Location determination techniques
First Claim
1. A method for estimating a set of properties of a target object in an environment, wherein the set of properties comprises location;
- the method comprising;
modelling the environment with a topology model and a data model, wherein the topology model indicates permissible locations and transitions within the environment, and the data model indicates at least one location-dependent physical quantity for each of several permissible locations indicated by the topology model;
modelling location changing characteristics of the target object with one or more motion models, wherein each motion model models a specific type of a target object and obeys permissible locations and transitions indicated by the topology model;
associating to the target object one or more co-located sensing devices, each of which is capable of making observations of one or more of the location-dependent physical quantities;
assigning to the target object a set of particles, each of which has a set of attributes, wherein the set of attributes comprises at least a location in relation to the topology model;
estimating the set of properties of the target object with the set of attributes of the particles assigned to the target object; and
updating the set of particles in a plurality of update cycles, wherein each update cycle comprises the following phases a) to c);
a) determining a degree of belief for each particle to accurately estimate the set of properties of the target object, using the data model and observations from at least one sensing device associated to the target object;
b) determining a weight for each particle based on at least the determined degree of belief; and
c) generating a set of new particles for update cycle n+1 wherein;
at least some of the new particles are based on one or more parent particles for update cycle n, wherein the likelihood of a particle for update cycle n to be selected as a parent particle for a new particle in update cycle n+1 is a non-decreasing function of the weight of the particle; and
the set of attributes of a new particle for update cycle n+1 is derived from the set of attributes of one or more parent particles for update cycle n by using at least one of the one or more motion models and a predetermined algorithm.
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Abstract
A method for estimating a target object properties, including location, in an environment. A topology model indicates permissible locations and transitions and a data model models a location-dependent physical quantity which is observed by the target object'"'"'s sensing device. Motion models model specific target object types, obeying the permissible locations and transitions. The target object is assigned a set of particles, each having a set of attributes, including location in relation to the topology model. The attributes estimate the target object properties The particles'"'"' update cycles include: determining a degree of belief for each particle to estimate the target object properties; determining a weight for each particle based on at least the determined degree of belief and generating new particles for update cycle n+1 in an evolutionary process.
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Citations
25 Claims
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1. A method for estimating a set of properties of a target object in an environment, wherein the set of properties comprises location;
- the method comprising;
modelling the environment with a topology model and a data model, wherein the topology model indicates permissible locations and transitions within the environment, and the data model indicates at least one location-dependent physical quantity for each of several permissible locations indicated by the topology model;
modelling location changing characteristics of the target object with one or more motion models, wherein each motion model models a specific type of a target object and obeys permissible locations and transitions indicated by the topology model;
associating to the target object one or more co-located sensing devices, each of which is capable of making observations of one or more of the location-dependent physical quantities;
assigning to the target object a set of particles, each of which has a set of attributes, wherein the set of attributes comprises at least a location in relation to the topology model;
estimating the set of properties of the target object with the set of attributes of the particles assigned to the target object; and
updating the set of particles in a plurality of update cycles, wherein each update cycle comprises the following phases a) to c);
a) determining a degree of belief for each particle to accurately estimate the set of properties of the target object, using the data model and observations from at least one sensing device associated to the target object;
b) determining a weight for each particle based on at least the determined degree of belief; and
c) generating a set of new particles for update cycle n+1 wherein;
at least some of the new particles are based on one or more parent particles for update cycle n, wherein the likelihood of a particle for update cycle n to be selected as a parent particle for a new particle in update cycle n+1 is a non-decreasing function of the weight of the particle; and
the set of attributes of a new particle for update cycle n+1 is derived from the set of attributes of one or more parent particles for update cycle n by using at least one of the one or more motion models and a predetermined algorithm. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 25)
- the method comprising;
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22. A property-estimation apparatus for estimating a set of properties of a target object in an environment, wherein the set of properties comprises location, the property-estimation apparatus comprising:
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a topology model for indicating permissible locations and transitions within the environment and a data model for indicating at least one location-dependent physical quantity for each of several permissible locations indicated by the topology model;
one or more motion models for modelling location changing characteristics of the target object, wherein each motion model models a specific type of a target object and obeys permissible locations and transitions indicated by the topology model;
an association of one or more co-located sensing devices to the target object, wherein each sensing device is capable of making observations of one or more of the location-dependent physical quantities;
means for assigning to the target object a set of particles, each of which has a set of attributes, wherein the set of attributes comprises at least a location in relation to the topology model;
a property estimator for estimating the set of properties of the target object with the set of attributes of the particles assigned to the target object; and
update means for updating the set of particles in a plurality of update cycles, wherein each update cycle comprises the following phases a) to c);
a) a determination of a degree of belief for each particle to accurately estimate the set of properties of the target object, using the data model and observations from at least one sensing device associated to the target object;
b) a determination of a weight for each particle based on at least the determined degree of belief; and
c) a generation of a set of new particles for update cycle n+1 wherein;
at least some of the new particles are based on one or more parent particles for update cycle n, wherein the likelihood of a particle for update cycle n to be selected as a parent particle for a new particle in update cycle n+1 is a non-decreasing function of the weight of the particle; and
the set of attributes of a new particle for update cycle n+1 is derived from the set of attributes of one or more parent particles for update cycle n by using at least one of the one or more motion models and a predetermined algorithm. - View Dependent Claims (23, 24)
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Specification