Abstract: Location estimation for a first target object assisted by a second target object, which have co-located signalling devices is provided. A positioning engine employs a data model of a location-dependent physical quantity and determines location probability distributions for the target objects. One signalling device sends positioning-assisting signals to the other which makes observations from it. The positioning engine uses observations on the physical quantity and the positioning-assisting signals to make a quantity observation set, and determines location probability distributions for the target objects. The positioning engine determines an updated first location probability distribution based on the location probability distributions and the positioning-assisting observation set.

Abstract: A method and apparatus for using at least one signal quality parameter in an application. A target device makes observations of at least one signal quality parameter in a wireless communication environment. Because there are gross differences between different target devices' observations, there are one or more device models that compensate for the differences. A selection logic selects an optimal device model for each target device. The observations made by the target device are processed with the selected device model and applied to an application, such as a positioning application or network planning.

Abstract: A technique for positioning a target object in a wireless communication environment. A data model models several sample points. Each sample point includes a location and a set of expected signal values therein. A set of relevance indicators indicates one or more sets of relevant sample points, which are subsets of the sample points in the data model. Signal values are observed at the target object's location. Based on the signal value observations and the set of relevance indicators, a current set of relevant sample points is determined and used, along with the signal value observations, to estimate the target object's location. Computational burden is reduced because sample points not included in the current set of relevant sample points can be omitted from calculations.

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.

Abstract: Location estimation for a first target object assisted by a second target object, which have co-located signalling devices is provided. A positioning engine employs a data model of a location-dependent physical quantity and determines location probability distributions for the target objects. One signalling device sends positioning-assisting signals to the other which makes observations from it. The positioning engineuses observations on the physical quantity and the positioning-assisting signals to make a quantity observation set, and determines location probability distributions for the target objects. The positioning engine determines an updated first location probability distribution based on the location probability distributions and the positioning-assisting observation set.

Abstract: A target device's location is estimated by a location estimation module (LEM) that comprises a probabilistic model (PM) for a plurality of sample points, each of which comprises a sample location and an expected distribution of signal values at that sample point. The location estimation module (LEM) makes a sequence (OS) of observations on, n=1, 2, 3 . . . , of signal values. Each observation corresponds to a respective location qn along the target device's path. The sequence of observations and the respective location constitute a hidden Markov model. The module estimates the target device's location qn based on the probabilistic model (PM) and the sequence of observations, wherein the sequence of observations comprises one or more future observations on+m for which m is a positive integer. In other words, the target device's location is estimated, at least partially, based on knowledge of its future.

Abstract: A target device's location in a radio network is estimated by maintaining a probabilistic model for several sample points that indicate expected distributions of signal values at a given location. The target device observes signal values, wherein the sequence of observations and the respective locations constitute a Hidden Markov Model. A graph models the topology of the positioning environment. The graph indicates several nodes which are permissible locations in the environment and several arcs which are permissible transitions between two nodes. The graph is used to estimate the target device's location based on the probabilistic model and the sequence of observations.

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.

Abstract: A technique for positioning a target object in a wireless communication environment. A data model models several sample points. Each sample point includes a location and a set of expected signal values therein. A set of relevance indicators indicates one or more sets of relevant sample points, which are subsets of the sample points in the data model. Signal values are observed at the target object's location. Based on the signal value observations and the set of relevance indicators, a current set of relevant sample points is determined and used, along with the signal value observations, to estimate the target object's location. Computational burden is reduced because sample points not included in the current set of relevant sample points can be omitted from calculations.

Abstract: A model construction module for constructing a probabilistic model of a wireless environment in which a target device communicates using signals that have a measurable signal value, such as signal strength. The model construction module forms several submodels of the wireless environment. Each submodel indicates a probability distribution for signal values at one or more locations in the wireless environment. The module combines the submodels to a probabilistic model of the wireless environment, such that the probabilistic model indicates a probability distribution for signal values at several locations in the wireless environment. Alternatively, the model may insert new locations to a single model based on a combination of existing locations. The combination of submodels or existing locations includes combining the inverse cumulative distribution functions of the submodels or existing locations.

