Accepted manuscript to appear in IJPRAI Accepted Manuscript Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. International Journal of Pattern Recognition and Artificial Intelligence Article Title: Amp-Phi: a CSI-based Indoor Positioning System Author(s): Zhou Tao-Yun, Lian Bao-Wang, Zhang Yi, Liu Sen DOI: 10.1142/S0218001418580053 Received: 28 November 2017 Accepted: 07 February 2018 To be cited as: Zhou Tao-Yun et al., Amp-Phi: a CSI-based Indoor Positioning System, International Journal of Pattern Recognition and Artificial Intelligence, doi: 10.1142/S0218001418580053 Link to final version: https://doi.org/10.1142/S0218001418580053 This is an unedited version of the accepted manuscript scheduled for publication. It has been uploaded in advance for the benefit of our customers. The manuscript will be copyedited, typeset and proofread before it is released in the final form. As a result, the published copy may differ from the unedited version. Readers should obtain the final version from the above link when it is published. The authors are responsible for the content of this Accepted Article. CR IP Accepted manuscript to appear in IJPRAI T Click here to download Manuscript (pdf) Modified.pdf Amp-Phi: a CSI-based Indoor Positioning System Zhou Tao-Yun1,2 Lian Bao-wang1,* Zhang Yi1 ,Liu Sen1 1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China 2. School of Information, Hunan University of Humanities, Science and Technology, Loudi, China *. Corresponding author DM AN US Abstract： With a rapid growth in the demand of Location Based Services (LBS) in indoor environments, localizations based on fingerprinting have attracted significant interests due to their convenience. Until to now, most of such methods are based on Received Signal Strength Indicator (RSSI), which is vulnerable to Non-Line-of-Sight (NLOS). In order to realize a high-precision indoor positioning, we propose a CSI-based Amp-Phi indoor positioning system which exploits the amplitude database. Firstly, according to the characteristics of the raw CSI information collected at different positions under different environments, we build a NLOS mitigation model and a phase error mitigation model respectively to process the amplitude and phase of CSI. Secondly, we analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. After being processed with the models, the CSI features can be used to distinguish different positions clearly, which provides a theoretical basis for the indoor positioning based on fingerprinting. Finally, we build a fingerprinting database based on the features of amplitude and phase, realize to locate the target’s position with the K-Nearest Neighbor (KNN) matching algorithm. Experiments implemented in different situations show that Amp-Pi system is reliable and robust, whose position accuracy is higher than that of PhaseFi, Horus and ML systems under the same condition. It can be used in many scenarios, such as the localization of robots in our daily life, doctors or patients in the hospital, people localization in large supermarket or museum and so on. Key words： Channel State Information (CSI), Indoor Positioning, KNN Matching Algorithm, EP TE Received Signal Strength Indicator (RSSI), Non-Line-of-Sight (NLOS) AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. and phase information of Channel State Information (CSI) at the same time to establish a fingerprinting T CR IP Accepted manuscript to appear in IJPRAI 1. Introduction Indoor positioning technology based on smart phones has many application scenarios since people stay inside buildings more than 80% of their daily life [1,2]. WLAN-based indoor location fingerprinting has been attractive owing to the advantages of open access and high accuracy. A large number of companies including both domestic and international based have devoted themselves to the research of indoor positioning and have already produced many positioning systems and solutions. Apple introduced i-Beacon in 2013 and began to use Bluetooth for indoor positioning, it opened the Core-Location API in IOS 8 system at the Apple Worldwide Developers Conference (WWDC) in 2014, which could allow the developers to capture the user’s precise position in indoors[3,4]. Currently, DM AN US Google has extended Google Maps to indoors, and has mapped museums, airports and shopping malls for at least 17 countries, such as the Tai Po Super City in Hong Kong [5,7]. There are also many teams in China who are engaged in the research of indoor positioning products, such as Local-Sense indoor positioning system, High German Maps and Smart Maps and so on[8]. receiver with some commercial WLAN equipment. The RSSI based methods [9-13] calculate the distance between two nodes and locate the unknown target with at least three known nodes, although they have a good performance in outdoors, the position errors in indoors are large due to the multipath effects. Such methods have three main disadvantages: First, due to the small-scale shadow fading caused by multipath effects in indoor environments, the RSSI values no longer increase monotonously with the propagation distance, which limits the ranging accuracy. Second, due to the NLOS, RSSI values at the same position will change greatly over time, and bring big errors even for a stationary device, the fluctuation of a typical laboratory environment is about 5dB in one minute [14-15]. Third, RSSI values only reflect the superimposed amplitude of multipath, but ignore the rich multipath information of subcarriers in an orthogonal frequency-division multiplexing (OFDM) system, so they can’t distinguish the multiple propagation paths one by one. Hence it is of critical importance to search for a much more