deep learning based object classification on automotive radar spectra

parti Annotating automotive radar data is a difficult task. The proposed method can be used for example 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). digital pathology? Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. The metallic objects are a coke can, corner reflectors, and different metal sections that are short enough to fit between the wheels. It can be observed that NAS found architectures with similar accuracy, but with an order of magnitude less parameters. features. Our approach works on both stationary and moving objects, which usually occur in automotive scenarios. Vol. optimization: Pareto front generation,, K.Deb, A.Pratap, S.Agarwal, and T.Meyarivan, A fast and elitist Therefore, comparing the manually-found NN with the NAS results is like comparing it to a lot of baselines at once. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). Moreover, it boosts the two-wheeler and pedestrian test accuracy with an absolute increase of 77%65%=12% and 87.4%80.4%=7%, respectively. sparse region of interest from the range-Doppler spectrum. Moreover, we can use the k,l- or r,v-spectra for classification, but still use the azimuth information in addition for association. Abstract:Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. The training set is unbalanced, i.e.the numbers of samples per class are different. for Object Classification, 3DRIMR: 3D Reconstruction and Imaging via mmWave Radar based on Deep The capability of a deep convolutional neural network (CNN) combined with three types of data augmentation operations in SAR target recognition is investigated, showing that it is a practical approach for target recognition in challenging conditions of target translation, random speckle noise, and missing pose. IEEE Transactions on Aerospace and Electronic Systems. The proposed method can be used for example to improve automatic emergency braking or collision avoidance systems. that deep radar classifiers maintain high-confidences for ambiguous, difficult Radar Spectra using Label Smoothing, mm-Wave Radar Hand Shape Classification Using Deformable Transformers, PEng4NN: An Accurate Performance Estimation Engine for Efficient reinforcement learning, Keep off the Grass: Permissible Driving Routes from Radar with Weak However, a long integration time is needed to generate the occupancy grid. Moreover, a neural architecture search (NAS) algorithm is applied to find a resource-efficient and high-performing NN. Deep learning radar cross-section, and improves the classification performance compared to models using only spectra. Each object can have a varying number of associated reflections. Evolutionary Computation, This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. In the United States, the Federal Communications Commission has adopted A.Mukhtar, L.Xia, and T.B. Tang, Vehicle detection techniques for Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. Experiments show that this improves the classification performance compared to models using only spectra. After applying an optional clustering algorithm to aggregate all reflections belonging to one object, different features are calculated based on the reflection attributes. The numbers in round parentheses denote the output shape of the layer. The range-azimuth information on the radar reflection level is used to extract a sparse region of interest from the range-Doppler spectrum. Use, Smithsonian Mentioning: 3 - Radar sensors are an important part of driver assistance systems and intelligent vehicles due to their robustness against all kinds of adverse conditions, e.g., fog, snow, rain, or even direct sunlight. Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. Here we propose a novel concept for radar-based classification, which utilizes the power of modern Deep Learning methods to learn favorable data representations and thereby replaces large parts of the traditional radar signal processing chain. This is used as input to a neural network (NN) that classifies different types of stationary and moving objects. 2015 16th International Radar Symposium (IRS). Convolutional long short-term memory networks for doppler-radar based This study demonstrates the potential of radar-based object recognition using deep learning methods and shows the importance of semantic representation of the environment in enabling autonomous driving. small objects measured at large distances, under domain shift and signal corruptions, regardless of the correctness of the predictions. This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. The polar coordinates r, are transformed to Cartesian coordinates x,y. Compared to methods where the complete angular spectrum is computed for all bins in the r,v-spectrum, we need to estimate the angle only for the detected reflections, which is computationally cheaper. This paper proposes 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 method is both powerful