Browsing by Author "Markow, John"

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    Examination of drone micro-Doppler and JEM/HERM signatures
    (IEEE, 2020-12-04) Markow, John; Balleri, Alessio
    Radars monitoring small targets often must increase their integration times to achieve sufficient signal-to-noise ratio (SNR) for maintaining a viable track. These longer integation times can prevent micro-Doppler signature extraction and instead result in Doppler signatures consisting of spectral lines to the radar's higher-level processing. Whether the radar operates in the micro-Doppler or spectral line regime depends on both radar parameters (e.g. waveforms, wavelengths and integration times) as well as target parameters (e.g. rotor length, rotational frequency, target reflectivity and geometry). Additionally, understanding the transition region between these regimes can further aid target recognition algorithms. This paper uses modelling, simulations and experimental data to refine the understanding of how a particular radar will observe a target Doppler signature in either of these regimes, highlighting the transition region between the two.
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    Statistical analysis of in-flight drone signatures
    (Institution of Engineering and Technology (IET), 2022-08-16) Markow, John; Balleri, Alessio; Catherall, Aled
    Drone-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.