stable and reliable information used to build a fingerprinting other than the RSSI. Recently, the IEEE 802.11 standardization is found to include rich channel properties in the form of channel state information based on OFDM technology. So more and more researchers concentrate on using the CSI to resolve the multipath effects existed in RSSI. With the availability of CSI from the TE physical layer, the Wi-Fi indoor positioning systems have gradually shifted from RSSI to CSI [15]. On one hand, instead of measuring the superimposed amplitude response of all subcarriers, it measures the frequency response of multiple subcarriers from one packet simultaneously, which can finely describe the frequency selectivity of the channel. On the other hand, by measuring the amplitude and phase of each subcarrier, it extends the single-valued RSSI to the frequency domain, and provides much richer EP and finer CSI in frequency domain [16]. By building some efficient models to preprocess the amplitude and phase of the raw CSI, the multipath effects can be mitigated to a certain extent, so CSI is much more stable and reliable than RSSI in indoor positioning [17]. In [18] a Fine-grained Indoor Fingerprinting System (FIFS) is proposed, where the CSI is the weighted average CSI values of multiple antennas. In [19] a PinLoc system is proposed, where it considers 1×1 m2 spots for training data and exploits the CSI information. FILA [20] proposes a new ranging model based on CSI expecting to reduce the signal propagation error existed in the traditional AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. Most fingerprinting-based systems so far rely on RSSI, which can be easily measured at the indoor positioning, and determines the final position with trilateral positioning method. However, the model is sensitive to environmental changes and needs to train the parameters in a specific indoor scenario to measure the distance accurately. In addition, ranges between the target and APs can’t be T CR IP Accepted manuscript to appear in IJPRAI measured in parallel, thus it can’t realize real-time position in WIFI scenario. Experimental results show that the 80% accuracy is about 2 m and 90% accuracy is about 3 m, with three APs being taken part in. CUPID [21] transforms the CSI to time domain and measures the range by considering the first bar of the energy distribution graph to be the line-of-sight. This will result in large errors in real environment, because there exists an uncertain time lag within the shortest delay. Experimental results show that the 80% accuracy is about 3m and 90% accuracy is about 4m, with five APs being taken part in. In [22-23], a DeepFi system is proposed, in which the fingerprinting is the weights of a network trained with a greedy learning algorithm in the offline training stage, and a probabilistic method is used to locate the target’s position in the online positioning stage. However, the phase information of DM AN US CSI is not used for the building of fingerprinting. In [24-25], a PhaseFi system is proposed where the phase information is used to build the fingerprinting database and a greedy learning algorithm is used to train the weights, however, it doesn’t use the amplitude information. In this paper, we propose a fingerprinting-based Amp-Phi indoor positioning system, in which the basic ideas can be concluded as: Firstly, according to the characteristics of the raw CSI information collected at different positions under different environments, we build a NLOS mitigation model and phase error mitigation model respectively to process the amplitude and phase of CSI. Secondly, we analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. After being processed with the models, the CSI features can be used to distinguish different positions clearly, which provides a theoretical basis for the indoor positioning based on fingerprinting. Finally, we build a fingerprinting database based on the amplitude and phase features of CSI and realize the target’s localization with the K-Nearest Neighbor (KNN) matching algorithm. We perform some experiments in two typical scenarios: one is the complex computer laboratory where exist a lot of NLOS due to the block of furniture and computers, the other is the relative simple living room, there is little NLOS for no large things exist. Both in the two environments, we compare the Amp-Phi system with the newest method PhaseFi proposed in 2016 [25], the most classical methods Horus [26] and ML [27] proposed in 2005. In the laboratory, the position accuracy of the four system is 4m,4.2m,5.4m and 6m respectively, and in the living room, the position accuracy of the four system is 1.7m,1.8m,2.5m and 3.7m respectively. That is to say, among the four systems, Amp-Phi performs best and the PhaseFi TE system takes the second place, this is because both of them use CSI which has much more fine-grained subcarriers information than RSSI, for Amp-Phi fuse the amplitude and phase information together, its position accuracy is better than that of PhaseFi which only use the phase information. The rest of the paper is organized as follows. In section 2, we collect the raw CSI information and build the CSI range model. In Section 3, we build a NLOS mitigation model and a phase error mitigation model respectively to process the amplitude and phase of CSI, and analyze the EP characteristics of CSI. In section 4, we build a fingerprinting database based on amplitude and phase at the same time and realize to locate the target’s position in indoors, and do some experiments in two typical scenarios. Finally, in section5 we conclude our work. 2 Collection of CSI Data and Establishment of Range Model AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. amplitude and phase information are used at the same time to build the fingerprinting database. The 2.1 Collection of CSI Data In the wireless communication protocol, CSI belongs to the physical layer information and can’t be obtained directly. TNS-CSI Tool, which is an integrated installation tool based on Linux 802.11n T CR IP Accepted manuscript to appear in IJPRAI can be used to extract CSI information and store them in real-time. The specific steps of obtaining the CSI are as follows: (1) Replace the laptop's wireless network card with Intel 5300 which have three antennas. For simplicity and without loss of generality, we just analyze the data collected from antenna A and antenna B, for the analysis methods of the three antennas are similar. (2) Install the Ubuntu11.04 operating system and replace the system software source with 11.04 Version for laptop. (3) Compile and install the TNS-CSI Tool, modify the firmware and NIC driver and recompile the kernel options. DM AN US When all the essential software has been installed, the CSI information is recorded in the xx.dat file and can be read by the TNS-CSI Tool. The experimental devices required to acquire CSI data is Fig.1 The experimental devices required to acquire CSI data The CSI is stored in a three-dimensional matrix with a size of NRX NTX 30 , NRX and NTX is the number of receive and transmit antennas respectively, 30 in the third dimension represents 30 OFDM subcarriers (in the following Tables it is abbreviated as Sub). There are 56 subcarriers in the CSI but only 30 subcarriers can be used when the bandwidth is 20 MHz, measurement information of CSI can be seen in Table.1. Table.1 Measurement Information of CSI Bandwidth Grouping Number of Sub 20MHz 2 30 40MHz 4 -28,-26,-24,-22,-20,-18,-16,-14,-12,-10,-8,-6,-4,-2,-1, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 28 -58,-54,-50,-46,-42,-38,-34,-30,-26,-22,-18,-14,-10,-6,-2, 2, 6, 10, 14, 18, 22, 26, 30, 34,38, 42, 46, 50, 54, 58 TE 30 Serial Number of Sub 2.2 Establishment of Range Model based on CSI CSI is compatible with IEEE 802.11a/g/n protocols, it can be acquired with a common wireless network card and an open source firmware [4]. CSI includes the channel properties of a communication EP link, describes how a signal propagates from the transmitter to the receiver and reveals the combined effects of the multipath, such as scattering, fading, and power decay with distance. In a narrowband flat-fading channel, the OFDM system in frequency domain can be modeled as: Y HX n (1) Where Y is the channel output vector which is always distorted by the multipath effects, X is the input vector which we need, namely the CSI information. H is the channel state information matrix AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. shown in Fig.1. of the subcarriers and n is the additive white Gaussian noise (AWGN). Usually n subjects to a normal distribution whose mean is zero and variance is 2 , namely, n ~ N (0, 2 ) . In order to extract X from Y successfully, it is necessary to estimate the channel state information matrix H , the estimation of H can be described as: Y Hˆ X T CR IP Accepted manuscript to appear in IJPRAI (2) We collect CSI from the nearby APs with a mobile device equipped with 802.11 NICs at each sample position, every group of CSI represents the amplitude and phase of an OFDM subcarrier. We modify the chipset firmware and divide the CSI into 30 groups subcarriers, which can be collected simultaneously at the receiver. Let H as: H [ H (1), H (2), , H (k ), , H ( K )]T , k [1, K ], K 30 (3) Where H (k ) is the channel state information of the kth subcarrier in frequency domain, it can also DM AN US be described with amplitude and phase as: H (k ) H (k ) e j sin( H ( k )) (4) Where H (k ) and H (k ) is the amplitude response and phase response of the kth subcarrier respectively. Process H (k ) with an Inverse Fast Fourier Transform (IFFT), we can get the channel state effects, h(t ) can be described as [3,19]: N h(t ) ai e ji (t i ) (5) i 1 Where i [1, N ] is the multipath component, N is the maximum number of paths, theoretically N must satisfies N B tmax ,B is the signal bandwidth and tmax is the maximum delay of indoor wireless signal propagation, means taking a down integer, (t i ) is the impact function of unit, i , ai and i is the time delay, amplitude attenuation and phase attenuation of the ith path respectively. We weight the amplitudes of 30 subcarriers and calculate the amplitude as: 1 K f CSI k H (k ) , k [1, K ], K 30 K k 1 f 0 (6) Where K is number of subcarriers, generally K 30 , f k f 0 is the weighting coefficient, f 0 is the channel central frequency and f k is the kth subcarrier frequency. Since CSI is physical layer information other than MAC information, the wireless propagation model built in [21] based on RSSI is no longer suitable to CSI. So, we adopt a refined indoor wireless TE propagation model, where the distance between the transmitter and the receiver can be calculated as: 1 c d 4 f 0 CSI 1 2 n (7) Where c 3.0 108 m / s is the light velocity, and n is the standard deviation of normal random variable and the path attenuation index respectively, they will vary according to the environments. Here EP we get the empirical values with 7dB and n 3 . From Eq.(7), we can see that under certain special condition, that is, when c , f 0 , and n are all known, the distance only relies on CSI . Let the position of the unknown target X and the known nodes ai is ( x, y) and ( xi , yi ) respectively, we need at least three known nodes to locate the target, namely the number nodes must satisfy i 3 . After resolving the distance according to Eq.