Strojniški vestnik - Journal of Mechanical Engineering 58(2012)6, 386-393 Paper received: 2011-12-20, paper accepted: 2012-04-25 DOI:10.5545/sv-jme.2011.278 © 2012 Journal of Mechanical Engineering. All rights reserved. *Corr. Author’s Address: University of Defence, Military academy, Pavla Jurišica Šturma 33, Belgrade, Serbia, asmilenko@beotel.net 386 Acoustic Experimental Data Analysis of Moving Targets Echoes Observed by Doppler Radars Andrić, M. – Bondžulić, B. – Zrnić, B. – Kari, A. – Dikić, G. Milenko Andrić 1,* – Boban Bondžulić 1 – Bojan Zrnić 2 – Aleksandar Kari 1 – Goran Dikić 1 1 University of Defence, Military Academy, Belgrade, Serbia 2 Ministry of Defence, Defence Technology Department, Belgrade, Serbia In this paper we describe the main tasks of ground surveillance radars for security and perimeter protection and targets detection. Our goal is to provide a balanced and comprehensive database to enable reproducible research results in the field of classification of ground moving targets (pattern recognition). Also, in this paper, we consider received radar echoes data of ground moving targets, and corresponding signals in time – frequency domain using spectrogram and cepstrum. The database, noted as RadEch Database, containing radar echoes from various targets. The objective of the paper is to identify and validate the intrinsic features characterizing the different classes of targets, and subsequently extract salient features for classification Keywords: cepstrum, classification, Doppler signature, feature extraction, radar echoes database, spectrogram 0 INTRODUCTION The main tasks of ground surveillance radars for defence and specific perimeter protection are detection and classification of ground moving targets. This process usually takes electro magnetic (EM) radars as the base sensors and Doppler effect to estimate radial velocities. EM radars, as sensors, are a well known technology for different surveillance and measurement purposes. Signal processing of EM radars shows some advantages if it transforms into the acoustic audio signals to the end users. Signals from EM radars are very sensitive on the jamming which causes difficulties in terms of its processing, digitalization and final recognizing of their sources. Acoustic signature as a diagnostic tool has different applications in mechanical engineering [1] and distribution of acoustic waves, its form and characteristic properties initiate different methodologies to estimate [2] behavior and performances of required reflected targets, spare parts, mechanical elements, environmental areas, and components, etc. Due to the above mentioned, in the most applications of ground surveillance radars, moving targets classification is performed using their transformation of EM signals to acoustic in aim to estimate audio-Doppler signature. The Doppler phenomenon describes the shift in the center frequency of an incident waveform due to the target motion with respect to the radar [3]. Radar produces an audio signal from the Doppler frequency of moving targets. Important classes of ground targets can be distinguished by their audio Doppler signature. While the operator recognizes the moving targets using the audio Doppler signatures by listening an audio channel, this concept leads to unsatisfactory performance, limited by the human operator’s senses. In aim to avoid this, miss data base of acoustic signatures transformed from EM radar signals in the loop, is necessary. To the best of our knowledge, there is only one database with a wide class of target echoes for low resolution surveillance radar. However, there are differences between database description given in [4]. Therefore, extensive experiments with various scenarios were carried out, represented in this paper, in order to obtain such a database (different targets and environments). The second problem to achieve reliable data about those targets in using audio signals from EM radars is the method of signal processing and recognizing types and states of the targets. Many current radar-based classification systems employ some type of Doppler or Fourier-based processing, followed by spectrogram and gait analysis to classify detected targets. In several studies it has been proved that spectrogram-based features could be used for discrimination purposes either between humans and other moving objects or between different persons [5] to [8]. Human spectrograms can be used to reveal information on human behaviour and to determine features about the human target being observed, such as size, gender, action, and speed, too. Research done in [5] had shown that the human spectrogram is the sum of Doppler shifted signals. Using Short Time Fourier Transform (STFT) and the chirplet transform, they extracted various parameters of the human gait from the signal. Research done in [6], has shown that the radar Doppler signatures