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

Deep Learning and Polar Transformation to Achieve a Novel Adaptive Automatic Modulation Classification Framework

Automatic modulation classification (AMC) is an approach that can be leveraged to identify an observed signal\u27s most likely employed modulation scheme without any a priori knowledge of the intercepted signal. Of the three primary approaches proposed in literature, which are likelihood-based, distribution test-based, and feature-based (FB), the latter is considered to be the most promising approach for real-world implementations due to its favorable computational complexity and classification accuracy. FB AMC is comprised of two stages: feature extraction and labeling. In this thesis, we enhance the FB approach in both stages. In the feature extraction stage, we propose a new architecture in which it first removes the bias issue for the estimator of fourth-order cumulants, then extracts polar-transformed information of the received IQ waveform\u27s samples, and finally forms a unique dataset to be used in the labeling stage. The labeling stage utilizes a deep learning architecture. Furthermore, we propose a new approach to increasing the classification accuracy in low signal-to-noise ratio conditions by employing a deep belief network platform in addition to the spiking neural network platform to overcome computational complexity concerns associated with deep learning architecture. In the process of evaluating the contributions, we first study each individual FB AMC classifier to derive the respective upper and lower performance bounds. We then propose an adaptive framework that is built upon and developed around these findings. This framework aims to efficiently classify the received signal\u27s modulation scheme by intelligently switching between these different FB classifiers to achieve an optimal balance between classification accuracy and computational complexity for any observed channel conditions derived from the main receiver\u27s equalizer. This framework also provides flexibility in deploying FB AMC classifiers in various environments. We conduct a performance analysis using this framework in which we employ the standard RadioML dataset to achieve a realistic evaluation. Numerical results indicate a notably higher classification accuracy by 16.02% on average when the deep belief network is employed, whereas the spiking neural network requires significantly less computational complexity by 34.31% to label the modulation scheme compared to the other platforms. Moreover, the analysis of employing framework exhibits higher efficiency versus employing an individual FB AMC classifier. Advisor: Hamid R. Sharif-Kashan

Sparse and Nonnegative Factorizations For Music Understanding

In this dissertation, we propose methods for sparse and nonnegative factorization that are specifically suited for analyzing musical signals. First, we discuss two constraints that aid factorization of musical signals: harmonic and co-occurrence constraints. We propose a novel dictionary learning method that imposes harmonic constraints upon the atoms of the learned dictionary while allowing the dictionary size to grow appropriately during the learning procedure. When there is significant spectral-temporal overlap among the musical sources, our method outperforms popular existing matrix factorization methods as measured by the recall and precision of learned dictionary atoms. We also propose co-occurrence constraints -- three simple and convenient multiplicative update rules for nonnegative matrix factorization (NMF) that enforce dependence among atoms. Using examples in music transcription, we demonstrate the ability of these updates to represent each musical note with multiple atoms and cluster the atoms for source separation purposes. Second, we study how spectral and temporal information extracted by nonnegative factorizations can improve upon musical instrument recognition. Musical instrument recognition in melodic signals is difficult, especially for classification systems that rely entirely upon spectral information instead of temporal information. Here, we propose a simple and effective method of combining spectral and temporal information for instrument recognition. While existing classification methods use traditional features such as statistical moments, we extract novel features from spectral and temporal atoms generated by NMF using a biologically motivated multiresolution gamma filterbank. Unlike other methods that require thresholds, safeguards, and hierarchies, the proposed spectral-temporal method requires only simple filtering and a flat classifier. Finally, we study how to perform sparse factorization when a large dictionary of musical atoms is already known. Sparse coding methods such as matching pursuit (MP) have been applied to problems in music information retrieval such as transcription and source separation with moderate success. However, when the set of dictionary atoms is large, identification of the best match in the dictionary with the residual is slow -- linear in the size of the dictionary. Here, we propose a variant called approximate matching pursuit (AMP) that is faster than MP while maintaining scalability and accuracy. Unlike MP, AMP uses an approximate nearest-neighbor (ANN) algorithm to find the closest match in a dictionary in sublinear time. One such ANN algorithm, locality-sensitive hashing (LSH), is a probabilistic hash algorithm that places similar, yet not identical, observations into the same bin. While the accuracy of AMP is comparable to similar MP methods, the computational complexity is reduced. Also, by using LSH, this method scales easily; the dictionary can be expanded without reorganizing any data structures

Automatic music genre classification

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfillment of the requirements for the degree of Master of Science. 2014.No abstract provided

A Study of Neologisms in Tolkien's The Lord of the Rings. Compounds, Affixation, and their translation into Spanish

