Use of a kinematic constraint in tracking constant speed, maneuvering targets

Alouani, A.T.; Blair, W.D.

doi:10.1109/cdc.1991.261780

Cited by 46 publications

(63 citation statements)

References 3 publications

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“…First, the perfect measurement approach can be extended to soft constraints by adding small nonzero measurement noise to the perfect measurements [9,10,39,40]. Second, soft constraints can be implemented by adding a regularization term to the standard Kalman filter [6] .…”

Section: Soft Constraintsmentioning

confidence: 99%

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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: Soft Constraintsmentioning

confidence: 99%

“…State equality constraints can be treated as perfect measurements with zero measurement noise [8][9][10]. If the constraints are given by (10), where D is an s x n matrix with s < n, then we can augment (2) with s perfect measurements of the state.…”

Section: Perfect Measurementsmentioning

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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“…Thus, sensor 1 is an abstraction of an automotive vision sensor providing position observations, 1 z ,  and sensor 2 is an abstraction of an automotive radar or laser sensor providing position and velocity (Doppler) observations, 2 z ,  which are calculated with reference to a Cartesian coordinate frame.…”

Section: Sensorsmentioning

confidence: 99%

State-of-the-art Versus Time-triggered Object Tracking in Advanced Driver Assistance Systems

Koplin

Elmenreich

2013

International Journal of Advanced Robotic Systems

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Most state-of-the-art driver assistance systems cannot guarantee that real-time images of object states are updated within a given time interval, because the object state observations are typically sampled by uncontrolled sensors and transmitted via an indeterministic bus system such as CAN. To overcome this shortcoming, a paradigm shift toward time-triggered advanced driver assistance systems based on a deterministic bus system, such as FlexRay, is under discussion.In order to prove the feasibility of this paradigm shift, this paper develops different models of a state-of-the-art and a time-triggered advanced driver assistance system based on multi-sensor object tracking and compares them with regard to their mean performance. The results show that while the state-of-the-art model is advantageous in scenarios with low process noise, it is outmatched by the time-triggered model in the case of high process noise, i.e., in complex situations with high dynamic.

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“…Firstly, the pseudomeasurement was utilized for various tracking problems by several researchers (see e.g., [1,15,16,20]). Later, Ohsumi and his colleagues applied the idea of pseudomeasurement to tracking problems of maneuvering ships ( [13,14]) and some identification problems in environmental pollution problems ( [11,12]), and also to identification problems of unknown exogenous inputs and related quantities ( [5,6,7,9,10]).…”

Section: X(t + 1) = Ax(t)+ Bu(t) + W(t) (1) Y(t) = C X(t) + V(t)mentioning

confidence: 99%

Identification of Unknown Parameters of Partially Observed Discrete-time Stochastic Systems by Using Pseudomeasurements

Tanikawa

Sawada

2014

Transactions of ISCIE

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We consider partially observed discrete-time linear stochastic systems and assume that some entries of the system matrices are unknown. We propose a new method which identifies these unknown entries and the state vectors of these systems simultaneously. The key idea of the proposed method is utilization of the pseudomeasurement which is a fictitious and additional observation process on the unknown entries and will be modified so as to work for the partially observed systems. Augmenting the pseudomeasurement with the original observation process, we derive the new identification method by applying the extended Kalman filter. The proposed method is consistent with the conventional method (without pseudomeasurement).

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Use of a kinematic constraint in tracking constant speed, maneuvering targets

Cited by 46 publications

References 3 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

State-of-the-art Versus Time-triggered Object Tracking in Advanced Driver Assistance Systems

Identification of Unknown Parameters of Partially Observed Discrete-time Stochastic Systems by Using Pseudomeasurements

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