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Automatic target classification under all conditions is a key challenge for modern radar and sonar systems. Echolocating nectar feeding bats are able to detect and select flowers of bat-pollinated plants, even in highly cluttered environments. It is thought, that these flowers have evolved to ease classification by bats, and that their echo-acoustic signatures contain critical information, that aids the bat in choosing the most suitable flowers. In investigating the features of these flowers that aid the bats search for nectar, the strategy underpinning the task of classification of static targets by bats may be understood, and this may additionally offer lessons for radar and sonar systems. In this article, the authors analyse a real set of data, containing high range resolution profiles of unpollinated corollas of Cobaea scandens, which is a type of flower that is pollinated by bats. Classification performance of a k-NN classifier and a Naive Bayesian classifier is assessed using information available in both the time and frequency domains.

Bistatic radar is a subject of considerable present interest. Despite this, current understanding of the properties of bistatic clutter, and in particular, bistatic sea clutter, is limited at its best. The purpose of this study is to present an analysis of the limited existing published radar data and to derive an empirical model, which expresses the variation of mean sigma super( 0) with the measurement geometry and sea conditions. This empirical model is then compared with electromagnetic (EM) scattering calculations using the composite model, (sometimes called the two-scale model) to show the extent, to which the EM model is able to reproduce the trends observed in the data. The results indicate, where improvements to bistatic EM modelling are required (very low grazing angles and out-of-plane scattering), and a general need for more radar data to extend the empirical sigma super( 0) model and expand it to the bistatic clutter statistics.

Presented is an ambiguity function analysis of Digital Radio Mondiale signals in the HF band to demonstrate their suitability for HF passive bistatic radar. The ambiguities and interference floor are studied for different integration times. It is shown that interference floors of -40dB are achieved with a 5s integration time and that the ambiguity function properties are virtually independent of broadcast content, resulting in a very useful illuminator of opportunity.

A radar target detection scheme based on wavelet determination of scatterer lifetime within the Doppler spectra of the non-Gaussian sea clutter was reported. It was demonstrated that the target detection was possible for slowly varying real target within the clutter spectrum based on a physical measure of scatterer lifetime. The method was found to be complementary to simple intensity thresholding when applied to be a low-observable time varying target.

This paper evaluates the classification performance of a dual polarised on receive, 24 GHz Frequency Modulated Continuous Wave (FMCW) radar system to autonomously identify micro-Doppler signatures of unique hand gestures. We employ an Eigen subspace feature selection technique on the calculated signal subspace in order to classify each gesture. Measurements using the dual polarised radar, permitting simultaneous recording of both the co-pol and cross-pol returns, are evaluated with this processing technique and results are reported herein. Our analysis displays the challenges presented by the high variance in individuals gestures and the limited additional information the cross polarised returns have provided to the classifier. Classification performance comparisons are presented when co, cross and dual polarised data are provided to the classifier. With this technique we achieve autonomous classification performance of up to 84.6% when Eigenvalue derived features are used for classification.