# A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
## 2 Related Works
Although the data-driven inertial navigation has a good performance in long-time tracking based on the sophisticated deep learning technology, it needs a large amount of data to train and extra equipment to get the ground-truth trajectory in advance, and for real-time positioning, the device will be under greater computational load. PDR position is a self-positioning method that consists of three key components of step detection, step length, and heading estimation that is a good choice for the real-time position. [PERSON] et al. proposed three-step length error models: Gaussian model, constant random model, and Gauss Markov model. He modeled the error of the gyroscope as a random constant deviation when establishing the PDR error model, so the heading error was considered to be linear with time [36]. [PERSON] et al. established the error models for four methods of measuring step length and discussed the systematic and random errors with the Taylor expansion [37]. [PERSON] et al. proposed the multipoint positioning algorithm based on the received RSSI for calibration to eliminate the cumulative error existing in the traditional PDR system by analyzing the characteristics of walking postures [38]. For fusion methods, [PERSON] et al. [39] proposed an adaptive system noise EKF algorithm to develop an integrated Wi-Fi/PDR system. The proposed filter could determine the dynamic noise of the transition matrix according to the movement (straight or turning) of the pedestrian and reduce the computational complexity of the matching fingerprint database by using an affinity clustering algorithm. The positioning error could be reduced to 2.32 m by the experiment. [PERSON] et al. [40] also presented a novel data fusion framework by using an EKF to integrate Wi-Fi localization with PDR. They developed a measurement model based on kernel density estimation to enable accurate Wi-Fi localization and adaptive measurement noise statistics estimation. The experiments show that the proposed method obtains comparable accuracy and greatly reduces computation cost compared with a particle filter. [PERSON] et al. [41] utilized a grid-based nonlinear Bayesian filter algorithm to fuse the Wi-Fi, BLE, and inertial navigation system (INS) sensor information to develop a calibration-free hybrid indoor positioning methodology. The experiments demonstrated that the performance of the fusion method is much better than the BLE fingerprint position method. However, the harsh indoor environment will lead to coarse errors in the wireless localization method while different motion states of pedestrians affect the performance of PDR, which both affect the accuracy of the dynamic and observation models. Adaptive and robust filters can be employed to mitigate the effects of large errors in the dynamic and observation models, respectively. [PERSON] et al. [42] proposed an adaptively robust filter based on a robust maximum-likelihood estimation to kinematic geodetic positioning and measurement. The method could not only balance the contribution between the updated parameters and measurement but also mitigated the influence of measurement outliers. [PERSON] et al. [43] also presented an adaptively robust filter with multi adaptive factors based on the principles of the adaptive KF and bifactor robust estimations for correlated observations. The proposed filter is more flexible in controlling the disturbing effects of the state components compared to the classified adaptive factors. [PERSON] et al. [44] proposed a robust KF using the Chi-squared test to detect measurement outliers. [PERSON] et al. [45] presented an adaptive and robust filter to combine the Wi-Fi and PDR information to develop an integrated system. The adaptive filter is based on scenario and motion state recognition and the robust filter is based on the Mahalanobis distance. The experiment results indicate that the proposed filter is better than the common EKF.