Concerning the insufficient consideration of the cumulative error and offset which online Global Positioning System (GPS) trajectory data compression based on motion state change and the insufficient key point evaluation of online GPS trajectory data compression based on the offset calculation, an online compression of GPS trajectory data based on motion state change, named Synchronous Euclidean Distance (SED) Limited Thresholds Algorithm (SLTA), was proposed. This algorithm used steering angle and speed change to evaluate information of trajectory point. At the same time, SLTA introduced the SED to limit offset of trajectory point. So SLTA could reach better information retention. The experimental results show that the trajectory compression ratio can reach about 50%. Compared with Thresholds Algorithm (TA), the average SED error (less than 5 m) of SLTA can be negligible. For other trajectory data compression algorithms, SLTA's average angel error is the lowest (1.5°-2.3°) and run time is the most stable. SLTA can stably and effectively do online GPS trajectory data compression.

The traditional training methods of Gaussian Mixture Model (GMM) are sensitive to the initial model parameters, which often leads to a local optimal parameter in practice. To resolve this problem, a new GMM optimization method was proposed based on Particle Swarm Optimization (PSO). It utilized Maximum Likelihood (ML) algorithm in the PSO iteration and provided a new architecture of hybrid algorithm. Because of the global optimization characteristic of the particle swarm optimizer method and the strong local searching capacity of ML, it can obtain model parameters with high precision. Experiment for text-independent speaker identification shows that this method can obtain more optimum GMM parameters and better results than the traditional method.