Currently, there is increasing interest in obtaining information about the location of an object, especially in the area of navigation solutions for visually deficient or impaired people [1
]. The range of services will expand significantly if a user’s location information can be provided. The location-based services refer to applications that depend on the user’s location to provide services in various categories, including navigation and tracking, leading to the enormous social and economic potential of indoor positioning services (IPS). Adaptive navigation technologies can enhance indoor way finding by visually impaired people [2
]. Unfortunately, the Global Positioning System (GPS) technology does not specify whether a location is close to walls, buildings, trees, buildings, and subways, as the power of the GPS satellite signal is weak, making it unusable for indoor GPS localization. It is common to use WiFi hotspots for detecting location in the indoor environment (such as office buildings, industrial facilities, or smart homes). However, since walls are obstacles that affect the signal WiFi access points, that data mechanism is not effective. In this case, the quantity and location of WiFi access points are very important when using wireless technology; moreover, such a solution is costly. WiFi-based fingerprinting can achieve good accuracy (up to 1.21 m with an accuracy of 98%), but it is slower (takes 5.43 s to estimate, while proximity-based solutions are cheaper, do not require calibration, and offer good accuracy [3
]. While there is a possibility of using a sort of guide based on an image recognition [4
], these systems need to locate some sort of markers or objects and target a camera at them, which is not feasible for a visually impaired person [5
]. This problem could be partially solved by machine vision [6
]. Most non-wireless systems refer to some sort of proprietary hardware, e.g., microelectromechanical systems (MEMS) [7
]; radio-frequency identification (RFID) systems [8
] are very rare in artificial laboratory environments and in reality.
The main task of indoor navigation is locating people or objects in indoor environments such as public buildings. The main challenges for indoor navigation are building and maintaining accurate maps; the availability of technology (sensors, devices) for localization, signal interference (reflection, attenuation, multipath, and blockage), and the calibration of equipment to collect enough measurement samples for actual use; and use in uncontrolled environments where the users have no control over the placing of the equipment that is necessary to support indoor positioning.
Current approaches to indoor localization can be categorized as infrastructure-based and infrastructure-less. Infrastructure-based approaches utilize and heavily depend upon the existing infrastructure such as WiFi or Global System for Mobile Communications (GSM). For example, Du et al. [9
] used crowdsourced WiFi signal data and built-in smartphone sensors to achieve high-positioning accuracy and low power consumption, which outperformed the GPS-based method. Huh and Seo [10
] used Bluetooth 4.0, received signal strength indication (RSSI), Bluetooth Low Energy (BLE), and trilateration to determine the position of the user in an indoor space. Kawai et al. [11
] used BLE RSSI data processed by multilayer perceptron (MLP) with an extended Kalman filter, reaching an error of 2.21 m. Lee et al. [12
] used Beacon to transmit a radio frequency (RF) signal and an audio signal of specific frequency. The smartphone calculates distance values using the Time Difference of Arrival (TDoA) method and uses them for trilateration. Naz et al. [13
] used Visible Light Communication (VLC), which includes both frequency and the variable phase of the transmitted signal, estimating the position of an object with a localization error when the signal passes through an optical channel, and achieving positioning accuracy within 1–2 cm. Uradzinski et al. [14
] used Zigbee wireless technology and employed data filtering, weighted k-nearest neighbors (KNN), and the Bayesian algorithm to calculate the pedestrian’s location, obtaining the average error less than or equal to 0.81 m.
Navigation cues can be provided using statistical methods applied on static floor fields in open space rooms [15
]. A virtual graph based on luminaire analysis was suggested as another possible approach in [16
]. Segura et al. [17
] used the ultra wideband (UWB) system for indoor navigation and achieved a positioning error of 20 cm (the anchor method by Großwindhager et al. [18
] also showed an error of 20 cm); however, this method is more difficult to apply (comparing to BLE and WiFi-based methods) due to the requirement of having specific equipment, which may not be available to regular users. Zhang et al. [19
] proposed the use of a combination of GPS, UWB, and MARG (magnetic, angular rate, and gravity), achieving a positioning error is 3.2 m, which might not be acceptable for a blind person. Zhou et al. [20
] proposed a combined method based on images from a smartphone camera capturing the surrounding scene and pedestrian dead reckoning (PDR) to determine the pedestrian’s trajectory with an accuracy of about 0.56 m. Using embedded inertial sensors [21
] and PDR [22
] by updating the current position through measuring the length and title of each step, enabled reaching an error of 1.96 m. A similar multisensory approach improved this up to 1.46 m [23
]. A combination with stereo cameras has improved the accuracy up to 0.677 m [24
]. A combination with Bluetooth beacons can provide an average error rate around 2.53% [25
The placement and density of signal transmitters such as BLE beacons may have a crucial impact on the accuracy of indoor localization. For example, Rezazadeh et al. [26
] proposed an improved beacon placement strategy that enables 21.7% higher precision than using normal iBeacon placement.