(7), we can locate the target with the trilateral localization algorithm, the range model can be described as: AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. information in time domain h(t ) , namely the Channel Impulse Response (CIR). Due to the multipath di ( x xi )2 ( y yi )2 (8) Image there exists a circle, the center is ( xi , yi ) and the radius is d i , theoretically there exist at least three such circles and they will intersect, the target position can be determined as the intersection of the T CR IP Accepted manuscript to appear in IJPRAI circles. Actually, due to the multipath effects, the circles will not intersect. In order to increase the position accuracy as much as possible, the CSI values must be as accurate as possible. Next we will discuss how to mitigate the multipath effects. 3 Establishment of Models for CSI Processing Due to the effects of multipath and other random noise, the raw CSI collected by the TNS-CSI Tool is always attenuated. Before utilizing them in indoor positioning, it is very necessary to process the CSI information. In this paper, we propose a novel CSI processing method, the basic idea is: (1) Collect the real CSI information from the physical layer with the TNS-CSI Tool, which is IFFT; DM AN US stored in the form of 3-D matrix in frequency domain, and then translate them into time domain with an (2) Set an amplitude threshold to recognize whether the link is a NLOS or not, and build a NLOS mitigation model to deal with the NLOS; phase error mitigation model to offset the phase error. The flow of CSI processing can be in Fig.2. Laptop with Intel5300 Access Point Amplitude processing Set a threshold for the amplitude Build NLOS mitigation model Output of processed phase CSI processing Phase processing Inverse Fast Fourier Transform (IFFT) Unwrap and estimate the minimum phase errors Build phase error mitigation model Output of processed amplitude Fig.2 Flow of CSI processing 3.1 Threshold-based NLOS Mitigation Method Firstly, we collect 125 groups CSI from antenna A and B separately in the laboratory. For simplicity TE and without loss of generality, we just randomly select one group CSI from them and analyze their signal-to-noise ratio (SNR). Fig.3 depicts the SNR of the 30 subcarriers, in which the SNR of antenna A and B change largely. Otherwise, due to the multipath effects, the SNR of different subcarriers on the same antenna vary greatly too, this phenomenon is always called frequency-selective fading caused by NLOS. Histogram of amplitude before being processed 15 Amplitude(dB) EP 28 26 24 10 5 SNR [dB] 22 0 20 18 Amplitude(dB) 14 12 10 0 5 RX Antenna A RX Antenna B 15 Subcarrier index 0.5 1 Delay (ms) Histogram of amplitude after being processed 1.5 10 5 0 10 0 15 16 AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. (3) Minimum the phase error with the Least Squares Estimation (LSE) algorithm and build a 20 Fig.3 SNR of one group CSI 25 30 0 0.5 1 Delay (ms) 1.5 Fig.4 Time domain histogram before and after NLOS mitigation T CR IP Accepted manuscript to appear in IJPRAI In order to mitigate the NLOS, we build a threshold-based NLOS mitigation model, the basic idea can be described as: (1) Collect CSI from the physical layer with a TNS-CSI Tool, the raw CSI data is stored in a 3-D matrix in frequency domain; (2) Translate the CSI information into time domain with an IFFT, and search for the amplitude maximum amax which may be attenuated by the NLOS; 1 2 (3) Set a threshold value ath amax for the ith path, if the received amplitude ai ath , we consider it directly. DM AN US it is a Line-of-Sight (LOS) and do nothing, otherwise, if ai ath , we consider it is a NLOS and remove Basing on the empirical values, the amplitude of signals before and after NLOS mitigation are compared in time domain, the results can be seen in Fig.4. In order to further validate the effectiveness and reliability of the proposed method, we collect one group CSI at the same position and compare the always below the blue one during the 30 subcarriers, it means that the frequency selective fading is improved to some extent after being processed, although the fluctuation range belonging to (14,23) is still large. However, Fig.6 tells us that such fluctuations have little effects on position accuracy, because the signal amplitude from two different positions 1m away can be clearly distinguished. 26 28 Original value Filtered value 24 22 Amplitude [dB] Amplitude [dB] 18 16 14 22 20 18 16 12 14 10 8 26 24 20 0 5 10 15 Subcarrier index 20 25 30 12 Position 1 Position 2 0 5 10 15 Subcarrier index 20 25 30 Fig.6 Filtered values in two different positions 1m away TE Fig.5 Comparison of original values with filtered values 3.2 Phase Error Mitigation Model In addition to the rich channel features of amplitude, there are also rich channel features of phase in CSI. We collect 50 groups CSI and analyze the phase of all the 30 subcarriers. Fig. 7(a) depicts the phase distribution of every subcarrier, in which we observe that the phase distribution is messy and EP follows no law. If not being processed beforehand, it is not convenient for us to further analyze the phase characteristics and extract the channel feature. In order to observe the phase changing laws intuitively, we unwrap the phase. Fig. 7(b) indicates the phase characteristics of all the subcarriers after unwrapping, in which all the subcarriers have a same changing trend and decrease monotonously with the subcarrier index increasing. However, due to the NLOS and time lag between the transmitter and receiver, there also exist phase shifts between the subcarriers. Here we try to use it together with AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. original values with the processed values. Fig.5 shows that the fluctuation of the red curve is almost amplitude information for indoor positioning. So it is very necessary for us to establish a phase error mitigation. 