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US 8,615,480 B2Granted
Method, device and system for the fusion of information originating from several sensors
AssigneeThales
InventorFrédéric Pichon
Life of this patent
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Abstract
The invention relates to a method, device and system for fusion of information originating from several sensors. The invention includes a mechanism for fusion of belief functions. To apply this mechanism, various information, knowledge and operations are modelled within the framework of the theory of belief functions: information provided by the sensors, knowledge regarding the propensity of the sensors to be in a given operating state, and merge operators for each operating state considered.
Claims8
A method for the fusion of information originating from two or more sensors (C1, C2) each producing respective belief functions (mC1, mC2) representing knowledge on a reasoning space (Ω), and comprising several states (E1, . . . , EN) each representing an operating state of the set of sensors, said method comprising:
- calculating a merged belief function (mF);
- and
- calculating conditional belief functions on the basis of the belief functions (mC1, mC2) produced by the sensors (C1, C2) and of a predefined base of operators comprising associations between states of sensors and merge operators, wherein a conditional belief function represents knowledge of a sensor on the reasoning space (Ω) when the set of sensors is in a given operating state (E1, . . . , EN), and wherein a step of calculating the merged belief function (mF) uses the conditional belief functions calculated and knowledge arising from a knowledge base regarding the operating state of the sensors, wherein the calculating the merged belief function (mF) is carried out using contextual information indicating that, if an object observed by the sensors (C1, C2) is of type A, then the sensors have a propensity p to be in a set of states E′, where A is a subset of the space of propositions (Ω) and E′ is a subset of a set E of the various states (E1, . . . , EN) of the sensors and wherein the merged belief function (mF) follows the following relation:
- mF=((⊕i=1, . . . ,NDmΩ[EiE×Ω)⊕D(⊕j=1, . . . ,KDmE[Aj]E×Ω))↓Ω
- where ⊕D is an operator called the unnormalized Dempster's rule, mΩ[Ei], with i=1, . . . , N, are conditional belief functions representing knowledge on the reasoning space (Ω) when the sensors are in various states (E1, . . . , EN), N being the number of operating states, and mE[Aj] is contextual knowledge regarding the propensity of the sensors to be in a given operating state when an object observed by the sensors is of type Aj, with j=1, . . . , K, Aj being a subset of Ω and such that A1, . . . , AK forms a partition of Ω, ↑, ↓ and being operations for manipulating belief functions being a vacuous extension, a marginalization and a deconditioning, respectively.
2 more elements
A physical device for acquiring data originating from several sensors each producing a belief function representing knowledge on a reasoning space and comprising several operating states, the device being configured to:
- calculate a merged belief function;
- calculate conditional belief functions, the device being linked to the sensors and to a predefined base of operators comprising associations between states of sensors and merge operators, a conditional belief function representing knowledge of a sensor on the reasoning space when said sensor is in a given operating state, and wherein the merged belief function is calculated using the conditional belief functions calculated and knowledge regarding the operating state of the sensors originating from a knowledge base regarding the operating state of the sensors,
- the device being further configured to calculate the merged belief function (mF) is calculated by using contextual information indicating that, if an object observed by the sensors (C1, C2) is of type A, then the sensors have a propensity p to be in a set of states E′, where A is a subset of the space of propositions (Ω) and E′ is a subset of a set E of the various states (E1, . . . , EN) of the sensors, and wherein the merged belief function (mF) follows the following relation:
- mF=((⊕i=1, . . . ,NDmΩ[EiE×Ω)⊕D(⊕j=1, . . . ,KDmE[Aj]E×Ω))↓Ω
- where ⊕D is an operator called the unnormalized Dempster's rule, mΩ[Ei], with i=1, . . . , N, are conditional belief functions representing knowledge on the reasoning space (Ω) when the sensors are in various states (E1, . . . , EN), N being the number of operating states, and mE[Aj] is contextual knowledge regarding the propensity of the sensors to be in a given operating state when an object observed by the sensors is of type Aj, with j=1, . . . , K, Aj being a subset of Ω and such that A1, . . . , AK forms a partition of Ω, ↑, ↓ and being operations for manipulating belief functions being a vacuous extension, a marginalization and a deconditioning, respectively.
2 more elements
A system for fusion of information comprising:
- at least two sensors each producing a belief function;
- and
- a physical device for acquiring data originating from the at least two sensors each producing a belief function representing knowledge on a reasoning space and comprising several operating states, the device being configured to:
- calculate a merged belief function;
- calculate conditional belief functions, the device being linked to the sensors and to a predefined base of operators comprising associations between states of sensors and merge operators, a conditional belief function representing knowledge of a sensor on the reasoning space when said sensor is in a given operating state, and wherein the merged belief function is calculated using the conditional belief functions calculated and knowledge regarding the operating state of the sensors originating from a knowledge base regarding the operating state of the sensors, the device being further configured to calculate the merged belief function (mF) is calculated by using contextual information indicating that, if an object observed by the sensors (C1, C2) is of type A, then the sensors have a propensity p to be in a set of states E′, where A is a subset of the space of propositions (Ω) and E′ is a subset of a set E of the various states (E1, . . . , EN) of the sensors, and wherein the merged belief function (mF) follows the following relation:
- mF=((⊕i=1, . . . ,NDmΩ[EiE×Ω)⊕D(⊕j=1, . . . ,KDmE[Aj]E×Ω))↓Ω
- where ⊕D is an operator called the unnormalized Dempster's rule, mΩ[Ei], with i=1, . . . , N, are conditional belief functions representing knowledge on the reasoning space (Ω) when the sensors are in various states (E1, . . . , EN), N being the number of operating states, and mE[Aj] is contextual knowledge regarding the propensity of the sensors to be in a given operating state when an object observed by the sensors is of type Aj, with j=1, . . . , K, Aj being a subset of Ω and such that A1, . . . , AK forms a partition of Ω, ↑, ↓ and being operations for manipulating belief functions being a vacuous extension, a marginalization and a deconditioning, respectively.
4 more elements
Dependencies are inferred from the claim text.