Newtonized Orthogonal Matching Pursuit for High-Resolution Target Detection in Sparse OFDM ISAC Systems

Integrated Sensing and Communication (ISAC) is a technology paradigm that combines sensing capabilities with communication functionalities in a single device or system. In vehicle-to-everything (V2X) sidelink, ISAC can provide enhanced safety by allowing vehicles to not only communicate with one another but also sense the surrounding environment by using sidelink signals. In ISAC-capable V2X sidelink, the random resource allocation results in an unstructured and sparse distribution of time and frequency resources in the received orthogonal frequency division multiplexing (OFDM) grid, leading to degraded radar detection performance when processed using the conventional 2D-FFT method. To address this challenge, this paper proposes a high-resolution off-grid radar target detection algorithm irrespective of the OFDM grid structure. The proposed method utilizes the Newtonized orthogonal matching pursuit (NOMP) algorithm to effectively detect weak targets masked by the sidelobes of stronger ones and accurately estimates off-grid range and velocity parameters with minimal resources through Newton refinements. Simulation results demonstrate the superior performance of the proposed NOMP-based target detection algorithm compared to existing compressed sensing (CS) methods in terms of detection probability, resolution, and accuracy. Additionally, experimental validation is performed using a bi-static radar setup in a semi-anechoic chamber. The measurement results validate the simulation findings, showing that the proposed algorithm significantly enhances target detection and parameter estimation accuracy in realistic scenarios.

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.

Accelerating Innovation in 6G Research: Real-Time Capable SDR System Architecture for Rapid Prototyping

The next global mobile communication standard 6G strives to push the technological limits of radio frequency (RF) communication even further than its predecessors: Data rates beyond 100 Gbit/s, RF bandwidths above 1 GHz, and sub-millisecond latency necessitate very high performance development tools to enable the extent of innovation required for 6G's likely features. We propose a new SDR firmware and software architecture designed explicitly to meet these challenging requirements. It relies on Ethernet and commercial off-the-shelf network and server components to maximize flexibility and to reduce costs. We analyze state-of-the-art solutions (USRP X440 and other RFSoC-based systems), derive architectural design goals, explain resulting design decision in detail, and exemplify our architecture's implementation on the XCZU48DR RFSoC. Finally, we prove its performance via measurements and outline how the architecture surpasses the state-of-the-art with respect to sustained RF recording while maintaining high Ethernet bandwidth efficiency. Building a micro-Doppler radar example, we demonstrate its real-time and rapid application development capabilities.

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.

Estimation of Signal Parameters using Deep Convolutional Neural Networks

This paper introduces a Deep Learning approach for signal parameter estimation in the context of wireless channel modeling. Our work is capable of multidimensional parameter estimation from a signal containing an unknown number of paths. The signal parameters are estimated relative to a predefined grid, providing quasi grid-free, hence, more accurate estimates than previous grid-limited approaches. It requires no prior knowledge of the number of paths, giving it an advantage in terms of complexity compared to existing solutions. Along with the description, we provide an initial performance analysis and a comparison with State-of-the-Art techniques and discuss future research directions.

Measurement Testbed for Radar and Emitter Localization of UAV at 3.75 GHz

This paper presents an experimental measurement platform for the research and development of unmanned aerial vehicles (UAVs) localization algorithms using radio emission and reflectivity. We propose a cost-effective, flexible testbed made from commercial off-the-shelf (COTS) devices to allow academic research regarding the upcoming integration of UAV surveillance in existing mobile radio networks in terms of integrated sensing and communication (ISAC). The system enables nanosecond-level synchronization accuracy and centimeter-level positioning accuracy for multiple distributed sensor nodes and a mobile UAV-mounted node. Results from a real-world measurement in a 16 km2 urban area demonstrate the system's performance with both emitter localization as well as with the radar setup.