Auto-regressive model based polarimetric adaptive detection scheme part I: Theoretical derivation and performance analysis

This paper deals with the problem of target detection in coherent radar systems exploiting polarimetric diversity. We resort to a parametric approach and we model the disturbance affecting the data as a multi-channel autoregressive (AR) process. Following this model, a new polarimetric adaptive detector is derived, which aims at improving the target detection capability while relaxing the requirements on the training data size and the computational burden with respect to existing solutions. A complete theoretical characterization of the asymptotic performance of the derived detector is provided, using two different target fluctuation models. The effectiveness of the proposed approach is shown against simulated data, in comparison with alternative existing solutions

Knowledge-Aided STAP Using Low Rank and Geometry Properties

This paper presents knowledge-aided space-time adaptive processing (KA-STAP) algorithms that exploit the low-rank dominant clutter and the array geometry properties (LRGP) for airborne radar applications. The core idea is to exploit the fact that the clutter subspace is only determined by the space-time steering vectors, {red}{where the Gram-Schmidt orthogonalization approach is employed to compute the clutter subspace. Specifically, for a side-looking uniformly spaced linear array, the} algorithm firstly selects a group of linearly independent space-time steering vectors using LRGP that can represent the clutter subspace. By performing the Gram-Schmidt orthogonalization procedure, the orthogonal bases of the clutter subspace are obtained, followed by two approaches to compute the STAP filter weights. To overcome the performance degradation caused by the non-ideal effects, a KA-STAP algorithm that combines the covariance matrix taper (CMT) is proposed. For practical applications, a reduced-dimension version of the proposed KA-STAP algorithm is also developed. The simulation results illustrate the effectiveness of our proposed algorithms, and show that the proposed algorithms converge rapidly and provide a SINR improvement over existing methods when using a very small number of snapshots.Comment: 16 figures, 12 pages. IEEE Transactions on Aerospace and Electronic Systems, 201

Classification Schemes for the Radar Reference Window: Design and Comparisons

In this paper, we address the problem of classifying data within the radar reference window in terms of statistical properties. Specifically, we partition these data into statistically homogeneous subsets by identifying possible clutter power variations with respect to the cells under test (accounting for possible range-spread targets) and/or clutter edges. To this end, we consider different situations of practical interest and formulate the classification problem as multiple hypothesis tests comprising several models for the operating scenario. Then, we solve the hypothesis testing problems by resorting to suitable approximations of the model order selection rules due to the intractable mathematics associated with the maximum likelihood estimation of some parameters. Remarkably, the classification results provided by the proposed architectures represent an advanced clutter map since, besides the estimation of the clutter parameters, they contain a clustering of the range bins in terms of homogeneous subsets. In fact, such information can drive the conventional detectors towards more reliable estimates of the clutter covariance matrix according to the position of the cells under test. The performance analysis confirms that the conceived architectures represent a viable means to recognize the scenario wherein the radar is operating at least for the considered simulation parameters.Comment: Accepted by IEEE Transactions on Aerospace and Electronic System

Modified GLRT and AMF framework for adaptive detectors

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE."This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."The well-known general problem of signal detection in background interference is addressed for situations where a certain statistical description of the interference is unavailable, but is replaced by the observation of some secondary (training) data that contains only the interference. For the broad class of interferences that have a large separation between signal-and noise-subspace eigenvalues, we demonstrate that adaptive detectors which use a diagonally loaded sample covariance matrix or a fast maximum likelihood (FML) estimate have significantly better detection performance than the traditional generalized likelihood ratio test (GLRT) and adaptive matched filter (AMI') detection techniques, which use a maximum likelihood (ML) covariance matrix estimate. To devise a theoretical framework that can generate similarly efficient detectors, two major modifications are proposed for Kelly's traditional GLRT and AMF detection techniques. First, a two-set GLRT decision rule takes advantage of an a priori assignment of different functions to the primary and secondary data, unlike the Kelly rule that was derived without this. Second, instead of ML estimates of the missing parameters in both GLRT and AMF detectors, we adopt expected likelihood (EL) estimates that have a likelihood within the range of most probable values generated by the actual interference covariance matrix. A Gaussian model of fluctuating target signal and interference is used in this study. We demonstrate that, even under the most favorable loaded sample-matrix inversion (LSMI) conditions, the theoretically derived EL-GLRT and FL-AMF techniques (where the loading factor is chosen from the training data using the EL matching principle) gives the same detection performance as the loaded AMF technique with a proper a priori data-invariant loading factor. For the least favorable conditions, our EL-AMF method is still superior to the standard AMF detector, and may be interpreted as an intelligent (data-dep- endent) method for selecting the loading factor.Yuri I. Abramovich, Nicholas K. Spencer, Alexei Y. Gorokho

