This paper investigates the benefit of using game theoretic sensor control for the tracking of a reactive intelligent target. Game theory is adopted as a robust strategy which facilitates the study of interactions between intelligent players, in contrast to the alternative control strategies which only consider non-reactive targets. A general multi-step ahead optimisation strategy is designed and studied in the context of a passive tracking operation. Numerical results demonstrate the advantages of employing game-theoretic control for optimal tracking of evasive targets.

We consider the problem of real-time estimation of sea state and wave-induced motions on a moving vessel using onboard inertial sensors without knowing vessel’s dynamic parameters (i.e., draught and breadth). This is crucial for vessel operational planning and performance, preventing structure failure, emissions reduction and fuel economy. This work proposes a new estimation approach by reformulating the conventional problem of sea state and vessel motion estimation (unknown input into a known dynamic system) as an input-state-parameter estimation problem of mass-spring-damper systems. We exploit the strong correlations between a vessel’s vertical displacement and its rotation to develop a new estimation algorithm–Parameter-Sharing Extended-Augmented Kalman Filter (PS-EAKF)–for the problem to estimate the unidentified vessel parameters together with vessel motion (heave and pitch) and sea state. Experimental data from a scale-model vessel in regular head seas demonstrate the effectiveness and robustness of the proposed approach.

We consider the problem of estimating unknown input forces on structural systems using only noisy acceleration measurement data. This is an important task for condition monitoring, for example, to predict fatigue damage in a structure's body or to reduce transmission of vibrations in marine vessels. In this paper, we propose a new idea to estimate an input force with a sinusoidal form by formulating a force identification problem without a direct feed-through system. Consequently, the minimum variance unbiased (MVU) filter can be implemented coupled with a fast Fourier transform algorithm to estimate unknown input forces accurately in real-time. Moreover, when the input force is completely unknown, the ensemble sampling method combined with an augmented Kalman filter can be formulated to significantly reduce computation time. Experimental results confirm the effectiveness of our proposed methods and show that the formulations investigated outperform other state-of-the-art methods in term of computational cost whilst not compromising estimation performance.

Despite imprecision and possible ambiguity expressed by spatially referring natural language statements, they are potentially useful “measurements” (also known as soft data) for target localisation and tracking. The likelihood functions of such measurements typically include the parameters that model the inherent uncertainty in soft data. Adopting a binary model for spatially referring statements involving the word “near”, the paper derives the theoretical posterior Cramér-Rao bound for the estimation (learning) of the parameter which features in the likelihood function. A numerical analysis of the bound is presented with an example demonstrating estimation/learning in practice.

The problem is target motion analysis (TMA) in situations where the variance (standard deviation) of additive white Gaussian measurement noise is unknown and time-varying. In particular, the paper examines a somewhat surprising result from the theoretical analysis based on the Cramer-Rao bound, which suggests that the best-achievable (second-order) error in target state estimation is unaffected by the lack of knowledge of the measurement noise variance. In order to examine this result, the paper develops three recursive Bayesian filters for TMA, which jointly estimate the target state and the measurement variance. The basis of all filters is the Cubature Kalman filter for bearings-only tracking, combined with (i) the variational Bayesian approach, (ii) the Rao-Blackwellised particle filter, and (iii) the interactive multiple-model (IMM), to deal with the unknown time-varying measurement variance. The paper presents extensive numerical simulation results and comparisons, which confirm that the lack of knowledge of the measurement noise variance is by no means a handicap for TMA.

The problem of single-sensor bearings-only tracking continues to present challenges to tracking algorithms, particularly in certain difficult scenarios such as ones with high bearing rates. In such scenarios, the performance of the recently introduced shifted Rayleigh filter (SRF) is compared with that of other techniques such as extended Kalman filter (EKF), unscented Kalman filter (UKF) and particle filter (PF). The results are also compared with the theoretical Cramer-Rao Lower Bound (CRLB). The SRF is a moment matching algorithm, and its key feature is that it generates the exact conditional distribution of target motion, given normal approximation to the prior. Simulations show that the SRF is superior to other moment matching algorithms such as EKF and UKF and is able to achieve comparable performance to PF while being orders of magnitude faster.

In multi-sensor multi-target bearings-only tracking we often see false intersections of bearings known as ghosts.When the bearing measurements from each sensor have been associated to form sequences termed threads, the problem is to associate pairs of threads to identify the true target intersections. In this paper we present two algorithms: (i) Classical Bayesian Thread Association (CBTA) and (ii) Monte Carlo Thread Association (MCTA), for this problem. The performance of these algorithms is compared using Monte Carlo simulations. Furthermore, we also compare their performance against the Rao-Blackwellised Monte Carlo Data Association (RBMCDA) algorithm, which uses unthreaded measurements, in order to ascertain the benefits of using thread information. Simulations show that MCTA is superior to CBTA, and that there is significant benefit in using thread information in this class of problems.

The problem is joint detection and tracking of possibly several objects moving through a region of interest. A wireless sensor network (WSN), deployed in the region, collects the acoustic energy measurements and sends them to the fusion center for processing. The problem is cast in the sequential Bayesian estimation framework and solved using a particle filter. The number of objects is unknown and can vary over time. The paper presents the algorithm and demonstrates its performance by computer simulations. The particle filter error performance is compared to the theoretical Cramer-Rao bound (CRB) for multiple target tracking with the described WSN.