Millimeter-wave integrated radar systems and techniques

Al‐Hourani, Akram; Evans, Robin J.; Farrell, Peter M.; Moran, Bill; Kandeepan, Sithamparanathan; Skafidas, Stan; Parampalli, Udaya

doi:10.1016/b978-0-12-811887-0.00007-9

Cited by 23 publications

(13 citation statements)

References 24 publications

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“…In order to characterize the interference, it is important to understand the architecture of next generation radar front-end [5]. As depicted in Figure 2, a generic radar front-end consists of a power amplifier (PA) connected to the transmit (TX) antenna, while a separate receive (RX) antenna is connected to a low-noise amplifier (LNA).…”

Section: Interference Modellingmentioning

confidence: 99%

“…This pulse train where each pulse has a different frequency (tone) is equivalent to a narrow synthetic pulse when processed using the inverse Fourier transform (IDFT) [35] to provide range and velocity information of a target. A detailed discussion on the signal processing of random stepped frequency waveform for a target’s distance and velocity estimation is given in [5,35,36,37,38], while the angle of arrival estimation made possible with the use of multiple receivers is given in [39].…”

Section: Pseudo-random Cyclic Orthogonal Sequencesmentioning

confidence: 99%

“…Currently automotive radars are limited to high-end vehicles due to development and integration costs. However, very low-cost consumer radar technology is imminent [2,3,4,5] and the global market volume of automotive radars is expected to experience a compound annual growth rate of 22.5% over the period of 2017–2023 [6]. Many spectrum management authorities have already regulated the spectrum bands around 24 GHz and 77 GHz [7] primarily for shared use by automotive radars.…”

Section: Introductionmentioning

confidence: 99%

“…Many spectrum management authorities have already regulated the spectrum bands around 24 GHz and 77 GHz [7] primarily for shared use by automotive radars. This sharing of the spectrum will result in overlapping frequency bands with inevitable adjacent channel interference [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Interference Mitigation in Automotive Radars Using Pseudo-Random Cyclic Orthogonal Sequences

Skaria

Al‐Hourani

Evans

et al. 2019

Sensors

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The number of small sophisticated wireless sensors which share the electromagnetic spectrum is expected to grow rapidly over the next decade and interference between these sensors is anticipated to become a major challenge. In this paper we study the interference mechanisms in one such sensor, automotive radars, where our results are directly applicable to a range of other sensor situations. In particular, we study the impact of radar waveform design and the associated receiver processing on the statistics of radar–radar interference and its effects on sensing performance. We propose a novel interference mitigation approach based on pseudo-random cyclic orthogonal sequences (PRCOS), which enable sensors to rapidly learn the interference environment and avoid using frequency overlapping waveforms, which in turn results in a significant interference mitigation with analytically tractable statistical characterization. The performance of our new approach is benchmarked against the popular random stepped frequency waveform sequences (RSFWS), where both simulation and analytic results show considerable interference reduction. Furthermore, we perform experimental measurements on commercially available automotive radars to verify the proposed model and framework.

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Section: Interference Modellingmentioning

confidence: 99%

Section: Pseudo-random Cyclic Orthogonal Sequencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Interference Mitigation in Automotive Radars Using Pseudo-Random Cyclic Orthogonal Sequences

Skaria

Al‐Hourani

Evans

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, radar sensors are newly emerging due to their superior recognition performance even in adverse lighting conditions and complex background. In addition, low-cost commercial miniature radars are becoming widely available, and are capable of capturing the signature of finer hand movements which can yield high classification accuracy at low processing cost [7].…”

Section: Introductionmentioning

confidence: 99%