The accuracy and reliability of a localization system can greatly affect the performance for location-based applications associated with the ubiquitous and pervasive systems, including location-based services (LBS), wireless social networks, Internet-of-Things (IoT), etc. Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS), Beidou, Global Orbiting Navigation Satellite System (GLONASS), Galileo, and Quasi-Zenith Satellite System (QZSS) are the simplest schemes for observing the positional information of a person and a vehicle outdoors. However, the availability of GNSS decreases markedly indoors, since GNSS signals may be blocked by obstacles and walls due to no line-of-sight to the satellite, noise, interference, etc. [1
Hence, many indoor localization methods were designed to facilitate the positioning in indoor environments recently, and various solutions have been suggested by employing wireless infrastructures, ultrasound, motion sensing devices, and vision. The indoor positioning schemes are typically broken down into three categories: solution with wireless infrastructures (such as WiFi access points and Bluetooth transmitters), one without the infrastructures (such as dead reckoning) and sensor fusion approach using Bayes filters.
The dead reckoning (DR) scheme locates users by computing the direction and distance of movement from a known position through inertial measurement units (e.g., gyroscope and accelerometer) without the help of external references (e.g., radio infrastructures and GNSS satellites) [3
]. However, inertial sensors may have large bias and lead to cumulative errors as time goes by. Furthermore, positioning inaccuracy for the DR method can be induced by random bouncing motions of the pedestrian.
A fingerprint-based method is one of the positioning approaches employing RSS values obtained by the wireless infrastructures, including cell towers [6
], fixed Bluetooth modules [7
], and 802.11 access points (APs) [10
]. The fingerprinting approach is performed through offline and online phases. In the first phase, it collects RSS fingerprint data at positions for the localization to construct a fingerprint database (map). Then, during the second phase, the localization is executed by retrieving positions corresponding to the received RSS values via the fingerprint database. Although the site survey needed to build the fingerprint database is labor-intensive, costly, and time-consuming, since the recorded RSS values at a position are distinguished from those from other positions, the RSS data matching-based approaches have been widely employed for indoor localization. Nonetheless, since RF signals are unpredictable and time-varying due to obstacles and multipath fading, the RSS fingerprinting schemes in practice cannot offer satisfactory positioning results for the user. For example, Molina et al. [13
] show that the Google geolocation API that determines indoor location via information about cell towers and WiFi APs in Google Maps can provide the average accuracy of less than 30 m in 50% for indoor environments with many obstructions.
For the localization system, Bayesian filters are used to combine sensory data form disparate sources to obtain better positioning accuracy. The Kalman filter (KF) and extended Kalman filter (EKF) as suboptimal Bayesian algorithms have been applied to indoor pedestrian navigation [14
]. However, they cannot be used for the navigation system with severe nonlinearity and non-Gaussian noises [15
]. On the contrary, the unscented Kalman filter (UKF) is founded on the intuition that it is easier to approximate a probability distribution than to approximate a nonlinear equation [16
]. The UKF, however, cannot apply to the nonlinear system models with highly non-Gaussian noise [17
]. The particle filtering (PF) can approximate the true posterior distribution of the state for the nonlinear/non-Gaussian system [17
The iBeacon, a novel type of Bluetooth module introduced by Apple, provides mobile devices (such as smartphone) with positional data using the Bluetooth 4.0 technology, also known as Bluetooth low-energy (BLE) [19
]. This Bluetooth module can be used to improve existing methods and to develop more reliable and accurate indoor localization algorithms [20
Among the filtering algorithms, the particle filtering algorithm offers the most accurate positioning results, and thus, it has been widely used for the indoor positioning. Various positioning methods that fuse positional information from the RSS fingerprint-based method and DR technique on smartphones using PF have been suggested to provide more accurate indoor positioning information [22
]. In general, they predict the pedestrian position using the movement and position information of the pedestrian obtained by inertial measurement units (IMUs), and then the predicted position is updated the estimated position using location data determined by the measured RSS values via the fingerprint map. GIFT [25
