Demands for indoor positioning based services (IPS) in commercial and military fields have spurred many positioning systems and techniques. Complex electromagnetic environments (CEEs) may, however, degenerate the accuracy and robustness of some existing single systems and techniques. To overcome this drawback, fusion-based positioning of multiple systems and/or techniques have been proposed to revamp the positioning performance in CEEs. In this paper, we survey the fusion-based indoor positioning techniques and systems from seminal works to elicit the state of the art within our proposed unified fusion-based positioning framework, which consists of three fusion characteristics: source, algorithm, and weight spaces. Different from other surveys, this survey summarizes and analyzes the existing fusion-based positioning systems and techniques from three characteristics. Meanwhile, discussions in terms of lessons, challenges, and countermeasures are also presented. This survey is invaluable for researchers to acquire a clear concept of indoor fusion-based positioning systems and techniques and also to gain insights from this survey to further develop other advanced fusion-based positioning systems and techniques in the future.

It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing multiple fingerprints gleaned from RSS for localization is a propitious strategy to overcome the RSS susceptibility. Brimful utilization via fusing multiple fingerprint functions which supplement each other are not harnessed by existing fusion-based techniques, resulting in low localization accuracy. This paper presents a novel and robust WiFi localization modus operandi by fusing DerIvative Fingerprints of RSS with MultIple Classifiers (DIFMIC). DIFMIC first constructs a multiple fingerprints group by gleaning hyperbolic location fingerprint (HLF) and signal strength differences fingerprint (DIFF) from RSS fingerprints. Then, it obtains Multiple Fingerprints Trained Classifiers (MFTCs) via training each basic classifier with each fingerprint. To fully leverage the inherent supplementation among fingerprints and classifiers, a two-layer fusion profile (weights) joint optimization algorithm with multiple constraints is proposed. We also propose a Fusion Profile Selection (FPS) algorithm to intelligently choose fusion weights from the two-layer fusion profile for a more accurate localization. DIFMIC shows more leverage in combining multiple information, thus exhibiting better robustness in WiFi positioning. Results from our experiments reflect that DIFMIC performs better than other existing methods in real environments.

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, autonomous parking, virtual reality, context awareness, condition monitoring, geolocation, smart manufacturing, as well as smart cities. It is well known that indoor localization based on some single fingerprints is rather susceptible to the changing environment. The efficiency of building single fingerprints from one localization system is also low. Recently, we first proposed a group of fingerprints (GOOF) based localization to improve the efficiency of building fingerprints, and then proposed an efficient fusion algorithm, namely, multiple classifiers multiple samples (MUCUS), to improve the accuracy of localization. However, the main drawbacks of MUCUS are the low localization efficiency and low accuracy when all classifiers show poor performance simultaneously. In this paper, based on the aforementioned GOOF, we propose a sliding window aided mode-based (SWIM) fusion algorithm to balance the localization accuracy and efficiency. SWIM first adopts windowing and sliding techniques to improve the localization efficiency, and then obtains a more accurate estimate by minimizing the entropy of multiple classifiers or multiple samples. This can guarantee our estimator to be robust to changing environment and larger noise level. We demonstrate the performance of our algorithms through simulations and real experimental data via two universal software radio peripheral platforms.

We propose a localization technique by fusing multiple classifiers based on received signal strengths (RSSs) of visible light in which different intensity-modulated sinusoidal signals emitted by LEDs are captured by photodiodes placed at various grid points. First, we obtain some approximate RSSs fingerprints by capturing the peaks of power spectral density of the received signals at each given grid point. Unlike the existing RSSs-based algorithms, several representative machine learning algorithms are adopted to train multiple classifiers based on these RSSs fingerprints. Then, two robust fusion localization algorithms, namely, grid-independent least square and grid-dependent least square (GD-LS), are proposed to combine the outputs of these classifiers. A singular value decomposition (SVD)-based LS (LS-SVD) method is proposed to mitigate the numerical stability problem when the prediction matrix is singular. Experiments conducted on an intensity-modulated direct detection system show that the probability of having mean square positioning error of less than 5 cm achieved by GD-LS is improved by 93.03% and 93.15%, respectively, as compared to those by the RSS ratio and RSS matching methods with the fast Fourier transform length of 2000.

The existing indoor localization approaches based on single fingerprints, such as received signal strength (RSS) and channel impulse response, are rather susceptible to the changing environment, multipath, and nonline-of-sight. It is well known that indoor localization can obtain higher positioning accuracy than the single-fingerprint-based methods by fusing multiple information sources (fingerprints/fingerprint functions). However, the existing fusion methods cannot fully exploit the intrinsic complementarity among multiple information sources and thus show lower accuracy. In this paper, we propose an accurate WiFi localization approach by Fusing A Group Of fingerprinTs (WiFi-FAGOT) via a global fusion profile (GFP). WiFi-FAGOT first constructs a WiFi-based GrOup Of Fingerprints (GOOF) in the offline phase, which consists of RSS, signal strength difference, and hyperbolic location fingerprint. Then, instead of direct localization by using the WiFi-based GOOF, we design multiple classifiers by training each fingerprint in the WiFi-based GOOF, namely GOOF classifiers. To fully leverage the intrinsic complementarity among different kinds of fingerprints, we propose a GFP construction algorithm by minimizing the average positioning error over the space of all GOOF classifiers. Finally, in the online phase, we derive a grid-dependent matching algorithm, namely, optimal classifier selection, to intelligently choose a fusion profile in the GFP for more accurate localization. Experimental results demonstrate that WiFi-FAGOT performs better than other systems in real complex indoor environments.

Transfer learning algorithms (TLAs) are often used to solve the distribution discrepancy issue in fingerprint-based indoor localization. However, existing TLAs cannot react well to real time changes in the environmental dynamics of the target space due to three remarkable shortcomings: a) redundant knowledge in source domain may lead to “negative transfer”; b) the required target domain samples to calculate the distributions are unrealistically feasible for real-time positioning; c) they cannot transfer knowledge efficiently across domains with heterogeneous feature spaces. In this paper, we propose TransLoc, a heterogeneous knowledge transfer framework for fingerprint-based indoor localization, which can perform knowledge transfer efficiently even with only one sample in the target domain. Specifically, we first refine the source domain according to the target domain by removing redundant knowledge in the source domain. Then, we derive a cross-domain mapping, which transfers the specific knowledge of one domain to another domain, to construct a homogeneous feature space. In this new feature space, the transfer weights are computed for training a classifier for target location prediction. To further train the framework efficiently, we combine the mapping and weights learning into a joint objective function and solve it by a three-step iterative optimization algorithm. Extensive simulation and real-world experimental results verify that TransLoc not only significantly outperforms state-of-the-art methods but is also very robust to changing environment.