Indoor Localization Algorithm Based on Array Antenna and Sparse Bayesian Learning
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摘要:
由于多径和非同源等因素的影响，传统基于蓝牙信号强度的室内定位方法的性能精度和稳定性都不高。针对基于蓝牙信号的复杂室内环境定位问题，该文提出基于低成本阵列天线的室内定位方法，该方法利用单通道轮采极化敏感阵列天线对蓝牙信号进行采样，然后结合暗室测量获得的准确阵列流形和极化快收敛稀疏贝叶斯学习(P-FCSBL)算法实现信源的角度估计，最后通过角度实现定位。该方法充分利用极化信息和角度信息来实现目标和多径信号的分离，同时对单信源的同时采样保证了估计的稳定性。最后通过实测数据处理验证了该方法的有效性。
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关键词:
- 室内定位 /
- 极化快收敛稀疏贝叶斯学习 /
- 极化敏感阵列天线
Abstract:Due to the influence of many factors such as multipath and multi-source, the traditional indoor localization algorithms based on Bluetooth signal strength have low performance in accuracy and stability. In order to solve the location problem in complex indoor environment based on Bluetooth signal, an indoor localization algorithm based on low-cost array antenna is developed. The algorithm utilizes single-channel using switch-antenna polarization sensitive array to sample Bluetooth signal, then combines the accurate array manifold measured in dark room and the algorithm of Polarized Fast Converging Sparse Bayesian Learning (P-FCSBL) to estimate the source’s angle, and finally gets the target location by angle. This algorithm makes full use of polarization information and angle information to separate target and multipath signal, and simultaneous sampling of one source ensures estimation stability. Finally, the effectiveness of the method is verified by the real data.
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初始化： $ {\left( {{\sigma ^2}} \right)^{\left( 1 \right)}}{\rm{ = }}{\widehat \sigma ^2}$ $ {{{v}}_i} = {{{S}}_i}{{{h}}_i},i = 1,2, ··· ,P$ $ {{D}} = \left[ {{{{v}}_1}\;{{{v}}_2}\; ··· \;{{{v}}_P}} \right]$ $ \gamma _i^{\left( 1 \right)} = \left| {{{v}}_i^{\rm{H}}{{x}}} \right|/\left| {{{v}}_i^{\rm{H}}{{{v}}_i}} \right|,i = 1,2, ··· ,P$ $ {{{C}}^{\left( 1 \right)}} = \displaystyle\sum\limits_{i{\rm{ = }}1}^P {\gamma _i^{\left( 1 \right)}{{v}}_i^{\rm{H}}{{{v}}_i} + {{\left( {{\sigma ^2}} \right)}^{\left( 1 \right)}}{{I}}} $ 迭代： $ {\mu ^{\left( j \right)}} = {{{\varGamma}} ^{\left( j \right)}}{{{D}}^{\rm{H}}}{\left( {{{{M}}^{\left( j \right)}}} \right)^{ - 1}}{{x}}$ $ {\left( {{\sigma ^2}} \right)^{\left( {j + 1} \right)}} = \left( {1/N} \right)\left[ {\left\| {{{x}} - {{D}}{{{\mu}} ^{\left( j \right)}}} \right\|_2^2 + {{\left( {{\sigma ^2}} \right)}^{\left( j \right)}}\displaystyle\sum\limits_1^P {\gamma _i^{\left( j \right)}{{v}}_i^{\rm{H}}{{\left( {{{{C}}^{\left( j \right)}}} \right)}^{ - 1}}{{{v}}_i}} } \right]$ $ \gamma _i^{\left( {j + 1} \right)} = {\left| {\gamma _i^{\left( j \right)}{{v}}_i^{\rm{H}}{{\left( {{{{C}}^{\left( j \right)}}} \right)}^{ - 1}}{{x}}} \right|^2},i = 1,2, ··· ,P$ $ {{{C}}^{\left( {j + 1} \right)}} = \displaystyle\sum\limits_{i{\rm{ = }}1}^P {\gamma _i^{\left( {j + 1} \right)}{{v}}_i^{\rm{H}}{{{v}}_i} + {{\left( {{\sigma ^2}} \right)}^{\left( {j + 1} \right)}}{{I}}} $ 直到得到一个满足要求的稀疏解。 
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表 1 两次实验定位误差分析统计表
实验 平均定位误差(m) 误差≤0.2 m的占比(%) 误差≤0.4 m的占比(%)) 误差≤1 m的占比(%) 轨迹1 RSS 1.0858 0 3.48 36.32 轨迹2 RSS 1.0196 0 3.98 40.80 轨迹1 FCSBL 0.1247 87.16 100 100 轨迹2 FCSBL 0.1843 58.29 98.10 100 轨迹1 P-FCSBL 0.0930 90.79 100 100 轨迹2 P-FCSBL 0.0990 93.23 100 100
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