scholarly journals Spiking Neural Network for Fourier Transform and Object Detection for Automotive Radar

2021 ◽  
Vol 15 ◽  
Author(s):  
Javier López-Randulfe ◽  
Tobias Duswald ◽  
Zhenshan Bing ◽  
Alois Knoll
Keyword(s):  
Fourier Transform ◽  
Temporal Structure ◽  
Autonomous Driving ◽  
Radar Data ◽  
Radar Signal ◽  
Constant False Alarm Rate ◽  
Automotive Radar ◽  
Sensory Data ◽  
Low Energy Consumption ◽  
Novel Algorithms

The development of advanced autonomous driving applications is hindered by the complex temporal structure of sensory data, as well as by the limited computational and energy resources of their on-board systems. Currently, neuromorphic engineering is a rapidly growing field that aims to design information processing systems similar to the human brain by leveraging novel algorithms based on spiking neural networks (SNNs). These systems are well-suited to recognize temporal patterns in data while maintaining a low energy consumption and offering highly parallel architectures for fast computation. However, the lack of effective algorithms for SNNs impedes their wide usage in mobile robot applications. This paper addresses the problem of radar signal processing by introducing a novel SNN that substitutes the discrete Fourier transform and constant false-alarm rate algorithm for raw radar data, where the weights and architecture of the SNN are derived from the original algorithms. We demonstrate that our proposed SNN can achieve competitive results compared to that of the original algorithms in simulated driving scenarios while retaining its spike-based nature.

scholarly journals Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3410
Author(s):  
Claudia Malzer ◽  
Marcus Baum
Keyword(s):  
Moving Objects ◽  
Hierarchical Cluster ◽  
Autonomous Driving ◽  
Radar Data ◽  
Background Information ◽  
Automotive Radar ◽  
Data Set ◽  
Radar Sensors ◽  
Small Clusters ◽  
Cluster Level

High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and density of available data points across different scans. Modified versions of the density-based clustering method DBSCAN have mostly been used so far, while hierarchical approaches are rarely considered. In this article, we explore the applicability of HDBSCAN, a hierarchical DBSCAN variant, for clustering radar measurements. To improve results achieved by its unsupervised version, we propose the use of cluster-level constraints based on aggregated background information from cluster candidates. Further, we propose the application of a distance threshold to avoid selection of small clusters at low hierarchy levels. Based on exemplary traffic scenes from nuScenes, a publicly available autonomous driving data set, we test our constraint-based approach along with other methods, including label-based semi-supervised HDBSCAN. Our experiments demonstrate that cluster-level constraints help to adjust HDBSCAN to the given application context and can therefore achieve considerably better results than the unsupervised method. However, the approach requires carefully selected constraint criteria that can be difficult to choose in constantly changing environments.

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

Vehicles ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 257-271
Author(s):  
Axel Diewald ◽  
Clemens Kurz ◽  
Prasanna Venkatesan Kannan ◽  
Martin Gießler ◽  
Mario Pauli ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Development Stage ◽  
Test System ◽  
Radar Signal ◽  
Radar Target ◽  
Current Development ◽  
Automotive Radar ◽  
Radar Echoes ◽  
Test Beds

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.

scholarly journals Multi-Input Deep Learning Based FMCW Radar Signal Classification

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1144
Author(s):  
Daewoong Cha ◽  
Sohee Jeong ◽  
Minwoo Yoo ◽  
Jiyong Oh ◽  
Dongseog Han
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Continuous Wave ◽  
Autonomous Driving ◽  
Radar Data ◽  
Classification Performance ◽  
Radar Signal ◽  
Fmcw Radar ◽  
Input Structure ◽  
Single Input

