Measurements and analysis of the radar signature of a new wind turbine design at
            <i>X</i>
            ‐band

Balleri, Alessio; Al-Armaghany, Allann; Griffiths, Hugh; Tong, Kin‐Fai; Matsuura, Takashi; Karasudani, Takashi; Ohya, Yuji

doi:10.1049/iet-rsn.2012.0026

Cited by 19 publications

(17 citation statements)

References 13 publications

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“…The Doppler centroid is an estimation of the centre of gravity of the micro-Doppler signature, whereas the Doppler bandwidth provides an estimate of the signature bandwidth around the centroid. These features have also been previously used in [23] when evaluating wind turbine signatures.…”

Section: Centroid Featuresmentioning

confidence: 99%

Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones

Ritchie

Fioranelli

Borrion

et al. 2017

IET Radar, Sonar & Navigation

Self Cite

135

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This paper presents the use of micro-Doppler signatures collected by a multistatic radar to detect and discriminate between micro-drones hovering and flying while carrying different payloads, which may be an indication of unusual or potentially hostile activities. Different features have been extracted and tested, namely features related to the Radar Cross Section of the micro-drones, as well as the Singular Value Decomposition (SVD) and centroid of the micro-Doppler signatures. In particular, the added benefit of using multistatic information in comparison with conventional radar is quantified. Classification performance when identifying the weight of the payload that the drone was carrying while hovering was found to be consistently above 96% using the centroid-based features and multistatic information. For the non-hovering scenarios classification results with accuracy above 95% were also demonstrated in preliminary tests in discriminating between three different payload weights.

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Section: Centroid Featuresmentioning

confidence: 99%

Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones

Ritchie

Fioranelli

Borrion

et al. 2017

IET Radar, Sonar & Navigation

Self Cite

135

View full text Add to dashboard Cite

show abstract

“…Two features based on the Doppler and bandwidth centroid of the micro-Doppler signatures have been identified as suitable for the loaded/unloaded classification [9]. The first parameter gives an indication of the centre of gravity of the micro-Doppler signature, and the second provides an estimate of the signature bandwidth around the centroid.…”

Section: Fig 2 Micro-doppler Signatures For the Drone Hovering: (A) mentioning

confidence: 99%

Classification of loaded/unloaded micro‐drones using multistatic radar

et al. 2015

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This letter presents preliminary results on the use of multistatic radar and micro-Doppler analysis to detect and discriminate between microdrones hovering carrying different payloads. Two suitable features related to the centroid of the micro-Doppler signature have been identified and used to perform classification, investigating also the added benefit of using information from a multistatic radar as opposed to a conventional monostatic system. Very good performance with accuracy above 90% has been demonstrated for the classification of hovering micro-drones.

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“…independence from any normalisation, so they can be applied also to non-calibrated data without losing information. These parameters have been calculated as in equation (1) and (2), where S(i,j) is the spectrogram at the i th Doppler bin and j th time bin, and fC is the Doppler centroid, BC the Bandwidth centroid [25]. Table 1 for each polarisation and turbine under test considered, indicating the error for each radar node (transmitter and receiver) and the minimum error obtained across different distributions in bold font for each case.…”

Section: S-band Datamentioning

confidence: 99%

“…The performance of realistic radar systems affected by wind farm clutter has also been characterised, for example for the effectiveness of Ground Moving Target Indication (GMTI) algorithms for targets within the wind farm area [17], for the behaviour of weather radar stations [18][19][20], for maritime radar systems [21][22], and for the detection and tracking of unauthorised aircraft entering restricted zones [23]. A different approach aims to develop totally new designs for wind farms to replace the conventional horizontal axis turbines with 3 blades, for instance vertical axis turbines such as those simulated and tested with a scaled model in [24], or wind lens turbines developed and tested experimentally in [25]. In [26], partial reshaping of the nacelle design combined with the application of RAM material has been considered to reduce the overall RCS, by taking also into account the costs of this intervention.…”

Section: Introductionmentioning

confidence: 99%

Practical investigation of multiband mono‐ and bistatic radar signatures of wind turbines

Fioranelli¹,

Ritchie

Balleri

et al. 2017

IET Radar, Sonar & Navigation

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The negative effects wind farm clutter has on the performance of radar systems for Air Traffic Control and Air Surveillance is well-known in the radar research community and several mitigation techniques have been proposed to address this problem. These include bistatic and multistatic radar systems providing multiple views of the area under surveillance, and hence potential additional information that can be used to improve the receiver performance. This paper presents the analysis of a set of experimental data collected simultaneously by two radar systems, one operating at S-band and one at Xband, of echoes from an operational wind farm in the UK near Oxford. This analysis presents several parameters extracted from the time domain data and the Doppler spectra, such as Doppler centroid and bandwidth of the micro-Doppler signature as well as amplitude statistics of the time domain returns.These parameters are characterised using data recorded at monostatic and bistatic nodes, as well as at different polarisation combinations.

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Measurements and analysis of the radar signature of a new wind turbine design at X ‐band

Cited by 19 publications

References 13 publications

Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones

Multistatic micro‐Doppler radar feature extraction for classification of unloaded/loaded micro‐drones

Classification of loaded/unloaded micro‐drones using multistatic radar

Practical investigation of multiband mono‐ and bistatic radar signatures of wind turbines

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