Abstract: A model construction module (MCM) for constructing a probabilistic model (PM) of a wireless environment (RN) in which a target device (T) communicates using signals that have a measurable signal value (x), such as signal strength. The model construction module forms several submodels (611–631) of the wireless environment (RN). Each submodel indicates a probability distribution (F1–F3) for signal values at one or more locations (Q1–QY) in the wireless environment. The module combines the submodels to a probabilistic model (PM) of the wireless environment (RN), such that the probabilistic model indicates a probability distribution for signal values at several locations in the wireless environment. Alternatively, the model may insert new locations to a single model based on a combination of existing locations. The combination of submodels or existing locations includes combining the inverse cumulative distribution functions of the submodels or existing locations.

Abstract: A method for locating terminals in a wireless network including several base stations. A probabilistic model of the wireless network indicates a probability distribution for signal values of several base stations at several locations in the wireless network. Each terminal makes a set of observations of signal values of a subset of the several base stations. The terminal's location is estimated based on the probabilistic model (PM) and the set of observations. The terminal recurrently measures at least one additional item, which is not used for locating the terminal. Each information tuple indicates the additional items and the time and the location at which the additional items were measured. The information tuples are used to determine the additional item as a function of time and location in the wireless network.

Abstract: A target device's location in a radio network is estimated by maintaining a probabilistic model for several sample points that indicate expected distributions of signal values at a given location. The target device observes signal values, wherein the sequence of observations and the respective locations constitute a Hidden Markov Model. A graph models the topology of the positioning environment. The graph indicates several nodes which are permissible locations in the environment and several arcs which are permissible transitions between two nodes. The graph is used to estimate the target device's location based on the probabilistic model and the sequence of observations.

Abstract: A method and apparatus for using at least one signal quality parameter in an application. A target device makes observations of at least one signal quality parameter in a wireless communication environment. Because there are gross differences between different target devices' observations, there are one or more device models that compensate for the differences. A selection logic selects an optimal device model for each target device. The observations made by the target device are processed with the selected device model and applied to an application, such as a positioning application or network planning.

Abstract: A target device's location is estimated by a location estimation module (LEM) that comprises a probabilistic model (PM) for a plurality of sample points, each of which comprises a sample location and an expected distribution of signal values at that sample point. The location estimation module (LEM) makes a sequence (OS) of observations on, n=1, 2, 3 . . . , of signal values. Each observation corresponds to a respective location qn along the target device's path. The sequence of observations and the respective location constitute a hidden Markov model. The module estimates the target device's location qn based on the probabilistic model (PM) and the sequence of observations, wherein the sequence of observations comprises one or more future observations on+m for which m is a positive integer. In other words, the target device's location is estimated, at least partially, based on knowledge of its future.

Abstract: A model construction module (MCM) for constructing a probabilistic model (PM) of a wireless environment (RN) in which a target device (T) communicates using signals that have a measurable signal value (x), such as signal strength. The model construction module forms several submodels (611-631) of the wireless environment (RN). Each submodel indicates a probability distribution (F1-F3) for signal values at one or more locations (Q1-QY) in the wireless environment. The module combines the submodels to a probabilistic model (PM) of the wireless environment (RN), such that the probabilistic model indicates a probability distribution for signal values at several locations in the wireless environment. Alternatively, the model may insert new locations to a single model based on a combination of existing locations. The combination of submodels or existing locations comprises combining the inverse cumulative distribution functions of the submodels or existing locations.

Abstract: A method for locating terminals (T) in a wireless network (RN) comprising several base stations. A probabilistic model (PM) of the wireless network indicates a probability distribution (4131-413n) for signal values of several base stations at several locations in the wireless network. Each terminal makes a set of observations (423) of signal values of a subset of the several base stations. The terminal's location (425) is estimated based on the probabilistic model (PM) and the set of observations. The terminal recurrently measures at least one additional item (424), which is not used for locating the terminal. Each information tuple (42) indicates the additional items (424) and the time (422) and the location (425) at which the additional items were measured. The information tuples (42) are used to determine the additional item (424) as a function (45) of time and location in the wireless network.