Multisensor Information Fusion Using Fuzzy Neural Network for Distance and Bearing Determination

Multisensor information fusion using fuzzy neural network provides a straightforward yet effective partitioning algorithm for determining obstacle distance and bearing. This approach leverages BP neural network implementation for obstacle environment classification and pattern recognition, delivering an efficient solution for mobile robot navigation and obstacle avoidance. The neural network architecture typically involves input layers for sensor data, hidden layers for feature extraction, and output layers for classification decisions.

The core algorithm integrates data from multiple sensors through fusion techniques to achieve more accurate obstacle positioning and attribute identification. By feeding sensor inputs into the BP neural network, the system learns to recognize various obstacle types through backpropagation training and classifies them based on extracted features. This enables mobile robots to accurately perceive and interpret obstacle positions and characteristics in complex environments, with the neural network continuously refining its weights through gradient descent optimization.

To validate algorithm effectiveness, we conducted a series of experiments implementing sensor data preprocessing, neural network training with momentum-based optimization, and real-time classification modules. Experimental results demonstrate that the fuzzy neural network-based multisensor fusion approach achieves high accuracy and robustness in obstacle distance and bearing determination. This provides a reliable and effective navigation solution for mobile robots, with the system showing consistent performance across varying environmental conditions.

In summary, multisensor information fusion using fuzzy neural network represents a simple and effective partitioning algorithm for obstacle distance and bearing detection. By employing BP neural network for obstacle environment classification and pattern recognition, it offers an efficient methodology for mobile robot navigation and obstacle avoidance, with scalable architecture suitable for real-time implementation in autonomous systems.