中文说明：资源描述应用背景关于许可的主要用户（浦）的位置的知识，可以使几个关键功能认知无线电（认知）网络，包括改进的时空感知，智能位置感知路由，以及协助频谱策略的执行。在本文中，我们考虑的可达到的精度PU的定位算法，联合利用接收信号强度（RSS）和到达方向（DOA）通过评价克莱默饶测量界（CRB）。以前的作品评价CRBRSS和DOA定位算法分别承担的DOA估计误差方差是一个固定不变的或相对独立的RSS。我们得到联合RSS / CRB DOA型PU在DOA估计的误差方差的数学模型作为一个功能定位算法RSS，对于一个给定的CR的位置。与实际定位算法和几个关键参数的影响，如节点数目，天线和样品的数目，信道跟踪方差和相关距离，对所能达到的精度进行彻底的分析和讨论。我们也从中获得了封闭形式渐近CRB统一随机CR的位置，和执行所需的CRS，渐近CRB数的理论和数值模拟研究对于一个给定的位置紧密接近的CRB的数值积分。
English Description：
Application background Application background Knowledge about the location of licensed primary-users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we consider the achievable accuracy of PU localization algorithms that jointly utilize received-signal-strength (RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only localization algorithms separately and assume DoA estimation error variance is a fixed constant or rather independent of RSS. We derive the CRB for joint RSS/DoA-based PU localization algorithms based on the mathematical model of DoA estimation error variance as a function of RSS, for a given CR placement. The bound is