Design of a Cyberattack Resilient 77 GHz Automotive Radar Sensor

Toker, Onur; Alsweiss, Suleiman

doi:10.3390/electronics9040573

Cited by 22 publications

(10 citation statements)

References 23 publications

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“…In Fig. 6, normalized detector outputs of the detectors D 1 and D 2 are plotted with respect to the attack signal scaling factor s. This plot is generated using a two randomly generated signature functions ρ ∈ S, but as observed in [5], generating different random signature functions results similar curves. Note that, normalized detector outputs were defined in the previous section.…”

Section: Numerical Example Using Real Radar Datamentioning

confidence: 96%

“…In [4], an attack resilient automotive radar design was introduced and its effectiveness was demonstrated using simulations. An similar attack resilient design was discussed in [5] and its effectiveness was demonstrated using real measured data, but without any theoretical performance analysis. In this paper, we present a new attack resilient automotive radar system, derive analytical bounds on its attack detection performance, prove that the total risk can be made as small as possible, and demonstrate all of these using real measured data obtained from Texas Instruments 77 GHz automotive radars.…”

Section: Introductionmentioning

confidence: 99%

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Physical-Layer Cyberattack and Interference Resilient Automotive Radars

Toker

2020

IEEE Access

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In this paper, we present a physical-layer attack and interference resilient automotive radar system, and derive analytical upper bounds for the probability of not detecting an attack, and the probability of false attack alarm. We consider a quite general attack model and prove that if the attack signal level is above a defined relative threshold, both the probability of false attack alarm and the probability of not detecting an attack converge to zero exponentially with the number of samples acquired during a single chirp, and the number of chirps used in a frame. We also derive an analytical formula for this relative threshold, and prove that by selecting shorter frame durations, and using lower noise RF equipment, the threshold can be made as small as possible. Basically, by proper selection of radar parameters arbitrarily small attack signals can be detected almost always with almost no false alarms. We also present a numerical example using real measured data obtained from two 77 GHz automotive radars operated at the same time. Also using real data, we show that the proposed system reduces the negative effects of undetected weak attacks which are below the above mentioned threshold.

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Section: Numerical Example Using Real Radar Datamentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Physical-Layer Cyberattack and Interference Resilient Automotive Radars

Toker

2020

IEEE Access

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“…k ) as the possible candidate setZ (1) , the next nonzero element of support vector can be found by evaluating ψ(z…”

Section: Bmp-based Mimo Ofdm Radar Imagingmentioning

confidence: 99%

“…Recently, interest in autonomous driving and connected car has grown because of implementing an intelligent transportation system. Although sensors such as light detection and ranging (Lidar) and cameras can be used to recognize the surrounding environment, vehicle radar technology using millimeter waves is drawing attention as it is not affected by the surrounding environment, such as bad weather or light intensity [ 1 , 2 ]. In order to increase the number of sensors fitted to vehicles and to communicate various sensing information and road information, inter-vehicle communication technology with a high data rate is needed.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing-Based Radar Imaging and Subcarrier Allocation for Joint MIMO OFDM Radar and Communication System

Hwang

Seo

Park

et al. 2021

Sensors

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In this paper, a joint multiple-input multiple-output (MIMO OFDM) radar and communication (RadCom) system is proposed, in which orthogonal frequency division multiplexing (OFDM) waveforms carrying data to be transmitted to the information receiver are exploited to get high-resolution radar images at the RadCom platform. Specifically, to get two-dimensional (i.e., range and azimuth angle) radar images with high resolution, a compressive sensing-based imaging algorithm is proposed that is applicable to the signal received through multiple receive antennas. Because both the radar imaging performance (i.e., the mean square error of the radar image) and the communication performance (i.e., the achievable rate) are affected by the subcarrier allocation across multiple transmit antennas, by analyzing both radar imaging and communication performances, we also propose a subcarrier allocation strategy such that a high achievable rate is obtained without sacrificing the radar imaging performance.

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“…Radar is a relatively cheaper (compared to Lidar) sensing technology that can be used to determine the range, velocity, and angle-of-arrival of multiple targets simultaneously in poor lighting conditions and inclement weather [5]. Various radar sensors centered around 77 GHz have been developed and tested recently for automotive applications [6]- [11].…”

Section: Introductionmentioning

confidence: 99%

High Fidelity Physics Simulation-Based Convolutional Neural Network for Automotive Radar Target Classification Using Micro-Doppler

et al. 2021

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Detection and classification of vulnerable road users (VRUs) such as pedestrians and cyclists is a key requirement for the realization of fully autonomous vehicles. Radar-based classification of VRUs can be achieved by exploiting differences in the micro-Doppler signatures associated with VRUs. Specifically, machine learning (ML) algorithms can be trained to classify VRUs using the spectral content of radar signals. The performance of these models depends on the quality and quantity of the data used during the training process. Currently, data collection is typically done through measurements or low fidelity physics, primitive-based simulations. The feasibility of carrying out measurements to collect training data is typically limited by the vast amounts of data required and practicality issues when using VRUs like animals. In this paper, we present a computationally efficient, high fidelity physics-based simulation workflow that can be used to obtain a large quantity of spectrograms from the micro-Doppler signatures of VRUs. The simulations are conducted on full-scale VRU models with a 77 GHz, frequency-modulated continuouswave (FMCW) radar sensor model. Here, we collect the spectrograms of 4 targets; car, pedestrian, cyclist and dog at different speeds and angles-of-arrival. This data is then used to train a 5-layer convolutional neural network (CNN) that achieves nearly 100% classification accuracy after 5 epochs. Studies are conducted to investigate the impact of training data size, velocity and observation time window size on the accuracy of the CNN. Results from this study demonstrate how an accuracy of 95% can be realized using spectrograms obtained over a 0.2 s time window.

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Design of a Cyberattack Resilient 77 GHz Automotive Radar Sensor

Cited by 22 publications

References 23 publications

Physical-Layer Cyberattack and Interference Resilient Automotive Radars

Physical-Layer Cyberattack and Interference Resilient Automotive Radars

Compressive Sensing-Based Radar Imaging and Subcarrier Allocation for Joint MIMO OFDM Radar and Communication System

High Fidelity Physics Simulation-Based Convolutional Neural Network for Automotive Radar Target Classification Using Micro-Doppler

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