This paper discusses the problem of streaming coupled tensor factorization based on sensor networks, where each sensor observes only some features of the targets, and the measurements from sensors are provided in a streaming tensor fashion. Moreover, the observed features of different sensors might overlap (i.e., coupled tensor), and there is no central processing unit to collect all sensor data. Then, in our work, the canonical polyadic (CP) decomposition is exploited to perform local tensor decomposition based on the measurements of each sensor, and average consensus (AC) for diffusing information throughout the network. The proposed method is verified via simulations.

In this work a new class of filters, called Lambda:Omicron Multiplicative Error Model (L:OMEM), is introduced with the aim to efficiently address tracking of maneuvering extended objects. In this context, two main challenges have to be tackled: (1) the tracked object moves with unknown time-varying speed and turning rate; (2) the tracked object can produce a large amount of measurements. Closed-form formulas and a novel method to reduce extended object tracking to a conventional point object tracking problem are derived, so that the novel filter results into an accurate and computationally cheap algorithm. Numerical simulations are presented to validate the effectiveness of the proposed approach, where the L:OMEM filter is compared to state-of-the-art filters for extended objects.

This paper considers the general problem of tracking a noncooperative target in the presence of multipath. The multipath effect occurs intermittently, according to a discrete-time Markov chain, and exerts an additional unknown measurement error (i.e, a bias), with biases autocorrelated if they occur across successive sampling times. We calculate the posterior Cramér-Rao bound (PCRB) for this problem by augmenting the target state with the multipath bias. An established, efficient Riccati recursion is then used to determine the PCRB, thereby providing mean squared error performance bounds for both the estimation of the target state and the multipath bias. The approach is demonstrated in a simulated scenario in which an airborne radar tracks a low altitude airborne target that is moving in a horizontal plane with nearly constant velocity, using measurements of azimuth. elevation and range. The measurements are intermittently corrupted by multipath bias resulting from specular reflection at the surface boundary. It is shown that the PCRB increases rapidly when the multipath effect occurs, indicating that optimal target tracking performance is significantly degraded at such times. Future work will compare the PCRB developed herein with alternative PCRB methodologies that do not implicitly condition on the multipath effects, and also compare the bound to the performance of a tracking algorithm that is designed to identify and adjust for the multipath effects.

The paper develops an information fusion system that aims at supporting a commander's decision making by providing an assessment of threat, that is an estimate of the extent to which an enemy platform poses a threat based on evidence about its intent and capability. Threat is modelled in the framework of the valuation-based system (VBS), by a network of entities and relationships between them. The uncertainties in the relationships are represented by belief functions as defined in the theory of evidence. Hence the resulting network for reasoning is referred to as an evidential network. Local computations in the evidential network are carried out by inward propagation on the underlying joint binary tree. This allows the dynamic nature of the external evidence, which drives the evidential network, to be taken into account by recomputing only the affected paths in the joint binary tree.

In this paper we investigate the k-connectivity threshold of distributed dense ad hoc heterogeneous wireless sensor network architecture. We consider the situation when sensors are deployed in the surveillance area according to a uniform distribution perturbed by a Gaussian noise. We derive analytically the minimum detection range which guarantees an emerging structure in the network, namely the connectivity, which becomes larger and larger as the number of sensors in the network increase. This allows the target track to be propagated almost surely throughout the network using the minimum possible amount of prime energy. We report the results of some simulation experiments which further support the theoretical results.

This paper compares two different approaches for sensor selection for distributed tracking: 1) The Frisbee method, and 2) Global Node Selection (GNS). The Frisbee method is based on the proximity of the nodes to the predicted location of the target; GNS is based on minimizing the unbiased Cramer Rao lower bound (CRLB). Both theoretical and experimental results indicate that the Frisbee method is as effective as GNS. Furthermore, the Frisbee method is attractive due to its very light computational load.

This work presents a deterministic approach to the problem of modelling the human behaviour in a command and control radar system and it considers the fusion of information between the operator and the system. The implementation and the results of a case study are presented where a human operator performs a tracking operation of multiple targets in a sea region. The mission performed by the operator is the surveillance of a coast area and the selection of a system action against possible threat targets, in order to check their identity. An analytical model of human memory has been investigated where the human decision maker is represented as a subsystem involved with two operational blocks, corresponding to the situation assessment process and the response selection process that he performs. The operator performance is evaluated by mean of his error probability in these two processes

This paper illustrates an emulator of an integrated system developed for predicting its performance in a typical border control surveillance scenario. The sensor suite emulated is constituted by: one spaceborne surveillance radar, one airborne radar for long range surveillance, one radar devoted to vessel traffic control (VTC) and one infrared (IR) camera. The collected data are sent to the command & control centre which allocates the proper means for facing the "potentially hostile" target intrusion. Reaction time and probability of correct reaction in presence of an unidentified moving object have been selected as representative of the integrated system performance. A key point to note is that the visualization is nowadays considered as an enabling technology; thus a great care has been dedicated to the presentation of results via movies realization

The paper deals with the problem of impact point prediction of ballistic targets (BT) by processing measurements acquired by two 3D surveillance radars. It is assumed that the radars acquire a limited number of measurements that do not encompass the whole target trajectory; thus the established target track has to be extrapolated ahead in time in order to predict the coordinates of the impact point. The updating and testing of the data extractor (DE) of a notional tri-dimensional surveillance radar system is presented in this paper; the modified hardware and software is capable of acquiring, managing and fusing tracks pertaining to the radar system housing the DE and to other systems connected to the DE itself

The paper presents an analysis of the phased array radar allocation demands, when tracking highly maneuverable anti-ship missiles (ASM) using a collocated radar/IRST sensor combination. The motion of the ASM is modeled using the quantized acceleration levels. The principal aim of this analysis is to determine an upper bound on the average radar update time. This bound follows from a Cramer-Rao type error bound for the estimation of linear jump Markov dynamic systems. Given a dynamic motion model of an ASM, the IRST/radar sensor characteristics and a tolerable level of target state estimation error, we can theoretically predict the maximum average update time required for the phased-array radar. The presented analysis allows us to quantify the IRST benefits in ASM defence, without a need for extensive Monte Carlo simulations

A new centroid formula for range-free sensor node location estimation using the proximity anchor positions is proposed. A detailed statistical analysis of the estimator is presented in one-dimension, taking into account the boundary conditions. The estimator is optimal in the minimum-mean square error sense for the one-dimensional case and outperforms the averaging centroid formula for N>2, where N is the number of proximity anchors