500 200 150 0 100 -500 Phase (deg) Phase (deg) 50 0 -50 -1000 -1500 -100 -2000 -150 0 5 10 15 Subcarrier index 20 25 30 -2500 0 5 10 15 Subcarrier index T 20 DM AN US -200 CR IP Accepted manuscript to appear in IJPRAI (a)Original phase 25 30 (b) Unwrapped phase Fig.7 Phase of 50 groups CSI at one receiver ˆk k 2 lk Z , k [1, K ], K 30 M （9） Where k denotes the true phase, δ is the timing offset at the receiver, β is an unknown phase offset, and Z is measurement noise, lk is the subcarrier index ranging from -28 to 28 of the kth subcarrier and M is the Fast Fourier Transform (FFT) size which equals to 64 in IEEE 802.11 a/g/n. Here, we define two variables a and b satisfy: ˆ ˆ1 K 1 2 a K lK l1 lK l1 M b 1 K 1 K 2 ˆk k K k 1 K k 1 KM K Suppose lk is symmetrical, then lk 0 , and b k 1 correction model as: K l k 1 k （10） （11） 1 K k . We build a linear phase error K k 1 (alk b) （12） According to LSE, we can obtain the minimum of , which can be described as: TE min (alk b) （13） Where a and b are the LSE values. Then the phase of the kth subcarrier after calibration can be rewritten as: k k m i n （14） EP After being processed according to the theoretical analysis above, the phase model can mitigate the phase shifts caused by and to some extent, and has a closely linear combination of the real phase. In order to validate the accuracy and reliability of the model in the actual situation, we collect 50 groups CSI from one antenna at two different positions 1m away and analyze their phase characteristics. Fig.8 compares the phase of CSI collected at the same position, in which the processed phase error is near zero, but there exists a slightly larger fluctuation between the 15th subcarrier(Sub-15) and the 17th AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. Generally, the phase of the kth subcarrier can be expressed as: subcarrier(Sub-17), compared with the raw phase, the phase error is mitigated greatly. Fig.9 depicts the processed phase of CSI collected at two different positions 1m away, in which the phase curves are nearly separate, although there is an overlap between Sub-15 and Sub-17. In the whole, the processed T CR IP Accepted manuscript to appear in IJPRAI phase information can be used to distinguish different positions to some extent in indoors. 20 500 10 0 0 -10 Phase (deg) Phase (deg) -500 -1000 -1500 -20 -30 -40 -50 -60 -2000 -80 0 5 Position 1 Position 2 -70 DM AN US -2500 Raw Processed 10 15 Subcarrier index 20 25 30 Fig.8 Comparison of raw and processed phase at the same position 0 5 10 15 Subcarrier index 20 25 30 Fig.9 Processed phase at two different positions 1m away After being processed with the amplitude model and phase model built in this paper respectively, a high precision indoor positioning. In order to popularize our models in different situations, we will discuss the features of CSI collected at different positions under different conditions in the next. 3.3 Amplitude Features of CSI (1) Features of CSI collected at different positions in the static environment. Firstly, in the static environment, we collect 1000 groups CSI from antenna A and B which is located at position 1 and position 2 simultaneously. Position 1 is 2m away from the Access Point (AP) and position 2 is 3m away from the AP. For simplicity and without loss of generality, we just analyze the CSI from antenna A, for Fig.2 has shown us antennas A has a better SNR of CSI, and we just select 3 representative subcarriers Sub-1, Sub-15 and Sub-27. Table.2 shows the details of the features under such condition. Table.2 Features of 3 subcarriers collected at two different positions in the static environment Features 3m away from AP(Position 2) Sub-1 Sub-15 Sub-27 24.6239961247 26.3842899998 24.7207832842 19.9703688908 20.7032770276 19.4606518966 22.1498932712 24.2751062011 22.7892487907 0.16717759414 0.27951936069 0.94181934806 TE Maximum Minimum Mean Variance 2m away from AP(Position 1) Sub-1 Sub-15 Sub-27 29.904316135 30.8963020249 29.9043161350 24.496923180 27.5137930813 27.51379308he 28.833473865 28.9244862737 28.8974689936 0.1340129538 0.10137200762 0.12109392173 In table.2 we observe that the features of all the subcarriers are different, no matter the CSI data is from position 1 and position 2. At position 1, there exist some small fluctuations between the subcarriers, but the maximum of the 3 subcarriers is nearly equal and around 30dB, with a small variance around 0.1.But at position 2, the fluctuation and variance grow larger, with the maximum is around 25 dB. In other words, much farther away from the AP, much worse the features grow. However, EP there is no overlap between the two positions, so it can be used to distinguish different positions. (2) Features of CSI collected at the same position in two different environments. We collect 1000 groups CSI from antenna A and B at the position 2m away from the AP under the static and shaking conditions respectively, for the same reasons, we just analyze the three subcarriers of CSI collected from antenna A. Table.3 shows the details of the features under such condition. Table.3 Features of 3 subcarriers collected at the position 2m away from the AP in two different environments AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. the features of CSI will be much more stable and reliable, and can be used to build a fingerprinting for Features Maximum Minimum Mean Sub-1 29.904316135 24.496923180 28.833473865 Static Sub-15 30.8963020249 27.5137930813 28.9244862737 Sub-27 29.9043161350 