the classic thin-wire model to include bistatic sensing configuration with an OFDM-like signal. Then, it extends further by incorporating multiple propellers and integrating the reflectivity of the drone's static parts. Measurements were performed to collect ground truth data for verification of the proposed model. Validation results show that the model generates micro-Doppler signatures closely resembling those obtained from measurements, demonstrating its potential as a tool for data generation. In addition, it offers a comprehensive approach to analyzing bistatic micro-Doppler effects.

Through-the-Wall Radar Human Activity Micro-Doppler Signature Representation Method Based on Joint Boulic-Sinusoidal Pendulum Model

With the help of micro-Doppler signature, ultra-wideband (UWB) through-the-wall radar (TWR) enables the reconstruction of range and velocity information of limb nodes to accurately identify indoor human activities. However, existing methods are usually trained and validated directly using range-time maps (RTM) and Doppler-time maps (DTM), which have high feature redundancy and poor generalization ability. In order to solve this problem, this paper proposes a human activity micro-Doppler signature representation method based on joint Boulic-sinusoidal pendulum motion model. In detail, this paper presents a simplified joint Boulic-sinusoidal pendulum human motion model by taking head, torso, both hands and feet into consideration improved from Boulic-Thalmann kinematic model. The paper also calculates the minimum number of key points needed to describe the Doppler and micro-Doppler information sufficiently. Both numerical simulations and experiments are conducted to verify the effectiveness. The results demonstrate that the proposed number of key points of micro-Doppler signature can precisely represent the indoor human limb node motion characteristics, and substantially improve the generalization capability of the existing methods for different testers.

Synthesis of Through-Wall Micro-Doppler Signatures of Human Motions Using Generative Adversarial Networks

Narrowband radar micro-Doppler signatures are heavily used to identify and classify human activities. When the radar is operated in through-wall environments, the complex electromagnetic propagation phenomenology introduces considerable distortions in the micro-Doppler signatures through attenuation and multipath. The problem is particularly severe in inhomogeneous wall scenarios involving multiple wall layers, air gaps, or metal reinforcements. Through-wall radar data collection using simulations and measurements involves significant time and effort. In this paper, we propose an alternative method of synthesizing through-wall radar micro-Doppler signatures from their free space counterparts using the generative adversarial network (GAN). We train the GAN using radar micro-Doppler signatures generated from electromagnetic simulations. We generate the radar data for different human motions, along different orientations, and under diverse through-wall conditions. The synthetic radar micro-Dopplers generated from the neural networks are then evaluated for their realism using a denoising autoencoder, which shows an excellent realism score.

Interference Motion Removal for Doppler Radar Vital Sign Detection Using Variational Encoder-Decoder Neural Network

The treatment of interfering motion contributions remains one of the key challenges in the domain of radar-based vital sign monitoring. Removal of the interference to extract the vital sign contributions is demanding due to overlapping Doppler bands, the complex structure of the interference motions and significant variations in the power levels of their contributions. A novel approach to the removal of interference through the use of a probabilistic deep learning model is presented. Results show that a convolutional encoder-decoder neural network with a variational objective is capable of learning a meaningful representation space of vital sign Doppler-time distribution facilitating their extraction from a mixture signal. The approach is tested on semi-experimental data containing real vital sign signatures and simulated returns from interfering body motions. The application of the proposed network enhances the extraction of the micro-Doppler frequency corresponding to the respiration rate is demonstrated.

Sounding-Based Evaluation of Multi-Sensor ISAC Networks for Drone Applications: Measurement and Simulation Perspectives

With the upcoming multitude of commercial and public applications envisioned in the mobile 6G radio landscape using unmanned aerial vehicles (UAVs), integrated sensing and communication (ISAC) plays a key role to enable the detection and localization of passive objects with radar sensing, while optimizing the utilization of scarce resources. To explore the potential of future ISAC architectures with UAVs as mobile nodes in distributed multi-sensor networks, the system's fundamental capability to detect static and dynamic objects that reveal themselves by their bi-static back-scattering needs to be evaluated. Therefore, this paper addresses simulation- and measurement based data acquisition methods to gather knowledge about the bistatic reflectivity of single objects including their Micro-Doppler signature for object identification as well as the influence of multipath propagation in different environments on the localization accuracy and radar tracking performance. We show exemplary results from simulation models, bi-static reflectivity measurements in laboratory environment and real-flight channel sounding experiments in selected scenarios showcasing the potential of synthetic and measured data sets for development and evaluation of ISAC algorithms. The presented measurement data sets are publicly available to encourage the academic RF community to validate future algorithms using realistic scenarios alongside simulations models.

Sensing with OFDM Waveform at mmWave Band based on Micro-Doppler Analysis

Joint communication and sensing (JCAS) technology has been regarded as one of the innovations in the 6G network. With the channel modeling proposed by the 3rd Generation Partnership Project (3GPP) TR 38.901, this paper investigates the sensing capability using the millimeter-wave (mmWave) band with an orthogonal frequency division multiplexing (OFDM) waveform. Based on micro-Doppler (MD) analysis, we present two case studies, i.e., fan speed detection and human activity recognition, to demonstrate the target modeling with micro-motions, backscattering signal construction, and MD signature extraction using an OFDM waveform at 28 GHz. Simulated signatures demonstrate distinct fan rotation or human motion, and waveform parameters that affect the MD signature extraction are analyzed. Simulation results draw the validity of the proposed modeling and simulation methods, which also aim to facilitate the generation of data sets for various JCAS applications.

Near out-of-distribution detection for low-resolution radar micro-Doppler signatures

Near out-of-distribution detection (OOD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OOD use case for radar targets detection extensible to other kinds of sensors and detection scenarios. We emphasize the relevance of OOD and its specific supervision requirements for the detection of a multimodal, diverse targets class among other similar radar targets and clutter in real-life critical systems. We propose a comparison of deep and non-deep OOD methods on simulated low-resolution pulse radar micro-Doppler signatures, considering both a spectral and a covariance matrix input representation. The covariance representation aims at estimating whether dedicated second-order processing is appropriate to discriminate signatures. The potential contributions of labeled anomalies in training, self-supervised learning, contrastive learning insights and innovative training losses are discussed, and the impact of training set contamination caused by mislabelling is investigated.

Extraction of Unaliased High-Frequency Micro-Doppler Signature using FMCW radar

Micro-Doppler signature is a potent feature that has been used for target identification and micro-motion parameter estimation. The extraction of high frequency micro-Doppler signature from frequency modulated continuous wave (FMCW) radar along with the target range and velocity is the problem considered in this article. The severe aliasing of the high micro-Doppler frequency spread is circumvented by the fast time processing in the proposed method. The use of range-Doppler (RD) filtering and empirical mode decomposition (EMD) enables effective out-of-band and in-band noise suppression. Simulation studies and experimental results present the effectiveness of the proposed approach.

Neural Style Transfer Enhanced Training Support For Human Activity Recognition

This work presents an application of Integrated sensing and communication (ISAC) system for monitoring human activities directly related to healthcare. Real-time monitoring of humans can assist professionals in providing healthy living enabling technologies to ensure the health, safety, and well-being of people of all age groups. To enhance the human activity recognition performance of the ISAC system, we propose to use synthetic data generated through our human micro-Doppler simulator, SimHumalator to augment our limited measurement data. We generate a more realistic micro-Doppler signature dataset using a style-transfer neural network. The proposed network extracts environmental effects such as noise, multipath, and occlusions effects directly from the measurement data and transfers these features to our clean simulated signatures. This results in more realistic-looking signatures qualitatively and quantitatively. We use these enhanced signatures to augment our measurement data and observe an improvement in the classification performance by 5% compared to no augmentation case. Further, we benchmark the data augmentation performance of the style transferred signatures with three other synthetic datasets -- clean simulated spectrograms (no environmental effects), simulated data with added AWGN noise, and simulated data with GAN generated noise. The results indicate that style transferred simulated signatures well captures environmental factors more than any other synthetic dataset.

FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogram

Micro-Doppler signatures contain considerable information about target dynamics. However, the radar sensing systems are easily affected by noisy surroundings, resulting in uninterpretable motion patterns on the micro-Doppler spectrogram. Meanwhile, radar returns often suffer from multipath, clutter and interference. These issues lead to difficulty in, for example motion feature extraction, activity classification using micro Doppler signatures ($\mu$-DS), etc. In this paper, we propose a latent feature-wise mapping strategy, called Feature Mapping Network (FMNet), to transform measured spectrograms so that they more closely resemble the output from a simulation under the same conditions. Based on measured spectrogram and the matched simulated data, our framework contains three parts: an Encoder which is used to extract latent representations/features, a Decoder outputs reconstructed spectrogram according to the latent features, and a Discriminator minimizes the distance of latent features of measured and simulated data. We demonstrate the FMNet with six activities data and two experimental scenarios, and final results show strong enhanced patterns and can keep actual motion information to the greatest extent. On the other hand, we also propose a novel idea which trains a classifier with only simulated data and predicts new measured samples after cleaning them up with the FMNet. From final classification results, we can see significant improvements.