Meaning
Iterative optimization routines solve for all instrument locations and target points simultaneously to achieve a global best fit. In usmn bundling, the software creates a mathematical model of the entire measurement network. This technique identifies the most likely position for every point by reducing the residuals across all observations.
The result is a more stable coordinate system than one built through sequential alignments.
Computational Process
The algorithm iteratively adjusts the parameters of the network until the error is minimized. This requires significant processing power for networks with many instruments and targets. Each measurement is weighted based on the distance and the known accuracy of the device.
Precision Gain
Multiple observations of the same point from different angles allow the software to cancel out random errors. This redundancy leads to a higher level of confidence in the final coordinates. The system provides statistical reports that show the uncertainty for every point in the network.
Redundant measurements are the primary way to improve the reliability of the spatial data.
Convergence Limit
Calculations stop when the improvement in the error falls below a predefined threshold. Large datasets with high levels of noise may take longer to reach this point. If the network is poorly constrained, the algorithm might fail to find a stable solution.
A successful convergence indicates that the network is mathematically sound and ready for use.