A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
Abstract
:1. Introduction
- Network based systems: these systems are build on the top of a wireless network deployed in the scenario and use the information of the wireless signals to estimate the position of the user carrying a wireless device.
- Inertial based systems: these systems use self-contained sensors that measure the motion of the user and estimate its position relative to the starting point without the need of any physical infrastructure deployed in the building.
- Hybrid systems: these systems jointly combine two or more different methods in order to enhance the estimation of position.
2. Network Based Systems
2.1. Range Based
2.1.1. Time
- The first TDoA method computes the difference in the ToA of a signal transmitted to two different receivers. For each TDoA measurement the transmitter must be in a hyperboloid with a constant range difference between the two receiver positions [4]. This method relaxes the synchronization constraint to the receivers.
- The second TDoA method is based on the difference in the ToA of two different signals with different propagation times. Usually, the first signal is the radio packet and the second one is a kind of sound signal due to the difference in the propagation speed between the electromagnetic waves (propagate at the speed of light ≈300,000 ) and the acoustic waves (propagation speed ≈340 ) [68]. This method does not need synchronization but the nodes must include additional hardware in order to send two kind of signals simultaneously.
2.1.2. Angle
- Use an array of sensors (for ultrasound systems) whose locations relative to the node center are known and use the difference in the ToA of the signal at each sensor to compute the AoA of the anchor node. In the case of using radio signals the array of sensors is replaced by an antenna array.
- Use two or more directional antennas pointing to different directions and with overlapping main beams. Then compute the AoA as a function of the ratio of the RSS of the individual antennas.
2.1.3. RSS
2.2. Range Free
- Proximity methods: these methods use the connectivity information to infer directly the position of the user based on the number of anchors in the neighbourhood.
- Fingerprinting methods: these methods are based on location dependent characteristics of the signals received from the wireless network. In a first step, a database of the characteristics and the real location where they were measured is collected. Then, in a second step, the position is estimated by selecting the position of the database sample that best matches the real data.
2.2.1. Proximity
2.2.2. Fingerprinting
3. Inertial Based Systems
- Strapdown systems: these systems integrate twice the acceleration of the user in order to estimate the position.
- Step and Heading Systems (SHS): these systems estimate the position by adding to the initial position estimation vectors representing the step length and the step heading of the user.
3.1. Strapdown Systems
3.2. Step and Heading Systems
- Identification of the subset of data of an individual step.
- Estimation of the step length.
- Estimation of the heading.
3.3. Simultaneous Localization and Mapping
4. Hybrid Positioning Systems
- RSS-IMU hybrid systems: here we include the methods that combine inertial measurements with RSS measurements either by using a propagation model or a fingerprinting approach.
- Map hybrid systems: here we embrace the methods that in addition to the RSS and/or IMU measurements also use the map of the building to enhance the performance of an IPS.
- Smartphone hybrid systems: here we include those RSS-IMU and Map hybrid systems that have been specifically designed for smartphones.
4.1. RSS-IMU Hybrid Systems
4.2. Map Hybrid Systems
4.3. Smartphone Hybrid Systems
5. Conclusions
Acknowledgments
Conflicts of Interest
References
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System | Type | Cost | Scalability | Anchors | Area () | Error | |
---|---|---|---|---|---|---|---|
Type | Value | ||||||
Harter et al. [12] | Time | Expensive | Limited | 100 | 280 | 95th | 0.09 m |
Priyantha et al. [13,95] | Time | Medium | Limited | 6 | <10 | 90th | 0.3 m |
Correa et al. [22] | RSS | Low | Yes | 6 | 530 | RMSE | 1.4 m |
Palumbo et al. [23] | RSS | Low | Yes | 8 | 36 | 75th | 1.8 m |
Yang et al. [19] | RSS | Low | Yes | 5 | 3400 | median | 3 m |
Lin et al. [27] | Proximity | Low | Yes | 12 | 300 | room detection | 97.2 % |
