In this paper, a novel time of arrival (TOA) estimation method is proposed based on an iterative cleaning process to extract the first path signal. The purpose is to address the challenge in dense multipath indoor environments that the power of the first path component is normally smaller than other multipath components, where the traditional match filtering (MF)-based TOA estimator causes huge errors. Along with parameter estimation, the proposed process is trying to detect and extract the first path component by eliminating the strongest multipath component using a band-elimination filter in fractional Fourier domain at each iterative procedure. To further improve the stability, a slack threshold and a strict threshold are introduced. Six simple and easily calculated termination criteria are proposed to monitor the iterative process. When the iterative ``cleaning'' process is done, the outputs include the enhanced first path component and its estimated parameters. Based on these outputs, an optimal reference signal for the MF estimator can be constructed, and a more accurate TOA estimation can be conveniently obtained. The results from numerical simulations and experimental investigations verified that, for acoustic chirp signal TOA estimation, the accuracy of the proposed method is superior to those obtained by the conventional MF estimators.

As the demand for indoor localization is increasing to support our daily life in large and complex indoor environments, sound-based localization technologies have attracted researchers’ attention because they have the advantages of being fully compatible with commercial off-the-shelf (COTS) smartphones, they have high positioning accuracy and low-cost infrastructure. However, the non-line-of-sight (NLOS) phenomenon poses a great challenge and has become the technology bottleneck for practical applications of acoustic smartphone indoor localization. Through identifying and discarding the NLOS measurements, the positioning performance can be improved by incorporating only the LOS measurements. In this paper, we focus on identifying NLOS components by characterizing the acoustic channels. Firstly, by analyzing indoor acoustic propagations, the changes of acoustic channel from the line-of-sight (LOS) condition to the NLOS condition are characterized as the difference of channel gain and channel delay between the two propagation scenarios. Then, an efficient approach to estimate relative channel gain and delay based on the cross-correlation method is proposed, which considers the mitigation of the Doppler Effect and reduction of the computational complexity. Nine novel features have been extracted, and a support vector machine (SVM) classifier with a radial-based function (RBF) kernel is used to realize NLOS identification. The experimental result with an overall 98.9% classification accuracy based on a data set with more than 10 thousand measurements shows that the proposed identification approach and features are effective in acoustic NLOS identification for acoustic indoor localization via a smartphone. In order to further evaluate the performance of the proposed SVM classifier, the performance of an SVM classifier is compared with that of traditional classifiers based on logistic regression (LR) and linear discriminant analysis (LDA). The results also show that a SVM with the RBF kernel function method outperforms others in acoustic NLOS identification.

This paper proposes an integrated positioning algorithm for mobile devices and achieves long-term and high-accuracy indoor pedestrian tracking under severe non-Line-of-Sight (NLOS) scenarios. The traditional fusion method for hand-held devices lacks zero-speed correction and cannot clear the accumulated error of the Pedestrian Dead Reckoning (PDR). Secondly, the PDR algorithm also requires user privacy data for high positioning accuracy. Hence, we propose a customized model with acoustic and PDR through self-updating parameters with two novel fusing strategies: Kalman Filter with Least-Square (KFLS) and Kalman Filter with Bayesian Parameter Estimation (KFBPE), which utilize numerical feedback and Bayesian distribution, respectively. Experiments with Huawei Mate 9 show that both methods above can effectively eliminate the outlier resulting from severe signal loss, regardless of hand-holding gestures, with no individual privacy data required. Extensive experimental results demonstrate that the proposed methods are more efficient for NLOS and perform much better than the baselines of traditional fusion frameworks like the standard Kalman Filter. KFBPE has a relatively smoother tracking result, which guarantees an average positioning accuracy of up to 25 cm under the circumstance of nearly thirty percent acoustic signal loss (or NLOS) at the same time.

Optimizing anchor deployment is critical to ensure the performance and positioning stability of indoor positioning systems in real-world applications. In this paper, a new anchor deployment optimization method is proposed to enhance the positioning performance of range-based positioning systems in the Non-Line-of-Sight (NLOS) environment without increasing the application cost. Firstly a new fitness function is proposed by simultaneously considering the mean geometric dilution of precision (GDOP) and the coverage of available positioning area in the indoor NLOS environment. Then, a search architecture based on a particle swarm optimization (PSO) algorithm is proposed to optimize anchor deployment. The initialization method of swarm’s position and velocity is given, and the calculation process of the search architecture is introduced in detail. The results obtained from numerical simulations and experimental investigations verified that, for range-based positioning systems in NLOS environment, the accuracy and stability can be significantly improved by optimizing the anchor deployment through our proposed method.

Fusion positioning technology requires stable and effective positioning data, but this is often challenging to achieve in complex Non-Line-of-Sight (NLoS) environments. This paper proposes a fusion positioning method that can achieve stable and no hop points by adjusting parameters and predicting trends, even with a one-sided lack of fusion data. The method combines acoustic signal and Inertial Measurement Unit (IMU) data, exploiting their respective advantages. The fusion is achieved using the Kalman filter and Bayesian parameter estimation is performed for tuning IMU parameters and predicting motion trends. The proposed method overcomes the problem of fusion failure caused by long-term unilateral data loss in traditional fusion positioning. The positioning trajectory and error distribution analysis show that the proposed method performs optimally in severe NLoS experiments.

We have designed, implemented and evaluated the novel single array acoustic localization system SAILoc. The SAILoc system can interact with the commercial-off-the-shelf (COTS) smart phone and it is easier to deploy than multiple sensors' system. We also propose a robust time delay estimation (TDE) algorithm which is based on frequency domain (FD-TDE) and has an advantage over generalized cross-correlation phase thransform (GCC-PHAT) in an indoor environment. Meanwhile, based on multiple sensor information, we propose to use Fusion-based time delay estimation (FD-TDE) to reduce the reverberation interference. The SAILoc's experiment is operated in room and corridor. Our experiment demonstrated that although the SAILoc's room environment performance is better than the corridor, they all have a good localization performance and the error is no more than 0.6m.