M Li, M Stolz
This paper preserves individual reflection points in assigned grid cells and employs a model-based clustering window to ensure stability and high true positive rates.
A Manjunath, Ying Liu, Bernardo Henriques
introduces a postprocessing architecture designed to cluster and track multiple radar detections from a single object in complex scenarios.
F Roos,Dominik Kellner, Jens Klappstein,Juergen Dickmann
Leveraging high-resolution image radars to estimate vehicle orientation swiftly in critical traffic situations. It adapts the orientated bounding box algorithm and introduces a quality function to select the optimal bounding box.
C Knill, Alexander Scheel, Klaus Dietmanyer
It introduces a measurement model designed to track vehicles with rectangular shapes in various traffic scenarios, utilizing Doppler information for improved tracking.
D Steinhauser, Alexander Kamann, Patrick Held
The process includes identifying pedestrian reflections, applying a CLEAN algorithm, clustering, and azimuth angle estimation. Real radar measurements are compared to motion capture references, demonstrating the ability to capture time-dependent motion behavior of various body parts, including legs, arms, and the torso.
S Abdulatif,Qian Wei, Fady Aziz
Focuses on micro-Doppler (μ-D) and micro-Range (μ-R) signatures for target classification. The study compares classical learning methods, ensemble classifiers, and a Deep Convolutional Neural Network (DCNN).
A Scheel, Christina Knill, Klaus Dietmayer
It introduces a Labeled Multi-Bernoulli filter, a probabilistic approach that exploits detailed sensor information, resolves measurement-to-object association ambiguities, and supports multi-sensor setups for redundancy and wider fields of view.
N Scheiner, Nils Appenrodt, Jurgen Dickmann
It introduces a classifier ensemble approach, enriched by one-vs-all correction classifiers, to efficiently classify traffic participants and identify hidden object classes not seen during training.
P.Joseph, B.Morris and K. V. S. Hari
This letter reports the measurements and results of tests conducted on the detection of unmanned aircraft systems (UASs) or drones using a millimeter-wave (mmWave) automotive radar sensor.
W.D. van Eedena, J.P. de Villiers, R. J. Berndt, W.A.J. Nel, E. Blaschc
A combined Gaussian mixture model and hidden Markov model (HMM) is developed to distinguish between slow moving animal and human targets using mel-cepstrum coefficients.
M Li, M Stolz,Zhaofei Feng
Retains individual reflection points in different grid cells, utilizes a clustering window for similarity in three dimensions, and incorporates a model-based approach to ensure stable results with high true positive rates, regardless of parameters and object types.
T. Wagner, R. Feger, A. Stelzer
Present a grid-based clustering algorithm, which directly utilizes the gridded result of a three-dimensional fast Fourier transform.
D. Solomitckii, M. Gapeyenko, V. Semkin, S. Andreev, Y. Koucheryavy
In this work, we propose to exploit 5G millimeter-wave deployments to detect violating amateur drones. We argue that the prospective 5G infrastructure may provide all the necessary technology elements to support efficient detection of small-sized drones.
V. R. J. Deville; C. M. Lievers; J. H. Manton
This paper demonstrates the feasibility of using tracking radar technology to provide non-invasive axle-based vehicle classification. A new algorithm that extracts the physical characteristics of the vehicle from its radar signal return is presented.
K Yoneda, Naoya Hashimoto,Ryo Yanase
Radar Perception and signal filtering processing in Extreme weather.
T Nagasaku,Kenji Kogo, Hiroshi Shinoda
Use chip to prove 77 Ghz Radar have outstanding performance for autonomous driving. Self-designed Chip applied in this paper.
Dominik Kellner, Michael Barjenbruch, Jens Klappstein, Jürgen Dickmann, Klaus Dietmayer
This approach analyzes the object's velocity profile obtained from two Doppler radar sensors, reducing reaction time in safety and advanced driving assistance applications.
F. Weishaupt, N. Appenrodt, J. F. Tilly, J. Dickmann , D. Heberling
For higher levels of driving automation, presenting a radar-based method utilizing a lower data level than typical peak detection point clouds (e.g. Constant False Alarm Rate (CFAR) detection) to address some challenges in static environment perception.
P. Zaumseil , D. Steinhauser , P. Held , A. Kamann, T. Brandmeier
This paper presents the application of a back projection algorithm to frequency-modulated continuous wave (FMCW) radar for increasing target localization in the automotive field.
V. Janoudi, P. Schoeder, T. Grebner, D. Schwarz, C. Waldschmidt J. Dickmann, N. Appenrodt
This paper presents a radar network consisting of two individual MIMO radar sensors equipped with L-shaped physical antenna arrays. Furthermore, the paper discusses the performance of the resulting virtual aperture in the context of DoA estimation.
Lei Xing; Fei Peng; Rui Pan
In order to improve the environmental perception performance of millimeter wave radar in static situation and in motion, this paper analyzed reasons for the insufficient measurement of millimeter wave radar and improved filter methods to reduce the impact of noise or environment on millimeter wave radar.
M. Stolz, M. Li, Z. Feng, M. Kunert, W. Menzel
In this paper a new approach for automotive radar data clustering is presented.
D. Guimarães, C. H. Lim
Present a spectrum sensing technique that combines the intermediate detections from multiple small sliding sensing windows of the received radar signal into a final decision.
J.Noh, Y.Kwon, J.Lee, H. Baek , and J. Lim
Propose adaptive-sliding-window-based energy detection (ASED) that adaptively applies the sliding window and estimates the pulsewidth using triple thresholds
N.Litov, H.Zhou and A.Mehta
Present a novel, low cost, high accuracy, high frequency radar cross section characterization method using commercially available.
