&lt;title&gt;Road-constrained target tracking and identification a particle filter&lt;/title&gt;

Agate, Craig S.; Sullivan, Kevin

doi:10.1117/12.506135

Cited by 29 publications

(27 citation statements)

References 12 publications

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“…Some of these approaches can be used with Kalman filtering, such as reparameterizing the problem [54]. Other approaches are specific to particle filtering, such as modifYing the particles' likelihood functions based on their level of constraint satisfaction [55,56] or generating process noise which ensures that the propagated particles satisty the constraints [57].…”

Section: Particle Filtersmentioning

confidence: 99%

Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

Simon

2010

IET Control Theory Appl.

777

468

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The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.

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Section: Particle Filtersmentioning

confidence: 99%

Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

Simon

2010

IET Control Theory Appl.

777

468

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“…In cases of multiple-hypothesis tracking of a single target, the belief updating is traditionally done with particle filters ( [4], [5], [6]). An exception is [19], which explores both the Gaussian sum approximation and particle filter approaches.…”

Section: Related Workmentioning

confidence: 99%

“…As the agent does not have perfect information of the targets' poses and their subsequent actions, it has to reason about its belief of their poses when planning to keep them well-localized. Traditionally, although targetsearch algorithms [1], [2], [3] necessarily involve planning under uncertainty, target-following algorithms [4], [5], [6] typically focus on performing accurate belief updating and data association, rather than tackling the decision-making challenges faced by the agent. Especially when multiple targets have to be tracked by a single agent, the agent has the additional challenge of reasoning about which target to concentrate on at every timestep.…”

Section: Introductionmentioning

confidence: 99%

Efficient planning under uncertainty for a target-tracking micro-aerial vehicle

Bachrach

Roy

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-A helicopter agent has to plan trajectories to track multiple ground targets from the air. The agent has partial information of each target's pose, and must reason about its uncertainty of the targets' poses when planning subsequent actions.We present an online, forward-search algorithm for planning under uncertainty by representing the agent's belief of each target's pose as a multi-modal Gaussian belief. We exploit this parametric belief representation to directly compute the distribution of posterior beliefs after actions are taken. This analytic computation not only enables us to plan in problems with continuous observation spaces, but also allows the agent to search deeper by considering policies composed of multistep action sequences; deeper searches better enable the agent to keep the targets well-localized. We present experimental results in simulation, as well as demonstrate the algorithm on an actual quadrotor helicopter tracking multiple vehicles on a road network constructed indoors.

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“…Previous work has shown that this tracking can be improved further by exploiting any additional known structure from the problem (e.g. road network) [7], [8]. However, it is not always possible to obtained such a structured set of constraints which dictate object movement (e.g.…”

Section: Introductionmentioning

confidence: 99%

Efficient tracking and querying for coordinated uncertain mobile objects

Larusso

Singh

2013

2013 IEEE 29th International Conference on Data Engineering (ICDE)

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Abstract-Accurately estimating the current positions of moving objects is a challenging task due to the various forms of data uncertainty (e.g. limited sensor precision, periodic updates from continuously moving objects). However, in many cases, groups of objects tend to exhibit similarities in their movement behavior. For example, vehicles in a convoy or animals in a herd both exhibit tightly coupled movement behavior within the group. While such statistical dependencies often increase the computational complexity necessary for capturing this additional structure, they also provide useful information which can be utilized to provide more accurate location estimates.In this paper, we propose a novel model for accurately tracking coordinated groups of mobile uncertain objects. We introduce an exact and more efficient approximate inference algorithm for updating the current location of each object upon the arrival of new (uncertain) location observations. Additionally, we derive probability bounds over the groups in order to process probabilistic threshold range queries more efficiently. Our experimental evaluation shows that our proposed model can provide 4X improvements in tracking accuracy over competing models which do not consider group behavior. We also show that our bounds enable us to prune up to 50% of the database, resulting in more efficient processing over a linear scan.

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<title>Road-constrained target tracking and identification a particle filter</title>

Abstract: 532 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/02/2015 Terms of Use: http://spiedl.org/terms Proc. of SPIE Vol. 5204 533 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/02/2015 Terms of Use: http://spiedl.org/terms

Cited by 29 publications

References 12 publications

Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

Kalman filtering with state constraints: a survey of linear and nonlinear algorithms

Efficient planning under uncertainty for a target-tracking micro-aerial vehicle

Efficient tracking and querying for coordinated uncertain mobile objects

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