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The multi-Bernoulli (MB) filter for extended targets has been derived recently. However, the implementation of the extended target (ET) MB filter for nonlinear non-Gaussian models has not been presented. In this paper, we propose the sequential Monte Carlo (SMC) implementation of the ET-MB filter for estimating multiple extended targets by using the SMC technique and measurement partitioning algorithm...
An unsupervised track classification approach based on appropriate discriminative and aggregative features derived from beamformed and normalized matched-filtered data is applied to sonar multistatic tracking and extended to include discretised track velocity and heading rate. A clustering algorithm based on the Latent Dirichlet Allocation model is proposed. It is demonstrated how low-level, highly...
How to fuse/combine state estimates that are obtained based on different models (e.g., a CV model, a CA model, and a CT model)? This paper provides a theoretical solution to such problems and beyond. Conventional multiple-model estimation methods use models defined in a common state space. In this paper, we discuss the advantage of using heterogeneous state space for different models in the multiple-model...
We consider long-haul sensor networks where sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors to improve the accuracy of the final estimates of certain target characteristics. In this work, we pursue artificial neural network...
For linear-Gaussian non-deterministic dynamics, that is, systems with non-zero process noise, it is well known that tracklet fusion based on equivalent measurement is optimal only for full communication rate, i.e., if the local posterior probabilities or estimates are communicated and fused after each observation and update time. Despite this constraint, tracklet fusion has become very popular because...
Track-to-track fusion is an important topic for distributed tracking. Compared with the centralized measurement fusion, the track-to-track fusion requires less communication resources and is suitable for practical implementation. Although having been widely investigated in the literature, the majority of track-to-track fusion algorithms assume synchronous communication. However, in practice, local...
This paper introduces a novel approach to robust tracking that combines Particle Filters (PFs) and estimation of physical constraints using Bayesian Networks (BNs). Heterogeneous Context Data (CD) describing the environment in which tracked objects move, is fused with the help of BNs. The resulting uncertain constraints are incorporated into the filtering process through a modification of the importance...
In joint tracking and classification (JTC) problems, both decision and estimation are involved and they affect each other. Good solutions for JTC require solving the two problems jointly. A joint decision and estimation (JDE) framework based on a generalized Bayes risk was recently proposed for solving the problem of inter-dependent decision and estimation. In the JDE framework, a conditional JDE...
A great deal of interest has been paid to target tracking for the last decades. When using Bayesian estimation algorithms, choosing relevant motion models is crucial for accurate localization. Information on the type of target and its maneuver capability can be helpful in the motion model design. Thus, joint tracking and classification (JTC) methods based on target features have been recently developed...
Many target tracking algorithms for radar systems assume homogeneous backgrounds of clutter. However, real backgrounds are rarely homogeneous. By estimating background intensity, and using the estimate in the likelihood measure, the tracking algorithm is given the ability to adapt to the background. In this work, a method for estimating the clutter intensity is introduced. The method is based on locally...
Conventional tracking algorithms rely upon the hypothesis of one detection per target for each frame. However, very fine spatial resolution radars represent widespread systems that provides data for which this hypothesis could be no longer valid. This problem is often called in the literature extended target tracking. In this paper we propose to use the well-established random matrix theory to deal...
In this paper, we consider the problem of guarding a valuable naval asset from a highly maneuverable threat via the use of autonomous unmanned surface vehicles (USVs) as dynamic obstacles. The objective of the defending agent is to maximize the amount of time it takes an intruder boat to enter the restricted area. Here we introduce a set of active blocking strategies which allow the defender to influence...
The use of heavy machinery is one of the main causes of accidents in sites as warehouses or construction. These vehicles have several blind spots that encumber their maneuvering and create a collision-prone environment. To ensure safety in these situations, an early warning system capable of avoiding these accidents is required. An innovative solution consists of the use of a on-board, low-cost, K-band...
In order to improve the tracking performances of a target with multiple motion models and a high level of maneuvering, this paper proposes a modified maneuver target tracking algorithm (i.e. MIMM-CSRF) based on the traditional Interacting Multiple Model Centralized Shifted Rayleigh Filter (IMM-CSRF) algorithm. A two-stage acceleration correction method based on median filtering and the "current"...
Important places inferring, the key technology in the intelligence mining from the big data, has important applications in tracking target users. In the paper, a method for important places inferring, aiming at LBS users is studied. The method includes pruning process and parameter estimation of Periodic Mobility Model. In the step of pruning process, to solve the problem that the location of target...
In order to implement Sequential Bayesian estimator using Monte carlo simulation and to get rid of limitations of Kalman filter, Particle filtering techniques plays a very crucial role for target tracking applications in state space where Importance sampling approximately distributed by posterior distribution with multimodel feature and robustness to noise. However as the particles becomes very large,...
This paper proposes a new pan-tilt-zoom (PTZ) tracking method to improve the robustness against occlusions and appearance changes by using motion likelihood map and scale change estimation as well as appearance correlation filter. For this purpose, we introduce a motion likelihood map constructed from motion detection result in addition to the correlation filter. The motion likelihood map is generated...
This paper considers the tracking problem for the maneuvering target with the motion subject to some known physical constrains. For the target tracking problem, the moving horizon estimation (MHE) approach is firstly introduced, by which we can treat the physical constraints on the target motion as some useful knowledge. Under the MHE framework, we then adopt the multiple model (MM) method to describe...
Dirichlet process (DP) mixtures were recently introduced to deal with switching linear dynamical models (SLDM). They assume the system can switch between an a priori infinite number of state-space representations (SSR) whose parameters are on-line inferred. The estimation problem can thus be of high dimension when the SSR matrices are unknown. Nevertheless, in many applications, the SSRs can be categorized...
The problem of multipath propagation in the tracking low-altitude targets with a radar is addressed. It is well known that multipath fading causes bias errors to the target altitude over the sea. Since the bias error caused by multipath propagation depends on a large number of parameters such as the frequency of the radar waveform, the actual target altitude, and range, it is difficult to estimate...
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