Practical Step Length Estimation Combining Multiple Indoor-Available Radio Signals and Accelerometer

Among various indoor positioning methods, pedestrian dead reckoning (PDR) stands out due to its advantages such as low-cost and no requirement for laborious surveys. Step length estimation (SLE) serves as a key component of PDR. However, most existing methods only utilize acceleration or angular velocity for SLE, which are susceptible to the measurement noise of low-cost inertial sensors as well as variations in walking habits and phone poses. In this paper, we propose a practical SLE algorithm that combines acceleration data with multiple indoor-available radio signals. Firstly, we investigate the outdoor-to-indoor and indoor propagation paths of multiple radio signals, based on which we summarize the similarities and disparities of these signals. Secondly, we explore the relationship between the received signal strength indicator (RSSI) of radio signals and step length, providing the theoretical basis for the proposed SLE algorithm. Thirdly, the availability of radio signals for SLE is evaluated, and the best signal channels are selected to eliminate moments with large errors. Finally, we employ support vector regression (SVR) to adaptively fuse the extracted acceleration features with RSSI features from the chosen channels to achieve SLE. Experimental results from diverse experimenters, sites, and phone poses demonstrate that the combination of multiple radio signals effectively enhances the available percentage of RSSI features. Additionally, the introduction of RSSI features contributes to the reduction of SLE errors, thereby improving the overall positioning accuracy.