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This paper introduces a novel method to score how well proposed fused image quality measures (FIQMs) indicate the effectiveness of humans to detect targets in fused imagery. The human detection performance is measured via human perception experiments. A good FIQM should relate to perception results in a monotonic fashion. The method computes a new diffuse prior monotonic likelihood ratio (DPMLR) to facilitate the comparison of the H(1) hypothesis that the intrinsic human detection performance is related to the FIQM via a monotonic function against the null hypothesis that the detection and image quality relationship is random. The paper discusses many interesting properties of the DPMLR and demonstrates the effectiveness of the DPMLR test via Monte Carlo simulations. Finally, the DPMLR is used to score FIQMs with test cases considering over 35 scenes and various image fusion algorithms.

Complex systems, represented as dynamic networks, comprise of components that influence each other via direct and/or indirect interactions. Recent research has shown the importance of using Higher-Order Networks (HONs) for modeling and analyzing such complex systems, as the typical Markovian assumption in developing the First Order Network (FON) can be limiting. This higher-order network representation not only creates a more accurate representation of the underlying complex system, but also leads to more accurate network analysis. In this paper, we first present a scalable and accurate model, BuildHON+, for higher-order network representation of data derived from a complex system with various orders of dependencies. Then, we show that this higher-order network representation modeled by BuildHON+ is significantly more accurate in identifying anomalies than FON, demonstrating a need for the higher-order network representation and modeling of complex systems for deriving meaningful conclusions. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of deploying a network of passive sensors to estimate the positions of an unknown number of stationary transient emitters. Since a completely connected network, which has a link between every pair of nodes, is not feasible because of the power and bandwidth constraints, we developed a distributed algorithm that relies only on local communications between neighboring sensors. This distributed algorithm requires information diffusion within the network with the goal that every node achieves all target location estimates as accurate as a fusion center with centralized access to all information. The locations of the emitters are not completely observable by any single sensor since bearings and times of arrival with origin uncertainty are the only available measurements. These measurements are modeled as a realization of a Poisson point process at each sensor. The problem is formulated as a constrained optimization problem, which is solved via an alternating direction method of multipliers in a distributed manner based on the expectation maximization and averaging consensus algorithms. Consensus on the number of candidate targets as well as the inter-node estimate association are addressed so that the distributed algorithm converges to the maximum likelihood estimate. A likelihood function based approach using the estimated probability of detection is presented to determine the number of targets. Simulation results show that the distributed algorithm converges very fast and the root mean square error of target locations is almost as small as that obtained using the centralized algorithm. It is also shown that one can accurately determine the number of targets using the estimated probability of detection. [ABSTRACT FROM AUTHOR]

A finite point process approach to multi-target localization from a transient signal is presented. After modeling the measurements as a Poisson point process, we propose a twofold scheme that includes an expectation maximization algorithm to estimate the target locations for a given number of targets and an information theoretic algorithm to select the number of targets. The proposed localization scheme does not require explicitly solving the data association problem and can account for clutter noise as well as missed detections. Although point process theory has been widely utilized for sequential tracking of multiple moving targets, the application of point process theory for multi-target localization from transient measurements has received very little attention. The optimal subpattern assignment metric is used to assess the performance and accuracy of the proposed localization algorithm. Implementation of the proposed algorithm on synthetic data yields desirable results. The proposed algorithm is then applied to the multi-shooter localization problem using acoustic gunfire detection systems. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops a block Wiener-based image registration method to align consecutive frames in a video sequence. The main purpose of this registration technique is to serve as a processing step in a moving target indication (MTI) algorithm, where subsequent steps include frame-to-frame differencing and thresholding. Experiments show that the block Wiener-based approach is computationally efficient and effective in terms of facilitating a MTI. These experiments compare the block Wiener-based against traditional global parametric registration, pixel-based Wiener registration, block-based motion compensation, and state-of-the-art phase-based registration. [Copyright &y& Elsevier]

In this article, we develop tractable mathematical models and approximate solution algorithms for a class of integer optimization problems with probabilistic and deterministic constraints, with applications to the design of distributed sensor networks that have limited connectivity. For a given deployment region size, we calculate the Pareto frontier of the sensor network utility at the desired probabilities for d-connectivity and k-coverage. As a result of our analysis, we determine (1) the number of sensors of different types to deploy from a sensor pool, which offers a cost vs. performance trade-off for each type of sensor, (2) the minimum required radio transmission ranges of the sensors to ensure connectivity, and (3) the lifetime of the sensor network. For generality, we consider randomly deployed sensor networks and formulate constrained optimization technique to obtain the localization performance. The approach is guided and validated using an unattended acoustic sensor network design. Finally, approximations of the complete statistical characterization of the acoustic sensor networks are given, which enable average network performance predictions of any combination of acoustic sensors. [ABSTRACT FROM AUTHOR]

Delay-and-sum (DS) beamforming is a simple processing method that can estimate the direction-of-arrival from multiple signal sources. The major advantage of DS beamforming is that it can handle wideband as well as narrowband signals. However, DS beamforming exhibits high computational complexity. The multiangle resolution fast beamformer was proposed as a computationally efficient approximation of DS beamforming, reducing the computational order of complexity from O(n³) to O(n²log n). In this paper, we introduce the pruned multiangle resolution fast beamformer to further reduce the computational complexity. The new algorithm includes an energy detector at intermediate stages of the fast beamformer to prune sectors that do not exhibit increasing energy consistent with coherent integration. Simulations are provided to assess the performance of the pruned fast beamformer. One use for the estimates from the pruned fast beamformer is to initialize high-resolution direction-ofarrival (DOA) estimators such as coherent signal subspace methods.' [ABSTRACT FROM AUTHOR]

This paper introduces a new direction-of-arrival (DOA) estimation algorithm for wideband sources called test of orthogonality of projected subspaces (TOPS). This new technique estimates DOAs by measuring the orthogonal relation between the signal and the noise subspaces of multiple frequency components of the sources. TOPS can be used with arbitrary shaped one-dimensional (1-D) or two-dimensional (2-fl) arrays. Unlike other coherent wideband methods, such as the coherent signal subspace method (CSSM) and WAVES, the new method does not require any preprocessing for initial values. The performance of those wideband techniques and incoherent MUSIC is compared with that of the new method through computer simulations. The simulations show that this new technique performs better than others in mid signal-to-noise ratio (SNR) ranges, while coherent methods work best in low SNR and incoherent methods work best in high SNR. Thus, TOPS fills a gap between coherent and incoherent methods. [ABSTRACT FROM AUTHOR]