Static Background Removal in Vehicular Radar: Filtering in Azimuth-Elevation-Doppler Domain

A significant challenge in autonomous driving systems lies in image understanding within complex environments, particularly dense traffic scenarios. An effective solution to this challenge involves removing the background or static objects from the scene, so as to enhance the detection of moving targets as key component of improving overall system performance. In this paper, we present an efficient algorithm for background removal in automotive radar applications, specifically utilizing a frequency-modulated continuous wave (FMCW) radar. Our proposed algorithm follows a three-step approach, encompassing radar signal preprocessing, three-dimensional (3D) ego-motion estimation, and notch filter-based background removal in the azimuth-elevation-Doppler domain. To begin, we model the received signal of the FMCW multiple-input multiple-output (MIMO) radar and develop a signal processing framework for extracting four-dimensional (4D) point clouds. Subsequently, we introduce a robust 3D ego-motion estimation algorithm that accurately estimates radar ego-motion speed, accounting for Doppler ambiguity, by processing the point clouds. Additionally, our algorithm leverages the relationship between Doppler velocity, azimuth angle, elevation angle, and radar ego-motion speed to identify the spectrum belonging to background clutter. Subsequently, we employ notch filters to effectively filter out the background clutter. The performance of our algorithm is evaluated using both simulated data and extensive experiments with real-world data. The results demonstrate its effectiveness in efficiently removing background clutter and enhacing perception within complex environments. By offering a fast and computationally efficient solution, our approach effectively addresses challenges posed by non-homogeneous environments and real-time processing requirements

System Design of Advanced Multi-Beam and Multi-Range Automotive Radar

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 김성철.As the number of vehicles on the road is increased, the incidence of traffic accident is gradually increased and the number of death on roads is also increased. Most accidents are due to carelessness of the driver. If the vehicle can actively recognize the dangerous situation and alert the driver to avoid accident, it will be a great help to the driver. As concern for safety and driver assistance increases, needs for driver assistance system (DAS) are consistently increasing. Moreover, with the grooming demand for autonomous driving, there has been paid a great attention to the incorporation of multiple sensors. Various sensors for safety and convenience are being introduced for automobiles. The detection performance of the automotive radar looks outstanding compared to other sensors such as Lidar, camera, and ultrasonic sensors, in poor weather conditions or environmental conditions of the roads. Among many applications using automotive radars, the adaptive cruise control (ACC) and the autonomous emergency braking (AEB) using forward looking radars are the most basic functions for safety and convenience. Using ACC and AEB functions, drivers can be guaranteed safety as well as convenience when visibility is poor under bad weather conditions. Generally, the radar system for ACC and AEB had been composed of singe longrange radar (LRR) and two of short-range radar (SRR) and the system cost was very expensive. However, the cost can be lowered by the concept of multi-beam, multirange (MBMR) radar which consist of integrated narrow long range beam and wide short range beam in a single radar sensor. In this dissertation, we propose an advanced MBMR radar for ACC and AEB using 77 GHz band and highly integrated RF ICs. The detection specifications are investii gated base on theoretical radar principles and effective design concepts are suggested to satisfy the specifications. We implemented an actually working forward looking MBMR radar and performed experiments to verify the detection performance. To overcome the limitation of radar hardware resources for cost-effective design, we propose novel signal processing schemes to recognize environment on roads which are regarded as impossible with automotive radar. Characteristics of an iron tunnel which deteriorate the detection performance of the radar are analyzed and a measure for the recognition is proposed. Moreover, the recognition method is expanded to harmonic clutters which are caused by man-made structures on roads containing periodic structures such as iron tunnels, guardrails, and sound-proof wall. The harmonic clutter suppression method is also proposed to enhance the quality of the received signal and improve the detection performance of the radar. All experiments are performed using the proposed MBMR radar to verify the detection performance and the usefulness of proposed signal processing methods for recognition and suppression of clutters on roads.1 Introduction 1 2 A Multi-Beam and Multi-Range FMCW Radar using 77 GHz Frequency Band for ACC and AEB 6 2.1 Introduction 6 2.2 System Design of Advanced MBMR Radar 7 2.3 Waveform and Signal Processing Structure Design 14 2.4 Advanced Singal Processing Technique for AEB 19 2.5 Design Results 20 2.6 Experimental Results 22 2.6.1 Anechoic Chamber 22 2.6.2 Field Test 27 2.7 Summary 29 3 Iron-tunnel Recognition 30 3.1 Introduction 30 3.2 Iron-Tunnel Recognition 32 3.2.1 Radar Model 32 3.2.2 Spectral Characteristics of an Iron-Tunnel 34 3.2.3 Measuring Spectrum Spreading 40 3.3 Experimental Result 45 3.3.1 Iron-Tunnel Recognition 45 3.3.2 Early Target Detection and Prevention of Target Drop 49 3.4 Summary 53 4 Clutter Suppression 55 4.1 Introduction 55 4.2 Clutter Recognition 57 4.2.1 Radar Model 57 4.2.2 Spectral Analysis of Road Environment 62 4.2.3 Proposed Clutter-recognition Method (Measuring Harmonics of Clutter) 64 4.3 Clutter Suppression 69 4.3.1 Proposed clutter suppression method 69 4.3.2 Verification using real data 71 4.4 Experimental results 74 4.5 Summary 81 5 Conclusion and Future Works 82 Bilbliography 85 Abstract (In Korean) 89Docto

