System architecture for a compact high range resolution frequency comb OFDM radar

With increasing demands on resolution and flexibility in current and future radar applications, the focus is moving to digital radar systems such as orthogonal frequency-division multiplexing (OFDM) radars. To achieve high bandwidths and consequently a high range resolution, high sampling rates are needed. To overcome this constraint, an approach called frequency comb OFDM radar has been developed. This paper presents a novel, hardware efficient implementation of such a frequency comb OFDM radar including a novel way of comb generation. Special attention is put on the suppression of unwanted frequency components. Measurements which demonstrate the functionality of the hardware efficient radar system in combination with the frequency comb OFDM technique are presented

Doppler Shift Tolerance of Accumulation and Outer Coding in MIMO-PMCW Radar

Phase-modulated continuous wave (PMCW) has been widely regarded as a promising modulation scheme for radar systems, e.g., in highly automated driving (HAD) applications. Although the so-called outer coding can efficiently enable the multiple-input-multiple-output (MIMO) operation of PMCW-based radar systems, the yielded processing gain in this multiplexing approach may be reduced at increasing Doppler shifts. In this context, this letter introduces a normalized Doppler shift parameter that enables predicting the Doppler-shift-induced degradation of the processing gain in a MIMO-PMCW radar system. Finally, simulation and measurement results confirm the usefulness of the introduced parameter in desigining MIMO-PMCW radars

Bistatic OFDM-based Joint Radar-Communication: Synchronization, Data Communication and Sensing

This article introduces a bistatic joint radar-communication (RadCom) system based on orthogonal frequency-division multiplexing (OFDM). In this context, the adopted OFDM frame structure is described and system model encompassing time, frequency, and sampling synchronization mismatches between the transmitter and receiver of the bistatic system is outlined. Next, the signal processing approaches for synchronization and communication are discussed, and radar sensing processing approaches using either only pilots or a reconstructed OFDM frame based on the estimated receive communication data are presented. Finally, proof-of-concept measurement results are presented to validate the investigated system and a trade-off between frame size and the performance of the aforementioned processing steps is observed.Comment: Accepted for presentation at the focused session "Joint Communication and Radar Sensing - a step towards 6G'' of the EuMW 202

Enabling Joint Radar-Communication Operation in Shift Register-Based PMCW Radars

This article introduces adaptations to the conventional frame structure in binary phase-modulated continuous wave (PMCW) radars with sequence generation via linear-feedbck shift registers and additional processing steps to enable joint radar-communication (RadCom) operation. In this context, a preamble structure based on pseudorandom binary sequences (PRBSs) that is compatible with existing synchronization algorithms is outlined, and the allocation of pilot PRBS blocks is discussed. Finally, results from proof-of-concept measurements are presented to illustrate the effects of the choice of system and signal parameters and validate the investigated PMCW-based RadCom system and synchronization strategy.Comment: Accepted for presentation at the focused session "Automotive PMCW Radars'' of the EuMW 202

Radar Target Simulation for Vehicle-in-the-Loop Testing

Automotive radar sensors play a vital role in the current development of autonomous driving. Their ability to detect objects even under adverse conditions makes them indispensable for environment-sensing tasks in autonomous vehicles. As their functional operation must be validated in-place, a fully integrated test system is required. Radar Target Simulators (RTS) are capable of executing end-of-line, over-the-air validation tests by looping back a received and afterward modified radar signal and have been incorporated into existing Vehicle-in-the-Loop (ViL) test beds before. However, the currently available ViL test beds and the RTS systems that they consist of lack the ability to generate authentic radar echoes with respect to their complexity. The paper at hand reviews the current development stage of the research as well as commercial ViL and RTS systems. Furthermore, the concept and implementation of a new test setup for the rapid prototyping and validation of ADAS functions is presented. This represents the first-ever integrated radar validation test system to comprise multiple angle-resolved radar target channels, each capable of generating multiple radar echoes. A measurement campaign that supports this claim has been conducted

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

The applicability of sensor-based human activity recognition in sports has been repeatedly shown for laboratory settings. However, the transferability to real-world scenarios cannot be granted due to limitations on data and evaluation methods. On the example of football shot and pass detection against a null class we explore the influence of those factors for real-world event classification in field sports. For this purpose we compare the performance of an established Support Vector Machine (SVM) for laboratory settings from literature to the performance in three evaluation scenarios gradually evolving from laboratory settings to real-world scenarios. In addition, three different types of neural networks, namely a convolutional neural net (CNN), a long short term memory net (LSTM) and a convolutional LSTM (convLSTM) are compared. Results indicate that the SVM is not able to reliably solve the investigated three-class problem. In contrast, all deep learning models reach high classification scores showing the general feasibility of event detection in real-world sports scenarios using deep learning. The maximum performance with a weighted f1-score of 0.93 was reported by the CNN. The study provides valuable insights for sports assessment under practically relevant conditions. In particular, it shows that (1) the discriminative power of established features needs to be reevaluated when real-world conditions are assessed, (2) the selection of an appropriate dataset and evaluation method are both required to evaluate real-world applicability and (3) deep learning-based methods yield promising results for real-world HAR in sports despite high variations in the execution of activities