Optimal motion strategies for range-only distributed target tracking

Zhou, Ke; Roumeliotis, Stergios I.

doi:10.1109/acc.2006.1657547

Cited by 13 publications

(10 citation statements)

References 26 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the distribution forx[k | k − 1] is not necessarily Gaussian in practice, it is commonly assumed that a Gaussian approximation is appropriate in many practical applications [1]- [4], [7], [8], [39]. Therefore the probability density functions in (12) are given by…”

Section: State Updatementioning

confidence: 99%

“…A DVANCES in embedded computing and wireless communication are facilitating the use of wireless sensor networks for a variety of new applications, including target localization and tracking with bearing-only sensors [1]- [3] and with range-only sensors [4]; optimal sensor placement and motion control strategies for target tracking [5]- [8]; formation and coverage control [9]- [12]; and environmental tracking and monitoring [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Estimation With State-Dependent Gaussian Observation Noise

Spinello

Stilwell

2010

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

Abstract-We consider the problem of estimating the state of a system when measurement noise is a function of the system's state. We propose generalizations of the extended Kalman filter and the iterated extended Kalman filter that can be utilized when the state estimate distribution is approximately Gaussian. The state estimate is computed by an iterative root-searching method that maximizes a maximum likelihood function. The new filter allows for the consistent treatment of a class of control problem involving nonlinear estimation from measurements with statedependent noise. The effectiveness of the estimation algorithm is illustrated for a control problem with a mobile bearing-only sensor.

show abstract

Section: State Updatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Estimation With State-Dependent Gaussian Observation Noise

Spinello

Stilwell

2010

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

show abstract

“…A computationally efficient optimization method was found in [9] for a similar application that used the trace of the covariance matrix, but it was for a much simpler measurement model than assumed here.…”

Section: B Maximizing Information (Mi)mentioning

confidence: 99%

“…models: range only [9], [10], bearing only [11], [12], range/bearing [2], [13] and more recently, range/rangerate/bearing [14]. These observation models are common due to the prevalent use of directional laser and radar sensors.…”

mentioning

confidence: 99%

Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

Zhan

Casbeer

Swindlehurst

2010

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

Unmanned Air Vehicles (UAVs) are playing an increasingly prominent role in both military and civilian applications. In this paper, we focus on the use of multiple UAV agents in a target tracking application where performance is improved by exploiting each agent's maneuverability. Local time-delay and Doppler measurements made at each UAV are used as inputs to an Extended Kalman Filter (EKF) which tracks the target's position and velocity. Two simple metrics are defined to quantify the accuracy of the tracking algorithm, and heading feedback to the UAVs is used to minimize the metric and improve tracking performance. A simplified version of one of the algorithms that reduces computational complexity is also presented. Simulations demonstrate the significant improvement that results when the UAV sensors are allowed to be optimally positioned during tracking.

show abstract

“…Using distance-only measurements, [19] determines optimal motions for multiple mobile sensors to minimize the error in the posterior estimate of the target position. Using the Fisher Information Matrix, [20] characterizes a condition for local system observability of tracking a moving target in a plane with range-only measurements.…”

Section: A Related Workmentioning

confidence: 99%

A pursuit game with range-only measurements

Bopardikar

Bullo

Hespanha

2008

2008 47th IEEE Conference on Decision and Control

View full text Add to dashboard Cite

Abstract-We address a discrete-time, pursuit-evasion game with alternate moves played between two kinds of players: the pursuer and the evader. The pursuer wishes to capture the evader while the evader's goal is to avoid capture. By capture, we mean that the distance between the players is no greater than 1 unit. We assume simple, first-order motion kinematics for the players. The pursuer can move with a step size of at most 1 unit while the evader can move with a maximum step size of β < 1 units. The pursuer is able to measure only its distance from the evader, before and after the evader's move. We propose a capture strategy and first show that for the game played in R 2 , if β < 0.5, then a single pursuer captures the evader in finite time. Next, we show that if the game is played in R 3 and if β < 0.5, then with a modified strategy, two identical cooperative pursuers capture the evader in finite time. Finally, we shed light on the performance of the capture strategy in the case of β ∈ [0.5, 1[ and the case of sensing errors via simulations. We also present a simulation study of a version of this game with simultaneous moves.

show abstract

Optimal motion strategies for range-only distributed target tracking

Cited by 13 publications

References 26 publications

Nonlinear Estimation With State-Dependent Gaussian Observation Noise

Nonlinear Estimation With State-Dependent Gaussian Observation Noise

Adaptive Mobile Sensor Positioning for Multi-Static Target Tracking

A pursuit game with range-only measurements

Contact Info

Product

Resources

About