Statistical analysis of in-flight drone signatures

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dc.contributor.authorMarkow, John
dc.contributor.authorBalleri, Alessio
dc.contributor.authorCatherall, Aled
dc.date.accessioned2022-08-23T08:29:42Z
dc.date.available2022-08-23T08:29:42Z
dc.date.issued2022-08-16
dc.description.abstractDrone-monitoring radars typically integrate many pulses in order to improve signal to noise ratio and enable high detection performance. Over the course of this coherent processing interval (CPI), many components of the drone signature change and the signature's amplitude and Doppler modulations may hinder coherent integration performance, even in the absence of range-Doppler cell migrations. A statistical characterisation of these fluctuations aides radar designers in selecting optimal CPI lengths. This paper presents a statistical analysis of experimental data of nine flying drones, collected with a frequency modulated continuous wave Ku-band radar, and examines the statistical features of the amplitude fluctuations of the drone body and blades as well as the signature decorrelation time. The method of moments is used to estimate the probability density function parameters of different drone spectral components with the aim of informing the development of improved theory for predicting drone signatures and ultimately increasing detection performance. Results show that, on average, the Weibull distribution provided the best mean square error fit to the data for most drone spectral components and drone types, with the Rayleigh distribution being the next best match. These results were further corroborated by a study of detection performance for a fluctuating target. Whilst decorrelation times of the various signatures varied significantly, even for the same drone, results show that an approximate inverse relationship between drone spectral component bandwidth and decorrelation time held, with individual spectral lines decorrelating after tens to hundreds of msec.en_UK
dc.identifier.citationMarkow J, Balleri A, Catherall A. (2022) Statistical analysis of in-flight drone signatures. IET Radar Sonar and Navigation, Volume 16, Issue 11, November 2022, pp. 1737-1751en_UK
dc.identifier.issn1751-8784
dc.identifier.urihttps://doi.org/10.1049/rsn2.12293
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18345
dc.language.isoenen_UK
dc.publisherInstitution of Engineering and Technology (IET)en_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleStatistical analysis of in-flight drone signaturesen_UK
dc.typeArticleen_UK

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