] uses a more stable gradient for RF signal data rather than the signal value to address biased and time-variant WiFi signal readings across sensing devices as well as changes in transmission power of WiFi APs. By comparing RSS values at neighboring positions, GIFT generates a fingerprint map based on RSS gradients called Gmap. Then, it estimates the position of users by integrating the RSS measurements and motion detection information using an extended particle filter; that is, their locations are predicted through mobility sensing results and are updated based on the comparison between Gmap and measured RSS values. SLAC [11
] is a fingerprint localization algorithm that performs system calibration and indoor positioning simultaneously, where user step counter readings and WiFi RSS fingerprints are jointly considered using a specialized particle filter. It learns parameters of step length model, calibrates RF signal data owing to heterogeneous devices, and meanwhile estimate the user location accurately by solving a convex optimization problem. In this fusion algorithm, the user location is predicted through walking displacements obtained by the step length model and is updated by the convex optimization localization and specialized particle filter. However, due to high computational cost required to solve the convex optimization problem, the smartphone-based localization system using SLAC may not be applied to real-time positioning applications. Furthermore, a large sample (particle) size of PF required for better localization accuracy as in [22
] can result in a substantial computation time when comparing to Kalman filtering-based methods. Table 1
summarizes the main technique, experimental environment, sample size, localization accuracy of the aforementioned PF-based localization methods along with the proposed algorithm in this paper.
In our previous work, the capability of estimating the user’s position by fusing DR and RSS fingerprinting approaches via the improved Kalman filtering agorithm, denoted the sigma-point Kalman particle filter (SKPF) has already been addressed [26
]. By utilizing the sample weighting scheme used in the PF and the deterministic sampling approach of the UKF, the SKPF method can provide more accurate localization results compared with UKF and KF. Our investigations are now updated with improvements in the fusion process of DR and fingerprinting method using an enhanced PF for the indoor positioning. The contributions of this study are as follows:
We design a sample weighting method that calculates a weight for each particle through the likelihood function of positional measurements based on kernel density estimation. Similar to the SKPF [26
], the enhanced PF scheme predicts the location of the user for every user step using the mobility sensing information determined by the gyroscope and accelerometer. Then, like SKPF, it also corrects the predicted position through the user’s positional observations obtained from the fingerprinting approach based on machine learning that uses WiFi and iBeacon RSS values as location features. The SKPF evaluates the weight of particles obtained from the deterministic sampling of the UKF through the likelihood function based on parametric technique (e.g., Gaussian function) as in general PF, such as sequential importance resampling (SIR) filter [18
]. On the contrary, the enhanced PF computes the weight of particles drawn by the importance sampling [27
] through the likelihood function calculated by Gaussian kernel density estimation (i.e., Parzen-window method) among nonparametric techniques [28
For the enhanced PF, the likelihood of positional measurements is represented by the target distribution, which is generated based on point mass representations using positional measurements obtained from the measured WiFi and iBeacon RSS values and pedestrian direction data using the fingerprinting algorithm. The RSS data received from WiFi and iBeacon APs permits the target distribution to reflect indoor wireless environments surrounding the user. Unlike the localization schemes [11
] shown in Table 1
that calculate the weight of samples through the parametric density estimation, the particle weight in the enhanced PF is determined by calculating a probability density function of the target distribution using the Parzen-window density estimation for better positioning results.
We propose a double-stacked particle filter (DSPF) as the improved PF. The DSPF estimates the location of the pedestrian using a separate particle presentation for both of the proposal and target distributions. Using the target distribution that reflects wireless circumstances surrounding the user through the multiple observations, the DSPF can conduct reliable position estimation in indoor wireless environments affected by considerable bias and errors. Also, the DSPF can perform accurate position estimations even with less particles due to the use of target distribution. Furthermore, the use of a small particle size guarantees a reduction in the computational cost and makes it possible for the DSPF to be applied to real-time localization applications.
