With the fast developing of mobile terminals, positioning techniques based on fingerprinting method draw attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve the system performance, on the one hand, in the paper, we propose a coarse positioning method based on random forest, which is able to customize several subregions, and classify test point to the region with an outstanding accuracy compared with some typical clustering algorithms. On the other hand, through the mathematical analysis in engineering, the proposed kernel principal component analysis algorithm is applied for radio map processing, which may provide better robustness and adaptability compared with linear feature extraction methods and manifold learning technique. We build both theoretical model and real environment for verifying the feasibility and reliability. The experimental results show that the proposed indoor positioning system could achieve 99% coarse locating accuracy and enhance 15% fine positioning accuracy on average in a strong noisy environment compared with some typical fingerprinting based methods.
With the fast developing of mobile terminals and wireless network techniques, location based services (LBS) are becoming unprecedented popular recent years. World prestigious research institutions have exerted great attention and effort on both indoor positioning and relative business applications, such as the cooperation between Alibaba and AutoNavi and the competition between Google and Baidu. Many emerging indoor positioning systems based on ultrasound, infrared, and radio frequency have been proposed recently [
Firstly, in order to avoid tremendous computation complexity and reduce the error margin of a large fingerprinting dataset, or radio map, clustering algorithm is normally implemented in indoor positioning systems to separate the database into several subradio maps to boost the performance. However, conventional methods, for example,
To solve the issues, we propose a coarse locating method based on random forest (RF) [
Secondly, deploying feature extraction algorithm on the radio map is able to decrease the noise and improve the positioning accuracy at the expense of increasing computation complexity [
In this paper, a kernel PCA approach based on the PCA method [
The remainder of this paper is organized as follows. In Section
This section includes a detailed introduction of traditional fingerprinting positioning system in indoor environment. At the beginning, RSS values are sampled on a grid of reference points (evenly distributed in the interesting region) by an administrator with the mobile terminal from all available access points (AP). A radio map thereby can be built with both RSS values and location coordinates. The current location of any customer can be estimated according to their RSS readings of the mobile device by matching the similarity between the received values and the built radio map. Thus, the radio map building and location matching are the two major procedures for the traditional fingerprinting methods.
As we presented before, the RSS values are sampled and recorded at known spots using a mobile device. However, the height and the direction of a device antenna may affect the sampled signals, which, to some extent, influence positioning accuracy of a positioning system (also communication states, type of WLAN card, and the driver version may have impact on it). Therefore, as a compromised resolution, we concern only holding-in-hand situation, which means that the user is holding the mobile for using LBS service; therefore terminal antenna normally is placed at 1.2 m and its direction is kept consistent while no data is transmitting by any AP, and RSS readings on each reference point are taken in four directions in average.
Defining RSS values sampled from AP
There are many approaches which can be deployed for location matching process: probabilistic methods such as Bayesian, deterministic methods such as
An analysis of WKNN is briefly presented below. After receiving signals from all available APs on a test point (TP), the RSS readings will be calculated for matching with the most similar point throughout the radio map. WKNN method measures the similarity between the TP and each RP by calculating their distance
It is obvious that the dimensionality of a radio map depends on both the number of RPs and quantity of deployed APs. Therefore, in the case of positioning quite a large area with many RPs and numerous APs, the size of radio map will be expanded largely and the computational burden will increase sharply. Besides, in case of some APs being breaking down, the fingerprinting system may be severely damaged due to the missed dimension, even out of work.
We design the new positioning system with two phases by and large, which are offline stage and online stage, respectively. The structure of the proposed system is illustrated in Figure
The structure of the proposed indoor positioning system.
The radio map of positioning area is established firstly in the offline stage as presented in Section
While in the online stage, a group of RSS values are received firstly by a mobile terminal. Then the terminal will be directly located to a subregion by applying RF classifiers, of which the process is also denoted by coarse locating procedure. After that, the sampled RSS values are extracted into feature space by the corresponding transfer matrix of the subregion in order to match with the radio map in feature space. Thereafter, online matching algorithm (WKNN) is employed for precise position estimation and finally the proposed positioning system will output the estimated location coordinates.
Moreover, different from some positioning systems, which are designed to upload the measured RSS to a central server for computing and estimating location [
Random forest substantially is an ensemble classifier including many decision trees [
Random forest classification is adopted in our proposed locating system for coarse positioning process. Its ensemble bagged forest classifier has advantages of resolving the overfitting problem and being able to process large scale dataset which meets the locating system requirement. The procedures of training random forest classifiers are briefly given in Algorithm
number of features
Selecting a subset (with replacement) of radio map dataset randomly with known label of class (i.e., to randomly select The rest part of radio map is reserved to test the error rate. Selecting Calculating the best split accordingly
After training the random forest classifiers, the generated classification model could be directly used in the proposed positioning system for coarse locating. For instance, we put the RSS values of a test point into the classifiers, and then the system output would illustrate which class the test point is located to.