Using the available map of a building or facility provides additional useful information for accurate indoor localization. For example, Wang et al. [27
] proposed a scheme for determining the location in the room by combining the floor map, WiFi data, and smartphone sensors with an average error of 3.135 m for KNN, and 4.99 m for PDR. A similar method in combination with the fingerprint algorithm allowed a reduction of around 37% [28
]. Xu et al. [29
] proposed using a grid-based indoor model to create a floor plan to track indoor location with an average accuracy of 92%. The RSSI combination method applied on BLE beacons with cartographic information without using the value of the beacon distance showed an error rate of 1.6 m [30
Infrastructure-less approaches require no support from the existing infrastructures or networks such as internet access points (APs). For example, Jeong et al. [31
] used only a smartphone as a mobile beacon that is capable of tracking its own position by using its motion sensor data. The smartphone broadcasts short-distance beacon messages and collects response messages from neighboring Internet of Things (IoT) devices along with the message’s signal strength and its position, thus obtaining less than a 20-cm position error in a real-world setting. Link et al. [32
] adopted sequence alignment algorithms from the field of bioinformatics for the accurate localization of a subject using only accelerometer and compass sensor data from a smartphone.
The fingerprinting technique, which has been used for both infrastructure-based and infrastructure-less approaches, involves the use of measurements (aka fingerprints) of some physical quantity such as received signal strength indication (RSSI). Tomazic et al. [33
] improved the fingerprinting method with the interval fuzzy model to calculate the confidence interval for the k-nearest neighbors (kNN) search in the database of fingerprints, thus achieving an improvement of localization results by 40%. Dari et al. [34
] used the received signal strength (RSS) received from the access point (AP), and applied the location fingerprint technique using the features of RSS’s fingerprint, while the position was determined by the k-nearest neighbor (KNN) method. Cha and Xiaoran [35
] used Naïve Bayes and WiFi fingerprinting for indoor localization. The router is used as the generator of the WiFi signal, the Naive Bayes models train the data, and the server calculates the position, reaching an accuracy of more than 80%. Raspopoulos [36
] used device-independent radio maps generated by deterministic channel modeling through three-dimensional (3D) ray tracing (RT) for WiFi RSSI-based fingerprinting. Song et al. [37
] proposed a channel state information (CSI) amplitude fingerprinting-based localization algorithm and multidimensional scaling (MDS) to calculate the Euclidean distance and time-reversal resonating strength (TRRS) between target and reference points. Finally, the KNN algorithm was used for location estimation. The final estimated position is obtained by the results of MDS and KNN, which reduces the positioning error. Subedi and Pyun [38
] improved traditional fingerprinting localization by combining it with weighted centroid localization (WCL), which allowed reducing the number of required fingerprint reference points by more than 40% while maintaining a similar localization error.
Surrounding walls, equipment, and obstacles, including human bodies, can attenuate and distort wireless positioning signals. The problem of obstacles was addressed in Wang et al. [39
]. The authors estimated the distance using the time-of-arrival (TOA) measurement model and applied residual analysis to identify the non-line-of-sight (NLOS) error. Finally, the particle swarm optimization with a constriction factor (PSO-C) was used to compute the position. A method to detect and prevent collisions with obstacles, based on the Kalman filter algorithm with time stamps (TSM-KF) using an RGB-Depth (RGB-D) camera was suggested by [40
]. Deng et al. [41
] focused on the problem of the body-shadowing impairment of RSS-based positioning, and derived a mathematical relation between the body-shadowing effect and positioning error.
The use of multiple data sources may require employing data fusion to produce more consistent and accurate results. Al-Qudsi et al. [42
] performed a fusion of data from a multi-band frequency modulated continuous wave (FMCW) radar system using a particle filter-based tracking method, and achieved a positioning error of less than 17 cm and 31 cm for outdoor and indoor conditions, respectively, outperforming the commercial systems. Seco and Jimenez [43
] combined the RSS of RF signals emitted from known location beacons together with combined with pedestrian dead reckoning (PDR) estimates of user walking. A centralized cooperative particle filter (PF) was applied to improve the localization result, reaching a location error of 1.6 m. Widyawan et al. [44
] employed the backtracking particle filter (BPF) to improve indoor localization performance, achieving up to 25% improvement.
An improvement of the results of localization can include a variety of the techniques. The examples include error compensation for the body-shadowing effect [45
], continuous feature scaling and outlier deletion [46
], collaborative localization using information from multiple nodes [47
], error correction using collision avoidance velocity and map-aided inertial dead reckoning (DR) [48
], probabilistic fingerprint (P-FP) using the probability density functions of the received signal strength algorithm (RSSA) [49
], the use of optimization algorithms to decrease the localization error considering different RSS thresholds for hybrid indoor positioning [50
], and particle swarm optimization (PSO) for fitting the signal attenuation curve, thus allowing the developed parametric model to locate the user’s position with the standard deviation of positioning of 1.15 m [51
For a survey of the remaining works in the area, the readers can consult the recent surveys in [52
In our previous article [55
], we presented the results of a series of experiments using signal strength received from the Bluetooth Low Energy (BLE) beacons and applied various positioning algorithms such as proximity, centroid localization, weighed-centroid localization, fingerprinting, etc. to determine the effect on positioning error. Then, we proposed a fuzzy logic [56
]-based scheme to select the most fitting positioning algorithm depending upon the strength of the signal, the number of beacons available, and the size of the room.
In this paper, we combined fuzzy logic type-2 for indoor positioning in the real-world environment, allowing a flexibility of complex environments (glass/metal corridors) such as our test building. We have adopted multi-fuzzy sets-based membership location distance methods and compared the results of using fuzzy logic type-1 and fuzzy logic type-2 with those obtained without using fuzzy logic.