Optimal sampling using singular value decomposition of the parameter variance space

The integration of mobile robotic vehicles with distributed sensor networks requires the development of methods for vehicle navigation to achieve sample selection and effectively estimate distributed task variables. In this paper, singular value decomposition (SVD) of the parameter variance space is introduced as a basis for optimal sample selection. Simulation results are used to evaluate the algorithm performance, and significant reduction in field prediction variance are achieved over more conventional incremental rectangular measurement grids. An example of field estimation sensors on an autonomous underwater vehicle (AUV) is described.