Joint Communication and Radar Sensing: An Overview

Thomä, Reiner S.; Dallmann, Thomas; Jovanoska, Snezhana; Knott, Peter; Schmeink, Anke

doi:10.23919/eucap51087.2021.9411178

Cited by 35 publications

(18 citation statements)

References 18 publications

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“…However, for simplicity, we assume that it is located within a circular region that spans the coverage area of the transmitters. Therefore, following the derivations in [19], the exact location of Eve (Ω E ) was defined as a point on a circular uncertain region with uncertainty given in (7).…”

Section: B Pls Analysis Of the Communication/radar Cohabiting Scenariomentioning

confidence: 99%

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Interference and noise cancellation for joint communication radar (JCR) system based on contextual information

Nnamani¹,

Sellathurai²

2023

Preprint

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This paper examines the separation of wireless communication and radar signals, thereby guaranteeing cohabitation and acting as a panacea to spectrum sensing. First, considering that the channel impulse response was known by the receivers (communication and radar), we showed that the optimizing beamforming weights mitigate the interference caused by signals and improve the physical layer security (PLS) of the system. Furthermore, when the channel responses were unknown, we designed an interference filter as a low-complex noise and interference cancellation autoencoder. By mitigating the interference on the legitimate users, the PLS was guaranteed. Results showed that even for a low signal-to-noise ratio, the autoencoder produces low root-mean-square error (RMSE) values.

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Section: B Pls Analysis Of the Communication/radar Cohabiting Scenariomentioning

confidence: 99%

“…While the latter presents complementary roles for both signals, the former describes a waveform that inseparably represents both signals. Overviews of the coexistence of communication and radar systems were presented in [5, part 1], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Interference and noise cancellation for joint communication radar (JCR) system based on contextual information

Nnamani¹,

Sellathurai²

2023

Preprint

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“…A key aspect of the co-existence of these two functions in an integrated system is the design of a joint waveform that is capable of transmitting information and performing radar sensing simultaneously. This convergence could be achieved based on different approaches as discussed in [4]- [7], [10]- [12]. For example, some research focuses on embedding information into traditional radar waveforms for joint application.…”

Section: Introductionmentioning

confidence: 99%

Combined Radar and Communications With Phase-Modulated Frequency Permutations

Han

Senanayake

Smith

et al. 2023

IEEE Open J. Commun. Soc.

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This paper focuses on the combined radar and communications problem and conducts a thorough analytical investigation on the effect of phase and frequency change on the communication and sensing functionality. First, we consider the classical stepped frequency radar waveform and modulate data using M -ary phase shift keying (MPSK). Two important analytical tools in radar waveform design, namely the ambiguity function (AF) and the Fisher information matrix (FIM) are derived, based on which, we make the important conclusion that MPSK modulation has a negligible effect on radar local accuracy. Next, we extend the analysis to incorporate frequency permutations and propose a new signalling scheme in which the mapping between incoming data and waveforms is performed based on an efficient combinatorial transform called the Lehmer code. We also provide an efficient communications receiver based on a modified Hungarian algorithm. From the communications perspective, we consider the optimal maximum likelihood (ML) detector and derive the union bound and nearest neighbour approximation on the block error probability. From the radar sensing perspective, we discuss the broader structure of the waveform based on the AF derivation and quantify the radar local accuracy based on the FIM. Extensive numerical examples are provided to illustrate the accuracy of our results.INDEX TERMS Joint communications and radar, maximum likelihood, ambiguity function, Fisher information matrix.

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“…Em um ambiente com espectro eletromagnético já extremamente ocupado e devido à necessidade de sensores mais eficientes em um cenário de guerra eletrônica, o uso de radares passivos se encaixam perfeitamente seja para fins de defesa, seja para aplicações civis. Ultimamente, para esse último, encontram-se em evidência estudos no âmbito de comunicação e sensoriamento conjunto, aproveitando sistemas de comunicação específicos para estimar a localização de alvos [3].…”

Section: Introductionunclassified

Estimação de posição e velocidade de um alvo com base em TDOA e FDOA em sistemas de radar passivo usando redes neurais feedforward

Pompeo¹,

Rodríguez²,

Apolinario³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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ResumoÐ Nesse trabalho, o uso de redes neurais do tipo feedfoward para estimação de posição e velocidade de alvos não cooperativos em sistemas de radar passivo é investigado. Medidas consideradas como variáveis de entrada da rede são as diferenças de tempo de chegada (TDOA, do inglês time-difference of arrival) e as diferenças de frequência Doppler (FDOA, do inglês frequency-difference of arrival) entre o sinal direto proveniente de um iluminador de oportunidade e o sinal refletido no alvo que chegam nos receptores. Em relação às entradas, os experimentos simulados utilizam somente informações de TDOA, somente informações de FDOA ou, então, as duas medidas conjuntamente. Além disso, com o intuito de determinar os parâmetros mais eficientes utilizados na rede, variam-se o número de neurônios, a quantidade de receptores e os níveis de erros associados às medidas. Finalmente, após eleger as melhores redes, os resultados são comparados a outras técnicas de estimação.Palavras-ChaveÐ Radar passivo, redes neurais feedforward, TDOA, FDOA.AbstractÐ In this paper, the authors investigate the use of feedforward neural networks to estimate the location and velocity of non-cooperative targets in passive radar systems. Time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements between the direct-path signals originated by an illuminator of opportunity and the target's reflected signals acquired by sensors are the possible network inputs. Concerning network inputs, simulated experiments use only TDOAs, only FDOAs, or both measurements. Furthermore, to determine efficient network parameters, the experiments vary the number of neurons, amount of receivers, and error degrees. Finally, the best network results are compared with other estimation techniques.KeywordsÐ passive radar, feedforward neural network, time difference of arrival (TDOA), frequency difference of arrival (FDOA).

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Joint Communication and Radar Sensing: An Overview

Cited by 35 publications

References 18 publications

Interference and noise cancellation for joint communication radar (JCR) system based on contextual information

Interference and noise cancellation for joint communication radar (JCR) system based on contextual information

Combined Radar and Communications With Phase-Modulated Frequency Permutations

Estimação de posição e velocidade de um alvo com base em TDOA e FDOA em sistemas de radar passivo usando redes neurais feedforward

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