# A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position
## 2 Related Works  
The BLE-based position systems have been widely used by utilizing the received signal strength (RSS) measurements. [PERSON] et al. pointed out that the radio signals of BLE had two features. One is that the signal can change dramatically in a small spatial change. The other is that the signal can be reported multiple times or not reported at all during a single scan. Both features will cause huge noises in both RSS and BLE beacon availability [22]. In addition to the features of the signal, multipath effect, device heterogeneity, and deployment are the sources of errors in the BLE fingerprint position [19,23]. To analyze the effects of dense deployment, [PERSON] et al. proposed a high-resolution proximity detection using an adaptive scanning mechanism fusion with spontaneous differential evolution [24]. [PERSON] et al. defined a coverage degree criterion by leveraging the Cramer Rao Lower Bound (CRLB) and the differential evolution algorithm to optimize the placement of Wi-Fi and BLE access points (APS) to hybrid two types of signals to fuse position based on the position performance analysis [23]. [PERSON] et al. applied three Bayesian filtering techniques to fit the BLE signal distance equation based on considering various errors and conduct comparison experiments to verify the significant modification in two environments [25]. [PERSON] et al. presented an in-depth experimental analysis of RSS and its effect on distance and position [26]. The position methods based on BLE and WiFi are the same, which can be divided into two categories: proximity detection, multilateration, and fingerprinting approaches. The research by [PERSON] et al. showed that under the same environment and conditions, the BLE-based position is more accurate than Wi-Fi because of its lower transmission power and unique channel hopping mechanism [27]. The key point of much research on proximity detection and multilateralism is a distance-based estimation [25,28]. Moreover, it is difficult to obtain an accurate distance model under complex environmental circumstances. The fingerprint method has become the popular approach in real-time positioning with many advantages of being infrastructure-free and easily realizable. RADAR was the first RSS-based fingerprint system that used a K-nearest neighbor (KNN) algorithm to estimate the location indoors [29]. [PERSON] et al. introduced an efficient and graph optimization-based way for estimating the beacon positions and the reference fingerprint map to combine range-based and fingerprint-based methods of BLE [21]. A self-adaptive weighted KNN algorithm PhaseFi proposed a deep network instead of the fingerprint database and estimated the position by a radial basis function probabilistic method [30]. The self-contained position method mainly contains two types: the data-driven inertial navigation method and the PDR method. The data-driven inertial navigation technology has been increasingly used in recent works which use the deep neural network with great potential in model-free generalization to regress pedestrian motion characteristics. It used inertial measurement units (IMU) in a short time and ground-truth motion trajectories to regress motion parameters (velocity and heading). Robust IMU double integration (RIDI) had made a breakthrough in coordinate frame normalization and used support vectors to regress a more accurate velocity vector [31]. IONet and RoNIN utilized trained neural networks to regress the magnitude of speed and the rate of heading angle change, showing the capability to obtain plain displacement [32,33]. ILIO demonstrated a network that regresses 3D displacement estimation and its uncertainty to tightly fuse the relative state measurement into a stochastic cloning EKF to solve for the pose, velocity, and sensor biases [34]. IDOL presented a two-stage, data-driven pipeline using a commodity smartphone that first estimates device orientations and then estimates device position to solve the problem of inaccurate orientation estimates [35].