Particle-Filter-Based Multisensor Fusion For Solving Low-Frequency Electromagnetic NDE Inverse Problems
Flaw profile characterization from NDE measurements is a typical inverse problem. A novel transformation of this inverse problem into a tracking problem, and subsequent application of a sequential Monte Carlo method called particle filtering, has been proposed by the authors in an earlier publication [1]. In this study, the problem of flaw characterization from multi-sensor data is considered. The NDE inverse problem is posed as a statistical inverse problem and particle filtering is modified to handle data from multiple measurement modes. The measurement modes are assumed to be independent of each other with principal component analysis (PCA) used to legitimize the assumption of independence. The proposed particle filter based data fusion algorithm is applied to experimental NDE data to investigate its feasibility.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1115854
- Report Number(s):
- PNNL-SA-80758
- Journal Information:
- IEEE Transactions on Instrumentation and Measurement, 60(6):2142-2153, Journal Name: IEEE Transactions on Instrumentation and Measurement, 60(6):2142-2153
- Country of Publication:
- United States
- Language:
- English
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