No abstract

This paper presents a new method to calculate the geometric dilution of precision (GDOP) of GPS by incorporating the concept of model predictive filtering in the training process of neural networks to learn the relationship between GDOP and the azimuth and elevation of satellite. This method overcomes the shortcomings of the traditional back propagation neural networks, such as the slow convergence speed and easily falling into local minimum. A model predictive filtering algorithm is developed by using network weights as system state variables to optimize the network weights based on the neural network's error correction. During the training process, the neural network model error is corrected by compensating the deviation between the actual and target output via the model predictive filtering. Experimental results and comparison analysis demonstrate that the proposed method can effectively approximate GDOP with improved accuracy and reduced training time.

An equivalent center of concentric circle model in target location based on self-organized sensor networks (ad-hoc) is derived. The model is a description of many different sensors in the area where there is one or more monitored target, and the number of sensors is assumed to be the certain stochastic distribution. By defining a proposal distribution about unknown parameters, the maximum posterior likelihood function is taken. Perfect sampling scheme has been applied to induce the runtime of coalesce of chain. We come to the samples from the posterior distribution of targets being monitored. Numerical simulation results indicate that the developed method is reasonable.