To solve the problems existing in the off-line construction method of location fingerprint database for location fingerprint positioning based on Received Signal Strength Indication (RSSI), including large workload of collecting all the fingerprint information in the location, low construction efficiency of the location fingerprint database, and the limited precision of interpolation, a high efficient off-line construction method of the location fingerprint database based on the Singular Value Thresholding (SVT) Matrix Completion (MC) algorithm improved by the Backtracking Search optimization Algorithm (BSA) was proposed. Firstly, using the collected location fingerprint data of some reference nodes, a low-rank matrix completion model was established. Then the model was solved by the low rank MC algorithm based on the SVT. Finally, the complete location fingerprint database could be reconstructed in the location area. At the same time, the BSA was introduced to improve the optimization process of MC algorithm with the minimum kernel norm as the fitness function to solve the problem of the fuzzy optimal solution and the poor smoothness of the traditional MC theory, which could further improve the accuracy of the solution. The experimental results show that the average error between the location fingerprint database constructed by the proposed method and the actual collected location fingerprint database is only 2.7054 dB, and the average positioning error is only 0.0863 m, but nearly 50% of the off-line collection workload can be saved. The above results show that the proposed off-line construction method of the location fingerprint database can effectively reduce the workload of off-line collection stage while ensuring the accuracy, significantly improve the construction efficiency of location fingerprint database, and improve the practicability of fingerprint positioning method to a certain extent.

For the difficulty of complex non-linear system modeling, a new system modeling algorithm based on the Takagi-Sugeno (T-S) Fuzzy Radial Basis Function (RBF) neural network optimized by improved Particle Swarm Optimization (PSO) algorithm was proposed. In this algorithm, the good interpretability of T-S fuzzy model and the self-learning ability of RBF neural network were combined together to form a T-S fuzzy RBF neural network for system modeling, and the network parameters were optimized by the improved PSO algorithm with dynamic adjustment of the inertia weight combined with recursive least square method. Firstly, the proposed algorithm was used to do the approximation simulation of a non-linear multi-dimensional function, the Mean Square Error (MSE) of the approximation model was 0.00017, the absolute error was not greater than 0.04, which shows higher approximation precision; the proposed algorithm was also used to build a dynamic flow soft measurement model and to finish related experimental study, the average absolute error of the dynamic flow measurement results was less than 0.15L/min, the relative error is 1.97%, these results meet measurement requirements well and are better than the results of the existing algorithms. The above simulation results and experimental results show that the proposed algorithm is of high modeling precision and good adaptability for complex non-linear system.

There are some deficiencies in traditional two-step algorithm for under-determined blind source separation, such as the value of K is difficult to be determined, the algorithm is sensitive to the initial value, noises and singular points are difficult to be excluded, the algorithm is lacking theory basis, etcetera. In order to solve these problems, a new two-step algorithm based on the potential function algorithm and compressive sensing theory was proposed. Firstly, the mixing matrix was estimated by improved potential function algorithm based on multi-peak value particle swarm optimization algorithm, after the sensing matrix was constructed by the estimated mixing matrix, the sensing compressive algorithm based on orthogonal matching pursuit was introduced in the process of under-determined blind source separation to realize the signal reconstruction. The simulation results show that the highest estimation precision of the mixing matrix can reach 99.13%, and all the signal reconstruction interference ratios can be higher than 10dB, which meets the reconstruction accuracy requirements well and confirms the effectiveness of the proposed algorithm. This algorithm is of good universality and high accuracy for under-determined blind source separation of one-dimensional mixing signals.