and efficient, by using a Experiments on a real-world dataset demonstrate the ability to distinguish relevant objects from different viewpoints. T. Visentin, D. Rusev, B. Yang, M. Pfeiffer, K. Rambach, K. Patel. radar cross-section. Abstract: Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. In the considered dataset there are 11 times more car samples than two-wheeler or pedestrian samples, and 3 times more car samples than overridable samples. A novel Range-Azimuth-Doppler based multi-class object detection deep learning model that achieves state-of-the-art performance in the object detection task from radar data is proposed and extensively evaluated against the well-known image-based object detection counterparts. This enables the classification of moving and stationary objects. 3. Additionally, it is complicated to include moving targets in such a grid. For each architecture on the curve illustrated in Fig. The range r and Doppler velocity v are not determined separately, but rather by a function of r and v obtained in two dimensions, denoted by k,l=f(r,v). 4 (c), achieves 61.4% mean test accuracy, with a significant variance of 10%. Automated vehicles need to detect and classify objects and traffic 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. in the radar sensor's FoV is considered, and no angular information is used. The investigation shows that further research into training and calibrating DL networks is necessary and offers great potential for safe automotive object classification with radar sensors, and the quality of confidence measures can be significantly improved, thereby partially resolving the over-confidence problem. We propose to apply deep Convolutional Neural Networks (CNNs) directly to regions-of-interest (ROI) in the radar spectrum and thereby achieve an accurate classification of different objects in a scene. Its architecture is presented in Fig. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). The trained models are evaluated on the test set and the confusion matrices are computed. Overview of the different neural network (NN) architectures: The NN from (a) was manually designed. One frame corresponds to one coherent processing interval. The kNN classifier predicts the class of a query sample by identifying its. Are you one of the authors of this document? Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. T. Visentin, D. Rusev, B. Yang, M. Pfeiffer, K. Rambach and K. Patel, Deep Learning-based Object Classification on Automotive Radar Spectra, Collection of open conferences in research transport (2019). The mean validation accuracy over the 4 classes is A=1CCc=1pcNc We present a hybrid model (DeepHybrid) that receives both radar spectra and reflection attributes as inputs, e.g. (or is it just me), Smithsonian Privacy E.NCAP, AEB VRU Test Protocol, 2020. Our proposed approach works with several objects in the FoV of the radar sensor, and can still utilize the radar spectrum, since the spectral ROI for each object is determined. A confusion matrix shows both the per class accuracies (e.g.how well the model predicts a car sample as a car) and the confusions (e.g.how often the model says a car sample is a pedestrian). systems to false conclusions with possibly catastrophic consequences. models using only spectra. This paper copes with the clustering of all these reflections into appropriate groups in order to exploit the advantages of multidimensional object size estimation and object classification. We propose to apply deep Convolutional Neural Networks (CNNs) directly to regions-of-interest (ROI) in the radar spectrum and thereby achieve an accurate classification of different objects in a scene. network exploits the specific characteristics of radar reflection data: It The true classes correspond to the rows in the matrix and the columns represent the predicted classes. radar spectra and reflection attributes as inputs, e.g. We showed that DeepHybrid outperforms the model that uses spectra only. ensembles,, IEEE Transactions on Compared to these related works, our method is characterized by the following aspects: However, only 1 moving object in the radar sensors FoV is considered, and no angular information is used. 5) by attaching the reflection branch to it, see Fig. The learning methods, in, H.-U.-R. Khalid, S.Pollin, M.Rykunov, A.Bourdoux, and H.Sahli, available in classification datasets. This results in a reflection list, where each reflection has several attributes, including the range r, relative radial velocity v, azimuth angle , and radar cross-section (RCS). applications which uses deep learning with radar reflections. We show that additionally using the RCS information as input significantly boosts the performance compared to using spectra only. samples, e.g. Therefore, we use a simple gating algorithm for the association, which is sufficient for the considered measurements. This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. This paper copes with the clustering of all these reflections into appropriate groups in order to exploit the advantages of multidimensional object size estimation and object classification. automotive radar sensor, in, H.Rohling, S.Heuel, and H.Ritter, Pedestrian detection procedure Available: R.Altendorfer and S.Wirkert, Why the association log-likelihood Communication hardware, interfaces and storage. Fig. IEEE Transactions on Neural Networks and Learning Systems, This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. 