Abstract: A method and apparatus for processing data, the data including a set of one or more system inputs; and a set of one or more system outputs; wherein each system output corresponds to a respective system input. Each system input can include a plurality of data points, such that at least one of these data points is from a different data source to at least one other of those data points. The method includes performing a kernel function on a given system input from the data and a further system input to provide kernelized data; and inferring a value indicative of a significance of data from a particular data source; wherein the inferring includes applying a regression technique to the kernelized data.

Abstract: A method and apparatus for processing data, the data including: a set of one or more system inputs; and a set of one or more system outputs; wherein each system output corresponds to a respective system input; each system input includes a plurality of data points, a first data point in the plurality and a second data point in the plurality being from a same raw data source, and the first data point being pre-processed using a different pre-processing method relative to a pre-processing method used to pre-process the second data point, the method including: for each of the first and second data points, inferring a value indicative of a significance of the pre-processing method used to pre-process that data point; wherein the inferring includes performing a machine learning algorithm on a given system input from the data and a further system input.

Abstract: A method and apparatus for selecting a value or change in value of a measurement variable for an observation of an object, comprising: receiving models for the object defined in terms of an observation parameter and a measurement variable; selecting values of the measurement variable; for each model, determining a value of the observation parameter for each selected value; for each selected value, determining a value of an expected classification potential level using the determined values; and selecting a value of the measurement variable dependent upon the potential level values; wherein the potential level is an expected level of: the information or lack of information, and/or the certainty or uncertainty, with which the object could be classified if a measurement of the observation parameter were taken of the object at the respective value of the measurement variable.

Abstract: A method and apparatus for processing an observation of an object to be classified, the method comprising using a particle filter in which an observation of an object to be classified is used to weight particles on a plurality of models for the object. The method may further comprise performing at least one further iteration of using the particle filter to weight the particles on the plurality of models for the object using a further observation of the object to be classified. The method may further comprise providing respective classification probabilities that each respective model corresponds to the object based on the weightings of the particles of the respective model. Each of the respective particles may be divided into a respective plurality of sub-particles.

Abstract: A method and apparatus for processing data, the data including a set of one or more system inputs; and a set of one or more system outputs; wherein each system output corresponds to a respective system input. Each system input can include a plurality of data points, such that at least one of these data points is from a different data source to at least one other of those data points. The method includes performing a kernel function on a given system input from the data and a further system input to provide kernelised data; and inferring a value indicative of a significance of data from a particular data source; wherein the inferring includes applying a regression technique to the kernelised data.

Abstract: A method and apparatus for processing data, the data including a set of one or more system inputs; and a set of one or more system outputs; wherein each system output corresponds to a respective system input; each system input includes a plurality of data points, such that at least one of these data points is from a different data source to at least one other of those data points, the method including performing a kernel function on a given system input from the data and a further system input to provide kernelised data; and inferring a value for further system output corresponding to the further system input; wherein the step of inferring includes applying a Gaussian Process to the kernelised data. The data sources may be heterogeneous data sources.

Abstract: A method of generating an indication of a probability of a hypothesis being correct based on a set of observations includes obtaining data representing first and second sets of observations. Data representing a set of hypotheses at least partially derivable from the first and the second set of observations can also be obtained. Plural data associations can be generated between at least some data in the first and second sets to indicate a probability of at least some of the generated data associations being correct. The data representing the set of hypotheses and the indication of the probability of at least some of the generated data associations being correct can be used to generate an indication of a probability of at least one of the hypotheses represented by the data being correct.

Abstract: A method and apparatus for processing an observation of an object to be classified, the method comprising using a particle filter in which an observation of an object to be classified is used to weight particles on a plurality of models for the object. The method may further comprise performing at least one further iteration of using the particle filter to weight the particles on the plurality of models for the object using a further observation of the object to be classified. The method may further comprise providing respective classification probabilities that each respective model corresponds to the object based on the weightings of the particles of the respective model. Each of the respective particles may be divided into a respective plurality of sub-particles.

Abstract: A method and apparatus for selecting a value or change in value of a measurement variable for an observation of an object, comprising: receiving models for the object defined in terms of an observation parameter and a measurement variable; selecting values of the measurement variable; for each model, determining a value of the observation parameter for each selected value; for each selected value, determining a value of an expected classification potential level using the determined values; and selecting a value of the measurement variable dependent upon the potential level values; wherein the potential level is an expected level of: the information or lack of information, and/or the certainty or uncertainty, with which the object could be classified if a measurement of the observation parameter were taken of the object at the respective value of the measurement variable.

Abstract: An exemplary system and method are for tracking a target in a decentralised network having a plurality of sensing nodes. Each node makes observations of a target, performs a multiple models tracking algorithm based on the observations, and updates tracking information stored therein. Each node communicates the updated track information to selected other nodes in the network. In response to receiving track information from another node, each node fuses the receiving track information with local track information.

Abstract: A method of tracking a target is applied to a decentralised network (100) comprising a plurality of sensing nodes (110, 120, 130, 140, 150, 160, 170) operable to make observations of the target. The method comprises the steps of: each node (110, 120, 130, 140, 150, 160, 170) performing a multiple models tracking algorithm, thereby updating track information stored at each said node; each node (110, 120, 130, 140, 150, 160, 170) communicating updated track information to selected other nodes in the network (100); and each node (110, 120, 130, 140, 150, 160, 170), in response to receiving track information from another node, conservatively fusing the receiving track information with local track information. In one embodiment, the multiple models tracking algorithm is an interacting multiple models tracking algorithm.