In autonomous driving vehicles, the emergency braking system uses lidar or radar sensors to recognize the surrounding environment and prevent accidents. The conventional classifiers based on radar data using deep learning are single input structures using range–Doppler maps or micro-Doppler. Deep learning with a single input structure has limitations in improving classification performance. In this paper, we propose a multi-input classifier based on convolutional neural network (CNN) to reduce the amount of computation and improve the classification performance using the frequency modulated continuous wave (FMCW) radar. The proposed multi-input deep learning structure is a CNN-based structure using a distance Doppler map and a point cloud map as multiple inputs. The classification accuracy with the range–Doppler map or the point cloud map is 85% and 92%, respectively. It has been improved to 96% with both maps.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

scholarly journals A Multi-Stage Clustering Framework for Automotive Radar Data

2019 ◽  
Author(s):  
Nicolas Scheiner ◽  
Nils Appenrodt ◽  
Jurgen Dickmann ◽  
Bernhard Sick
Keyword(s):  
Radar Data ◽  
Automotive Radar ◽  
Multi Stage

scholarly journals Signal processing algorithm of ship navigation radar based on azimuth distance monitoring

2019 ◽  
Vol 10 ◽  
pp. 12
Author(s):  
Yuxin Qin ◽  
Yu Chen
Keyword(s):  
Signal Processing ◽  
Processing Algorithm ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Good Precision ◽  
Clutter Suppression ◽  
Constant False Alarm Rate ◽  
Sea Clutter ◽  
Signal Processing Algorithm ◽  
Ship Navigation

The effect of ship navigation radar signal processing has a great impact on the overall performance of the radar system. In this paper, the signal processing algorithm is studied. Firstly, the principle of radar azimuth and distance monitoring is introduced, then the pulse accumulation algorithm and median filtering algorithm are analyzed, and finally a sea clutter suppression algorithm based on sensitivity time control (STC) and a rain and snow clutter suppression algorithm based on constant false alarm rate are designed to improve the target monitoring performance of radar. In the test of the algorithm, the radar signal processing algorithm designed in this study has good precision as monitoring error of the target's azimuth and distance is controlled within 1%; and it also has a better suppression effect of sea clutter and rain and snow clutter, which can suppress the clutter well, improve the target clarity, and ensure the safe navigation of the ship. The experiment proves the effectiveness of the proposed algorithm and provides some theoretical basis for the better processing of radar signals, which is beneficial to improve the environment perception ability of ships in harsh environments and promote the further development of the navigation industry.

scholarly journals Nyquist Zone Index and Chirp Rate Estimation of LFM Signal Intercepted by Nyquist Folding Receiver Based on Random Sample Consensus and Fractional Fourier Transform

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1477 ◽  
Author(s):  
Xinqun Liu ◽  
Tao Li ◽  
Xiaolei Fan ◽  
Zengping Chen
Keyword(s):  
Fourier Transform ◽  
Random Sample ◽  
Fractional Fourier Transform ◽  
Estimation Method ◽  
Ultra Wideband ◽  
Estimation Methods ◽  
Radar Signal ◽  
Chirp Rate ◽  
Time Frequency ◽  
Random Sample Consensus

The Nyquist folding receiver (NYFR) can achieve a high-probability interception of an ultra-wideband (UWB) signal with fewer devices, while the output of the NYFR is converted into a hybrid modulated signal of the local oscillator (LO) and the received signal, which requires the matching parameter estimation methods. The linear frequency modulation (LFM) signal is a typical low probability of intercept (LPI) radar signal. In this paper, an estimation method of both the Nyquist Zone (NZ) index and the chirp rate for the LFM signal intercepted by NYFR was proposed. First, according to the time-frequency characteristics of the LFM signal, the accurate NZ and the rough chirp rate was estimated based on least squares (LS) and random sample consensus (RANSAC). Then, the information of the LO was removed from the hybrid modulated signal by the known NZ, and the precise chirp rate was obtained by using the fractional Fourier transform (FrFT). Moreover, a fast search method of FrFT optimal order was presented, which could obviously reduce the computational complexity. The simulation demonstrated that the proposed method could precisely estimate the parameters of the hybrid modulated output signal of the NYFR.

Experimental study on low‐THz automotive radar signal attenuation during snowfall

2019 ◽  
Vol 13 (9) ◽  
pp. 1421-1427 ◽  
Author(s):  
Fatemeh Norouzian ◽  
Emidio Marchetti ◽  
Edward Hoare ◽  
Marina Gashinova ◽  
Costas Constantinou ◽  
...  
Keyword(s):  
Experimental Study ◽  
Radar Signal ◽  
Signal Attenuation ◽  
Automotive Radar
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