The database of radar echoes from various targets with spectral analysis

Andrić, Milenko; Bondžulić, Boban; Zrnic, B.

doi:10.1109/neurel.2010.5644074

Cited by 23 publications

(14 citation statements)

References 8 publications

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“…The algorithm has been tested on real Ku band radar data, with short range within radar and target (100 -1000 m) [23][24][25]. The analysis has been conducted on an As used previously, from all the available samples, 70% are used for training, while the other 30% are used for testing.…”

Section: A Experimental Results On Ku Band Radar Datamentioning

confidence: 99%

“…The choice of the spectrogram, rather than other time-frequency distributions, is motivated by its robustness with respect to interference terms present in the so-called energy distributions [22]. An example of the spectrogram of s(n) for a running human observed with a 16 GHz carrier frequency radar [23][24][25] is shown in Fig. 3(a).…”

Section: B Feature Extraction Algorithmmentioning

confidence: 99%

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A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials

Clemente

Pallotta

Maio

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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Phase modulation induced by target micromotions introduces sidebands in the radar spectral signature returns. Time-frequency distributions facilitate the representation of such modulations in a micro-Doppler signature that is useful in the characterization and classification of targets. Reliable micro-Doppler signature classification requires the use of robust features that are capable of uniquely describing the micromotion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper, the application of the pseudo-Zernike moments for micro-Doppler classification is introduced. Specifically, the proposed algorithm consists of the extraction of the pseudo-Zernike moments from the cadence velocity diagram (CVD). The use of pseudo-Zernike moments allows invariant features to be obtainedManuscript that are able to discriminate the content of two-dimensional matrices with a small number of coefficients. The analysis has been conducted both on simulated and on real radar data, demonstrating the effectiveness of the proposed approach for classification purposes.

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Section: A Experimental Results On Ku Band Radar Datamentioning

confidence: 99%

Section: B Feature Extraction Algorithmmentioning

confidence: 99%

A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials

Clemente

Pallotta

Maio

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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show abstract

“…Detailed description of database can be found in [13], and database is freely available for download at [14].…”

Section: Vegetation Clutter (Trees Bush)mentioning

confidence: 99%

Analysis of Radar Doppler Echoes from Various Ground Moving Targets

Andrić

Bondžulić

Bujaković

et al. 2011

International Conference on Aerospace Sciences and Aviation Tec

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The main tasks of ground surveillance radars for security and perimeter protection are detection and classification of moving ground targets. In typical radar systems, target detection is fully automated, but the target classification requires human involvement. In this paper, we consider received radar echoes data of ground moving targets, and corresponding signals in timefrequency domain using spectrograms and cepstrum. 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. We will show examples on Radar Echoes Database. This database, named RadEch Database, cointaines radar echoes that are collected in controlled test enviroments at the premises of Military Academy-Republic of Serbia. Database purpose is to enable reproducible research results in the field of classification of ground moving targets (pattern recognition).

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“…Some of these reported recognition accuracies, e.g. Bilik et al [1] used greedy learning of Gaussian mixture model for a wide range of ground surveillance targets such as walking person(s), tracked or wheeled vehicles, animals and clutter; Smith et al [2] used dynamic time warping for micro-Doppler signature classification; Eryildirim and Onaran [3] used Mel-frequency cepstral coefficients to classify stationary and moving targets using ground surveillance pulse-Doppler radar; Molchanov et al [4] used discrete cosine transform for ground moving target classification; Liao et al [5] used RELAX features for identification of ground targets from sequential high-range-resolution radar signatures; Andrićet al used spectral analysis [6] and used spectrogram based features [7] to classify radar echoes from moving ground targets. The number of classes and accuracy achieved in these methods will be given in the 'Results and Discussion' section of this Letter.…”

mentioning

confidence: 99%

Ground moving target recognition using log energy entropy of wavelet packets

Göksu

2018

Electron. lett.

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Automatic target recognition of ground surveillance radars is a beneficial practice that reduces cost, error, duration of detection and response time. The identification of echoes from eight classes using an RF pulsed Doppler radar is studied. Instead of traditional methods, the wavelet packet analysis (WPA) is used. WPA, by providing arbitrary time-frequency resolution, enables analysing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. WPA subimages are further analysed to obtain feature vectors of log energy entropy in all cases except one case where norm entropy is used. These features are fed into a multilayer perceptron for classification which is done in a hierarchical scheme and composed of binary classification steps. Hundred per cent accuracy was possible in all steps except two which were realised at 99.5 and 98.2%. An overall classification rate of 98.2% was achieved. The results were compared with other published results that report accuracy in the same application field. The proposed method achieved the highest accuracy although it had the highest number of classes. The method provides a promising tool for the automatic recognition of ground surveillance radar targets.

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The database of radar echoes from various targets with spectral analysis

Cited by 23 publications

References 8 publications

A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials

A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials

Analysis of Radar Doppler Echoes from Various Ground Moving Targets

Ground moving target recognition using log energy entropy of wavelet packets

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