Carmine Clemente

Developments in target micro-Doppler signatures analysis: radar imaging, ultrasound and through-the-wall radar

Target motions, other than the main bulk translation of the target, induce Doppler modulations around the main Doppler shift that form what is commonly called a target micro-Doppler signature. Radar micro-Doppler signatures are generally both target and action specific and hence can be used to classify and recognise targets as well as to identify possible threats. In recent years, research into the use of micro-Doppler signatures for target classification to address many defence and security challenges has been of increasing interest. In this article, we present a review of the work published in the last 10 years on emerging applications of radar target analysis using micro-Doppler signatures. Specifically we review micro-Doppler target signatures in bistatic SAR and ISAR, through-the-wall radar and ultrasound radar. This article has been compiled to provide radar practitioners with a unique reference source covering the latest developments in micro-Doppler analysis, extraction and mitigation techniques. The article shows that this research area is highly active and fast moving and demonstrates that micro-Doppler techniques can provide important solutions to many radar target classification challenges.

Robust PCA for micro-doppler classification using SVM on embedded systems

In this paper, a novel feature extraction technique for micro-Doppler classification and its real-time implementation using a support vector machine classifier on a low-cost, embedded digital signal processor are presented. The effectiveness of the proposed technique is improved through exploitation of the outlier rejection capabilities of robust principal component analysis (PCA) in place of classic PCA.

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

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 obtained 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.

GNSS-Based Passive Bistatic Radar for Micro-Doppler Analysis of Helicopter Rotor Blades

The alternative use of the Global Navigation Satellite System (GNSS) has recently initiated a number of studies that aim to exploit this system as an illuminator of opportunity for a passive radar system. A passive bistatic radar (PBR) configuration using a GNSS as illuminator in near forward scattering zone for micro-Doppler analysis is proposed. It is known that the received signal power is the main issue for this kind of passive radar. It is demonstrated that the enhancement achievable in received signal power strength when operating in a forward scattering mode can cope with this issue. The analysis focuses on the case of helicopters rotor blades where the Doppler shift is very high and a relatively large wavelength is useful in reducing the maximum Doppler shift. The power budget analysis for this kind of configuration and target is presented. This work demonstrates the possibility of detecting these kinds of targets and to measure their micro-Doppler signatures. The theoretical analysis is supported with simulations that demonstrate the effectiveness of the proposed configuration for micro-Doppler signature analysis for helicopter rotor blades.

Invariant Rules for Multipolarization SAR Change Detection

This paper deals with coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multipolarization synthetic aperture radar (SAR) change detection assuming the availability of reference and test images collected from N multiple polarimetric channels. At the design stage, the change detection problem is formulated as a binary hypothesis testing problem, and the principle of invariance is used to come up with decision rules sharing the constant false alarm rate property. The maximal invariant statistic and the maximal invariant in the parameter space are obtained. Hence, the optimum invariant test is devised proving that a uniformly most powerful invariant detector does not exist. Based on this, the class of suboptimum invariant receivers, which also includes the generalized likelihood ratio test, is considered. At the analysis stage, the performance of some tests, belonging to the aforementioned class, is assessed and compared with the optimum clairvoyant invariant detector. Finally, detection maps on real high-resolution SAR data are computed showing the effectiveness of the considered invariant decision structures.

Processing of synthetic Aperture Radar data with GPGPU

Synthetic aperture radar processing is a complex task that involves advanced signal processing techniques and intense computational effort. While the first issue has now reached a mature stage, the question of how to produce accurately focused images in real-time, without mainframe facilities, is still under debate. The recent introduction of general-purpose graphic processing units seems to be quite promising in this view, especially for the decreased per-core cost barrier and for the affordable programming complexity. The authors explain, in this work, the main computational features of a range-Doppler Synthetic Aperture Radar (SAR) processor, trying to disclose the degree of parallelism in the operations at the light of the CUDA programming model. Given the extremely flexible structure of the Single Instruction Multiple Threads (SIMT) model, the authors show that the optimization of a SAR processing unit cannot reduce to an FFT optimization, although this is a quite extensively used kernel. Actually, it is noticeable that the most significant advantage is obtained in the range cell migration correction kernel where a complex interpolation stage is performed very efficiently exploiting the SIMT model. Performance show that, using a single Nvidia Tesla-C1060 GPU board, the obtained processing time is more than fifteen time better than our test workstation.

Vibrating Target Micro-Doppler Signature in Bistatic SAR With a Fixed Receiver

Bistatic synthetic aperture radar (BSAR) provides strategical, technical and economical advantages in radar imaging. Motions and micro-motions of objects in an illuminated scene introduces Doppler and micro-Doppler effects in the received radar echoes. Combining the advantages introduced by the bistatic configuration and the usefulness of the micro-Doppler signature characterization will provide a powerful tool for military and civil remote sensing applications such as target recognition and classification. In this paper, a vibrating micro-Doppler signature for a BSAR system with fixed receiver is analyzed and compared to the signature obtained in a monostatic SAR system. The micro-Doppler effect is derived for a vibrating target in the bistatic SAR. The corresponding bistatic factor is shown to be a function of the bistatic acquisition geometry. Also, the effect of the target vibration on the focused image is shown to be influenced by the acquisition geometry. The derived model is useful for micro-Doppler classification. Simulations for 94 GHz and 10 GHz are given and the results confirm the derived model.

Range Doppler and chirp scaling processing of synthetic aperture radar data using the fractional Fourier transform

Synthetic aperture radar (SAR) systems are used to form high-resolution images from radar backscatter signals. The fractional Fourier transform (FrFT), which is a generalised form of the well-known Fourier transform, has opened up the possibility of a new range of potentially promising and useful applications that involve the use and detection of chirp signals that include pattern recognition and SAR imaging. In this study a time variant problem associated with the use of the FrFT for SAR processing is addressed and a new algorithm is presented that resolves this problem. Two new FrFT-based SAR processing algorithms are presented namely the FrRDA and the eFrCSA that are shown to improve the well-established range-Doppler and chirp-scaling algorithms for SAR processing. The performance of the algorithms are assessed using simulated and real Radarsat-1 data sets. The results confirm that the FrFT-based SAR processing methods provide enhanced resolution yielding both lower side lobes effects and improved target detection.

Pseudo-Zernike based multi-pass automatic target recognition from multi-channel SAR

The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provides the opportunity to exploit diversities to mitigate uncertainty. In this study, the authors address the problem of automatic target recognition (ATR) from synthetic aperture radar platforms. The authors approach exploits both channel (e.g. polarisation) and spatial diversity to obtain suitable information for such a critical task. In particular they use the pseudo-Zernike moments (pZm) to extract features representing commercial vehicles to perform target identification. The proposed approach exploits diversities and invariant properties of pZm leading to high confidence ATR, with limited computational complexity and data transfer requirements. The effectiveness of the proposed method is demonstrated using real data from the Gotcha dataset, in different operational configurations and data source availability.

Pseudo-Zernike moments based radar micro-Doppler classification

Reliable micro-Doppler signature classification requires the use of robust features describing uniquely 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 demonstrating the effectiveness of the proposed approach by classifying real data. The use of pseudo-Zernike moments allows invariant features to be obtained that are able to discriminate the content of two-dimensional matrices with a small number of coefficients.