Automotive Radar Waveform Parameters Randomisation for Interference Level Reduction

Self Cite

Mutual interference in automotive radar is expected to become a major issue owing to the rapid increase in the number of vehicles on the road equipped with radar. The phenomenology of interference in frequency modulated continuous wave radar is presented. Interference is empirically analysed at every signal processing stage in the victim radar by means of experimentally verified simulation modelling. Knowledge of how interference manifests in different domains provides a useful tool to develop algorithms for interference detection, mitigation and/or avoidance. The receiver's filter response is analysed to minimise the interference duration and increase the effectiveness of time-domain mitigation techniques. A innovative method of interference parameter extraction by using spectrograms is also introduced.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

“…If the target reflections remain coherent during a CPI consisting of M chirps, the signal power increases as

M^{2}

, whereas the thermal noise power increases as M . As for the interference, its power after integration depends on its correlation property during CPI [20]. Schipper et al.…”

Section: Interference Analysis In Various Domainmentioning

confidence: 99%

“…This analysis shows that destroying the interference coherency can decrease the interference level and may maximise SIR improvement. The effectiveness of waveform randomisation to reduce the detrimental effect of periodic and semiperiodic interferences was shown in our earlier work [20].…”

Section: Interference Analysis In Various Domainmentioning

confidence: 99%

Phenomenology of automotive radar interference

Norouzian

Pirkani

Hoare

et al. 2021

Self Cite

“…This section focusses on the study of interference statistical characteristics and its comparison with that of white Gaussian noise at the decision‐making point (‘D’ in Figure 5). First, different types of FMCW interference [31] received by the victim radar have been characterised in terms of the correlation between the interference pulses represented by

ρ_{a b}

, where a and b represents the interference pulse number. An example is shown in Figure 3b where

ρ_{12}

represents the correlation between interference pulse with a = 1 and b = 2, and so on.…”

Section: Statistical Analysis Of Interferencementioning

confidence: 99%

“…Semi‐periodic interference is the case when interference pulses occur at a different time and instantaneous frequency with respect to some victim chirps during

T_{C P I}

, and for the remaining victim chirps the interference sequence repeats and becomes periodic. Aperiodic interference always happens at a random time and instantaneous frequency with respect to each victim chirp during

T_{C P I}

[31]. The parameters for each interference types are summarised in Table 1 where

B W_{s} = B W_{v} / B W_{int}

.…”

Section: Statistical Analysis Of Interferencementioning

confidence: 99%

“…In the case of aperiodic interference, non‐coherent gain is expected resulting in

10 \log (N_{C P I})

trend for interference level increase. For semi‐periodic interference, the interference power also increases by

10 \log (N_{C P I})

for interference pulses which are aperiodic (non‐coherent gain; start of

T_{C P I}

) and by

20 \log (N_{C P I})

where the interference sequence repeats periodically [31].…”

Section: Statistical Analysis Of Interferencementioning

confidence: 99%

See 1 more Smart Citation

Automotive interference statistics and their effect on radar detector

Pirkani

Norouzian

Hoare

et al. 2021

Self Cite

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.

5G‐based passive radar on a moving platform—Detection and imaging

Maksymiuk,

Gomez del Hoyo,

Abratkiewicz

et al. 2024

In recent times, the development of radar systems for automotive applications has gathered significant interest due to the increasing necessity of vehicle situational awareness for new active protection systems and the intensive development of higher autonomous level driving solutions. Therefore, the number of active radars in automotive applications is growing, causing spectrum sharing and interference between radars operating in the same band. Alternative solutions without dedicated electromagnetic transmissions, such as joint communication and radar systems, or even no transmission at all, such as passive radars, have emerged in recent years and are promising solutions to help mitigate the interference problem. A passive automotive radar based on 5G communication signals is proposed as an alternative to active radars to provide situational awareness. The data downlink transmissions provided by an operative 5G base station were combined with a dual‐channel passive radar (PR) system deployed on a moving platform to provide moving target detection and radar imaging of the vehicle's surroundings. The outcomes show a possibility of commensal utilisation of the new telecommunication standard for automotive radar applications. The idea of mounting the PR receiver on a moving platform was tested using simulated and real‐life data, which shows great potential for joining the new radio (NR) with sensing capabilities using PR. The theory, numerical experiments, and measurement results are dealt with a cooperative 5G base station and PR demonstrator.