P. Addesso

Sequential Sampling in Sensor Networks for Detection With Censoring Nodes

A wireless sensor network designed according to the sensor network with mobile agents (SENMA) architecture is engaged in a detection task, with a mobile agent (MA) that sequentially queries the networks nodes. The focus is on the effect of censoring: sensors respond to the query from the MA only if the local observations are deemed sufficiently informative; otherwise, they stay silent. Delivered data, if any, can be either unquantized or quantized to a single bit. The study is limited to shift-in-mean problems, involving two simple statistical hypotheses, where the noise distribution must be an even function but is otherwise arbitrary. Simple analytical relationships characterizing the tradeoff between the detection delay and the energy consumption of the network are derived, and examples of their applications are provided.

Adaptive localization techniques in WiFi environments

Indoor localization of a mobile user can be performed by using the off-the-shelf 802.11 (WiFi) infrastructure. However most of the existing position estimators are based on a stationary environment assumption that turns out to be rarely true in practice. We analyze two different approaches for the simultaneous estimation of the position and of the signal statistical model. The first uses a discrete state approach and is based on the Expectation-Maximization (EM) algorithm; the second employs a continuous state space and Kalman or Particle Filtering methodology. Numerical simulations and implementation show the effectiveness of the latter for real-time applications in nonstationary environments.

Characterization of escape times of Josephson junctions for signal detection.

The measurement of the escape time of a Josephson junction might be used to detect the presence of a sinusoidal signal embedded in noise when use of standard signal processing tools can be prohibitive due to the extreme weakness of the source or to the huge amount of data. In this paper we show that the prescriptions for the experimental setup and some physical behaviors depend on the detection strategy. More specifically, by exploitation of the sample mean of escape times to perform detection, two resonant regions are identified. At low frequencies there is a stochastic resonance or activation phenomenon, while near the plasma frequency a geometric resonance appears. Furthermore, detection performance in the geometric resonance region is maximized at the prescribed value of the bias current. The naive sample mean detector is outperformed, in terms of error probability, by the optimal likelihood ratio test. The latter exhibits only geometric resonance, showing monotonically increasing performance as the bias current approaches the junction critical current. In this regime the escape times are vanishingly small and therefore performance is essentially limited by measurement electronics. The behavior of the likelihood ratio and sample mean detector for different values of incoming signal to noise ratio is discussed, and a relationship with the error probability is found. Detectors based on the likelihood ratio test could be employed also to estimate unknown parameters in the applied input signal. As a prototypical example we study the phase estimation problem of a sinusoidal current, which is accomplished by using the filter bank approach. Finally we show that for a physically feasible detector the performances are found to be very close to the Cramer-Rao theoretical bound. Applications might be found, for example, in some astronomical detection problems (where the all-sky gravitational and/or radio wave search for pulsars requires the analysis of nearly sinusoidal long-lived waveforms at very low signal-to-noise ratio) or to analyze weak signals in the subterahertz range (where the traditional electronics counterpart is difficult to implement).

A Class of Cloud Detection Algorithms Based on a MAP-MRF Approach in Space and Time

A recurrent concern in cloud detection approaches is the high misclassification rate for pixels close to cloud edges. We tackle this problem by introducing a novel penalty term within the classical maximum a posteriori probability-Markov random field (MAP-MRF) approach. To improve the classification rate, such term, for which we suggest two different functional forms, accounts for the predictable motion of cloud volumes across images. Two mass tracking techniques are proposed. The first one is an effective and efficient implementation of the probability hypothesis density (PHD) filter, which is based on Gaussian mixtures (GMs) and relies on finite set statistics (FISST). The second one is a region matching procedure based on a maximum cross-correlation (MCC) that is characterized by low computational load. Through extensive tests on simulated images and real data, acquired by the SEVIRI sensor, both methods show a clear performance gain in comparison with classical spatial MRF-based algorithms.

MAP-MRF Cloud Detection Based on PHD Filtering

Temporal correlation has been recently taken into consideration to improve the performances of cloud detection algorithms. We exploit this concept within the Maximum A Posteriori Markov Random Field MAP-MRF framework by adding a penalization term which is determined according to the history of cloud masses. Multi Target Tracking of clouds is accomplished by methods of FInite Set Statistics (FISS) and several particle-based implementations are compared among them and with other previous methods both on simulated and real data.

Sequential Bayesian Methods for Resolution Enhancement of TIR Image Sequences

The availability of remotely sensed image sequences characterized by both spatial and temporal high resolution is crucial in many applications, ranging from agriculture to Earth surface hazard monitoring. To date, image sequences presenting such desirable characteristics in both domains are not directly obtainable by a single device and thus a viable solution is represented by the joint use of multisensor information. In this work, we propose a solution, based on Bayesian sequential estimation, for fusing two image sequences characterized by complementary features. Together with the assessment of two different sequential estimation approaches, a novel method for constructing a sharpened observations is presented here. The proposals are then evaluated by employing different datasets acquired by the SEVIRI and MODIS sensors, showing remarkable improvements with respect to classical approaches.

Estimation of Target Location Via Likelihood Approximation in Sensor Networks

A fully decentralized sensor network, without fusion center, is deployed to estimate the position of a target. Taking advantage of the limited communication range of the nodes, and exploiting their (unknown) location inside the surveyed area, the likelihood profile is approximately reconstructed. A distributed ML-like estimator is, therefore, proposed and its asymptotic performance is investigated analytically, while computer experiments assess the behavior of the estimator in nonasymptotic regimes. The differences between one- and two-dimensional scenarios are also discussed.

Interplay between detection strategies and stochastic resonance properties

Abstract We discuss how to exploit stochastic resonance with the methods of statistical theory of decisions. To do so, we evaluate two detection strategies: escape time analysis and strobing. For a standard quartic bistable system with a periodic drive and disturbed by noise, we show that the detection strategies and the physics of the double well are connected, inasmuch as one (the strobing strategy) is based on synchronization, while the other (escape time analysis) is determined by the possibility to accumulate energy in the oscillations. The analysis of the escape times best performs at the frequency of the geometric resonance, while strobing shows a peak of the performances at a special noise level predicted by the stochastic resonance theory. We surmise that the detection properties of the quartic potential are generic for overdamped and underdamped systems, in that the physical nature of resonance decides the competition (in terms of performances) between different detection strategies.

All-sky search for long-duration gravitational wave transients with LIGO

We present the results of a search for long-duration gravitational wave transients in two sets of data collected by the LIGO Hanford and LIGO Livingston detectors between November 5, 2005 and September 30, 2007, and July 7, 2009 and October 20, 2010, with a total observational time of 283.0 days and 132.9 days, respectively. The search targets gravitational wave transients of duration 10 - 500 seconds in a frequency band of 40 - 1000 Hz, with minimal assumptions about the signal waveform, polarization, source direction, or time of occurrence. All candidate triggers were consistent with the expected background; as a result we set 90% confidence upper limits on the rate of long-duration gravitational wave transients for different types of gravitational wave signals. We also report upper limits on the source rate density per year per Mpc^3 for specific signal models. These are the first results from an all-sky search for unmodeled long-duration transient gravitational waves.

Escape time characterization of pendular Fabry-Perot

In a pendular Fabry-Perot interferometer the system placed inside one of the minimum of the optomechanical potential undergoes an escape if it crosses the point of sudden change of reflectivity near the top of the potential well. We demonstrate that the loss of information that occurs retaining only the sequence of escapes, rather than the full trajectory, is mild if suitable signal processing techniques are applied to reveal the noise intensity or the presence of a coherent signal.