PARTICLE SAMPLING METHOD AND SENSOR FUSION AND FILTERING METHOD

• US 20060190217A1
• Filed: 02/15/2006
• Published: 08/24/2006
• Est. Priority Date: 02/17/2005
• Status: Active Grant

• Current Assignee: Sungkyunkwan University Foundation For Corporate Collaboration (SungKyunKwan University)
• Original Assignee: Sungkyunkwan University Foundation For Corporate Collaboration (SungKyunKwan University)
• Inventors: BAEK, Seung-Min, LEE, Jang-Yong, LEE, Suk-Han, LEE, Seong-Soo
• Granted Patent: US 7,379,844 B2
• Time in Patent Office: Days
• US Class Current: 702/181
• CPC Class Codes: G05B 13/04 involving the use of models or simulators
• CPC Class Codes: G05B 17/02 electric

Abstract

Disclosed is a technique for obtaining an_estimate and variance of each variable based on a constraint manifold. Particles (or samples) are sampled in order to filter and fuse ambiguous data or information on at least one state variable of a system using the particles. The sampling is carried out in consideration of an influence which non-linearity of the constraint manifold of_a system model, an observation model or another system model exerts on a probability distribution of the state variable. With this construction, it is possible to reduce decrease of fusion and filtering performance, decrease a Gaussian approximation error, and detect mismatched information.

Claims

1. A particle sampling method for sampling particles in order to filter and fuse ambiguous data or information on at least one state variable of a system using the particles, characterized in that

sampling is carried out especially in consideration of the influence of the non-linearity of system dynamic model, observation model and/or other system constraints, represented by a constraint manifold defined in the state-observation space, on the probability distribution of state variables.

2. The particle sampling method according to claim 1, characterized by the steps of:

uniformly sampling the particles on an axis of a previous state variable in order to find, from the previous state variable, a probability distribution of a current state variable estimated by an equation for the system model or other system model;

finding estimated weights of particles of the current state variable according to the equation of the system model or other system model and of each particle of the current state variable, from a weight allocated by the sampled particles of the previous state variable and information on a prior probability of the previous state variable;

finding a prior probability distribution of a current state estimated from the estimated weight of the sampled particles of the current state variable and from the estimated weight of each particle of the current state variable; and

obtaining particles of an observation variable estimated by an equation for the observation model from the current state variable in order to obtain the particles of the observation variable.

3. The particle sampling method according to claim 1, characterized by the steps of:

when the sampling performed on a manifold in a geometrical space of an arbitrary model equation at regular intervals is defined as “

Uniform Sampling on Constraint Manifold,”

previously performing the sampling in the geometrical space from numerous sample meeting an equation for the system model or other system model at regular intervals, and obtaining particles of a previous state variable and a current state variable;

when an interval divided uniformly on an axis of the previous state variable is defined as a bucket, finding a weight of each particle of the current state variable estimated through a weight allocated by the particles of the previous state variable and prior probability information of the previous state variable and through a number of the particles existing in the bucket;

finding a prior probability distribution of a current state estimated from the estimated weight of the sampled particles of the current state variable and from the estimated weight of each particle of the current state variable; and

previously performing the sampling in the geometrical space from the numerous samples meeting an equation for the observation model at regular intervals, and obtaining the particles of the previous state variable and particles of an observation variable.

4. The particle sampling method according to claim 2 or 3, characterized by the step of obtaining the samples which meet the non-linearity of the constraint manifold of the system model, the observation model or the other system model as well as the constraint manifold by a Monte Carlo method.

5. The particle sampling method according to claim 3, characterized in that, when the number of the samples existing in each bucket is denoted by m, the weight of the particles of the current state variable is found by:

Weight of particles of the current state variable=Weight of samples existing in each bucket/m.

6. A fusion and filtering method for fusing and filtering ambiguous data or information on at least one state variable of a system using particles, characterized by the steps of:

setting a data or information fusing and filtering space composed of state and observation variables;

defining a constraint manifold having various constraints in the fusing and filtering space;

calculating a joint probability distribution on the constraint manifold using a prior probability distribution of the variables;

calculating a marginal probability distribution of the variables from the joint probability distribution; and

obtaining estimate and variance of each of the variables.