Ambiguity function analysis for passive radar system performance

Johnson, Nicholas; Chergui, Mohamed; Sternberg, Oren; Rockway, J. W.; Jones, W. Linwood

doi:10.1109/milcom.2016.7795439

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…When the transmit waveform is not quite matched with the environment, the AF analysis is very important. As it is known, AF is applied in traditional radar waveform as a measure of capabilities in terms of object detection, rejection of clutter, range and Doppler resolution, accuracy of measurement, and overall ambiguity [32]. The calculation of an AF is performed by passing a waveform through its own matched filter.…”

Section: Numerical Simulation Resultsmentioning

confidence: 99%

Joint optimisation of transmit waveform and receive filter for cognitive radar

Zhang

Wang

Liu

2018

IET Radar, Sonar & Navigation

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Section: Numerical Simulation Resultsmentioning

confidence: 99%

Joint optimisation of transmit waveform and receive filter for cognitive radar

Zhang

Wang

Liu

2018

IET Radar, Sonar & Navigation

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“…In this section, the OFDM‐IM RadCom waveform performance is firstly evaluated by way of computing its ambiguity function [17]. Then, we focus on using CS method to obtain the Radar range‐velocity profile in the proposed OFDM‐IM RadCom system.…”

Section: Radar Performance and Pa Efficiencymentioning

confidence: 99%

“…The theoretical relation between the PA efficiency and the PAPR is plotted in [27, Figure 1]. As an example, the class-A PA efficiency α can be described as 𝛼 = Q exp(−q ⋅ 𝜒 dB ), (17) with χ dB denoting the PAPR in dB. For the class-A operation, Q = 58.7% and q = 0.1247 [28].…”

Section: Pa Efficiencymentioning

confidence: 99%

Index modulation for OFDM RadCom systems

Huang

Ding

Ouyang

et al. 2021

The Journal of Engineering

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This paper studies a novel Radar and wireless communication fusion system namely, Rad-Com, using orthogonal frequency division multiplexing index modulation waveforms. From the communication perspective, information is conveyed not only through modulated orthogonal frequency division multiplexing subcarriers but also by the active subcarrier indices that are dynamically updated at the transmitted symbol rates. When compared with conventional orthogonal frequency division multiplexing communication systems, only a subset of subcarriers is activated in the proposed orthogonal frequency division multiplexing index modulation system, which reduces signal peak-to-average ratios, leading to enhanced power amplifier efficiency. From the Radar sensing perspective, the Radar range-velocity profile obtained using compressed sensing approach is simulated and presented. It is shown that orthogonal frequency division multiplexing index modulation Rad-Com system can enhance the transmission data rates when low-order modulation schemes are adopted e.g. binary phase shift keying, and maintain similar Radar performance compared with the orthogonal frequency division multiplexing counterpart when the system is carefully designed. 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.

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“…The ambiguity function is a useful tool to analyse external radiation sources of passive radar [8], and it is generalised to multiple input multiple output (MIMO) radar in [9], which confirms that radar beamforming not only affects the distance and Doppler resolution but also affects the angular resolution. Literature [10] analyses the type of sidelobe of transmitting and receiving beam through ambiguity function and solves some related optimisation problems of suppressing sidelobe.…”

Section: Introductionmentioning

confidence: 99%