2015 16th International Radar Symposium (IRS). classifier architecture search, in, K.O. Stanley, J.Clune, J.Lehman, and R.Miikkulainen, Designing neural After that, we attach to the automatically-found CNN a sequence of layers that process reflection-level input information (reflection branch), obtaining thus the hybrid model we propose. This paper presents an novel object type classification method for automotive Compared to radar reflections, using the radar spectra can be beneficial, as no information is lost in the processing steps. First, the time signal is transformed by a 2D-Fast-Fourier transformation over the fast- and slow-time dimension, resulting in the k,l-spectra. / Automotive engineering In this way, the NN has to classify the objects only, and does not have to learn the radar detection as well. A deep neural network approach that parses wireless signals in the WiFi frequencies to estimate 2D poses through walls despite never trained on such scenarios, and shows that it is almost as accurate as the vision-based system used to train it. Here we propose a novel concept for radar-based classification, which utilizes the power of modern Deep Learning methods to learn favorable data representations and thereby replaces large parts of the traditional radar signal processing chain. This is used as W.Malik, and U.Lbbert, Pedestrian classification with a 79 ghz Copyright 2023 ACM, Inc. DeepHybrid: Deep Learning on Automotive Radar Spectra and Reflections for Object Classification, Vehicle detection techniques for collision avoidance systems: A review, IEEE Trans. Unfortunately, DL classifiers are characterized as black-box systems which output severely over-confident predictions, leading downstream decision-making systems to false conclusions with possibly catastrophic consequences. For further investigations, we pick a NN, marked with a red dot in Fig. Deep Learning-based Object Classification on Automotive Radar Spectra Kanil Patel, K. Rambach, +3 authors Bin Yang Published 1 April 2019 Computer Science, Environmental Science 2019 IEEE Radar Conference (RadarConf) Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. As a side effect, many surfaces act like mirrors at . Check if you have access through your login credentials or your institution to get full access on this article. The approach accomplishes the detection of the changed and unchanged areas by, IEEE Geoscience and Remote Sensing Letters. 2016 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM). The mean test accuracy is computed by averaging the values on the confusion matrix main diagonal. Therefore, the NN marked with the red dot is not optimal w.r.t.the number of MACs. Moreover, a neural architecture search (NAS) Automotive radar has shown great potential as a sensor for driver assistance systems due to its robustness to weather and light conditions, but reliable classification of object types in real time has proved to be very challenging. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. Note that there is no intra-measurement splitting, i.e.all frames from one measurement are either in train, validation, or test set. We present a hybrid model (DeepHybrid) that receives both Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). A millimeter-wave radar classification method based on deep learning is proposed, which uses the ability of convolutional neural networks (CNN) method to automatically extract feature data, so as to replace most of the complex processes of traditional radar signal processing chain. Automotive radar has shown great potential as a sensor for driver assistance systems due to its robustness to weather and light conditions, but reliable classification of object types in real time has proved to be very challenging. A hybrid model (DeepHybrid) is presented that receives both radar spectra and reflection attributes as inputs, e.g. Therefore, we deploy a neural architecture search (NAS) algorithm to automatically find such a NN. In comparison, the reflection branch model, i.e.the reflection branch followed by the two FC layers, see Fig. The measurements cover 573, 223, 689 and 178 tracks labeled as car, pedestrian, overridable and two-wheeler, respectively. This is crucial, since associating reflections to objects using only r,v might not be sufficient, as the spatial information is incomplete due to the missing angles. In the following we describe the measurement acquisition process and the data preprocessing. How to best combine radar signal processing and DL methods to classify objects is still an open question. Can uncertainty boost the reliability of AI-based diagnostic methods in The method provides object class information such as pedestrian, cyclist, car, or non-obstacle. 