E. Hyun, Y. Jin
Propose a Doppler-spectrum feature-based human–vehicle classification scheme for anFMCW(frequency-modulated continuous wave) radar sensor.
E. Schubert, F. Meinl, M. Kunert, W. Menzel
This paper copes with the clustering of all challenges into appropriate groups in order to exploit the advantages of multidimensional object size estimation and object classification
Markus Andres, Peter Feil, Wolfgang Menzel
3D scattering center analysis enables to derive radar system specifications for future automotive applications. Synthetic aperture radar (SAR) and SAR in combination with digital beam forming (DBF) will be presented to extract the location of scattering centers at 77 GHz.
Cheng Wang; Jianfei Tong; Gaofeng Cui; Xiaoyan Zhao; Weidong Wang
This paper explores spectrum sharing challenges between V2V communication and automotive radar and introduces an interference cancellation algorithm to enhance V2V communication performance.
Y. Almalioglu; M. Turan; C. X. Lu; N. Trigoni; A. Markham
Introduce Milli-RIO, an MMWave radar-based solution making use of a single-chip low-cost radar and inertial measurement unit sensor to estimate six-degrees-of-freedom ego-motion of a moving radar.
J. Schlichen-maier; M. Steiner; T. Grebner; C. Waldschmidt
A method for collaborative target list processing of multiple radar sensors simultaneously estimating multiple extended targets is presented and discussed.
F. Weishaupt; J. F. Tilly; N. Appenrodt; J. Dickmann; D. Heberling
This paper demonstrates how to achieve the necessary stability in the scattering information of an automotive multiple input multiple output (MIMO) millimeter wave radar.
Xingyu Zhang; Liping Guo; Weiwei Meng
In this paper, the traffic information perception technology based on the radar and video fusion is investigated, which organically integrates the detection data of millimeter wave radar and visual sensors.
K Werber, Matthias app, Jens Klappstein, Jurgen Dickmann
The first approach utilizes amplitude-based information, while the second employs an occupancy grid-mapping approach with a modified inverse sensor measurement model.
L Sless, Gilad Cohen
Employs deep learning techniques to learn the inverse sensor model for occupancy grid mapping from clustered radar data
M Rapp, Markus Hahn, Jurgen Dickmann, Klaus Dietmayer
It presents a robust Monte-Carlo based localization approach that incorporates stochastic analysis of past observations. The model employs a grid-based Markov chain and extends it with a Lévy process to assess reliability and prediction for each grid cell.
O Schumann, Markus Hahn, Jurgen Dickmann
Unlike previous methods that relied on clustered reflections and manually selected features, this approach eliminates the need for clustering algorithms and manual feature selection.
D. M. Zasada, M. C. Arabadjis, L. D. Tromp
This paper discusses simulating a cognitive perception-action cycle and applying its algorithms to a notional medium PRF fighter radar featuring an adaptive advanced electronically scanned array.
S. Haykin
Survey on cogonitive Radar: Three ingredients 1) intelligent signal processing,; 2) feedback from the receiver to the transmitter; and 3) preservation of the information content of radar returns (tracking)
Huan Zhu, Zhangqin Zhu, Feng Su, Jianzhi Zhang
the key algorithms in intelligent cognitive radar and the effects in improving the radar performance of these algorithms are discussed
Donglin Tan; Junfeng Wang
A novel cognitive waveform designing method is presented for radar target detection based on cognitive radar theory. (to effectively improve the SNR)
M. Z. Ikram, A. Ahmad
In this paper, we present a new method for sensor calibration that is easy to execute, is repeatable with minimal supervision, and yields close-toaccurate results.
D. Kellner, M. Barjenbruch, J. Klappstein, J. Dickmann, K. Dietmayer
A robust algorithm using radar sensors to instantly determine the complete 2D motion state of the ego-vehicle. It evaluates the relative motion between at least two Doppler radar sensors and their received stationary reflections (targets).
B.Zhu, Y.Sun, J.Zhao, S. Zhang, P. Zhang, D. Song
1.the millimeter-wave radar in-the-loop (mmRil) testing for intelligent vehicles is proposed in this study. 2.A geometric model of radar target detection and a power attenuation model under bad weather conditions are built to make the simulated target more similar to the real target in terms of its attributes.
Arien P. Sligar
In this paper, an investigation of a machine learning based radar perception algorithm for object detection is implemented, along with a novel, automated workflow for generating large-scale virtual datasets used for training and testing.
M. Dudek, R. Wahl, D. Kissinger, R. Weigel, G. Fischer
In this paper proposes a millimeter-wave radar system simulation environment for frequency-modulated continuous-wave (FMCW) radar using a 3D ray tracing channel simulator is presented.
J. Dickmann, J. Klappstein, M. Hahn, N. Appenrodt, H. Bloecher, K. Werber, A. Sailer
An overview on state of the art automotive radar usage is presented and the changing requirements from detection and ranging towards radar based environmental understanding for highly automated and autonomous driving deduced.
J. S. Patel, F. Fioranelli, D. Anderson
This review explores radar-based techniques currently utilised in the literature to monitor small unmanned aerial vehicle (UAV) or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market.
J. Dickmann, J. Klappstein, M. Hahn, M. Muntzinger, N. Appenrodt, C. Brenk, A. Sailer
A survey. The paper will provide an overview on state of the art automotive radar usage on the basis of the DAIMLER car platforms, will give an outline on future requirements for highly automated driving and will present recent approaches in radar based environmental perception.
A Davoli; G. Guerzoni; G. M. Vitetta
A comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed