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Details
Inventors: Segawa, Hiroyuki;
Assignee: Sony Corporation (Tokyo, JP)
Primary Examiner: Hoff; Marc S.
Assistant Examiner: Suarez; Felix
Attorney, Agent or Firm: Bell, Boyd & Lloyd LLC

In estimating a state variable of a target on the basis of a previously estimated state variable and an observation value, a prediction value of a next state variable is found on the basis of the previously estimated state variable by a prediction process pre, and the state variable is updated by a first update process ed on the basis of the resultant prediction value and the observation value so as to find an update value. By a second update process opt, whether the update value meets the boundary condition or not is checked, and when the update value does not meet the boundary condition, a new update value is found which minimizes the Mahalanobis distance from the prediction value in the state space and the square sum or the absolute value sum of the Mahalanobis distance from the observation value in the observation space under the boundary condition. Thus, a parameter of motion information or the like of the target can be optimally estimated under the boundary condition or constraint.

DETAILED DESCRIPTION What is claimed is: 1.
An information processing method for estimating a state variable of a target on the basis of a previously estimated state variable and an observation value, the method comprising: a prediction step of finding a prediction value of a next state variable on the basis of the previously estimated state variable; and an update step of updating the state variable on the basis of the prediction value obtained by the prediction step and the observation value and outputting an estimation value; the update step including a step of finding an update value which minimizes the Mahalanobis distance from the prediction value in the state space and the square sum or the absolute value sum of the Mahalanobis distance from the observation value in the observation space under a boundary condition.
2.
The information processing method as claimed in claim 1, wherein the update step includes: a step of finding the update value of the state variable on the basis of the prediction value obtained by the prediction step and the observation value; a step of checking whether the update value meets the boundary condition or not; and a step of finding a new update value which minimizes the Mahalanobis distance from the prediction value in the state space and the square sum or the absolute value sum of the Mahalanobis distance from the observation value in the observation space under the boundary condition, when the update value does not meet the boundary condition.
3.
The information processing method as claimed in claim 2, wherein quadratic programming is used for finding the new update value which minimizes the square sum or absolute value sum of the Mahalanobis distance under the boundary condition.
4.
The information processing method as claimed in claim 2, wherein nonlinear programming is used for finding the new update value which minimizes the square sum or absolute value sum of the Mahalanobis distance under the boundary condition.
5.
The information processing method as claimed in claim 1, wherein the state variable includes motion information and torque information of the target



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