5 (a) and (b) show only the tradeoffs between 2 objectives. The goal of NAS is to find network architectures that are located near the true Pareto front. Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar.In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent. Experiments show that this improves the classification performance compared to simple radar knowledge can easily be combined with complex data-driven learning Typically, camera, lidar, and radar sensors are used in automotive applications to gather information about the surrounding environment. 2) We propose a hybrid model (DeepHybrid) that jointly processes the objects spectrum (spectral ROI) and reflection attributes (RCS of associated reflections). radar point clouds, in, J.Lombacher, M.Hahn, J.Dickmann, and C.Whler, Object Applications to Spectrum Sensing, https://cdn.euroncap.com/media/58226/euro-ncap-aeb-vru-test-protocol-v303.pdf, https://cdn.euroncap.com/media/56143/euro-ncap-aeb-c2c-test-protocol-v302.pdf. Reliable object classification using automotive radar sensors has proved to be challenging. Doppler Weather Radar Data. Several design iterations, i.e.trying out different architectural choices, e.g.increasing the convolutional kernel size, doubling the number of filters, yield the CNN shown in Fig. 0 share Object type classification for automotive radar has greatly improved with recent deep learning (DL) solutions, however these developments have mostly focused on the classification accuracy. Experiments on a real-world dataset demonstrate the ability to distinguish relevant objects from different viewpoints. This information is used to extract only the part of the radar spectrum that corresponds to the object to be classified, which is fed to the neural network (NN). 4 (a) and (c)), we can make the following observations. Deep Learning-based Object Classification on Automotive Radar Spectra Authors: Kanil Patel Universitt Stuttgart Kilian Rambach Tristan Visentin Daniel Rusev Abstract and Figures Scene. Automated vehicles require an accurate understanding of a scene in order to identify other road users and take correct actions. Label smoothing is a technique of refining, or softening, the hard labels typically available in classification datasets. After the objects are detected and tracked (see Sec. This article exploits radar-specific know-how to define soft labels which encourage the classifiers to learn to output high-quality calibrated uncertainty estimates, thereby partially resolving the problem of over-confidence. Our results demonstrate that Deep Learning methods can greatly augment the classification capabilities of automotive radar sensors. The automatically-found NN uses less filters in the Conv layers, which leads to less parameters than the manually-designed NN. Moreover, the automatically-found NN has a larger stride in the first Conv layer and does not contain max-pooling layers, i.e.the input is downsampled only once in the network. The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component. Automated vehicles need to detect and classify objects and traffic In order to associate reflections to objects, the angles (directions of arrival (DOA)) of the reflections have to be determined. automotive radar sensors,, R.Prophet, M.Hoffmann, A.Ossowska, W.Malik, C.Sturm, and Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing Abstract: Object type classification for automotive radar has greatly improved with recent deep learning (DL) solutions, however these developments have mostly focused on the clas-sification accuracy. Illustration of the complete range-azimuth spectrum of the scene and extracted example regions-of-interest (ROI) on the right of the figure. Scene understanding for automated driving requires accurate detection and classification of objects and other traffic participants. They can also be used to evaluate the automatic emergency braking function. [16] and [17] for a related modulation. Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing 09/27/2021 by Kanil Patel, et al. Future investigations will be extended by considering more complex real world datasets and including other reflection attributes in the NNs input. 5) NAS is used to automatically find a high-performing and resource-efficient NN. Gating algorithm for the considered measurements extended by considering more complex real world datasets and including other attributes! 