Phenomenology of automotive radar interference

Norouzian, Fatemeh; Pirkani, Anum; Hoare, Edward; Cherniakov, M.; Gashinova, Marina

doi:10.1049/rsn2.12096

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…In contrast, the maximum speeds encountered in indoor environments are typically only a few meters per second. The literature on interference considerations can essentially be divided into examining interference probability [42]- [45] and its impacts on detection quality [46]- [50]. Interference investigations with real measurements have been done in [51]- [53].…”

Section: Related Work a Interference Modelmentioning

confidence: 99%

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

Reitz,

Künzle,

Franchi

et al. 2024

IEEE Access

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Frequency-modulated continuous-wave (FMCW) radars are becoming increasingly important in industrial applications due to their low cost, simple signal processing, and high resolution. The research is driven by the growing trend of radar applications in industrial settings, including tasks like autonomous robot navigation, radar-based monitoring of humans, and enhancing safety across various scenarios, such as smart factories, traffic control, or environmental monitoring. The network of distributed sensor nodes is a feature to be implemented by the sixth generation of wireless mobile networks. As a result, 6G will become an enabler technology that will develop even further use cases and advance the expansion of these technologies. Therefore, the mutual interference of these sensor systems is becoming highly relevant since they can be avoided by scheduling the individual resources of the sensor nodes by a network structure. This paper investigates the interference behavior of FMCW radar sensors in a dense indoor scenario. In these environments, in particular, the reliability of the sensor system plays an important role, and disruption can have critical consequences like not recognizing a target, triggering a false alarm, or the complete failure of the sensing. To correctly classify the expected interference in the overall context, the paper investigates the physical layer effects on the performance, such as multipath propagation, channel spread, delay, Doppler shift, and attenuation. By evaluating these effects using state-of-the-art signal processing, it was revealed that a high channel spread in particular and the dynamics of the radar channel can significantly reduce the detection quality, as the interference power can exceed the received power by up to 20 dB, depending on the scenario. The simulation results given in this paper show that a potential communication between the radars and compliance with a synchronization delay, the interference problems can be reliably prevented with an appropriately designed anti-aliasing filter. Our simulations have quantified the detection performance under interference through the entire signal chain by evaluating the physical layer, the radar signal processing with non-idealities, and the hardware. The paper has investigated interference probability in indoor environments by examining radars with uncoordinated transmissions. Integrating findings from the physical propagation characteristics, the deviations in chirp durations, and the frequency of interference cases offers insights into the potential risk of interference.

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Section: Related Work a Interference Modelmentioning

confidence: 99%

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

Reitz,

Künzle,

Franchi

et al. 2024

IEEE Access

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“…When the ratio

k = k_{int} / k_{v}

is close to 1,

Δ k

is a small value resulting in a reduced non‐uniform spectral spread of the interference pulse, which is the well‐known spectrum of the two‐sided LFM signal [28]. As the value of

k

deviates from 1, interference components spread wider across a larger number of frequency bins, thus becoming more uniform [29]. The example cases for different values of

k

, along with an AWGN floor for comparison, are shown in Figure 4.…”

Section: Fmcw Interference Modellingmentioning

confidence: 99%

“…This was carried out by identifying the following: (1) interference cases with a higher NRMSE value and a power greater than

10 \log (N_{C P I})

after integration. However, the cases with high interference power (due to a specific relationship between victim and interferer parameters [29]), but distribution matching well with a Gaussian (after visual inspection) were not considered, and (2) interference cases with lower interference power (due to reduced number of victim and interferer chirps overlap) but having a deviation from Gaussian (visual inspection). The NRMSE value of these considered cases was averaged to estimate the NRMSE threshold for the identification of critical cases which shows a deviation from Gaussian distribution.…”

Section: Statistical Analysis Of Interferencementioning

confidence: 99%

Automotive interference statistics and their effect on radar detector

Pirkani

Norouzian

Hoare

et al. 2021

IET Radar Sonar & Navi

Self Cite

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Millimetre‐wave frequency‐modulated continuous wave (FMCW) radars are at present widely deployed in the autonomous vehicles. The growing usage of such sensors, as a vital part of a robust future autonomous sensing system, sees the potential for significant increase in mutual interference and adverse effects on sensor operation. Effective target detection in the background of interference requires knowledge of the interference statistics. In the case that such statistics are found to be similar to that of additive white Gaussian noise (AWGN), then classical well‐established detection techniques can be applied. Conversely, if statistics are found to be different, traditional techniques (matched filtering) will not be optimal. Here, a statistical analysis of mutual interference within an FMCW victim radar is presented. The majority of cases show a low correlation between the interference pulses received at the victim radar, with close to a Gaussian distribution. Some specific cases show a high correlation between the interference pulses in the victim radar chirps with a sinusoidal‐like distribution, which degrades the victim radar’s detection performance. The presented analysis is validated by experimental data for various interference cases.

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“…They will enable more secure road traffic by anticipating potential collisions and ensure safer driving. However, a rapid increase in radar on the road without regulation on how to share the available radar band will likely lead to harmful interference being generated [ 2 ], whose effects can vary from reducing the range of the radar, blinding it, or generating false targets [ 3 ]. Multiple projects have been started to investigate the automotive radar interference mechanisms, such as the European funded project MOSARIM [ 2 ] and the publicly funded German project IMIKO [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…By adding a gate in position 2, it is possible to reshuffle the order of shortest path computation, forcing another sub-graph to change its color. In this example, the first gate has the order[1,2,3] and the second one has the order[3,1,2]. The temporal adjacency matrix is illustrated by the small graphs, left of the coloring graph.…”

mentioning

confidence: 99%

Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars

Roudiere¹,

Martinez²,

Maréchal

et al. 2023

Sensors

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The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g., ETSI) provide requirements in terms of maximum transmit power but do no mandate specific radar waveform parameters nor channel access scheme policies. Techniques for interference mitigation are thus becoming very important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. In our previous work, we have shown that organizing the radar band into time-frequency resources that do not interfere with each other vastly reduces the amount of interference by facilitating band sharing. In this paper, a metaheuristic is presented to find the optimal resource sharing between radars, knowing their relative positions and thereby the line-of-sight and non-line-of-sight interference risks during a realistic scenario. The metaheuristic aims at optimally minimizing interference while minimizing the number of resource changes that radars have to make. It is a centralized approach where everything about the system is known (e.g., the past and future positions of the vehicles). This and the high computational load induce that this algorithm is not meant to be used in real-time. However, the metaheuristic approach can be extremely useful for finding near optimal solutions in simulations, allowing for the extraction of efficient patterns, or as data generation for machine learning.

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Phenomenology of automotive radar interference

Cited by 14 publications

References 23 publications

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

Automotive interference statistics and their effect on radar detector

Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars

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