基于贝叶斯压缩感知UWB单站定位方法

UWB single station localization based on Bayesian Compressive Sensing

摘要:

脉冲超宽带(IR-UWB)能够在无线定位中取得较高的精确度，但是存在ADC瓶颈问题，利用压缩感知理论(CS)对信号压缩采样可以显著降低信号采样速率。本文将贝叶斯压缩感知应用于UWB单站定位，接收节点利用L型天线阵列接收信号，对信号压缩采样，由贝叶斯压缩感知重构算法(BCS)还原信号并估计时延参数，最后由定位算法解算位置信息。基于IEEE 802.15.4a信号模型的仿真结果表明，该方法最低能以20%的奈奎斯特采样速率获得分米级的定位精确度。

Abstract:

Impulse Radio-Ultra Wideband(IR-UWB) can achieve wireless localization in a higher accuracy, whereas there exists an ADC bottleneck problem. Using Compressive Sensing(CS), signal sampling rate can be significantly reduced. An algorithm based on Bayesian Compressive Sensing(BCS) is proposed with UWB single station for localization. The receiving nodes adopt L-shaped antenna array to sample the signal compressively; the signal could be reconstructed by using BCS algorithm, and the signal delay parameters are estimated. The location information can be solved by the localization algorithm. The simulation results using IEEE 802.15.4a signal model indicate that the proposed method can obtain the positioning accuracy of decimeter level at 20% of the Nyquist sampling rate.