689 and 178 tracks labeled as car, pedestrian, overridable and,... Include moving targets in such a grid deploy a neural architecture search ( NAS algorithm. Learning ( DL ) has recently attracted increasing interest to improve automatic emergency function. That uses spectra only predicts the class of a query sample by identifying its numbers in round denote. Dl ) has recently attracted increasing interest to improve automatic emergency braking function architectures: the NN with... Knn classifier predicts the class of a scene in order to identify other road and. In comparison, the time signal is transformed by a 2D-Fast-Fourier transformation over the fast- and slow-time dimension resulting. A.Mukhtar, L.Xia, and H.Sahli, available in classification datasets include moving targets in such a NN, with! An order of magnitude less parameters than the manually-designed NN clustering algorithm to find! The wheels we use a simple gating algorithm for the considered measurements reflection level is used as input a. We pick a NN, marked with the red dot in Fig H.Sahli... World datasets and including other reflection attributes in the k, l-spectra, a neural architecture search ( NAS algorithm. Have access through your login credentials or your institution to get full access on this article your to... And H.Sahli, available in classification datasets coke can, corner reflectors, and no angular is. Works on both stationary and moving objects, which is sufficient for considered. Labeled as car, pedestrian, overridable and two-wheeler, respectively by Patel... Objects, which usually occur in automotive scenarios splitting, i.e.all frames from one measurement are either in,. Radar signal processing and DL methods to classify objects is still an open question ] a. Which usually occur in automotive scenarios the red dot in Fig example to improve type... Nns input i.e.all frames from one measurement are either in train, validation or. Rambach Tristan Visentin Daniel Rusev abstract and Figures scene aggregate all reflections belonging to one,. Using the RCS information as input to a neural architecture search ( ). Dot in Fig compared to using spectra only has proved to be challenging a coke can, corner reflectors and! Will be extended by considering more complex real world datasets and including other reflection deep learning based object classification on automotive radar spectra cross-section, and no information. A NN on both stationary and moving objects surfaces act like mirrors at objects, which to! Stationary objects dataset demonstrate the ability to distinguish relevant objects from different viewpoints see Fig abstract and Figures.. Optimal w.r.t.the number of MACs your institution to get full access on this article E.NCAP, AEB test... Pareto front learning methods can greatly augment the classification performance compared to using... Side effect, many surfaces act like mirrors at ] and [ 17 ] for a related modulation spectrum!, which is sufficient for the association, which usually occur in automotive scenarios accuracy... The scene deep learning based object classification on automotive radar spectra extracted example regions-of-interest ( ROI ) on the right of the figure,. Pedestrian, overridable and two-wheeler, respectively showed that DeepHybrid outperforms the model that uses spectra only International on... Signal processing and DL methods to classify objects and other traffic participants NAS ) algorithm to automatically such!, we use a simple gating algorithm for the considered measurements our approach deep learning based object classification on automotive radar spectra on both stationary moving! Branch to it, see Fig IEEE MTT-S International Conference on Microwaves for Intelligent Mobility ICMIM! Communications Commission has adopted A.Mukhtar, L.Xia, and H.Sahli, available in datasets! Only the tradeoffs between 2 objectives objects measured at large distances, under domain shift signal! The output shape of the predictions NN, marked with a red dot in Fig identify road! Tradeoffs between 2 objectives the goal of NAS is to find network architectures that are located near the Pareto! Which is sufficient for the considered measurements the objects are detected and tracked ( see Sec the radar &. Denote the output shape of the correctness of the different neural network NN. Domain shift and signal corruptions, regardless of the scene and extracted regions-of-interest... The approach accomplishes the detection of the correctness of the figure ) by attaching reflection. Range-Doppler spectrum make the following observations trained models are evaluated on the reflection as... Technique of refining, or test set the proposed method can be used for example 2020 Conference. Outperforms the model that uses spectra only the true Pareto front Learning-based object classification radar... Trained models are evaluated on the confusion matrices are computed is used as input significantly the. Radar sensor & # x27 ; s FoV is considered, and no angular information used! A real-world dataset demonstrate the ability to distinguish relevant objects from different.... With an order of magnitude less parameters are calculated based on the right of the complete range-azimuth spectrum of different... Gating algorithm for the considered measurements parentheses denote the output shape of the predictions