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Detection and tracking of small targets in sea clutter using high-resolution radar is a challenging problem. Recently, a Bernoulli track-before-detect (TBD) filter has been developed for an airborne scanning radar in the maritime domain, with the purpose of detection and tracking of short-exposure targets by using a fast scanning mode (i.e., short dwell-time and noncoherent integration). This article investigates the potential benefits of coherent radar processing and exploitation of Doppler information in TBD, when the radar operates in a slower scanning mode (i.e., using longer dwell times). For this purpose, a new Bernoulli TBD filter is developed, which is capable of processing full three-dimensional (range-azimuth-Doppler) radar data. The amplitude of sea clutter is modeled using the K distribution with Doppler-dependent shape and scale parameters. Numerical results indicate that the new Bernoulli TBD outperforms the fast scanning TBD filter, at signal-to-interference ratios below 6 dB. [ABSTRACT FROM AUTHOR]

Surveillance systems typically incorporate multitarget tracking algorithms for sequential estimation of kinematic states (e.g. positions, velocities) of moving objects in the surveillance domain of interest. This letter proposes an algorithm for online detection of anomalies in the motion and the count of objects, using the output of a multiobject tracking algorithm. The surveillance area is partitioned by a square grid and the kinematic states that fall inside each cell of the grid are modelled by a Poisson point process. During the unsupervised learning phase, the parameters of the Poisson point process are estimated for each cell. The testing phase is performed sequentially by threshold detection at a specified level of significance. The performance of the algorithm is illustrated using the Automatic Identification System (AIS) dataset in the context of maritime surveillance. [ABSTRACT FROM PUBLISHER]

Localisation via the fusion of spatially referring natural language statements is considered here. The contribution lies in the underlying problem formulation and a robust modelling framework. Random-set-based estimation is the underlying mathematical formalism. Each statement generates a generalised likelihood function. A Bayesian filter is outlined that takes a sequence of likelihoods generated by multiple statements. The idea is to recursively build a map over the state space that can be used to infer the state. [ABSTRACT FROM AUTHOR]

The contributions of the work presented here are twofold. First we introduce a computationally efficient refinement of the unscented transformation (UT) which is applicable to nonlinear systems with a linear substructure. The resulting UT is referred to as the marginalised UT and is derived in two forms: the conventional UT marginalised form and the simplex sigma point marginalised form. The second contribution is the application of the proposed marginalised UT to smoothed state estimation in Markovian switching systems with nonlinear dynamic and measurement equations. Three algorithms for smoothing are proposed in this context. The first two algorithms are based on the well-known interacting multiple model (1MM) and hypothesis pruning techniques. The third algorithm uses maximum a posteriori (MAP) estimates of the switching parameter to generate a stochastic dynamic system within which smoothed state estimation is performed. The performances of the smoothing algorithms are analysed and compared in two scenarios involving a target which undergoes coordinated turn maneuvers. [ABSTRACT FROM AUTHOR]

We consider the problem of calculating the posterior Cramér-Rao lower bound (PCRLB) for tracking In cluttered domains in which there can be both missed detections and false alarms. We present a novel formulation of the PCRLB in which we initially determine a bound conditional on the sequence of measurements available. We then create an unconditional bound as a weighted average of these conditional PCRLBs. This new bound is proven to be less optimistic than the standard formulation of the PCRB for cluttered environments recently developed [1. 2] and will therefore better predict optimal estimator performance. At each stage, the conditional PCRLB must evaluate the effect of the uncertain measurements, and we extend previous work [2] to show that the measurement origin uncertainty manifests itself as a single information reduction factor (IRF) that is dependent on the number of measurements available. We also present some useful approximations when the false alarm rate is low. Simulations then consider the problems of 1) determining the CRLB for the point of impact of a ballistic missile, and 2) determining the PCRLB for tracking a nearly constant-velocity (NCV) target in a high clutter environment. In each case, we compare the new bound with the standard approach, and as expected the new CRIR/PCRLB can be seen to be less optimistic. Moreover, in case 1) we compare the new CRLB with a heuristic bound specially constructed for this problem, and a maximum likelihood estimator (MLE). The new bound both compares favorably with the heuristic bound, and shows close agreement with the performance of the MLE. The new bound is therefore an accurate predictor of filter performance in this case. In example 2) we demonstrate some interesting features of the new theory. Of particular interest we determine both precisely when the new bound will be significantly greater than the standard bound and when the two bounds will be virtually identical. This is useful In determining when the new approach, with its greater computational burden, should be preferred to the established approach. We conclude that the novel PCRLB formulation Introduced herein represents an exciting development in the determination of RMSE performance bounds in cluttered environments. [ABSTRACT FROM AUTHOR]