Bolic et al. [25] | Proximity | Low | Yes | 24 | 8 | RMSE | 0.32 m |
Bahl et al. [30] | Fingerprinting | Medium | Limited | 3 | 980 | 75th | 4.69 m |
Han et al. [31] | Fingerprinting | Medium | Limited | 3400 | 192,200 | 75th | 3–9 m |
Youssef et al. [35] | Fingerprinting | Medium | Limited | 21 | 1700 | 90th | 1.4 m |
Wu et al. [34] | Magnetic fingerprinting | Low | Limited | 0 | 4000 | 90th | 2.5 m |
Foxlin et al. [38] | Inertial | Low | Yes | 0 | 75 | % travelled path | 0.3 % |
Jimenez et al. [39] | Inertial | Low | Yes | 0 | 3600 | % travelled path | 0.3–1.5 % |
Angermann et al. [44] | Inertial | Low | Limited | 0 | 600 | RMSE | 1–2 m |
System | Accuracy | Drift | Cost | Calibration | Integration with Network | Hardware | Scalability problems | |
---|---|---|---|---|---|---|---|---|
Computational | Monetary | |||||||
Time | High | No | Low | Medium | No | Yes | Transceiver, accurate clocks | Synchronisation |
Angle | High | No | Low | Medium | No | Yes | Transceiver, multiple antennas | Synchronisation |
RSS | Low | No | Low | Low | Easy | No | Transceiver | No |
Proximity | Poor | No | Low | Low | No | No | Transceiver | No |
Fingerprinting | Medium | No | High | Low | Laborious | No | Transceiver | Calibration |
PDR | High | Yes | Medium | Low | No | No | IMU | No |
SLAM | High | Closed loops | High | Low | No | No | IMU | No |
System | Technologies | RSS | IMU | Anchors | Area () | Error (m) | Cost | Scalability | |
---|---|---|---|---|---|---|---|---|---|
Position | Method | ||||||||
Frank et al. [47] | WiFi, MEMS | Fingerprinting | Foot | SHS | 11 | Floor | 1.65 | Medium | Limited by calibration |
Schmid et al. [48] | WSN, MEMS | Propagation model | Hip | SHS | 62 | 1125 | 4 | Low | Yes |
Tarrío et al. [49] | WSN, MEMS | Propagation model | Waist | SHS | 9 | 100 | 2.3 | Low | Yes |
Correa et al. [64] | WSN, MEMS | Propagation model | Waist | SHS | 8 | 155 | 0.9 | Low | Yes |
Jiménez et al. [50] | RFID, MEMS | Propagation model | Foot | Strapdown | 71 | 2200 | 1.35 | Low | Yes |
System | Technologies | RSS | IMU | Anchors | Area () | Error | Cost | Scalability | ||
---|---|---|---|---|---|---|---|---|---|---|
Position | Method | Type | Value (m) | |||||||
Evennou et al. [51] | WiFi, MEMS | Fingerprinting | Belt | SHS | 4 | 1600 | RMSE | 1.53 | Medium | Limited by calibration |
Woodman et al. [52] | WiFi, MEMS | Fingerprinting | Foot | SHS | 33 | 8725 | 90th | 0.73 | Medium | Limited by calibration |
Wang et al. [53] | WiFi, MEMS | Fingerprinting | N/A | Step | 5 | 1000 | RMSE | 4.3 | Medium | Limited by calibration |
Klingbeil et al. [54] | WSN, MEMS | Proximity | Belt | SHS | 9 | Floor | RMSE | 1.2 | Low | Yes |
System | Technologies | Fusion Method | Area () | Error | Cost | Scalability | ||||
---|---|---|---|---|---|---|---|---|---|---|
WiFi | IMU | Magnetic | Map | Type | Value (m) | |||||
Pei et al. [56] | Yes | Yes | No | No | HMM | Building | RMSE | 4.55 | Medium | Limited by calibration |
Faragher et al. [57] | Yes | Yes | Yes | Yes | SLAM | 450 | 95th | 2.7 | Medium | Limited by calibration and complexity |
Liu et al. [59] | Yes | Yes | No | Yes | HMM | Floor | RMSE | 3.1 | Medium | Limited by calibration |
Radu et al. [60] | Yes | Yes | No | Yes | PF | Floor | 90th | 6 | Medium | Limited by calibration |
Moder et al. [61] | Yes | Yes | No | Yes | PF | Building | 90th | 2.3 | Medium | Limited by calibration |
Chen et al. [62] | Yes | Yes | No | Yes | KF | 3800 | RMSE | 1 | Medium | Limited by calibration |
Li et al. [63] | Yes | Yes | Yes | No | EKF | 8400 | RMSE | 2.9 | Medium | Limited by calibration |
Correa et al. [67] | Yes | Yes | No | No | EKF | 6000 | RMSE | 1.4–3.4 | Low | Yes |
Zou et al. [65] | Yes | Yes | No | No | PF | 600 | Mean | 0.6 | Medium | Limited by calibration |
Chen et al. [66] | Yes | Yes | No | Yes | KF | 425 | RMSE | 1.28 | Low | Yes |
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Correa, A.; Barcelo, M.; Morell, A.; Vicario, J.L. A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications. Sensors 2017, 17, 1927. https://doi.org/10.3390/s17081927
Correa A, Barcelo M, Morell A, Vicario JL. A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications. Sensors. 2017; 17(8):1927. https://doi.org/10.3390/s17081927
Chicago/Turabian StyleCorrea, Alejandro, Marc Barcelo, Antoni Morell, and Jose Lopez Vicario. 2017. "A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications" Sensors 17, no. 8: 1927. https://doi.org/10.3390/s17081927