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There are two problems with fingerprint matching indoor positioning technology based on Bluetooth Low Energy; in the process of collecting Bluetooth signals, due to the shadow effect and multipath effect, the signal is prone to violent fluctuations, abnormal values and special values, and Kalman filtering is commonly used only for Linear systems, for one-dimensional non-linear Bluetooth signals, are prone to data dispersion, causing positioning errors. The commonly used k-means clustering algorithm in the region division stage requires the number of clusters and the value of k to be specified in advance, which is more sensitive to the initial value and cannot determine the number of reference points according to the positioning environment. Based on the above problems, the extended Kalman filter (EKF) optimization database is first proposed. The nonlinear system adopts local Taylor expansion to form a linear system, and the problem of Kalman filter's sensitivity to nonlinear data is solved by the regression iteration method. The multi-dimensional similarity propagation clustering algorithm (MAP) is proposed, and the Pearson correlation coefficient dimension based on RSSI is proposed to redefine the similarity matrix elements. The results show that the fingerprint database of the proposed combination algorithm is optimized, the reference point area is more detailed, the algorithm adaptability is higher, and the average positioning accuracy is improved.

A simulation study on four packet optical integrated networks shows that flows are fully restorable on 41%–94% dual fiber-cut scenarios where single failure restoration is enabled on the packet layer and no protection is available on the optical layer. This ratio increases to 94%-99.7% after the optical layer adds the path protection. This demonstrates the incompetence of simple combination of single failure protections on both layers using existing techniques, and the necessity of protection coordination across layers for full dual failure restorability.