Weakly supervised deep learning method for vulnerable road user detection in FMCW radar

Millimeter-wave radar is currently the most effective automotive sensor capable of all-weather perception. In order to detect Vulnerable Road Users (VRUs) in cluttered radar data, it is necessary to model the time-frequency signal patterns of human motion, i.e. the micro-Doppler signature. In this paper we propose a spatio-temporal Convolutional Neural Network (CNN) capable of detecting VRUs in cluttered radar data. The main contribution is a weakly supervised training method which uses abundant, automatically generated labels from camera and lidar for training the model. The input to the network is a tensor of temporally concatenated range-azimuth-Doppler arrays, while the ground truth is an occupancy grid formed by objects detected jointly in-camera images and lidar. Lidar provides accurate ranging ground truth, while camera information helps distinguish between VRUs and background. Experimental evaluation shows that the CNN model has superior detection performance compared to classical techniques. Moreover, the model trained with imperfect, weak supervision labels outperforms the one trained with a limited number of perfect, hand-annotated labels. Finally, the proposed method has excellent scalability due to the low cost of automatic annotation

A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics

We survey the current state of millimeterwave (mmWave) radar applications in robotics with a focus on unique capabilities, and discuss future opportunities based on the state of the art. Frequency Modulated Continuous Wave (FMCW) mmWave radars operating in the 76--81GHz range are an appealing alternative to lidars, cameras and other sensors operating in the near visual spectrum. Radar has been made more widely available in new packaging classes, more convenient for robotics and its longer wavelengths have the ability to bypass visual clutter such as fog, dust, and smoke. We begin by covering radar principles as they relate to robotics. We then review the relevant new research across a broad spectrum of robotics applications beginning with motion estimation, localization, and mapping. We then cover object detection and classification, and then close with an analysis of current datasets and calibration techniques that provide entry points into radar research.Comment: 19 Pages, 11 Figures, 2 Tables, TRO Submission pendin

Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges

Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of radar data presents some challenges: it is characterized by low resolution, sparsity, clutter, high uncertainty, and lack of good datasets. These challenges have limited radar deep learning research. As a result, current radar models are often influenced by lidar and vision models, which are focused on optical features that are relatively weak in radar data, thus resulting in under-utilization of radar's capabilities and diminishing its contribution to autonomous perception. This review seeks to encourage further deep learning research on autonomous radar data by 1) identifying key research themes, and 2) offering a comprehensive overview of current opportunities and challenges in the field. Topics covered include early and late fusion, occupancy flow estimation, uncertainty modeling, and multipath detection. The paper also discusses radar fundamentals and data representation, presents a curated list of recent radar datasets, and reviews state-of-the-art lidar and vision models relevant for radar research. For a summary of the paper and more results, visit the website: autonomous-radars.github.io

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter

This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conventional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter distribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range-Doppler domain. The proposed approach is based on a unified NN model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the neural network training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CA-CFAR), the ordered-statistic CFAR (OS-CFAR), and the adaptive normalized matched-filter (ANMF) detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.Comment: Accepted to IEEE Transactions on Aerospace and Electronic System

Radar-STDA: A High-Performance Spatial-Temporal Denoising Autoencoder for Interference Mitigation of FMCW Radars

With its small size, low cost and all-weather operation, millimeter-wave radar can accurately measure the distance, azimuth and radial velocity of a target compared to other traffic sensors. However, in practice, millimeter-wave radars are plagued by various interferences, leading to a drop in target detection accuracy or even failure to detect targets. This is undesirable in autonomous vehicles and traffic surveillance, as it is likely to threaten human life and cause property damage. Therefore, interference mitigation is of great significance for millimeter-wave radar-based target detection. Currently, the development of deep learning is rapid, but existing deep learning-based interference mitigation models still have great limitations in terms of model size and inference speed. For these reasons, we propose Radar-STDA, a Radar-Spatial Temporal Denoising Autoencoder. Radar-STDA is an efficient nano-level denoising autoencoder that takes into account both spatial and temporal information of range-Doppler maps. Among other methods, it achieves a maximum SINR of 17.08 dB with only 140,000 parameters. It obtains 207.6 FPS on an RTX A4000 GPU and 56.8 FPS on an NVIDIA Jetson AGXXavier respectively when denoising range-Doppler maps for three consecutive frames. Moreover, we release a synthetic data set called Ra-inf for the task, which involves 384,769 range-Doppler maps with various clutters from objects of no interest and receiver noise in realistic scenarios. To the best of our knowledge, Ra-inf is the first synthetic dataset of radar interference. To support the community, our research is open-source via the link \url{https://github.com/GuanRunwei/rd_map_temporal_spatial_denoising_autoencoder}

Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays

Massive MIMO (multiple-input multiple-output) is no longer a "wild" or "promising" concept for future cellular networks - in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies - once viewed prohibitively complicated and costly - is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin