Real-time object detection can greatly help in automatic ship recognition. Most often though, image-based ship classifiers do not take into account all the class-specific geometric information available in the perspective projection performed by coastal surveillance cameras. This paper introduces therefore a novel Bayesian filter to jointly track the vessel kinematic states and classify it by fusing the labeled bounding boxes detected on incoming frames originating from multiple fully calibrated cameras. Simulation results show that the proposed recursive filter is able to improve the overall classification accuracy compared to a single-frame ship classifier. Finally, we were able to track and consistently classify a real-world ship using coastal surveillance cameras surrounding the Guanabara Bay at Rio de Janeiro.

In this paper, we address the problem of tracking a moving object while the sensing platform is also in motion, leveraging an Extended Kalman Filter (EKF). We consider two reference frames for describing the object's state: (i) a global inertial frame and (ii) a local frame associated with the platform/camera. We use spherical coordinates measurements (distance$\rho$, azimuth$\alpha_{k}$, elevation$\beta_{k}$, and radius$r_{k}$) from an onboard camera. In both scenarios, these spherical measurements are integrated into an EKF to estimate the object's position, velocity, and radius. The primary contribution of this paper is unifying the derivation of the Extended Kalman Filter (EKF) in both global and local frames, clarifying how to handle measurement conversion when the platform is in motion. We detail the key transformations, state update equations, and correction steps for each scenario, and provide simulation examples to compare the complexities and performance of both formulations.

Modern aviation often relies on global navigation satellite systems (GNSS) for positioning accuracy. However, today's electronic warfare technology presents an integrity risk for such systems. Therefore, it is increasingly desirable for aircraft not to depend exclusively on GNSS. Celestial navigation, on the other hand, is a promising alternative due to its resistance to electromagnetic interference. This paper describes a new way to solve the aircraft positioning problem through an errorstate Kalman filter (ESKF) for autonomous astronomical-based navigation by adapting an observation model developed for planetary rovers to the context of aviation. Its efficiency to reduce inertial navigation system errors is demonstrated through Monte Carlo simulations of randomly generated aircraft trajectories.

We propose in this paper two adapt-then-combine (ATC) distributed particle filters for cooperative estimation of 3D orientations. The first algorithm represents rotations as elements of the Special Orthogonal Group and builds Gaussian parametric approximations on a Lie Algebra to fuse posterior probability densities. The second algorithm, in turn, represents the orientations as unit-norm quaternions and resorts to directional statistics, fusing von Mises-Fisher parametric approximations. The proposed algorithms performances are then evaluated via numerical simulations.

We present in this paper a fully distributed algorithm for simultaneous positioning of cooperative aircraft and distributed tracking of a noncooperative target. We model the probability of connection between the aircraft and the probability of an aircraft detecting the target and implement the simultaneous positioning and tracking algorithm using dynamic factor graphs that incorporate geometric average fusion.

In this paper, we address the distributed data fusion problem considering a real scenario with multiple sensor sites geographically scattered around a bay. The advance of IoT, with more and more objects being connected, delivering and sharing huge amount of data, represents a big challenge. A timely fusion, using data obtained from different sources, like IoT and others, to provide efficient, reliable and accurate information to the decision makers is a requirement of modern data fusion systems. The paper presents a conceptual approach for a distributed data fusion system, applied in a maritime environment, where the common operational picture is obtained through a tactical datalink network. The concept and implementation of the proposed system follows the paradigm of Network Centric Warfare, which is an information age theory of warfare.

This paper formulates the general Adapt-then-Combine (ATC) and Random Exchange (RndEx) diffusion filters for an arbitrary nonlinear state-space model. Subsequently, we propose two novel marginal Particle Filter implementations of the general ATC and RndEx filters using respectively a pure Sequential Monte Carlo (SMC) strategy and a hybrid Gaussian/SMC methodology. The proposed algorithms are assessed via simulation in a numerical example of cooperative target tracking with received-signal-strength (RSS) sensors.

We introduce in this paper the Random Exchange Diffusion Bernoulli Filter (RndEx-BF), which enables joint target detection and tracking by a network of collaborative sensors. RndEx-BF is a fully distributed algorithm that, unlike consensus-based solutions, does not require iterative internode communication between sensor measurements. Internode communication cost is further reduced by a novel hybrid GMM/SMC implementation of the proposed filter. Experimental results show that RndEx-BF approaches the performance of a flooding-based implementation of the optimal centralized Bernoulli filter with much lower bandwidth requirements.

We introduce in this paper a new fully distributed particle filter (PF) algorithm based on random information diffusion that is capable of performing joint multi-frame detection and tracking of a single moving emitter using a cooperative network of multiple received-signal-strength (RSS) sensors. Unlike previous consensus-based distributed PF schemes, the proposed Random Exchange Diffusion Particle Filter (ReDif-PF) does not require multiple iterative inter-node communication in the time interval between the arrival of two consecutive sensor measurements. Inter-node communication cost is further reduced by suitable parametric approximations.

We introduce in this paper a novel cooperative particle filter algorithm for tracking a moving emitter using received-signal strength (RSS) measurements with unknown observation noise variance. In the studied scenario, multiple RSS sensors passively observe independently attenuated and perturbed versions of the same broadcast signal transmitted by an emitter which is moving through the sensor field and cooperate to estimate the emitter state. The new algorithm differs from previous methods by employing a parametric approximation to reduce the associated communication burden.