Domenico Gaglione

Model-based sparse recovery method for automatic classification of helicopters

The rotation of rotor blades of a helicopter induces a Doppler modulation around the main Doppler shift. Such a non-stationary modulation, commonly called micro-Doppler signature, can be used to perform classification of the target. In this paper a model-based automatic helicopter classification algorithm is presented. A sparse signal model for radar return from a helicopter is developed and by means of the theory of sparse signal recovery, the characteristic parameters of the target are extracted and used for the classification. This approach does not require any learning process of a training set or adaptive processing of the received signal. Moreover, it is robust with respect to the initial position of the blades and the angle that the LOS forms with the perpendicular to the plane on which the blades lie. The proposed approach is tested on simulated and real data.

Automatic Target Recognition of Military Vehicles With Krawtchouk Moments

The challenge of automatic target recognition of military targets within a synthetic aperture radar scene is addressed in this paper. The proposed approach exploits the discrete-defined Krawtchouk moments, which are able to represent a detected extended target with few features, allowing its characterization. The proposed algorithm provides robust performance for target recognition, identification, and characterization, with high reliability in the presence of noise and reduced sensitivity to discretization errors. The effectiveness of the proposed approach is demonstrated using the MSTAR dataset.

Fractional fourier based waveform for a joint radar-communication system

The increasing demand of spectrum resources and the need to keep the size, weight and power consumption of modern radar as low as possible, has led to the development of solutions like joint radar-communication systems. In this paper a novel Fractional Fourier Transform (FrFT) based multiplexing scheme is presented as a joint radar-communication technique. The FrFT is used to embed data into chirp sub-carriers with different time-frequency rates. Some optimisation procedures are also proposed, with the objective of improving the bandwidth occupancy and the bit rate and/or Bit Error Ratio (BER). The generated waveform is demonstrated to be robust to distortions introduced by the channel, leading to low BER, while keeping good radar characteristics compared to a widely used Linear Frequency Modulated (LFM) pulse with same duration and bandwidth.

Micro-Doppler Based Recognition of Ballistic Targets Using 2D Gabor Filters

The capability to recognize ballistic threats, is a critical topic due to the increasing effectiveness of resultant objects and to economical constraints. In particular the ability to distinguish between warheads and decoys is crucial in order to mitigate the number of shots per hit and to maximize the ammunition capabilities. For this reason a reliable technique to classify warheads and decoys is required. In this paper the use of micro-Doppler signatures in conjunction with the 2-Dimensional Gabor filter is presented for this problem. The effectiveness of the proposed approach is demonstrated through the use of real data.

Reuse of fractional waveform libraries for MIMO radar and electronic countermeasures

A fundamental aspect in the hardware-software design of modern radar systems, for example MIMO or Low Probability of Intercept Radar, is to operate in electromagnetically crowded environments. Proper radar waveform design is central to effective solutions in such systems. In this paper cross-interference and waveform reuse for a set of waveform libraries based on the fractional Fourier transform are presented and analysed. The results demonstrate the potential of the novel libraries in increasing the number of available waveforms and for stealth transmissions.

On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets

The ability to discriminate between ballistic missile warheads and confusing objects is an important topic from different points of view. In particular, the high cost of the interceptors with respect to tactical missiles may lead to an ammunition problem. Moreover, since the time interval in which the defense system can intercept the missile is very short with respect to target velocities, it is fundamental to minimize the number of shoots per kill. For this reason, a reliable technique to classify warheads and confusing objects is required. In the efficient warhead classification system presented in this paper, a model and a robust framework is developed, which incorporates different micro-Doppler-based classification techniques. The reliability of the proposed framework is tested on both simulated and real data.

GNSS based passive bistatic radar for micro-Doppler based classification of helicopters: Experimental validation

The capability of using illuminators of opportunity for target classification is of great interest to the radar community. In particular the alternative use of Global Navigation Satellite System (GNSS) has recently initiated a number of studies that aim to exploit this source of illumination for passive radar. We recently introduced the concept of a GNSS based passive radar for extraction of micro-Doppler signatures from helicopter rotor blades with the aim of identify these kind of targets. In this paper we present the experimental validation of our concept with real data from two different models of helicopter.

Fractional Fourier Transform Based Co-Radar Waveform: Experimental Validation

A Fractional Fourier Transform (FrFT) based waveform design for joint radar-communication systems (Co-Radar) that embeds data into chirp sub-carriers with different time-frequency rates has been recently presented. Simulations demonstrated the possibility to reach data rates as high as 3.660 Mb/s while maintaining good radar performance compared to a Linear Frequency Modulated (LFM) pulse that occupies the same bandwidth. In this paper the experimental validation of the concept is presented. The system is considered in its basic configuration, with a mono-static radar that generates the waveforms and performs basic radar tasks, and a communication receiver in charge of the pulse demodulation. The entire network is implemented on a Software Defined Radio (SDR) device. The system is then used to acquire data and assess radar and communication capabilities.

Label Consistent K-SVD for sparse micro-Doppler classification

Secondary motions of targets observed by radar introduce non-stationary returns containing the so-called micro-Doppler information. This is characterizing information that can be exploited to enhance automatic target recognition systems. In this paper, the challenge of classifying the micro-Doppler return of helicopters is addressed. A robust dictionary learning algorithm, Label Consistent K-SVD (LC-KSVD), is applied to identify effectively and efficiently helicopters. The effectiveness of the proposed algorithm is demonstrated on both synthetic and real radar data.

Krogager decomposition and Pseudo-Zernike moments for polarimetric distributed ATR

Automatic Target Recognition (ATR) is one of the most challenging areas of the modern radar signal processing field. In this paper a recognition algorithm for full-polarimetric SAR images, that is robust with respect to rotations and target roll, is presented. It is based on the use of the pseudo-Zernike moments and the Krogager decomposition components, and exploits multiple sources of information such as polarization and spatial diversity. The effectiveness of the proposed approach has been demonstrated with real full polarimetric SAR data.