27.513793081 28.8974689936 Sub-1 31.1509437759 5.22844797393 25.9836520954 shaking Sub-15 31.9239178249 12.8007232708 25.9516584155 Sub-27 31.4862610796 4.76506285601 26.1509926896 Variance 0.1340129538 0.10137200762 0.12109392173 11.7195454563 T CR IP Accepted manuscript to appear in IJPRAI 8.36597587670 8.75879844164 In table.3 we can see that the features at the position 2m from the AP under static condition are the same as those described in table.2. Under the shaking condition, the maximum is around 32 dB, just 2dB lower than that of the static situation, which can be offset by the NLOS mitigation model we build in this paper, however, the fluctuation of the variance is around 9, far larger than that of the static situation. (3) Features at the position 3m away from the AP in three different environments. In the typical laboratory situation, we collect 1000 groups CSI at the position 3 m away from the AP, with one person normal walking and three people frequently walking respectively. The details of DM AN US the features in the three different environments are listed in Table.4. Table.4 Features of 3 subcarriers at the position 3m away from the AP in three different environments Features Static Sub-15 26.38428 20.70327 24.27510 0.279519 Sub-27 24.720783 19.460651 22.789248 0.9418193 People Normal Walking Sub-1 Sub-15 Sub-27 30.73841 32.02819 30.73841 13.26735 23.60832 16.74933 26.70212 28.29877 27.60693 3.264671 1.529897 2.075351 People Frequently Walking Sub-1 Sub-15 Sub-27 31.71303 32.47817 32.13443 15.27619 21.90516 14.90306 26.73988 28.34911 27.63048 3.363349 3.315166 3.499226 In Table.4 we can see that the features of people normal walking and frequently walking show little differences, the maximum is around 31 dB, larger than that of the static situation, which can be processed with our NLOS mitigation model. But the variance changes largely, almost 10 times larger than that of the static situation. Namely, the features of CSI are robust, which provide us a theoretical basis for the fingerprinting-based indoor positioning. 3.4 Phase Features of CSI Under different conditions, we collect 1000 group CSI at position 1 and position 2 which is 2m and 3m away from the AP respectively. Same as the above, we just analyze the features of the three representative subcarriers, which are listed in Table.5 in detail. Table.5 Phase features of CSI at different positions under different conditions Features Maximum Minimum Mean Variance 2m away from the AP in static Sub- 1 Sub- 15 Sub-27 15.39180 -8.08073 -48.31308 8.32206 -14.5882 -66.99182 11.60136 -11.3748 -57.37984 1.09282 1.03514 12.48378 3m away from the AP in static Sub-1 Sub- 15 Sub- 27 16.23347 3.57844 -44.6495 -11.6468 -17.0459 -152.344 1.01421 -4.13689 -57.8902 10.80412 5.21004 34.08984 2m away from the AP in dynamic Sub- 1 Sub-15 Sub- 27 65.87126 64.12693 -1.43003 -47.2488 -45.3004 -122.248 9.23752 -9.50770 -55.2267 51.42345 37.27274 80.26890 Table.5 lists phase features under three different conditions, in which there is no phase statistics law in the first three features for us to use. However, the variance varies greatly between the three TE conditions and without overlap, which also can be used to distinguish different environmental status in indoor positioning. 4 The Amp-Phi System 4.1 System Architecture EP Basing on the theoretical basis discussed above, in this paper we propose a novel Amp-Phi system, the architecture of which is shown in Fig.10. The equipment Amp-Phi system requires is a laptop equipped with an Intel 5300 NIC and an access point which is compatible with IEEE 802.11a/g/n protocols and its coordinates are pre-known to the receiver. CSI is physical layer information and unable to be read directly, so we had to slightly modify the laptop’s firmware and kernel as in [22] to collect the CSI information. The Intel 5300 has three antennas, each of them receives information from AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. Maximum Minimum Mean Variance Sub-1 24.62399 19.97036 22.14989 0.167177 30 subcarriers, so we can collect 90 groups CSI simultaneously in total, then we process the amplitude and phase with the models built in this paper and implement an indoor positioning. There are many positioning methods, such as time of arrival (TOA), time difference of arrival (TDOA), time of fly (TOF), fingerprinting, trilateration and so on, each of them has its own advantages and disadvantages. T CR IP Accepted manuscript to appear in IJPRAI For the fingerprinting-based localization doesn’t need to know the AP’s position or the wireless signal propagation model, it is one of the most popular methods. However, most of the fingerprinting-based localization is based on RSSI which is vulnerable to NLOS and limit its position accuracy. Unlike RSSI, CSI which has rich channel characteristic has a strong anti-interference ability, so more and more researchers begin to study the indoor positioning based on CSI. But most of them are based on amplitude features, in [24-25], XU etc. begin to use the phase features for fingerprinting. In this paper, we propose an Amp-Phi system which tries to fuse the amplitude and phase features of CSI together to build a fingerprinting database for the indoor positioning. The architecture of Offline Training Revceived Real-time CSI at Uknown Position Online Positioning Amplitude Processing with NLOS Mitigation Model Extract Amplitude Features Fingerprinting Database Phase Processing with Phase Error Mitigation Model Extract Phase Features Amplitude Processing with NLOS Mitigation Model Extract Amplitude Features Phase Processing with Phase Error Mitigation Model Extract Phase Features KNN Matching Algorithm Estimated Target Position Fig.10. Architecture of the proposed Amp-Phi system Fingerprinting-based indoor positioning usually has two operational steps: the offline training and the online positioning. In the offline training stage, Amp-Phi firstly processes the raw CSI information collected from the AP with the amplitude and phase models, then extracts the features of amplitude and phase, and finally builds a fingerprinting database based on the features. In the online positioning stage, the Amp-Phi uses a similar method to acquire the features of amplitude and phase extracted from the CSI information, and locates the target with a KNN matching algorithm. 