We have implemented the DSPF-based localization system on smartphones, and performed indoor positioning experiments in a campus bulling. Experimental results indicate that the DSPF can offer more accurate localization performance compared with the UKF and KF, and can achieve localization results that are as accurate as PF, while it provides better computational efficiency than PF.
This paper is organized in the following manner. Section 2
demonstrates the overall positioning system configuration. Components of the indoor positioning system are discussed in more detail in Section 3
. Section 4
describes the experimental environment used for the performance analysis, and Section 5
provides results from pedestrian localization experiments in the indoor environments and compares DSPF with PF, UKF, and KF. Finally, Section 6
summarizes the localization performance for the DSPF algorithm.
2. System Configuration
indicates the positioning system used in this study, which is operated on a web server and a mobile phone client. The mobile phone is employed to obtain the motion and position information of the pedestrian from its IMU sensors and then to locate the pedestrian using the data. The machine learning algorithm for localizing the user is carried out on the server. Also, the server is used to process positional query data received from the phone via web. The position estimation system is composed of localization schemes and sensors.
The sensors include motion sensing devices and radio modules in the phone. The radio modules consist of iBeacon and WiFi receivers, and they offer RSS values obtained from iBeacon transmitters and WiFi APs for localization algorithms. The motion sensing devices contain the gyroscope used to obtain the orientation and angular velocity and the acceleration gauge used to determine a three-axis accelerations. Sensing information obtained from IMU devices (gyroscope and accelerometer) and radio modules are employed for position estimate in the localization schemes, including the DSPF algorithm and machine learning.
The localization methods are performed by online positioning and offline training phases. In the training step, RSS values received by the radio modules are recorded at chosen positions through the mobile phone. Moreover, pedestrian direction information obtained from the IMU sensors are also collected, since the body of the user similar to the obstacle can have a great impact on the RF signals between the radio transmitters and receivers. Then, the recorded information are transmitted into web server and are converted into fingerprinting database (map) using the machine learning algorithm.
During the positioning phase, the displacement calculation, positional measurement inference, and DSPF are executed for estimating the position of the pedestrian. DSPF is performed through dead reckoning and update (correction) steps, in which pedestrian locations are inferred using a user motion model described in Section 3.3
During the prediction phase, referred to as dead reckoning (DR), the location of the user for every step of the user is predicted using the movement direction information determined by the gyroscope and accelerometer and the user’s displacement calculated from measurements of the accelerometer. A more detailed description for both the direction determination and displacement (step length) calculation is addressed in Section 3.1
In the correction step, the pedestrian position obtained from the prediction phase is corrected using the positional measurement for the user, which is gained from external observers, such as GNSS. However, because of the GNSS’s unavailability indoors, observations employed in the update phase are gained from the fingerprint database constructed by the machine learning algorithm in the offline training step. When position queries with pedestrian heading data and measured RF signals are sent by the mobile phone into the web server side, the optimal pedestrian position that matches query data (RSS readings and heading angle) is estimated through fingerprinting map. Then, the machine learning algorithm transmits the estimated location of the pedestrian back to the mobile phone. More details on the machine learning approach for estimating the positional measurement are explained in Section 3.2
. The DSPF algorithm based on the two-phase process (prediction and update) is discussed further in Section 3.4
4. Experiment Setup
Several localization experiments are constructed to execute the fingerprinting method based on the machine learning and to compare the pedestrian positioning accuracy for the filters indoors, such as UKF, KF, DSPF, and PF. Our localization system is performed on a web server and a smartphone client (iPhone5S). The server is employed to provide the positional measurements used in the DSPF algorithm using the fingerprinting scheme. The smartphone is used to localize the user through the DSPF algorithm that fuses noisy user motion information obtained from the DR and positional measurements obtained from the server. A detailed description of our positioning system can be found in Section 2
. The indoor test site for our experiments is indicated in Figure 4
. The test site has the dimensions of about 37.3 by 26.5 m. In this study, we aim to examine the feasibility and accuracy of the DSPF as a positioning algorithm in indoor environments. Hence, our experiments were performed in a corridor and a room in a building that are often used to verify the performance of the positioning algorithms as shown in Table 1
rather than a complex environment; however, we need to examine the positioning performance of the DSPF for pedestrians that move from place to place in more complex indoor environments in the future, including the airport (large open space), shopping mall, and library.
