Micro-Doppler Signatures - Review, Challenges, and Perspectives

Griffiths

2016

Fioranelli, F., Ritchie, M., and Griffiths, H. (2016) Centroid features for classification of armed/unarmed multiple personnel using multistatic human microdoppler. IET Radar, Sonar and Navigation, (doi:10.1049/iet-rsn.2015.0493) This paper is a postprint of a paper submitted to and accepted for publication in IET Radar, Sonar and Navigation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/118776/ AbstractThis paper analyses the use of human micro-Doppler signatures collected using a multistatic radar system to identify and classify unarmed and potentially armed personnel walking within a surveillance area. The signatures were recorded in a series of experimental tests and analysed through Short Time FourierTransform followed by feature extraction and classification. Features based on Singular ValueDecomposition and on the centroid of the micro-Doppler signature are proposed and their suitability for armed vs unarmed classification purposes discussed. It is shown that classification accuracy above 95%can be achieved using a single feature. Features based on the centroid of the signatures are shown to be also effective in cases where there are two people walking together in the same direction and at similar speed, and one of them may be armed or not, i.e. for targets not easily separable in range or in Doppler.

Section: Introductionmentioning

confidence: 99%

Centroid features for classification of armed/unarmed multiple personnel using multistatic human micro‐Doppler

Fioranelli

Griffiths

2016

“…Humans have particular characteristic micro-Doppler signatures as a result of the motion of their body and limbs [1,2]. This has been investigated over a number of years in the context of security, warfare, and search and rescue applications to perform target recognition and classification in cluttered environments [3]. Micro-Doppler signatures collected with a monostatic radar have been analysed using Short Time Fourier Transform (STFT) and suitable features extracted from the spectrograms, achieving successful classification of different human activities [4,5].…”

Section: Introductionmentioning

confidence: 99%

Aspect angle dependence and multistatic data fusion for micro‐Doppler classification of armed/unarmed personnel

Fioranelli

Griffiths

2015

This paper discusses the analysis of multistatic micro-Doppler signatures and related features to distinguish and classify unarmed and potentially armed personnel. The application of radar systems to distinguish different motion types has been previously proposed and this work aims to further investigate the applicability of this in more scenarios. Real data have been collected using a multistatic radar system in a series of experiments involving several individuals performing different movements. Changes in classification accuracy as a function of different aspect angle between the direction in which the target faces and the line-of-sight of the radar nodes are analysed. Multiple data fusion methodologies are proposed, showing that significant improvement of the classification accuracy can be achieved when using separate classification at each node followed by a voting procedure to reach the final decision. This is beneficial especially at those aspect angles for which micro-Doppler detection is less favourable.

“…Far-field measurements appear to be therefore infeasible with the available radar systems. This issue was also mentioned in [15,43], arguing that it is not unlikely that wind farms, especially those with large and tall turbines, can be located in the near-field of practical radar systems and therefore the analysis of near-field data is still significant and reasonable. …”

Section: Experimental Setup and Radar Systemsmentioning

confidence: 99%

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

Fioranelli¹,

Balleri

et al. 2017

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.