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IEEE Trans Cybern ; 48(6): 1898-1909, 2018 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-28693003


This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

IEEE Trans Cybern ; 48(7): 2114-2127, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-28809721


This paper presents a distributed supervisory control algorithm that enables opportunistic sensing for energy-efficient target tracking in a sensor network. The algorithm called Prediction-based Opportunistic Sensing (POSE), is a distributed node-level energy management approach for minimizing energy usage. Distributed sensor nodes in the POSE network self-adapt to target trajectories by enabling high power consuming devices when they predict that a target is arriving in their coverage area, while enabling low power consuming devices when the target is absent. Each node has a Probabilistic Finite State Automaton which acts as a supervisor to dynamically control its various sensing and communication devices based on target's predicted position. The POSE algorithm is validated by extensive Monte Carlo simulations and compared with random scheduling schemes. The results show that the POSE algorithm provides significant energy savings while also improving track estimation via fusion-driven state initialization.

IEEE Trans Cybern ; 47(1): 93-104, 2017 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26960235


This paper addresses the problem of target detection in dynamic environments in a semi-supervised data-driven setting with low-cost passive sensors. A key challenge here is to simultaneously achieve high probabilities of correct detection with low probabilities of false alarm under the constraints of limited computation and communication resources. In general, the changes in a dynamic environment may significantly affect the performance of target detection due to limited training scenarios and the assumptions made on signal behavior under a static environment. To this end, an algorithm of binary hypothesis testing is proposed based on clustering of features extracted from multiple sensors that may observe the target. First, the features are extracted individually from time-series signals of different sensors by using a recently reported feature extraction tool, called symbolic dynamic filtering. Then, these features are grouped as clusters in the feature space to evaluate homogeneity of the sensor responses. Finally, a decision for target detection is made based on the distance measurements between pairs of sensor clusters. The proposed procedure has been experimentally validated in a laboratory setting for mobile target detection. In the experiments, multiple homogeneous infrared sensors have been used with different orientations in the presence of changing ambient illumination intensities. The experimental results show that the proposed target detection procedure with feature-level sensor fusion is robust and that it outperforms those with decision-level and data-level sensor fusion.

IEEE Trans Syst Man Cybern B Cybern ; 41(3): 783-91, 2011 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-21172754


This paper presents a statistical-mechanics-inspired procedure for optimization of the sensor field configuration to detect mobile targets. The key idea is to capture the low-dimensional behavior of the sensor field configurations across the Pareto front in a multiobjective scenario for optimal sensor deployment, where the nondominated points are concentrated within a small region of the large-dimensional decision space. The sensor distribution is constructed using location-dependent energy-like functions and intensive temperature-like parameters in the sense of statistical mechanics. This low-dimensional representation is shown to permit rapid optimization of the sensor field distribution on a high-fidelity simulation test bed of distributed sensor networks.

Inteligência Artificial , Técnicas de Apoio para a Decisão , Modelos Teóricos , Movimento (Física) , Reconhecimento Automatizado de Padrão/métodos , Transdutores , Algoritmos , Simulação por Computador
IEEE Trans Syst Man Cybern B Cybern ; 40(6): 1492-504, 2010 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-20236903


The quality of service of a network performing cooperative track detection is represented by the probability of obtaining multiple elementary detections over time along a target track. Recently, two different lines of research, namely, distributed-search theory and geometric transversals, have been used in the literature for deriving the probability of track detection as a function of random and deterministic sensors' positions, respectively. In this paper, we prove that these two approaches are equivalent under the same problem formulation. Also, we present a new performance function that is derived by extending the geometric-transversal approach to the case of random sensors' positions using Poisson flats. As a result, a unified approach for addressing track detection in both deterministic and probabilistic sensor networks is obtained. The new performance function is validated through numerical simulations and is shown to bring about considerable computational savings for both deterministic and probabilistic sensor networks.

Algoritmos , Inteligência Artificial , Redes de Comunicação de Computadores , Modelos Estatísticos , Reconhecimento Automatizado de Padrão/métodos , Transdutores , Simulação por Computador , Distribuição de Poisson
J Acoust Soc Am ; 112(6): 2735-41, 2002 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-12508993


A numerical optimization approach is presented to optimize passive broadband detection performance of hull arrays through the adjustment of array shading weights. The approach is developed for general hull arrays in low signal-to-noise ratio scenarios, and is shown to converge rapidly to optimal solutions that maximize the array's deflection coefficient. The beamformer is not redesigned in this approach; only the shading weights of the conventional beamformer are adjusted. This approach allows array designers to use the array to minimize the impact of known sources of noise on detection at the beamformer output while maintaining acoustic array gain against an unknown source. The technique is illustrated through numerical examples using hull-borne structural noise as the noise source; however, the design concept can be applied to other design parameters of the array such as element position, material selection, etc.