Peter Jancovic

TL;DR: In this article, an experimental study is undertaken of the feasibility of forward scattering radar (FSR) and its application to automatic ground target classification, which extracts features from the radar measurements by using Fourier transform and principle component analysis and uses a nearest neighbor classifier.

TL;DR: The proposed detection method shows high detection accuracy of bird tonal components and significant recognition accuracy improvements over the Mel-frequency cepstral coefficients, in both standard and noise-compensated models, and strong robustness to mismatch between the training and testing conditions.

TL;DR: The performances of several classification methods are compared, including Gaussian Mixture Model–Universal Background Model (GMM–UBM), GMM–Support Vector Machine (G MM–SVM) and i-vector based approaches, and the utility of different frequency bands for speaker, age-group and gender recognition from children’s speech is assessed.

TL;DR: The concept of a novel forward scattering micro-radar wireless network for ground targets detection and recognition is presented and signal processing strategies used for target detection, parameter estimation and automatic target recognition are briefly explained and supported with experimental results.

TL;DR: The theory and implementation of the probabilistic union model, and a combination of the union model with conventional noise reduction techniques to deal with a mixture of stationary noise and unknown, nonstationary noise, are introduced.