34 research outputs found
Advanced signal processing solutions for ATR and spectrum sharing in distributed radar systems
Previously held under moratorium from 11 September 2017 until 16 February 2022This Thesis presents advanced signal processing solutions for Automatic
Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems.
Two Synthetic Aperture Radar (SAR) ATR algorithms are described for
full- and single-polarimetric images, and tested on the GOTCHA and the
MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments,
that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to
the availability of several images from multiple sensors through the implementation of a simple fusion rule.
A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by
the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating
the number, the length and the rotation speed of the blades, parameters
that are peculiar for each helicopter’s model. The algorithm is extended to
deal with the identification of multiple helicopters flying in formation that
cannot be resolved in another domain. Moreover, a fusion rule is presented
to integrate the results of the identification performed from several sensors
in a distributed radar system. Tests performed both on simulated signals
and on real signals acquired from a scale model of a helicopter, confirm
the validity of the algorithm.
Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp
sub-carriers generated through the Fractional Fourier Transform (FrFT),
with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a
cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based
waveform is extensively tested and compared with Orthogonal Frequency
Division Multiplexing (OFDM) and LFM waveforms, in order to assess
both its radar and communication performance.This Thesis presents advanced signal processing solutions for Automatic
Target Recognition (ATR) operations and for spectrum sharing in distributed radar systems.
Two Synthetic Aperture Radar (SAR) ATR algorithms are described for
full- and single-polarimetric images, and tested on the GOTCHA and the
MSTAR datasets. The first one exploits the Krogager polarimetric decomposition in order to enhance peculiar scattering mechanisms from manmade targets, used in combination with the pseudo-Zernike image moments. The second algorithm employs the Krawtchouk image moments,
that, being discrete defined, provide better representations of targets’ details. The proposed image moments based framework can be extended to
the availability of several images from multiple sensors through the implementation of a simple fusion rule.
A model-based micro-Doppler algorithm is developed for the identification of helicopters. The approach relies on the proposed sparse representation of the signal scattered from the helicopter’s rotor and received by
the radar. Such a sparse representation is obtained through the application of a greedy sparse recovery framework, with the goal of estimating
the number, the length and the rotation speed of the blades, parameters
that are peculiar for each helicopter’s model. The algorithm is extended to
deal with the identification of multiple helicopters flying in formation that
cannot be resolved in another domain. Moreover, a fusion rule is presented
to integrate the results of the identification performed from several sensors
in a distributed radar system. Tests performed both on simulated signals
and on real signals acquired from a scale model of a helicopter, confirm
the validity of the algorithm.
Finally, a waveform design framework for joint radar-communication systems is presented. The waveform is composed by quasi-orthogonal chirp
sub-carriers generated through the Fractional Fourier Transform (FrFT),
with the aim of preserving the radar performance of a typical Linear Frequency Modulated (LFM) pulse while embedding data to be sent to a
cooperative system. Techniques aimed at optimise the design parameters and mitigate the Inter-Carrier Interference (ICI) caused by the quasiorthogonality of the chirp sub-carriers are also described. The FrFT based
waveform is extensively tested and compared with Orthogonal Frequency
Division Multiplexing (OFDM) and LFM waveforms, in order to assess
both its radar and communication performance
MACHe - Model-based algorithm for classification of helicopters
Secondary motions of a target, such as rotating blades of a helicopter's main rotor, induce a Doppler modulation around the main Doppler shift. This represents a unique feature of the target itself, known as micro-Doppler signature, and can be used for classification purposes. In this student research highlight a model-based automatic helicopter classification algorithm is presented. It is a parametric classification approach based on a sparse signal recovery method and it is independent of both the initial position of the blades and the aspect angle. The algorithm is tested on simulated and real data
Automatic recognition of military vehicles with Krawtchouk moments
The challenge of Automatic Target Recognition (ATR) of military targets within a Synthetic Aperture Radar (SAR) scene is addressed in this paper. The proposed approach exploits the discrete defined Krawtchouk moments, that 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 presence of noise and reduced sensitivity to discretization errors. The effectiveness of the proposed approach is demonstrated using the MSTAR dataset
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 the 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
3D Localization and Tracking Methods for Multi-Platform Radar Networks
Multi-platform radar networks (MPRNs) are an emerging sensing technology due
to their ability to provide improved surveillance capabilities over plain
monostatic and bistatic systems. The design of advanced detection,
localization, and tracking algorithms for efficient fusion of information
obtained through multiple receivers has attracted much attention. However,
considerable challenges remain. This article provides an overview on recent
unconstrained and constrained localization techniques as well as multitarget
tracking (MTT) algorithms tailored to MPRNs. In particular, two data-processing
methods are illustrated and explored in detail, one aimed at accomplishing
localization tasks the other tracking functions. As to the former, assuming a
MPRN with one transmitter and multiple receivers, the angular and range
constrained estimator (ARCE) algorithm capitalizes on the knowledge of the
transmitter antenna beamwidth. As to the latter, the scalable sum-product
algorithm (SPA) based MTT technique is presented. Additionally, a solution to
combine ARCE and SPA-based MTT is investigated in order to boost the accuracy
of the overall surveillance system. Simulated experiments show the benefit of
the combined algorithm in comparison with the conventional baseline SPA-based
MTT and the stand-alone ARCE localization, in a 3D sensing scenario
Fractional fourier transform 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 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 have a good rejection 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
Quickest Detection and Forecast of Pandemic Outbreaks: Analysis of COVID-19 Waves
The COVID-19 pandemic has, worldwide and up to December 2020, caused over 1.7
million deaths, and put the world's most advanced healthcare systems under
heavy stress. In many countries, drastic restriction measures adopted by
political authorities, such as national lockdowns, have not prevented the
outbreak of new pandemic's waves. In this article, we propose an integrated
detection-estimation-forecasting framework that, using publicly available data
published by the national authorities, is designed to: (i) learn relevant
features of the epidemic (e.g., the infection rate); (ii) detect as quickly as
possible the onset (or the termination) of an exponential growth of the
contagion; and (iii) reliably forecast the epidemic evolution. The proposed
solution is validated by analyzing the COVID-19 second and third waves in the
USA.Comment: Submitted to IEEE Communications Magazine, feature topic "Networking
Technologies to Combat the COVID-19 Pandemic