4.2 Experiment Results In order to validate the effectiveness and reliability of Amp-Phi system in our real life, we TE implement some experiments in different typical environments. We select the laboratory in the School of Electronic Information of Northwestern Polytechnical University. In this 5m × 8 m laboratory, it is full of computers and furniture which will block the line of sight and result in a complex wireless signal propagation environment. We perform two kind of experiments in such an environment, one is under the static condition with the receiver 2m away from the AP and the other is under the condition EP that some people normally working with the receiver 2m away from the AP. Target is random working, the position results are show intuitively in Fig.11 and Fig.12 respectively. AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. Revceived CSI at different known positions DM AN US Amp-Phi system is showed in Fig.10. T People normally working with the receiver 2m away from the AP Under the static condition with the receive 2m away from the AP 340 990 980 330 970 320 960 950 310 940 300 930 920 290 910 True path Amp-Phi 900 860 870 880 890 900 910 920 930 940 280 950 True path Amp-Phi 270 580 600 620 640 660 680 700 DM AN US 890 850 CR IP Accepted manuscript to appear in IJPRAI Fig.11. Positioning results under static condition 720 Fig.12. Positioning results under dynamic condition Fig.13 demonstrates the Cumulative Distribution Function (CDF) of position errors of Amp-Phi system and other three classical schemes (PhaseFi, Horus and ML) in the laboratory with someone distance error about 1.8m, PhaseFi has a distance error about 2m, Horus and ML have position errors about 2.7m. It means that among the four methods, Amp-Phi perform best for it fuses the amplitude and phase features of CSI, which is robust than RSSI. 1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 CDF CDF 0.6 0.5 0.4 0.3 Amp-Phi PhaseFi Horus ML 0.2 0.1 0 0.5 0.4 0.3 0 1 2 3 4 Position Error(m) 5 6 0.2 0.1 7 Fig.13. CDF of position errors in the laboratory condition 0 0 0.5 1 1.5 2 2.5 Position Error(m) 3 Amp-Phi PhaseFi Horus ML 3.5 4 Fig.14. CDF of position errors of the living room In order to validate the performance of Amp-Phi in other scenarios, we perform some experiments TE in the living room. Because there doesn't exist any large obstacles, most of the wireless links can be regarded as LOS. Fig.14 depicts the CDF of position errors of Amp-Phi system and other three classical schemes (PhaseFi, Horus and ML) in the living room. In such a relative simple scenario, more than 60% of the test points in Amp-Phi have an error around 1m, and PhaseFi, Horus and ML have the same test points with an error around 1.2m, 1.8m and 2.3m respectively, Amp-Phi performs best and EP PhaseFi takes the second place. So due to the fined-grained subcarrier information of CSI, Amp-Phi and PhaseFi methods outperform the RSS-based methods such as Horus and ML. 5. Conclusion In this paper, we propose a fingerprinting-based Amp-Phi indoor positioning system, in which the amplitude and phase information are fused together. Firstly, according to the characteristics of the raw CSI information collected at different positions in different environments, we build a NLOS mitigation AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. working normally. Under such complex condition, around 60% of the test points, Amp-Phi has a model and a phase error mitigation model to process amplitude and phase of CSI respectively. Secondly, we analyze the statistical characteristics of CSI carefully, including maximum, minimum, mean and variance. Finally, we build a fingerprinting database based on the amplitude and phase features of CSI and locate the target’s position with a KNN matching algorithm. We perform some experiments in two T CR IP Accepted manuscript to appear in IJPRAI typical scenarios: one is in the complex computer laboratory where exists a lot of NLOS, and the other is in the relative simple living room where exists little NLOS. In both environments, we compare the Amp-Phi system with PhaseFi, Horus and ML. Around 60% of the test points, the position accuracy of the four systems in the laboratory is 4m, 4.2m, 5.4m and 6m respectively, and the position accuracy of the four systems in the living room is 1.7m, 1.8m, 2.5m and 3.7m respectively. That is to say, the position accuracy of Amp-Phi is little better than that of the newest methods PhaseFi proposed in 2016[25], and far better than that of the most classical methods Horus [26] and ML[27] proposed in 2005 . Due to the widespread of WIFI signal and convenience of CSI, the Amp-Phi system can be used in DM AN US many scenarios, such as the localization of robots in our daily life, doctors or patients in the hospital, people localization in large supermarket or museum and so on. However, the position accuracy is only about 0.2m better than that of PhaseFi which adopts the machine learning. So in the future work, we will try to locate a target in indoors with the machine learning and further