Multi-sensor data fusion by a hybrid methodology – A comparative study

• A hybrid architecture for multi-sensor data fusion using soft computing.
• Autonomous design of expert system based on rough set (RS) and NN.
• RS is used for data mining and identifying the crucial inputs.
• NN based model is compared with RS–NN hybrid network.
• Compared results showed RS–NN hybrid model is more efficient.

Multi-sensor data fusion is considered as an inherent problem in wireless sensor network applications. It is widely assumed as a sturdy non linear system in view of the complexities involved in its operation. An accurate and precise methodical solution is therefore a complicated task to accomplish. It is crucial for the sensory systems that they should not be influenced in terms of accuracy and precision by any means. To address these issues a hybrid model employing rough set (RS) with back-propagation neural network (BPNN) is used to ameliorate the data fusion capability of the system with an illustrative example. Experimental results have demonstrated an escalating improvement in the predictive accuracy of the hybrid model as compared to the traditional BPNN model.