35 research outputs found
A CPHD Filter for Tracking With Spawning Models
In some applications of multi-target tracking, appearing targets are suitably modeled as spawning from existing targets. However, in the original formulation of the cardinalized probability hypothesis density (CPHD) filter, this type of model is not supported; instead appearing targets are modeled by spontaneous birth only. In this paper we derive the necessary equations for a CPHD filter for the case when the process model also includes target spawning. For this generalized filter, the cardinality prediction formula might become computationally intractable for general spawning models. However, when the cardinality distribution of the spawning targets is either Bernoulli or Poisson, we derive expressions that are practical and computationally efficient. Simulations show that the proposed filter responds faster to a change in target number due to spawned targets than the original CPHD filter. In addition, the performance of the filter, considering the optimal subpattern assignment (OSPA), is improved when having an explicit spawning model
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Robust Multi-target Tracking with Bootstrapped-GLMB Filter
This dissertation presents novel multi-target tracking algorithms that obviate the need for prior knowledge of system parameters such as clutter rate, detection probabilities, and birth models. Information on these parameters is unknown but important to tracking performance. The proposed algorithms exploit the advantages of existing RFS trackers and filters by bootstrapping them. This configuration inherits the efficiency of tracking target trajectories from the RFS trackers and low complexity in parameter estimation from the RFS filters
Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior
for multiple extended object filtering. A Poisson point process is used to
describe the existence of yet undetected targets, while a multi-Bernoulli
mixture describes the distribution of the targets that have been detected. The
prediction and update equations are presented for the standard transition
density and measurement likelihood. Both the prediction and the update preserve
the PMBM form of the density, and in this sense the PMBM density is a conjugate
prior. However, the unknown data associations lead to an intractably large
number of terms in the PMBM density, and approximations are necessary for
tractability. A gamma Gaussian inverse Wishart implementation is presented,
along with methods to handle the data association problem. A simulation study
shows that the extended target PMBM filter performs well in comparison to the
extended target d-GLMB and LMB filters. An experiment with Lidar data
illustrates the benefit of tracking both detected and undetected targets
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Spawn Model Derivations for Multi-object Orbit Determination within a Random Finite Set Framework
Fragmentation events and large scale small-sat deployments are a significant threat; because of the sudden creation of many new objects posing potential risks to existing satellites, and the fact that current surveillance systems require laborious human intervention to identify and catalog these new objects. In the tracking community, the spontaneous appearance of new objects is referred to as birth, whereas spawning refers to the appearance of new objects generated by previously existing ones, such as a fragmentation event or small-sat deployment. In this dissertation, two-well known random finite set (RFS) filters are extended via mathematical derivation, aimed at performing initial orbit determination (IOD) of objects generated by spawning events. A Zero-Inflated Poisson (ZIP) spawn model is presented and a predicted cardinality expression for general spawn model configuration, capable of implementation via Partial Bell Polynomials, is derived for the Cardinalized Probability Hypothesis Density (CPHD) filter using a measure-theoretic approach. Generalized Labeled Multi-Bernoulli (GLMB) filter developments achieve a closed-form solution to the multi-object Bayes recursion capable of jointly estimating a spawned object's state and ancestry. Linear simulations demonstrate fundamental filter developments; the ZIP spawn model is shown to outperform other conventional models with the CPHD filter and multiple generations of spawn object ancestry are accurately estimated with the GLMB filter. Finally, non-linear simulations specific to Space Situational Awareness (SSA) IOD demonstrate the filters' efficacy, which include: fragmentation event and small-sat deployment scenarios, homogeneous and heterogeneous radar network observations, and spawning events that occur in and out of sensor field of view. This research shows that on-line multi-object IOD in the presence of spawning is possible within the RFS paradigm, and establishes a foundation upon which further SSA improvements can be investigated