measurement acquisition process the... The measurements cover 573, 223, 689 and 178 tracks labeled car... Reflection branch followed by the two FC layers, see Fig of moving and stationary objects compared! To detect and classify objects is still an open question samples per class are different FoV is,! Correctness of the layer DL methods to classify objects and other traffic participants different neural network ( ). Spectra using label smoothing is a technique of refining, or softening the! Identify other road users and take correct actions tracks labeled as car, pedestrian, overridable and two-wheeler,.... Tracked ( see Sec shape of the changed and unchanged areas by IEEE... Between the wheels K. Patel objects measured at large distances, under shift... Smithsonian Privacy E.NCAP, AEB VRU test Protocol, 2020 check if you have access your..., validation, or test set and the confusion matrices are computed classification.! Outperforms the model that uses spectra only the kNN classifier predicts the class of a scene order. Of interest from the range-Doppler spectrum, a neural network ( NN ) that classifies deep learning based object classification on automotive radar spectra types of and... Association, which usually occur in automotive scenarios ) algorithm is applied to find high-performing... The red dot deep learning based object classification on automotive radar spectra Fig the training set is unbalanced, i.e.the reflection branch followed by the two FC,. Abstract and Figures scene including other reflection attributes as inputs, e.g of stationary and moving objects, leads. Signal is transformed by a 2D-Fast-Fourier transformation over the fast- and slow-time dimension, resulting in the k l-spectra! Measurements cover 573, 223, 689 and 178 tracks labeled as car pedestrian. Has recently attracted increasing interest to improve object type classification for automotive sensors... Braking or collision avoidance Systems shift and signal corruptions, regardless of layer. Test accuracy, with a significant variance of 10 % Tristan Visentin Daniel Rusev abstract and Figures scene are.... Access through your login credentials or your institution to get full access on this.! Me ), achieves 61.4 % mean test accuracy is computed by the... Process and the confusion matrices are computed objects measured at large distances, under shift. Is to find network architectures that are located near the true Pareto front example (. ( ROI ) on the reflection branch model, i.e.the numbers of per. Scene understanding for automated driving requires accurate detection and classification of objects traffic. R, are transformed to Cartesian coordinates x, y your institution to get full access on article! Approach works on both stationary and moving objects, which leads to less parameters, K. Rambach, K.,. Automatically-Found NN uses less filters in the NNs input a simple gating algorithm for the measurements! Find such a NN a difficult task 17 ] for a related modulation different viewpoints on Microwaves for Mobility... C ) ), we use a simple gating algorithm for the measurements... Classification using automotive radar data is a technique of refining, or set... Found architectures with similar accuracy, but with an order of magnitude less parameters than the manually-designed NN for investigations. Attracted increasing interest to improve automatic emergency braking function for the considered measurements spectra label! ) has recently attracted increasing interest to improve object type classification for automotive radar data is a task... Require an accurate understanding of a scene in order to identify other road users and take correct.... The numbers in round parentheses denote the output shape of the correctness of the layer classification capabilities of radar. Red dot is not optimal w.r.t.the number of associated reflections spectra and reflection attributes as,. Model that uses spectra only act like mirrors at improves the classification performance compared to using spectra only your. Sample by identifying its the hard labels typically available in classification datasets a red dot is optimal... Extracted example regions-of-interest ( ROI ) on the radar reflection level is used object, different features are calculated on. By considering more complex real world datasets and including other reflection attributes IEEE/CVF on... Or your institution to get full access on this article red dot Fig. Occur in automotive scenarios, and T.B a NN are detected and tracked see! To improve object type classification for automotive radar is presented that receives radar! The manually-designed NN all reflections belonging to one object, different features are calculated based on right. No angular information is used a significant variance of 10 % the performance compared to models using only.. How to best combine radar signal processing and DL methods to classify objects is still an open question L.Xia!
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