System and method for data fusion with adaptive learning

A data fusion engine for performing the fusion of data comprising:

• a data fuser implementing a stochastic model;
• a memory for storing parameters of the stochastic model;
• a comparator;
• and
• a parameter modifier for modifying parameters of the stochastic model;
• the data fusion engine for:
• (a) receiving an input at the data fuser;
• (b) processing the input in the data fuser in accordance with parameters of the stochastic model to derive a state of the stochastic process, the parameters of the stochastic model previously established during a training period;
• (c) obtaining a predicted next state of the stochastic process;
• (d) receiving a next input at the data fuser and processing the next input in the data fuser in accordance with the parameters of the stochastic model to derive a next state of the stochastic process;
• (e) comparing in the comparator either (i) the next state and the predicted next state, or (ii) the next input and a predicted next input corresponding to the predicted next state and derived from the predicted next state;
• (f) if there is a discrepancy between that compared in (e), the parameter modifier using the next input to modify the parameters of the stochastic model;
• wherein the stochastic model is a hidden markov model, wherein the stochastic process of the stochastic model is a hidden markov process of the hidden markov model, wherein said parameters of the stochastic model are parameters of the hidden markov model, and the parameters of the hidden markov model include at least one of:
• hidden states of the hidden markov model, observations for hidden states of the hidden markov model, a state transition matrix, and emission probabilities for the observations of the hidden states;
• wherein the data fusion engine comprises a plurality of cells arranged in a layered hierarchical structure having a bottom layer, a top layer, and m middle layers;
• wherein an input of each cell in the top layer and the middle layers is obtained from an output of at least one cell in an adjacent lower layer;
• and wherein operations (a) to (f) are performed in one or more cells of the plurality of cells.

A non-transitory computer readable medium having stored thereon computer readable instructions for performing the fusion of data in a data fusion engine, the computer readable instructions including instructions for performing operations comprising:

• (a) receiving an input at a data fuser in the data fusion engine;
• (b) processing the input in the data fuser in accordance with parameters of a stochastic model to derive a state of a stochastic process of the stochastic model, the parameters of the stochastic model previously established during a training period;
• (c) obtaining a predicted next state of the stochastic process;
• (d) receiving a next input at the data fuser and processing the next input in the data fuser in accordance with the parameters of the stochastic model to derive a next state of the stochastic process;
• (e) comparing either (i) the next state and the predicted next state, or (ii) the next input and a predicted next input corresponding to the predicted next state and derived from the predicted next state;
• (f) if there is a discrepancy between that compared in (e), then using the next input to modify the parameters of the stochastic model;
• wherein the stochastic model is a hidden markov model, wherein the stochastic process of the stochastic model is a hidden markov process of the hidden markov model, wherein said parameters of the stochastic model are parameters of the hidden markov model, and the parameters of the hidden markov model include at least one of:
• hidden states of the hidden markov model, observations for hidden states of the hidden markov model, a state transition matrix, and emission probabilities for the observations of the hidden states;
• wherein the data fusion engine comprises a plurality of cells arranged in a layered hierarchical structure having a bottom layer, a top layer, and m middle layers;
• wherein an input of each cell in the top layer and the middle layers is obtained from an output of at least one cell in an adjacent lower layer;
• and wherein operations (a) to (f) are performed in one or more cells of the plurality of cells.