1. Information Fusion Yu Cai

2. Research Article “Comparative Analysis of Some Neural Network Architectures for Data Fusion”, Authors: Juan Cires, PA Romo, PJ Zufiria, IEEE International Conference on Neural Networks, 1995

3. Abstract The various characteristics of fusion algorithms yield different design alternatives for the architecture of the neural network. These alternatives are summarized with comparative results. This paper validate the use of neural network for data fusion and provide a design framework for future work

4. Introduction A data fusion system combines the information from several sensors, or several sensor information processing modules to reduce the uncertainty of the information or to produce information that is not available from any of the sensors by themselves.

5. Classification of data fusion By the type of information to be fused –Congruent information data fusion: from same type sensors or a sensor over a period of time –Complementary information data fusion: from different type sensors By the level of abstraction –Like images: signal, pixel, feature or symbolic level –Centralized architecture: low level fusion with low level data input –Distributed architecture: sensor fusion locally, output high level data for further fusion

6. Classification of data fusion By the interaction of fusion modules –Strongly coupled: modules output depends on each other –Loosely coupled: independent modules with little/no interaction By functional point of view –Positional fusion: the position/state of the observed object. –Identity fusion: the identity of those object.

7. Neural network An artificial neural network can be defined as a set of processing elements (neurons), a specific topology of weighted interconnection between these elements, and a learning law which updates the connection weights. Neurons provide non-linear input/output transfer functions Neural network topology fit into: a feed forward topology and a recursive topology Learning law includes supervised learning, unsupervised learning and reinforcement learning.

8. Why neural network for data fusion Adaptive fusion inference: –Neural network can infer the relationship between the fusion output and the multiple inputs Incomplete information generalization –Information is noisy, distorted and incomplete Non-linear filtering of noise Parallel data computing

9. Neural network for data fusion The architecture of neural network reflects the different characteristics of fusion algorithms, and the types of relations between modules. –Different types of information => different inputs to neural network –Level of data fusion =>where to use neural network –The coupling alternatives => interconnection of the neural networks

10. Simulation Simulation Environment –Video image sensor: 25*25 pixel –Ultrasound sensor: 32*1 pixel –Objects on table: sphere, block, others Goal: Train neural networks for using sensor data to estimate the object position (center of mass).

11. Image by sensor

12. System

13. Neural network A, B Loosely coupled system C vs. Strongly coupled system D After get A and B, the types of C: –C-NLC: C is a neural network, and output non linear combination of A and B –C-Retrain: the whole system ABC is further retrained –C-Avg: average A and B –C-OLC: get an optimal linear combination of A and B by minimizing the mean squared error –C-N-OLC: compute weights for this linear combination using neural network

14. Result

15. Discussion B is better than A because of high resolution of ultrasound than imaging (32*1 vs 25*25) Loosely coupled C is better than strongly coupled D For blocks, all the loosely get similar result; but for sphere, c-retrain is the best.

16. Result 2

17. Discussion 2 A and B is not fully trained with high error C-retrain still performs the best.

18. Conclusion It is possible to perform data fusion with neural network, without knowledge of the characteristic input signal. Neural network perform well in the presence of noise. The result show a modular, loosely coupled architecture perform better than a monolithic, strongly coupled architecture. Within the loosely coupled, C-retrain seems to be the best