MVAR ANALYSIS OF IEEG SIGNALS TO DIFFERENTIATE CONSCIOUS STATES

Neuroscience is a highly multidisciplinary and rapidly evolving research field. An important recent challenge of this discipline is the investigation of the so-called connectome. According to its original meaning, connectome is the map of the all brain neural connections. In this framework, the cognitive processes are not seen as localized in specific loci, but stored and processed in a distributed manner. Connectome aims to map and under-stand the organization of neural interactions trying, at the same time, to explain the role of functional units within the brain system. In particular, one of the most difficult and un-solved tasks in neuroscience is the identification of the areas, connections or brain func-tions that are called neuronal correlates of consciousness (NCCs). In this thesis the neural activity was explored by analysing human brain signals ac-quired during medical procedure. Signals from patients with drug resistant epilepsy were acquired by means of electrodes placed deep in the cortex (intracranial electroencephalog-raphy, EEG-iEEG), positioned in order to localize the epileptogenic focus. The technique, called stereotactic EEG (SEEG), guided and flanked by detailed 3D images, also pro-vides for periodical intracranial single-pulse electrical stimulation (SPES) to highlight are-as of interest. The continuous recording of the EEG activity took place for several days, and signals were grouped in two datasets: one acquired during wakefulness (WAKE) and the other one during the Non-Rapid Eye Movement sleep (NREM), stage 3. The signals were processed by means of two methods based on a multivariate auto-regressive model (MVAR). The first method was DTF (Directed Transfer Function), that is an estimator of the information flow between structures, depending on the signal fre-quency; it is able to describe which structure influences another. The second one was ADTF (Adaptive DTF) that permits to study the time-variant signal features, capturing their temporal dynamics. In addition to these connectivity analysis, feature extraction and classification techniques have been employed. The main aim of the dissertation is to evaluate methods and carry out analyses useful to distinguish between conscious and unconscious states, corresponding to WAKE and NREM respectively, studying at the same time the brain connectivity in response to Single Pulse Electrical Stimulation in intracranial EEG data. Massimini\u2019s group (Department of Biomedical and Clinical sciences \u201cL. Sacco\u201d, Uni-versit\ue0 degli Studi di Milano) revealed a different behavior for signals from the two states, WAKE and NREM: they noted a reactivation of the signal around 300 ms after the system perturbation in WAKE and, in contrast, a period of neural silence (down-state) in NREM condition. A hypothesis about the origin of the reactivation phenomenon is a feedback activity, i.e. the result of the activity from the rest of the network. In the thesis, the ADTF method was chosen to shed light on the down-state effect, paying attention to a defined temporal slice of data. The analysis was completed by the application of the DTF procedure, that was chosen to compare the two consciousness states and underline their differences in the frame of network connectivity. The analysis carried out lead to the following results: \uf0a7 Indication of useful combinations of features and techniques able to distinguish the states of interest \uf0a7 Observations of neural connection changes over frequency and time consider-ing causal relationships \uf0a7 Comparison of connectivity results using different re-referencing styles \uf0a7 Endorsement of the anatomical-functional importance of some channels corre-sponding to specialized brain areas. As conclusion of the analysis it was possible to identify a series of anatomical-functional brain features useful to discriminate the two mentioned states, therefore to speculate on the possibility to differentiate conscious and unconscious states with computational tools

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas