Synchronizing network radar using all-in-view GPS-disciplined oscillators

Sandenbergh, J.S.; Inggs,

doi:10.1109/radar.2017.7944470

Cited by 17 publications

(31 citation statements)

References 7 publications

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“…Coherent detection at the auxiliary receiver involves oscillators’ synchronisation, which is a major topic by itself that we did not expand on. We only point out that some systems use global positioning system [16] and some use direct detection [17].…”

Section: Discussionmentioning

confidence: 99%

Contribution of auxiliary coherent radar receiver to target's velocity estimation

Cohen

Levanon

2018

IET Radar, Sonar & Navigation

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Recent progress in bistatic radar techniques can be used to improve performances of classical monostatic radar. A prominent limitation of coherent radar is its inability to measure the complete velocity vector (magnitude and direction) of a detected target. A single coherent detection can provide range-rate only. At least two detections, separated in time, are needed to estimate the target's velocity vector. This study discusses how the velocity vector can be determined by two simultaneous detections spaced in distance. The second detection is obtained by an auxiliary distant bistatic coherent receiver; an approach proposed in the 1990s to enhance meteorological radar. Being a very simple case of a distributed radar system allows for a simple demonstration of how to calculate the target's position and velocity vector and how to analyse the estimation accuracy, including geometric dilution of precision plots of the velocity error. Also discussed are two methods to identify correct data association when more than one target is detected.

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Section: Discussionmentioning

confidence: 99%

Contribution of auxiliary coherent radar receiver to target's velocity estimation

Cohen

Levanon

2018

IET Radar, Sonar & Navigation

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“…Another way to adjust a stable local oscillator (LO), e.g., based on the rubidium frequency standard, is to employ reference signals broadcasted by the Global Positioning System (GPS) [24]. Here, the pulse per second (PPS) signal provides a time, frequency, and phase reference for a local GPS-disciplined oscillator (GPSDO) and enables coherent integration, e.g., up to one second [25]. All previously mentioned approaches rely on the availability of auxiliary synchronization signals.…”

Section: Classification Of Distributed Radar Systemsmentioning

confidence: 99%

Coherent Automotive Radar Networks: The Next Generation of Radar-Based Imaging and Mapping

Gottinger

Hoffmann

Christmann³

et al. 2021

IEEE J. Microw.

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“…NeXtRAD has been developed to improve overall capabilities following the experience of using NetRAD in trials. The new radar is capable of operating in dual-band configuration (L-and Xbands, albeit not simultaneously, but alternating by pulse repetition interval, PRI), collecting polarimetric data at Xband, and operating in multistatic geometries across baselines of the order of hundreds of metres by means of GPS-Disciplined Oscillators [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Measurements and discrimination of drones and birds with a multi‐frequency multistatic radar system

Palamà

Fioranelli

Ritchie

et al. 2021

IET Radar Sonar & Navi

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This article presents the results of a series of measurements of multistatic radar signatures of small UAVs at L-and X-bands. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter-receiver and two bistatic receivers. NeXtRAD is capable of recording simultaneous bistatic and monostatic data with baselines and two-way bistatic range of the order of a few kilometres. The paper presents an empirical analysis with range-time plots and micro-Doppler signatures of UAVs and birds of opportunity recorded at several hundred metres of distance. A quantitative analysis of the overall signal-to-noise ratio is presented along with a comparison between the power of the signal scattered from the drone body and blades. A simple study with empirically obtained features and four supervised-learning classifiers for binary drone versus non-drone separation is also presented. The results are encouraging with classification accuracy consistently above 90% using very simple features and classification algorithms.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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Synchronizing network radar using all-in-view GPS-disciplined oscillators

Cited by 17 publications

References 7 publications

Contribution of auxiliary coherent radar receiver to target's velocity estimation

Contribution of auxiliary coherent radar receiver to target's velocity estimation

Coherent Automotive Radar Networks: The Next Generation of Radar-Based Imaging and Mapping

Measurements and discrimination of drones and birds with a multi‐frequency multistatic radar system

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