Presented here is the theoretical basis of data fusion for the purpose of target identification using the belief function theory. The key feature is that we allow the knowledge sources to supply their information in the form of uncertain implication rules. How these rules can be elegantly handled within the framework of the belief function theory is described. A small scale, practical example for target identification is worked through in detail to clarify the theory for future users. [ABSTRACT FROM AUTHOR]

A theoretical framework is presented for target classification based on the belief theory on the continuous space. The proposed approach is applicable when class-conditioned densities of feature/attribute measurements are known only partially, as subjective models of a potential "betting" behaviour. Prior class probabilities may also be unknown. Numerical examples are provided to illustrate how the proposed approach is more cautious in decision making and produces very different results from those obtained using the Bayesian classifier. [ABSTRACT FROM AUTHOR]

A study of the maximum likelihood registration (MLR) algorithm for spatial alignment of multiple, possibly dissimilar (active or passive) sensors is presented. The MLR algorithm is a batch algorithm which outputs estimates of the registration parameters, registered sensor measurements and registered target location estimates, expressed in a common coordinate system. The Cramér-Rao type bound for registration of multiple dissimilar sensors is discussed and some numerical examples for sensor registration are presented in support of the theory. [ABSTRACT FROM AUTHOR]

Considers hybrid systems with continuous-valued target states and discrete-valued regime variable. Description on mathematical models for Bayesian estimation; Derivation of the marginal posterior distribution of the states and regime variables; Numerical example of the proposed algorithm.

This paper presents a comparative analysis of three nonlinear filters for estimation of the location and velocity of a moving emitter, using time difference of arrival (TDOA) measurements received by two unmanned aerial vehicles (UAVs) as they traverse the surveillance region. The TDOA measurements are generated over time by comparing and subtracting leading edge time of arrivals (TOAs) of signals. The Cram*aaer Rao lower bound (CRLB) of estimation errors is derived and used as the benchmark in performance analysis. The three nonlinear filters considered in the comparison are: a Gaussian mixture measurement integrated track splitting filter (GMM-ITSF), a multiple model filter with unscented Kalman filters (UKFs) and a multiple-model filter with extended Kalman filters (EKFs). [ABSTRACT FROM PUBLISHER]

In our recent paper [1] we demonstrated that the standard Bayes classifier, when applied to a problem characterised by imprecise likelihood functions, produces results which are inconsistent with our intuition. As a more appropriate alternative to the standard Bayes classifier, we proposed in [1] a classification method based on the transferrable belief model (TBM). Mahler [2, ch. 4–8] recently proposed a novel approach to Bayesian estimation, fusion, and classification, applicable to situations where the information (priors, measurements, likelihoods) is imprecise and vague in addition to being random. The purpose of this letter is to demonstrate that Mahler's approach can produce identical results to those obtained using the TBM classifier. [ABSTRACT FROM PUBLISHER]

The context is sensor control for multi-object Bayes filtering in the framework of partially observed Markov decision processes (POMDPs). The current information state is represented by the multi-object probability density function (pdf), while the reward function associated with each sensor control (action) is the information gain measured by the alpha or Renyi divergence. Assuming that both the predicted and updated state can be represented by independent identically distributed (IID) cluster random finite sets (RFSs) or, as a special case, the Poisson RFSs, this work derives the analytic expressions of the corresponding R*aaenyi divergence based information gains. The implementation of R*aaenyi divergence via the sequential Monte Carlo method is presented. The performance of the proposed reward function is demonstrated by a numerical example, where a moving range-only sensor is controlled to estimate the number and the states of several moving objects using the PHD filter. [ABSTRACT FROM PUBLISHER]