On Time-Reversal Imaging by Statistical Testing

This letter is focused on the design and analysis of computational wideband time-reversal imaging algorithms, designed to be adaptive with respect to the noise levels pertaining to the frequencies being employed for scene probing. These algorithms are based on the concept of cell-by-cell processing and are obtained as theoretically-founded decision statistics for testing the hypothesis of single-scatterer presence (absence) at a specific location. These statistics are also validated in comparison with the maximal invariant statistic for the proposed problem.Comment: Reduced form accepted in IEEE Signal Processing Letter

On the Maximal Invariant Statistic for Adaptive Radar Detection in Partially-Homogeneous Disturbance with Persymmetric Covariance

This letter deals with the problem of adaptive signal detection in partially-homogeneous and persymmetric Gaussian disturbance within the framework of invariance theory. First, a suitable group of transformations leaving the problem invariant is introduced and the Maximal Invariant Statistic (MIS) is derived. Then, it is shown that the (Two-step) Generalized-Likelihood Ratio test, Rao and Wald tests can be all expressed in terms of the MIS, thus proving that they all ensure a Constant False-Alarm Rate (CFAR).Comment: submitted for journal publicatio

Rician MIMO Channel- and Jamming-Aware Decision Fusion

In this manuscript we study channel-aware decision fusion (DF) in a wireless sensor network (WSN) where: (i) the sensors transmit their decisions simultaneously for spectral efficiency purposes and the DF center (DFC) is equipped with multiple antennas; (ii) each sensor-DFC channel is described via a Rician model. As opposed to the existing literature, in order to account for stringent energy constraints in the WSN, only statistical channel information is assumed for the non-line-of sight (scattered) fading terms. For such a scenario, sub-optimal fusion rules are developed in order to deal with the exponential complexity of the likelihood ratio test (LRT) and impractical (complete) system knowledge. Furthermore, the considered model is extended to the case of (partially unknown) jamming-originated interference. Then the obtained fusion rules are modified with the use of composite hypothesis testing framework and generalized LRT. Coincidence and statistical equivalence among them are also investigated under some relevant simplified scenarios. Numerical results compare the proposed rules and highlight their jammingsuppression capability.Comment: Accepted in IEEE Transactions on Signal Processing 201

Performance Analysis and Design of Maximum Ratio Combining in Channel-Aware MIMO Decision Fusion

In this paper we present a theoretical performance analysis of the maximum ratio combining (MRC) rule for channel-aware decision fusion over multiple-input multiple-output (MIMO) channels for (conditionally) dependent and independent local decisions. The system probabilities of false alarm and detection conditioned on the channel realization are derived in closed form and an approximated threshold choice is given. Furthermore, the channel-averaged (CA) performances are evaluated in terms of the CA system probabilities of false alarm and detection and the area under the receiver operating characteristic (ROC) through the closed form of the conditional moment generating function (MGF) of the MRC statistic, along with Gauss-Chebyshev (GC) quadrature rules. Furthermore, we derive the deflection coefficients in closed form, which are used for sensor threshold design. Finally, all the results are confirmed through Monte Carlo simulations.Comment: To appear in IEEE Transactions on Wireless Communication

Tracking the Tracker from its Passive Sonar ML-PDA Estimates

Target motion analysis with wideband passive sonar has received much attention. Maximum likelihood probabilistic data-association (ML-PDA) represents an asymptotically efficient estimator for deterministic target motion, and is especially well-suited for low-observable targets; the results presented here apply to situations with higher signal to noise ratio as well, including of course the situation of a deterministic target observed via clean measurements without false alarms or missed detections. Here we study the inverse problem, namely, how to identify the observing platform (following a two-leg motion model) from the results of the target estimation process, i.e. the estimated target state and the Fisher information matrix, quantities we assume an eavesdropper might intercept. We tackle the problem and we present observability properties, with supporting simulation results.Comment: To appear in IEEE Transactions on Aerospace and Electronic System

HUBUNGAN SUPERVISI KLINIS, PENGALAMAN MENGAJAR GURU DAN IKLIM ORGANISASI DENGAN KETERAMPILAN GURU DALAM PEMBELAJARAN IPA DI SMP NEGERI KOTA SALATIGA

The purpose of this study was to assess and analyze: (1) the relationship of clinical supervision with the skills of teachers in the Learning Science in Secondary Schools Salatiga. (2) the relationship between teaching experience of teachers with the skills of teachers in the Learning Science in Secondary Schools Salatiga. (3) the relationship between organizational climate with the skills of teachers in learning science at the Junior High School Salatiga. (4) the relationship between the clinical supervision, teaching experience of teachers and the climate of the organization with the skills of teachers in learning science at the Junior High School Salatiga. Research conducted a research relationship/ correlation which aims to find the relationship of independent variables to the dependent variable. The population in this study are science teacher Salatiga totaling 65 teachers. Determination of the number of samples based on based Arikunto if the sample is under 100 then the whole sample is used so that the number of samples in the study 65 teachers. The data collection technique using Likert scale questionnaire. Data analysis techniques using correlation analysis techniques and multiple regression analysis to test the prerequisite test for normality, linearity testing, and independence testing. Based on these results it can be concluded: (1) There is a relation variable clinical supervision, teaching experience, and organizational climate on learning the skills of teachers in junior high school science Salatiga simultaneously, as shown by the results of the calculation obtained rxy price of 0,658 > 0,244 means that the relationship significant at 5% level. R² value of 0,433 means that the variable clinical supervision, teaching experience, and organizational climate together may explain the variable skills of teachers in learning science by 43,3%. While the remaining 56,7% is explained by other variables not examined. (2) There is a relation variable clinical supervision with the skills of teachers in Junior High School Science Learning Salatiga, as shown by the product moment correlation coefficient of 0,375 > 0,244 and p = 0,001 means that the relationship is significant at the 5% level. (3) There is a relation variable of teaching experience with the skills of teachers in Junior High School Science Learning Salatiga, it is indicated product moment correlation coefficient of 0,341 > 0,244 and p= 0.003 means that the relationship is significant at the 5% level. (4) There is a relationship of organizational climate variables with the skills of teachers in junior high school pembelajara Salatiga, as shown by the product moment correlation coefficient of 0,518 > 0,244 and p= 0,000, meaning that the relationship is significant at the 5% level. The test results show that the assumptions of classical regression model is not biased or problems classical assumptions (normality, linearity, multicollinearity, and heteroscedasticity) that can be expressed BLUE (Best, Linear, Unbiased, Estimator). Keywords: Clinical Supervision, Master Teaching Experience, Organizational Climate, Teacher Skills in Learning Science

Data fusion in wireless sensor networks: A statistical signal processing perspective

The role of data fusion has been expanding in recent years through the incorporation of pervasive applications, where the physical infrastructure is coupled with information and communication technologies, such as wireless sensor networks for the internet of things (IoT), e-health and Industry 4.0. In this edited reference, the authors provide advanced tools for the design, analysis and implementation of inference algorithms in wireless sensor networks. The book is directed at the sensing, signal processing, and ICTs research communities. The contents will be of particular use to researchers (from academia and industry) and practitioners working in wireless sensor networks, IoT, E-health and Industry 4.0 applications who wish to understand the basics of inference problems. It will also be of interest to professionals, and graduate and PhD students who wish to understand the fundamental concepts of inference algorithms based on intelligent and energy-efficient protocols