Bistatic Micro-Doppler Analysis of a Vertical Takeoff and Landing (VTOL) Drone in ICAS Framework

Integrated Communication and Sensing (ICAS) is a key technology that enables sensing functionalities within the next-generation mobile communication (6G). Joint design and optimization of both functionalities could allow coexistence, therefore it advances toward joint signal processing and using the same hardware platform and common spectrum. Contributing to ICAS sensing, this paper presents the measurement and analysis of the micro-Doppler signature of Vertical Takeoff and Landing (VTOL) drones. Measurement is performed with an OFDM-like communication signal and bistatic constellation, which is a typical case in ICAS scenarios. This work shows that micro-Doppler signatures can be used to precisely distinguish flight modes, such as take-off, landing, hovering, transition, and cruising.

Resolution-Adaptive Micro-Doppler Spectrogram for Human Activity Recognition

The rising demand for remote-sensing systems for detecting hazardous situations has led to increased interest in radar-based human activity recognition (HAR). Conventional radar-based HAR methods predominantly rely on micro-Doppler spectrograms for recognition tasks. However, spectrograms frequently fail to effectively capture micro-Doppler signatures because of their limited linear resolution. To address this limitation, we propose a time--frequency domain representation method that adaptively adjusts the resolution based on activity characteristics. This approach nonlinearly transforms the resolution to focus on the most relevant frequency range for micro-Doppler signatures. We validate the proposed method by training deep-learning-based HAR models on datasets generated using the adaptive representation method. Experimental results demonstrate that the models trained using the proposed method achieve superior recognition accuracy compared with those trained using conventional methods.

SDR-Based Metal Classification using Spectrogram Images from Micro-Doppler Signatures

Metallic materials such as brass, copper, and aluminum are used in numerous applications, including industrial manufacturing. The vibration characteristics of these objects are unique and can be used to identify these objects from a distance. This research presents a methodology for detecting and classifying these metallic objects using the vibration dynamics induced by their micro-Doppler signatures. The proposed approach utilizes image processing techniques to extract pivotal features from spectrograms. These spectrograms originate from micro-Doppler signatures of data collected during controlled laboratory experiments where signals were transmitted towards vibrating metal sheets, and the ensuing reflections were recorded using a software-defined radio (SDR). The spectrogram data was augmented using geometric transformation to train a convolutional neural network (CNN) based machine learning model for object classification. The results indicate that the proposed CNN model achieved an accuracy of more than 95% in classifying metals into brass, copper, and aluminum. This research could be used to understand the foundations of classifying spectrogram images using micro-Doppler signatures for its applications towards enhancing the sensing capabilities in industrial and defense applications.

Generalizable Indoor Human Activity Recognition Method Based on Micro-Doppler Corner Point Cloud and Dynamic Graph Learning

Through-the-wall radar (TWR) human activity recognition can be achieved by fusing micro-Doppler signature extraction and intelligent decision-making algorithms. However, limited by the insufficient priori of tester in practical indoor scenarios, the trained models on one tester are commonly difficult to inference well on other testers, which causes poor generalization ability. To solve this problem, this paper proposes a generalizable indoor human activity recognition method based on micro-Doppler corner point cloud and dynamic graph learning. In the proposed method, DoG-{\mu}D-CornerDet is used for micro-Doppler corner extraction on two types of radar profiles. Then, a micro-Doppler corner filtering method based on polynomial fitting smoothing is proposed to maximize the feature distance under the constraints of the kinematic model. The extracted corners from the two types of radar profiles are concatenated together into three-dimensional point cloud. Finally, the paper proposes a dynamic graph neural network (DGNN)-based recognition method for data-to-activity label mapping. Visualization, comparison and ablation experiments are carried out to verify the effectiveness of the proposed method. The results prove that the proposed method has strong generalization ability on radar data collected from different testers.

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.

On Input Formats for Radar Micro-Doppler Signature Processing by Convolutional Neural Networks

Convolutional neural networks have often been proposed for processing radar Micro-Doppler signatures, most commonly with the goal of classifying the signals. The majority of works tend to disregard phase information from the complex time-frequency representation. Here, the utility of the phase information, as well as the optimal format of the Doppler-time input for a convolutional neural network, is analysed. It is found that the performance achieved by convolutional neural network classifiers is heavily influenced by the type of input representation, even across formats with equivalent information. Furthermore, it is demonstrated that the phase component of the Doppler-time representation contains rich information useful for classification and that unwrapping the phase in the temporal dimension can improve the results compared to a magnitude-only solution, improving accuracy from 0.920 to 0.938 on the tested human activity dataset. Further improvement of 0.947 is achieved by training a linear classifier on embeddings from multiple-formats.

Static Reflectivity and Micro-Doppler Signature of Drones for Distributed ICAS

The integration of wireless communication and radar sensing is now getting a huge interest from researchers of two big societies, wireless communication and radar. The road map to the final goal and individual solutions to the challenges might differ in developing the Integrated Communication and Sensing (ICAS) system. However, the electromagnetic signature of the targets will be still valid for all variants of the ICAS system because the detection, localization and classification of the targets are involved. Therefore, this paper presents a study on static reflectivity and micro-Doppler signatures of drones together. To acquire the required data, the state-of-the-art measurement system, BiRa, is used.

Modelling Micro-Doppler Signature of Drone Propellers in Distributed ISAC

Integrated Sensing and Communication (ISAC) comprises detection and analysis of non-cooperative targets by exploiting the resources of the mobile radio system. In this context, micro-Doppler is of great importance for target classification, in order to distinguish objects with local movements. For developing algorithms for target classification, it is necessary to have a large amount of target signatures. Aiming to generate these data, this paper proposes a mathematical model for the micro-Doppler of drone rotating propellers, and validate the proposed model by comparing it to measured micro-Doppler. Results show that the proposed mathematical model can generate micro-Doppler data very similar to those from measurement data.

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.