Picture water droplets

Developing mathematical theories of the physical world: Open Access research on fluid dynamics from Strathclyde

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Mathematics & Statistics, where continuum mechanics and industrial mathematics is a specialism. Such research seeks to understand fluid dynamics, among many other related areas such as liquid crystals and droplet evaporation.

The Department of Mathematics & Statistics also demonstrates expertise in population modelling & epidemiology, stochastic analysis, applied analysis and scientific computing. Access world leading mathematical and statistical Open Access research!

Explore all Strathclyde Open Access research...

Performance analysis of co-located and distributed MIMO radar for micro-doppler classification

Bugra Ozcan, Mustafa and Gurbuz, Sevgi Zubeyde and Persico, Adriano Rosario and Clemente, Carmine and Soraghan, John (2016) Performance analysis of co-located and distributed MIMO radar for micro-doppler classification. In: European Radar Conference 2016, EuRAD 2016, 2016-10-03 - 2016-10-07.

[img]
Preview
Text (Ozcan-etal-EURAD2016-performance-analysis-co-located-distributed-MIMO-radar)
Ozcan_etal_EURAD2016_performance_analysis_co_located_distributed_MIMO_radar.pdf
Accepted Author Manuscript

Download (520kB) | Preview

Abstract

Over the past few years, the use of Multiple Input Multiple Output (MIMO) radar has gained increased attention as a way to mitigate the degredation of micro-Doppler classification performance incurred when the aspect angle approaches 90 degrees. In this work, the efficacy of co-located MIMO radar is compared with that of distributed MIMO. The performance anaylsis is accomplished for three different classification problems: 1) discrimination of a walking group of people from a running group of people; 2) identification of individual human activities, and 3) classification of different types of walking. In the co-located configuration each radar is placed side by side so as to form a line. In the distributed configuration, the radar positions are separated to observe the subjects from different angles. Starting from the cadence velocity diagram (CVD), the Pseudo-Zernike moments based features are extracted because of their robustness with respect to unwanted scalar and angular dependencies. Two different approaches to integrate the features obtained from multi-aspect data are compared: concatenation and principal component analysis (PCA). Results show that a distributed MIMO configuration and use of PCA to fuse multiperspective features yields higher classification performance as compared to a co-located configuration or feature vector concatenation.