Statistical Analysis of Automotive Radar Interference

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

doi:10.1109/radarconf2043947.2020.9266653

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…Interference distribution can be approximated by a sinusoidal PDF due to the deterministic and periodic behaviour of interference across the victim chirps [26]. � Semi-synchronous interference: NRMSE value in critical region with a sinusoidal distribution is also observed in this case (diagonal region in Figure 7) as interference is within the victim radar IF bandwidth for its entire sweep time and all the victim chirps during T CPI .…”

Section: Interference Distributionmentioning

confidence: 71%

“…As expected, the highest correlation is observed in case of periodicity between the interference pulses. In the absence of any periodicity (aperiodic interferences), an average correlation close to 0 is observed, indicating that

ρ_{a v}

largely depends on the type of interference [26].…”

Section: Statistical Analysis Of Interferencementioning

confidence: 99%

“…The spread of interference in frequency domain with f as the frequency vector depends on the waveform parameters [26] and is limited by

f_{L}

and

f_{H}

expressed as [27]:

f_{L} (f) = \frac{f_{LPF} - (normalΔ f_{c} + f ‐ k_{int} τ_{int})}{\sqrt{normalΔ k}}

f_{H} (f) = \frac{f_{LPF} + (normalΔ f_{c} + f ‐ k_{int} τ_{int})}{\sqrt{normalΔ k}}

…”

Section: Fmcw Interference Modellingmentioning

confidence: 99%

“…The spread of interference in frequency domain with f as the frequency vector depends on the waveform parameters [26] and is limited by f L and f H expressed as [27]:…”

Section: Fmcw Interference Modellingmentioning

confidence: 99%

“…The observed critical cases in Figure 8 (NRMSE > 0.5 dB) mainly arise due to the reasons stated below, with their example cases discussed later in this section. Synchronous interference: The highest NRMSE value is observed, which appears along the diagonal of Figure 7. Interference distribution can be approximated by a sinusoidal PDF due to the deterministic and periodic behaviour of interference across the victim chirps [26]. Semi‐synchronous interference: NRMSE value in critical region with a sinusoidal distribution is also observed in this case (diagonal region in Figure 7) as interference is within the victim radar IF bandwidth for its entire sweep time and all the victim chirps during

T_{C P I}

. Periodic/semi‐periodic interference: Periodicity between interference pulses causes a deviation from a random Gaussian process, resulting in a high NRMSE value.…”

Section: Statistical Analysis Of Interferencementioning

confidence: 99%

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Automotive interference statistics and their effect on radar detector

Pirkani

Norouzian

Hoare

et al. 2021

IET Radar Sonar & Navi

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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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Section: Interference Distributionmentioning

confidence: 71%

ρ_{a v}

largely depends on the type of interference [26].…”

Section: Statistical Analysis Of Interferencementioning

confidence: 99%

“…The spread of interference in frequency domain with f as the frequency vector depends on the waveform parameters [26] and is limited by

f_{L}

and

f_{H}

expressed as [27]:

f_{L} (f) = \frac{f_{LPF} - (normalΔ f_{c} + f ‐ k_{int} τ_{int})}{\sqrt{normalΔ k}}

f_{H} (f) = \frac{f_{LPF} + (normalΔ f_{c} + f ‐ k_{int} τ_{int})}{\sqrt{normalΔ k}}

…”

Section: Fmcw Interference Modellingmentioning

confidence: 99%

“…The spread of interference in frequency domain with f as the frequency vector depends on the waveform parameters [26] and is limited by f L and f H expressed as [27]:…”

Section: Fmcw Interference Modellingmentioning

confidence: 99%

T_{C P I}

. Periodic/semi‐periodic interference: Periodicity between interference pulses causes a deviation from a random Gaussian process, resulting in a high NRMSE value.…”

Section: Statistical Analysis Of Interferencementioning

confidence: 99%

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