Kernel PCA (KPCA) method is responsible for the feature extraction procedure in the proposed system. Extracting precise features of radio map could reduce the interference and enhance the positioning performance. A brief introduction of KPCA algorithm is given below.
Consistent with the representation of the radio map defined in fingerprinting method, the RSS values of a subregion thereby can be denoted by
The proposed indoor positioning system evaluation will be presented based on simulation model and real environment, respectively, in this section. Both the RF based coarse locating accuracy and KPCA feature extraction performance are examined and compared in detail.
In order to validate the proposed methods for indoor positioning, a system simulation is implemented firstly. Figure
Floor plan of a research center and the APs deployment.
Figure
Distributed RSS received from AP18 with one sampling based on FDTD propagation model with sampling interval of 0.5 m.
Under the simulated ideal environment, the number of nearest neighbors
Positioning systems comparison based on different feature extraction algorithms in simulated ideal environment; traditional fingerprinting method performs best.
However, when we enhance the ambient noise (especially adding on one of APs in consideration of abnormal state), as shown in Figure
Positioning systems comparison based on different feature extraction algorithms in simulated strong noise environment; KPCA-based method performs best.
The system simulation theoretically proves feasibility and robustness of the proposed positioning system. However, it is worth emphasizing that the theoretical model is based on the assumption that the indoor area is in a relatively ideal environment, although random noise has been added on (it affects little because we take average RSS value and the noise would approach to zero in average).
Factors such as interference from ISM band, movement of people, and electrical devices may also affect the RSS values. Therefore, in order to validate the effectiveness of the theoretical model and test the proposed system in practical, we sample the RSS readings 100 times at each direction on each RP (north, south, east, and west, 400 times in all with sampling rate two times per sec) by a mobile terminal and record their location coordinates in the real office environment as shown in Figure
Distributed average RSS value of 400 samples received from AP23 by a mobile terminal in real environment with sampling interval of 0.5 m.
A comparison between measured RSS in real environment and the RSS simulated by FDTD model is presented in Figure
Comparison of measured RSS and estimated RSS from AP18; RSS values below −70dBm will be blocked off in systems.
We firstly verify the proposed random forest coarse positioning method (different coarse positioning methods in the simulated environment are precise and close to each other, so we only test it in real environment). According to Table
Performance of FCM and
FCM |
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Clusters | Number of RPs | Classifying correctly | Clusters | Number of RPs | Classifying correctly |
1 | 149 | 144 | 1 | 211 | 195 |
2 | 180 | 176 | 2 | 157 | 142 |
3 | 187 | 185 | 3 | 167 | 167 |
4 | 123 | 89 | 4 | 144 | 121 |
5 | 189 | 166 | 5 | 149 | 100 |
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Classification accuracy: 82.1% | Classification accuracy: 87.6% |
In terms of the SVM method (optimized by genetic algorithm) [
Performance of SVM and RF for classification.
Support vector machine | Random forest | ||||
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Regions | Number of RPs | Locating correctly | Regions | Number of RPs | Locating correctly |
1 | 422 | 401 | 1 | 422 | 419 |
2 | 53 | 35 | 2 | 53 | 49 |
3 | 141 | 137 | 3 | 141 | 140 |
4 | 83 | 79 | 4 | 83 | 82 |
5 | 129 | 109 | 5 | 129 | 129 |
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Coarse locating accuracy: 91.9% | Coarse locating accuracy: 98.9% |
For the fine positioning performance, according to Figure
Positioning systems comparison based on different feature extraction algorithms in real environment, KPCA-based method performs best.
The proposed indoor positioning system is running on LG Nexus.
In conclusion, traditional fingerprinting method is able to provide the best accuracy in an ideal environment theoretically. Any forms of feature extraction would cause the information loss, which inevitably does harm to the positioning performance. However, ideal condition rarely exists in real environment. The abilities of noise suppression and precise feature extraction of the target radio map are supposed to be the key points to evaluate locating systems. The proposed positioning system is able to provide nearly 99% coarse locating accuracy with customized range. And its KPCA-based feature extraction method may provide outstanding robustness and boost 15% confidence probability in real noisy environment compared to traditional fingerprinting method (87% and 72%, resp., under the circumstance that error distance is within 2 meters).
In this paper, we propose an indoor positioning system which includes random forest method for coarse localization and KPCA algorithm for feature extraction. The structure of proposed system is designed for mobile computation, where the offline stage is responsible for most of the data computing procedure by powerful central server, and all the processed subradio maps and generated functions (classifiers and matrixes) created by offline phase would be stored and transmitted to mobile terminal and then applied in the online stage independently for real time positioning procedure. Our future work will also focus on dynamic indoor positioning and information fusion from built-in sensors such as gyroscope, accelerometer, thermometer, and barometer will be developed and implemented in the positioning system.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The research is supported by National Natural Science Foundation of China (Grant no. 61071105) and the National Science and Technology Major Project (2012ZX03004003).