As far as neologisms are concerned, translation is an essential and complex element in the international distribution of novels, since at the linguistic level each language has its own cultural and grammatical structure, and the best possible adaptation is necessary so that the message conveyed by the author in the source language is complete in the target language. This project focuses on the morphological analysis and translation of a selection of neologisms from the trilogy written by Tolkien, The Lord of the Rings. Specifically, the study focuses on compound and derived neologisms, and the different types of translation that follow. These two types of word formation will be taken into account for the analysis of a corpus of 50 neologisms from the trilogy. The results show that translation patterns followed depend directly on the characteristics of the neologism to be translated, and on specific linguistic factors in the target language.Respecto a los neologismos, la traducción resulta un elemento esencial y complejo a la hora de la distribución de las novelas a nivel internacional, a nivel lingüístico cada lengua tiene estructura cultura y gramatical propia, y es necesaria la mejor adaptación posible para que el mensaje que transmite el autor en la lengua origen sea total en la lengua meta. Este proyecto se centra en el análisis morfológico, etimológico y la traducción de una selección de neologismos de la trilogía escrita por Tolkien, El Señor de los Anillos. El estudio se centra en neologismos compuestos y derivados, y los distintos tipos de traducción seguidos. Estos dos tipos de formación de palabras contarán para el análisis de un corpus de 50 neologismos. Los resultados derivados de este análisis muestran que los patrones de traducción dependen de las características del neologismo traducido, y los factores lingüísticos específicos en la lengua meta.Departamento de Filología InglesaGrado en Estudios Inglese

時間周波数領域でのてんかん脳波識別に関する研究 ‐平均二乗根に基づく特徴抽出に着目して‐

Epilepsy affects over 50 million people on an average yearly world wide. Epileptic Seizure is a generalised term which has broad classification depending on the reasons behind its occurrence. Parvez et al. when applied feature instantaneous bandwidth B2AM and time averaged bandwidth B2FM for classification of interictal and ictal on Freiburg data base, the result dipped low to 77.90% for frontal lobe whereas it was 80.20% for temporal lobe compare to the 98.50% of classification accuracy achieved on Bonn dataset with same feature for classification of ictal against interictal. We found reasons behind such low results are, first Parvez et al. has used first IMF of EMD for feature computation which mostly noised induce. Secondly, they used same kernel parameters of SVM as Bajaj et al. which they must have optimised with different dataset. But the most important reason we found is that two signals s1 and s2 can have same instantaneous bandwidth. Therefore, the motivation of the dissertation is to address the drawback of feature instantaneous bandwidth by new feature with objective of achieving comparable classification accuracy. In this work, we have classified ictal from healthy nonseizure interictal successfully first by using RMS frequency and another feature from Hilbert marginal spectrum then with its parameters ratio. RMS frequency is the square root of sum of square bandwidth and square of center frequency. Its contributing parameters ratio is ratio of center frequency square to square bandwidth. We have also used dominant frequency and its parameters ratio for the same purpose. Dominant frequency have same physical relevance as RMS frequency but different by definition, i.e. square root of sum of square of instantaneous band- width and square of instantaneous frequency. Third feature that we have used is by exploiting the equivalence of RMS frequency and dominant frequency (DF) to define root mean instantaneous frequency square (RMIFS) as square root of sum of time averaged bandwidth square and center frequency square. These features are average measures which shows good discrimination power in classifying ictal from interictal using SVM. These features, fr and fd also have an advantage of overcoming the draw back of square bandwidth and instantaneous bandwidth. RMS frequency that we have used in this work is different from generic root mean square analysis. We have used an adaptive thresholding algorithm to address the issue of false positive. It was able to increase the specificity by average of 5.9% on average consequently increasing the accuracy. Then we have applied morphological component analysis (MCA) with the fractional contribution of dominant frequency and other rest of the features like band- width parameter’s contribution and RMIFS frequency and its parameters and their ratio. With the results from proposed features, we validated our claim to overcome the drawback of instantaneous bandwidth and square bandwidth.九州工業大学博士学位論文 学位記番号：生工博甲第323号 学位授与年月日：平成30年6月28日1 Introduction|2 Empirical Mode Decomposition|3 Root Mean Square Frequency|4 Root Mean Instantaneous Frequency Square|5 Morphological Component Analysis|6 Conclusion九州工業大学平成30年

Stochastic expansions using continuous dictionaries: L\'{e}vy adaptive regression kernels

This article describes a new class of prior distributions for nonparametric function estimation. The unknown function is modeled as a limit of weighted sums of kernels or generator functions indexed by continuous parameters that control local and global features such as their translation, dilation, modulation and shape. L\'{e}vy random fields and their stochastic integrals are employed to induce prior distributions for the unknown functions or, equivalently, for the number of kernels and for the parameters governing their features. Scaling, shape, and other features of the generating functions are location-specific to allow quite different function properties in different parts of the space, as with wavelet bases and other methods employing overcomplete dictionaries. We provide conditions under which the stochastic expansions converge in specified Besov or Sobolev norms. Under a Gaussian error model, this may be viewed as a sparse regression problem, with regularization induced via the L\'{e}vy random field prior distribution. Posterior inference for the unknown functions is based on a reversible jump Markov chain Monte Carlo algorithm. We compare the L\'{e}vy Adaptive Regression Kernel (LARK) method to wavelet-based methods using some of the standard test functions, and illustrate its flexibility and adaptability in nonstationary applications.Comment: Published in at http://dx.doi.org/10.1214/11-AOS889 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org