improve the position Acknowledgment This work was financially supported by National Major Special Science and Technology (NO.GFZX0301040115), the National Natural Science Foundation of China (No. 61301094 and No. 61571188), the Construct Program of the Key Discipline in Hunan Province, China, the Aid program for Science and Technology Innovative Research Team in Higher Educational Institute of Hunan Province, and the Planned Science and Technology Project of Loudi City, Hunan Province, China. Bibliography 1. Ahmed Makki, Abubakr Siddig, Mohamed Saad et al.,Survey of WiFi Positioning Using Time-based Techniques. Computer Networks, 2015 (88), pp:218-233 2. Sudhir Kumar, Rajesh M. Hegde, Niki Trigoni. Gaussian Process Regression for Fingerprinting based Localization. Ad Hoc Networks, 2016 (51), pp:1-10 TE 3. K. Wu, J. Xiao, Y. Yi, et al., CSI-Based Indoor Localization. IEEE Transactions on Parallel and Distributed System, 2013,24(7), pp:1300-1309 4. Iyad Husni Alshami, Noor Azurati Ahmad, Shamsul Sahibuddin, et al., Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments. Sensors, 2017, 17(1789), pp:1-29 5.Igor Bisio, Fabio Lavagetto, Mario Marchese, et al., Smart Probabilistic Fingerprinting for EP WiFi-based Indoor Positioning with Mobile Devices. Pervasive and Mobile Computing, 2016 (31), pp:107-123 6. Yogita Chapre, Aleksandar Ignjatovic, Aruna Seneviratne, et al., CSI-MIMO: An Efficient Wi-Fi Fingerprinting Using Channel State Information with MIMO. Pervasive and Mobile Computing, 2015 (23), pp: 89-103 7.Lingjun Gao. Channel State Information Fingerprinting Based Indoor Localization: a Deep Learning AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. accuracy. Approach[D]. Auburn University,2015 8. 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Jiang Xiao, Kaishun Wu, Youwen Yi, et al., FIFS: Fine-grained Indoor Fingerprinting System, IEEE ICCCN, 2012, pp: 1-7 19. Souvik Sen, Božidar Radunovic´, Romit Roy Choudhury, et al., Spot Localization Using PHY Layer Information, MobiSys’12, 2012, pp:183-196 20. K. Wu, J. Xiao, Y. Yi, M. Gao, et al., FILA: Fine-grained Indoor Localization, IEEE INFOCOM, 2012, pp:2210-2218 21.S. Sen, J. Lee. K. Han and P. Congdon, Avoiding Multipath to Revive in Building WiFi Localization, ACM MobiSys’13, 2013, pp: 249-262 TE 22. Xuyu Wang, Lingjun Gao, Shiwen Mao, et al., DeepFi: Deep Learning for Indoor Fingerprinting Using Channel State Information, IEEE Wireless Communications and Networking Conference (WCNC), 2015, pp:1666-1671 23.Xuyu Wang, Lingjun Gao, Shiwen Mao, et al., CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach. IEEE Transaction of Vehicular Technology, 2017, 66(1), pp:763-776 24. Xuyu Wang, Lingjun Gao, and Shiwen Mao, PhaseFi: Phase Fingerprinting for Indoor Localization EP with a Deep Learning Approach, IEEE Globecom, San Diego, 2015, pp:1-6 25. Xuyu Wang, Lingjun Gao, and Shiwen Mao. CSI Phase Fingerprinting for Indoor Localization with a Deep Learning Approach. IEEE Internet of Thing Journal, 2016, 3(6),pp:1113-1123 26. M. Youssef and A. Agrawala. The Horus WLAN Location Determination System. In Proc. ACM MobiSys’05, Seattle, WA, 2005, pp:205-218 27. M. Brunato and R. Battiti, Statistical Learning Theory for Location Fingerprinting in Wireless AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. Newsletter. 2014,11(10), pp:55-60 LANs, Elsevier Computer Netw., 2005, 47(6), pp:825-845 T CR IP Accepted manuscript to appear in IJPRAI Taoyun Zhou received the B.S. degree in Electronic Information Engineering from Hunan University of Science and Technology, Xiangtan, China in 2004 and the M.S. degree in Communication and Information System from Northwestern Polytechnical University, Xi’an, China in2007. She is currently working toward the Ph.D. degree with the Communication and Information System, Northwestern Polytechnical University, Xi’an, China.Her research interests include indoor localization, wireless communications and satellite navigation. Email: [email protected] Baowang Lian received the Ph.D. degree in Communication and Information System from Northwestern Polytechnical University, Xi’an, China in 2006. He is currently the Professor and the Director of Texas DM AN US Instruments DSPs Laboratory, Director of Shaanxi high-reliability wireless communications engineering technology research center. His research interests include satellite communications, navigation and positioning, broadband wireless communication technology and communication signal processing technology. Dr. Lian the member of China Satellite Navigation Panel, deputy director of Shaanxi Institute of in Air Force Engineering University. He has won 6 provincial and ministerial level science and technology awards in recent years. Email: [email protected] Yi Zhang received the Ph.D. degree in Communication and Information System from Northwestern Polytechnical University, Xi’an, China in 2010. She is currently the Professor of Northwestern Polytechnical University.Her research interests include satellite navigation and positioning technology, communication, telemetry remote control system of information transmission and processing. E-mail ： [email protected] Sen Liu is currently working toward the MS degree with the Communication and Information System, Northwestern Polytechnical University, Xi’an, China. His research interests include WIFI indoor EP TE localization, wireless communications and satellite navigation. E-mail：[email protected] AC C Int. J. Patt. Recogn. Artif. Intell. Downloaded from www.worldscientific.com by MCMASTER UNIVERSITY on 03/20/18. For personal use only. Electronics Radar Navigation Professional Committee and academic committee deputy director of military navy key laboratory
