Spectrum Sharing Solution for Automotive Radar

Mishra, Kumar Vijay; Zhitnikov, Andrey; Eldar, Yonina C.

doi:10.1109/vtcspring.2017.8108413

Cited by 24 publications

(5 citation statements)

References 36 publications

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“…• Sweep interval: The minimum elapsed time, T radar,i , between two consecutive sweeps across the same ith sector, must be set such that every vehicle which passes over that sector is detected. T radar,i (19) is restricted by the sector length, r i , and the maximum velocity expected in a vehicle, v max .…”

Section: Radar Comprehensive Configuration -System Designmentioning

confidence: 99%

“…In [17], a general-purpose spectrum sharing radar is presented using the recently proposed concept of cognitive radar that exploits sub-Nyquist processing [18]. The automotive radar application modifications are suggested in [19] but these works employ IEEE 802.22 cognitive radio for communications.…”

Section: Related Workmentioning

confidence: 99%

“…8a shows the time a single beamforming procedure requires in terms of ϕ Sradar . Even if the radar increases the beamforming time for a single BHI, as per (19), we only repeat this procedure every T radar . In contrast, legacy 802.11ad for V2I requires such a repetition every ≈30 ms [4], which implies ≈35% of the overall access time.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar

Muns

Mishra

Guerra

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Mobility scenarios involving short contact times pose a challenge for high bandwidth data transfer between autonomous vehicles and roadside base stations (BS). Millimeter wave bands are a viable solution as they offer enormous bandwidth in the 60GHz band with several Gbps data transfer rates. However, beamforming is used as a default mode in this band, which requires accurate and continuous alignment under relative motion. We propose a method in which an off-the-shelf IEEE 802.11ad WiFi router is configured to serve as the BS as well as a radar exploiting special structure of 802.11ad preamble. We embed the radar functionality within standards-compliant operations that do not modify the core structure of the frames beyond what is defined by the 802.11ad protocol. This not only reduces the beam training time, but also ensures scalability with increasing vehicular traffic because radar allows accurate ranging of up to 0.1m at distances up to 200m. We further analyze the ensuing cost-benefit trade-off between the time allotted to the proposed in-band radar and communication modes. Our results reveal 83% reduction on the overhead incurred during the beam training achieved for a specific simulated vehicular scenario over the classical 802.11ad operation.

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Section: Radar Comprehensive Configuration -System Designmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar

Muns

Mishra

Guerra

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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“…Further, reduction in system resources enables the radar to share them among different applications. For instance, technologies like multi-function integrated radio-frequency (RF) aperture systems [4][5][6], spectral coexistence [7,8], cognitive radars [9,10] and reconfigurable arrays [11] provide multiple services such as surveillance, tracking, and communication using a single system.…”

Section: Introductionmentioning

confidence: 99%

TenDSuR: Tensor-Based 4D Sub-Nyquist Radar

Mishra

Liu

et al. 2019

IEEE Signal Process. Lett.

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We propose Tensor-based 4D Sub-Nyquist Radar (TenDSuR) that samples in spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while simultaneously recovering the target's direction, Doppler velocity, and range without loss of native resolutions. We formulate the radar signal model wherein the received echo samples are represented by a partial third-order tensor. We then apply compressed sensing in the tensor domain and use our tensor-OMP and tensor completion algorithms for signal recovery. Our numerical experiments demonstrate joint estimation of all three target parameters at the same native resolutions as a conventional radar but with reduced measurements. Furthermore, tensor completion methods show enhanced performance in off-grid target recovery with respect to tensor-OMP. IndexTerms-sub-Nyquist MIMO radar, tensor decomposition, spectrum sharing, canonical polyadic decomposition (CPD), parallel factor analysis (PARAFAC)

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“…During past few years, autonomous vehicles or self-driving cars have witnessed enormous development in vehicular control [1], environmental sensing [2], in-vehicle entertainment [3], efficient resource utilization [4], and inter-vehicular synchronization [5]. An ongoing challenge is automotive target detection and recognition in order to avoid road accidents and boost automotive safety.…”

Section: Introductionmentioning

confidence: 99%

Doppler-Resilient 802.11ad-Based Ultra-Short Range Automotive Joint Radar-Communications System

Duggal¹,

Vishwakarma²,

Mishra³

et al. 2019

Preprint

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We present an ultra-short range IEEE 802.11ad-based automotive joint radar-communications (JRC) framework, wherein we improve the radar's Doppler resilience by incorporating Prouhet-Thue-Morse sequences in the preamble. The proposed processing reveals detailed micro-features of common automotive objects verified through extended scattering center models of animated pedestrian, bicycle, and car targets. Numerical experiments demonstrate 2.5% reduction in the probability-of-false-alarm at low signal-to-noise-ratios and improvement in the peak-to-sidelobe level dynamic range up to Doppler velocities of ±144 km/hr over conventional 802.11ad JRC.

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Spectrum Sharing Solution for Automotive Radar

Cited by 24 publications

References 36 publications

Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar

Beam Alignment and Tracking for Autonomous Vehicular Communication using IEEE 802.11ad-based Radar

TenDSuR: Tensor-Based 4D Sub-Nyquist Radar

Doppler-Resilient 802.11ad-Based Ultra-Short Range Automotive Joint Radar-Communications System

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