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

Duggal, Gaurav; Vishwakarma, Shelly; Mishra, Kumar Vijay; Ram, Shobha Sundar

doi:10.1109/taes.2020.2990393

Cited by 94 publications

(72 citation statements)

References 37 publications

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“…As seen in (Kumari et al, 2017a), the velocity estimation in this case is also poor due to the short duration of the probing signal. More recently, a Doppler-resilient variant of GCS (Pezeshki et al, 2008) has been employed to improve the radar performance (Duggal et al, 2020), especially in case of multiple moving point targets or a single target with multiple point scatterers to extract micro-Doppler and micro-range features (Duggal et al, 2019).…”

Section: Ieee 80211ad-based Radarmentioning

confidence: 99%

A Survey on Joint Communication-Radar Systems

2021

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Concerns for spectrum congestion have spurred extensive research efforts on efficient spectrum management. Therefore, devising schemes for spectrum sharing between radar and wireless communication systems has become an important area of research. Joint communications-radar (JCR) systems are among the several approaches proposed to achieve this objective. In JCR systems, additional components and processes are added to an existing standardized communication platform to enable radar functions. Moreover, the communication waveform is used as an integrated JCR waveform, i.e., the same signal is used to communicate information to a receiver and to perform radar detection and estimation operations for a nearby target. The most common application of JCR systems is found in vehicle-to-vehicle (V2V) communication scenarios. In this article, an overview of the spectrum sharing methods is presented, with a focus on JCR systems in automotive and other applications. We first review the recent works on IEEE 802.11p- and IEEE 802.11ad-based radars. A basic description of the modeling of a JCR system and channels is presented, followed by discussions on the main components and processes employed in various JCR systems. We are mainly interested in how radar detection and estimation functions are performed in conjunction with the communication receiver functions with minimal alterations to the existing system. At the end of the paper, some performance trade-offs between the communication and radar sub-systems are also discussed.

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Section: Ieee 80211ad-based Radarmentioning

confidence: 99%

A Survey on Joint Communication-Radar Systems

2021

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“…Also, a zero mean white Gaussian noise with σ 2 w R = 1 is considered at the radar receiver. We consider −10 dB Signal to Noise Ratio (SNR) for the target of interest, while the Clutter to Noise Ratio (CNR) and Interfernece to Noise Ratio (INR) at the range bin of the target are 20 dB, 30 dB, respectively 3 .…”

Section: Performance Analysismentioning

confidence: 99%

“…Further, we randomly select the incident and departure angles between −90 to 90 2 Iteration increases when all the entries of X were updated. 3 Assuming σ 2 s , σ 2 n , σ 2 c , and σ 2 ν indicating the power of signal, noise, clutter and interference, then SNR = degrees. At the communications, we uniformly select SNR of every user in the interval [5,25] dB and INR in the interval [10,30] dB.…”

Section: Performance Analysismentioning

confidence: 99%

“…Current approaches for mitigating this problem are broadly grouped in two classes. In spectral co-design [2,3], a common waveform is employed by the radar and communications system while sharing the hardware resources at the transmit (Tx) and/or receive (Rx). In spectral coexistence, radar and communications individually address the suppression of interference from the other system.…”

Section: Introductionmentioning

confidence: 99%

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Information Theoretic Approach for Waveform Design in Coexisting MIMO Radar and MIMO Communications

Alaee-Kerharoodi

Bhavani

Mishra

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We investigate waveform design for coexistence between a multipleinput multiple-output (MIMO) radar and MIMO communications (MRMC), with a radar-centric criterion that leads to a minimal interference in the communications system. The communications use the traditional mode of operation in Long Term Evolution (LTE)/Advanced (FDD), where we formulate the design problem based on information-theoretic criterion with the discrete phase constraint at the design stage. The optimization problem, is nonconvex, multi-objective and multi-variable, where we propose an efficient algorithm based on the coordinate descent (CD) framework to simultaneously improve radar target detection performance and the communications rate. The numerical results indicate the effectiveness of the proposed algorithm in designing discrete phase set of sequences, potentially binary.

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“…data with artefacts that would not be realistically present in experimental data. Although not presenting any classification work on radar data, the investigation in [27] is of interest to show how micro-Doppler signatures of pedestrians (plus other automotive targets) appear when using special waveforms based on 512-bit Golay codes that enable joint radar-communication functionalities. All the above papers that apply deep learning and deep neural networks to the classification of radar data have used convolutional neural networks in various architectures.…”

Section: Introductionmentioning

confidence: 99%

Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks

et al. 2020

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Recognition of human movements with radar for ambient activity monitoring is a developed area of research that yet presents outstanding challenges to address. In real environments, activities and movements are performed with seamless motion, with continuous transitions between activities of different duration and a large range of dynamic motions, compared with discrete activities of fixed-time lengths which are typically analysed in the literature. This paper proposes a novel approach based on recurrent LSTM and Bi-LSTM network architectures for continuous activity monitoring and classification. This approach uses radar data in the form of a continuous temporal sequence of micro-Doppler or range-time information, differently from from other conventional approaches based on convolutional networks that interpret the radar data as images. Experimental radar data involving 15 participants and different sequences of 6 actions are used to validate the proposed approach. It is demonstrated that using the Dopplerdomain data together with the Bi-LSTM network and an optimal learning rate can achieve over 90% mean accuracy, whereas range-domain data only achieved approximately 76%. The details of the network architectures, insights in their behaviour as a function of key hyper-parameters such as the learning rate, and a discussion on their performance across are provided in the paper.

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

Cited by 94 publications

References 37 publications

A Survey on Joint Communication-Radar Systems

A Survey on Joint Communication-Radar Systems

Information Theoretic Approach for Waveform Design in Coexisting MIMO Radar and MIMO Communications

Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks

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