Our experiments were conducted using iBeacons (Estimote) and WiFi APs (ipTime N104T), whose locations are denoted by blue pentagons and pink triangles marked as shown in this Figure 4
, respectively. The wireless network composed of both WiFi APs and iBeacons works on 2.4 GHz band. Each WiFi AP was equipped with a wireless adapter that provide a high-speed data transmission of up to 150 Mbps using IEEE 802.11n. The iBeacon that needs a maximum 10 mW transmission power via the Bluetooth Low Energy (BLE) technology offers a maximum 100 m wireless coverage and a data transfer speed of up to one Mbps. We used iPhone5S as a mobile host to record RSS fingerprints from iBeacons and WiFi APs and to localize the pedestrian, which operates with IMU sensors (such as gyroscope and accelerometer) as well as Bluetooth 4.0 and WiFi 802.11n adapters. The WiFi RSS information on iPhone5s was obtained from private Apple80211 framework [47
]. This is because public APIs for scanning RSS values of Wi-Fi networks are not provided by Apple. For the smartphone, the measurements of the gyroscope and accelerometer is updated every 10 ms, and the sampling frequency of iBeacon and WiFi receivers is one Hz.
In our experiments, 50 pedestrians of age range from 20 to 40 participated, striding at various speeds. As can be seen in Figure 4
, the positions where fingerprint information composed of heading angle data and RSS values are collected by the mobile host of the user are schematically represented by orange circles and green squares. They are located at intervals of one meter with corresponding sequence number to each physical location. We recorded over 100 location data at every physical position to build the fingerprint map.
Owing to the obstacles between RF receivers and transmitters, RSS information received from one radio transmitter may change remarkably for different positions. Therefore, we intentionally deployed all radio transmitters (iBeacons and WiFi APs) in the same place (i.e., the lecture room) in order to analyze the effect of the obstructions (such as walls) on RSS values in indoor environments. Considering this deployment, our experiments are divided into two test cases for the performance analysis of our system: TC1 and TC2.
For TC1, the user with the smartphone strides on the positions of the green squares clockwise. This case reflects the good wireless environments between the RF transmitters and receivers (mobile host), because there are no obstacles. On the other hand, for TC2, the pedestrian strides on the locations of orange circles clockwise. This test case indicates the poor radio environments in which RF signals are blocked because of walls between RF transmitters and receivers. For TC1 and TC2, the ground truth information were obtained by measuring their x
-axis and y
-axis coordinates using a measuring tape while walking along the physical locations (green squares and orange circles in Figure 4
The mean packet success rate computed using RF signals from the transmitters in the hallway (orange circles) of TC2 is shown in Figure 5
. The RF signals from the transmitters can be used to compute the packet success rate corresponding to each RF transmitter. The mean packet success rate can provide a reasonable indication of how the wireless environment is good at a given location. For example, the farther the pedestrians walk on the physical locations from the RF transmitters, the more the packet success rate decreases, as shown in Figure 5
that indicates the mean packet success rate obtained from pedestrians’ smartphones for each test position of TC2. The mean packet success rate values for the iBeacons and WiFi APs at all physical locations for TC2 are approximately 44% and 90% respectively, while those for TC1 are about 100%, together. Note that the positioning systems in this experiment use only the RSS values obtained from the RF transmitters to locate the user, but do not require to receive communication packets from them.