Adaptive beamforming using neural network and fuzzy logic model for measurement data fusion

In the information technology age, multisource multi-sensor information fusion encompasses the theory , methods and tools conceived and used for exploiting synergy in the information acquired from multiple sources databases, sensors. In Multi-sensor data fusion technology, array processing is an area of signal processing that has powerful tools for extracting information from signals collected using an array of sensors. A sensor array captures spatially propagating signals arriving from a certain direction and processes them to obtain useful information. To this end, we intend to linearly combine the signals from all the sensors with coefficients in a manner, so as to estimate transmitted data radiating from a particular direction. Current methods to calculate the weight coefficients are complex. simple and effective weight coefficient calculation method is considered and which is based on where the best features of neural network and fuzzy logic is combined. This technique is based on the optimum beamformer and makes it robust to an faulty estimate of the Direction Of Arrival (DOA) even when powerful interferences are within the uncertainty range of the desired source or contact. The new modified beamformer algorithm is the product of an effort to provide more efficient procedure for real time implementation and a better estimate of the position and spectrum of the contact which is helpful in measurement data fusion or localization.