Doppler Shift Mitigation in Acoustic Positioning Based on Relative Speed of Base Station for Smartphones

To mitigate the tracking error of moving targets caused by the Doppler shift in acoustic indoor positioning system (AIPS), a novel mitigation method is proposed in this article. It first uses the audio arrival times (AATs) of two adjacent positioning cycles to estimate the relative speed of base station (RSoBS), then uses the estimated RSoBS to update the parameters of the reference signal, reconstructs a new reference signal that matches the received signal, and, finally, substitutes the reconstructed reference signal into the audio detection algorithm to obtain a new AAT estimation, which is then fed back to update the RSoBS estimation. The above process is repeated until a stable RSoBS is obtained, and the AAT estimation corresponding to the final stable RSoBS is the final corrected AAT estimation, which is used to estimate the position of the moving target, thereby improving the tracking accuracy of AIPS. Both simulations and on-site experiments are used to verify the performance of the proposed method. The simulation results show that the ranging deviation caused by the Doppler shift is close to a linear relationship with RSoBS, and the errors of the estimated positions can be effectively mitigated by the proposed method under a low noise level. The on-site experiments show that about 90% of the RSoBS estimation errors are within 0.25 m/s, and the positioning accuracy is improved by about 0.26 m@90%. The proposed method has important application value in improving the tracking accuracy of AIPS for moving targets.

Automated summary

This article proposes a novel method to mitigate the tracking error of moving targets caused by the Doppler shift in acoustic indoor positioning system (AIPS). The method estimates the relative speed of the base station using the audio arrival times (AATs) of two adjacent positioning cycles and updates the parameters of the reference signal accordingly. The reconstructed reference signal is then used in the audio detection algorithm to obtain a new AAT estimation, which is fed back to update the relative speed estimation. The proposed method improves the tracking accuracy of AIPS and has important application value. Simulation and on-site experiments demonstrate the effectiveness of the method under low noise level.