Allocating the weight to each local density is essential in generalized covariance intersection (GCI) fusion. However, such a problem has not been fully addressed in the existing literature and still remains an open issue. In this paper, we propose a deep learning enhanced framework that dynamically optimizes GCI fusion weights by leveraging sensor node dependent local variables, resulting in the GCINet for fusion of probability density functions (PDFs). The key innovation lies in the employment of contextual based variables (e.g., measurement noise) as input to a neural network, which is trained by minimizing a suitably defined cost function. The proposed approach eliminates the need for manual weight tuning and overcomes the limitations of traditional optimization-based methods reliant on, e.g., Shannon entropy or Chernoff information. Application of proposed GCINet to distributed extended object tracking (EOT) application is discussed. Simulation results show that the proposed GCINet achieves superior accuracy compared to GCI fusion under equal as well as heuristically designed fusion weights.

This paper presents an approach for 2-dimensional extended object tracking (EOT). The extended object (EO) is represented as a superellipse characterized by kinematic and shape states, with the latter uniquely specified in terms of four parameters. An approximated measurement model is proposed accounting for the fact that the measurements can be generated from any position inside or on the contour of the EO. Then, EOT is performed by iteratively estimating the kinematic state via a Kalman filter, while the posterior of the shape state is represented and propagated in particle filter form due to the strong nonlinearity of the resulting shape measurement model. Simulation results are provided to assess the performance of the proposed method.

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.

Passive location and tracking of radio emitters is of great research value in civilian and defense applications. Among the existing methods, localization based on received signal strength indicator (RSSI) has been widely used due to its advantages in terms of low cost and easy implementation. However, most RSSI-based localization methods rely on the assumption that the emitter has been detected. Moreover, the emitter signal is supposed to propagate with the simplified path-loss model in which the shadow effects caused by obstacles are not considered. As a result, there are still gaps between the aforementioned methods and practical applications. In this paper, we consider the combined path-loss and shadowing model, which has been empirically confirmed in both outdoor and indoor radio propagation environments. Joint detection and tracking of an emitter is proposed by modeling the state of the emitter as Bernoulli random finite set, characterized by an existence probability and a spatial probability density function. Compared to existing studies, this paper works upon more practically appealing signal propagation model, and achieves better performance in real-time emitter detection and tracking. Moreover, the proposed method also provides explicit estimates of the unknown shadowing-related parameters, which can be adopted in further applications such as spectrum cartography and radio map construction. The feasibility of the proposed method is assessed via simulation experiments.

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.

Target state estimation with Doppler-only sensors has attracted a lot of attention due to its wide potential applications in target localization and tracking. While existing Doppler-only tracking methods rely on the assumption that Doppler sensors have been correctly registered, in many practical cases there can be significant registration errors which imply measurement biases and thus performance degradation in target state estimation. Motivated by this issue, the present paper addresses the problem of jointly estimating target state and sensor biases based on Doppler-only measurements. The proposed method consists of two phases, i.e., (1) raw estimation of the target state without considering sensor biases, followed by (2) a bias compensation step that relies on linearization of the measurement function and joint estimation of target state-sensor biases via a least square method. The Cramer-Rao lower bound (CRLB) in estimating sensor biases is evaluated and the performance of the proposed method is also assessed via simulations.

This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed target detection and tracking over a peer-to-peer sensor network. A consensus Bernoulli filter with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when a suitable measure of discrepancy between the current local posterior and the one predictable from the last transmission exceeds a preset threshold. Two information-theoretic criteria, i.e. Kullback-Leibler divergence and Hellinger distance, are adopted in order to measure the discrepancy between random finite set densities. The performance of the proposed event-triggered consensus Bernoulli filter is evaluated through simulation experiments.

A key issue in multi-sensor surveillance is the capability to surveil a much larger region than the field-of-view (FoV) of any individual sensor by exploiting cooperation among sensor nodes. Whenever a centralized or distributed information fusion approach is undertaken, this goal cannot be achieved unless a suitable fusion approach is devised. This paper proposes a novel approach for dealing with different FoVs within the context of Generalized Covariance Intersection (GCI) fusion. The approach can be used to perform multi-object tracking on both a centralized and a distributed peer-to-peer sensor network. Simulation experiments on realistic tracking scenarios demonstrate the effectiveness of the proposed solution.

In this paper, consensus-based Kalman filtering is extended to deal with the problem of joint target tracking and sensor self-localization in a distributed wireless sensor network. The average weighted Kullback-Leibler divergence, which is a function of the unknown drift parameters, is employed as the cost to measure the discrepancy between the fused posterior distribution and the local distribution at each sensor. Further, a reasonable approximation of the cost is proposed and an online technique is introduced to minimize the approximated cost function with respect to the drift parameters stored in each node. The remarkable features of the proposed algorithm are that it needs no additional data exchanges, slightly increased memory space and computational load comparable to the standard consensus-based Kalman filter. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation experiments on both a tree network and a network with cycles as well as for both linear and nonlinear sensors.

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.