认知定位系统的自适应精度研究

为了能够实现定位精度的自适应,本文分析了认知无线电定位系统(cognitive positioning systems,CPS)的原理。该系统利用频谱及环境感知等模块中的传感器单元来获取所需的定位参数,并将其与到达时间(time of arrival,TOA)定位估计算法相结合,以克拉美罗下界(Cramer-Rao lower bound,CRLB)为准则分析最大似然(maximum likelihood,ML)定位精度自适应算法。本文还对单径和多径环境条件下定位精度自适应性能进行了比较。仿真结果表明,该算法能够实现定位精度的自适应,并能通过改善系统可用的频带带宽、分散频谱的数目、信号的调制方式等因素来提高定位精度。

The Research about Precision of the Self-adaptive in Cognitive Positioning System

In order to realize positioning precision of the self-adaptive, this paper studies the principle of the cognitive positioning system. The system uses the modules of spectrum and environment-aware sensor unit in cognitive radio systems to obtain the required positional parameters and combine with TOA algorithm. In addition, this paper also analyzes the maximum likelihood positioning precision self-adaptive algorithm, which based on Cramer-Rao lower bound. It also compares the single path environment with the multipath to analyze positioning precision of the self-adaptive. The relative simulation results show that this algorithm can realize positioning precision of the self-adaptive, and improve the precision of the cognitive radio positioning system by improving bandwidth, the number of dispersed Spectrum, signal modulation method and other factors.