Training of neural network for multi-source data fusion

Details Training of neural network for multi-source data fusion Training of neural network for multi-source data fusion

1992-11-19

1994-01-04

MacDonald, Allen R.

395 22, G06F 1518

Patent

active

052767701

ABSTRACT:
A method of training a multilayer perceptron type neural network to provide a processor for fusion of target angle data detected by a plurality of sensors. The neural network includes a layer of input neurons at least equal in number to the number of sensors plus the maximum number of targets, at least one layer of inner neurons, and a plurality of output neurons forming an output layer. Each neuron is connected to every neuron in adjacent layers by adjustable weighted synaptic connections. The method of training comprises the steps of (a) for each sensor, designing a plurality of the input neurons for receiving any target angle data, the number of designated input neurons for each sensor being at least as large as the maximum number of targets to be detected by the sensor; (b) for a known set of targets having a known target angle for each sensor, applying a signal related to each known target angle to the designated input neurons for each of the sensors, wherein the output neurons will produce an initial output; (c) for a selected one of the sensors, designating a plurality of the output neurons to correspond to the input neurons designated for the selected sensor and applying the signal related to the known target angles for the selected sensor to the designated output neurons to provide a designated output signal wherein the difference between the initial output and the designated output signal is used to adapt the weights throughout the neural network to provide an adjusted output signal; and (d) repeating steps (a)-(c) until the adjusted output signal corresponds to a desired output signal.

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