Researches On Optimization Algorithm Of Wireless Location

In resource management, social economic activities, daily life and other areas, the wireless locating technology is playing a more and more major role. In the cellular positioning system, users can locate other users to be accessed by connecting the client software on common mobile terminals (such as cell phones) to wireless networks. This technology will be widely used, especially in searching, transportation, mobile e-business, emergency rescue and material tracing. Therefore, many governments and companies devote much manpower and resources to research and trial-manufacture of the wireless cellular location system.With the evolution of the3G mobile communication system, new technologies are used. For example, by using intelligent antenna, the value of a signal angle can be more precise. This makes it possible to use AOA positioning. This paper studies the optimized positioning and tracing algorithm of the cellular network communication system based on the signal angle.First of all, on the basis of analyzing present wireless location technology and algorithm, this paper puts its emphasis on the AOA location algorithm and provides the optimized algorithm in which the single reflection statistical channel model based on the geometric structure is provided and the non line-of-sight transmission (NLOS) influence is reduced. To improve the accuracy of the location estimation, in this algorithm, the angle expansion led by each of the multipath cannot be longer than the maximum angle expansion. This enhances the precision of wireless location. Due to the need for iterative computation searching for the most advantage, this algorithm is bigger than the least-squares (LS) algorithm. However, simulation results show that the algorithm’s performance is better than the LS algorithm. Thereafter, the AOA positioning algorithm based on BP neural networks are provided. The AOA algorithm corrects the NLOS and then performs positioning by using the proper algorithm. Simulation results also show that neural-based wireless positioning algorithm is precise in all circumstances. In different signaling channels, the AOA can perform positioning more precisely than location algorithms that do not correct NLOS errors by using neural networks.In order to achieve continuous and real-time location, mobile station location needs to be turned from static location to dynamic tracking location. A location tracking algorithm based on neural networks is proposed. The algorithm is able to correct the NLOS errors in the AOA measurement result by using the BP neural network. Then, the positions of MS can be estimated by appropriate algorithm. Furthermore, interworking with the correlation detection gate, the MS is tracked by the algorithm. The simulation results show that the algorithm performance is better than LS algorithm not only in the static state but also in the dynamic state. This algorithm is also applicable when a mobile station is static or moving at a low speed.