Location Fingerprinting Research Articles

LRF-WiVi: A WiFi and Visual Indoor Localization Method Based on Low-Rank Fusion.

In this paper, a WiFi and visual fingerprint localization model based on low-rank fusion (LRF-WiVi) is proposed, which makes full use of the complementarity of heterogeneous signals by modeling both the signal-specific actions and interaction of location information in the two signals end-to-end. Firstly, two feature extraction subnetworks are designed to extract the feature vectors containing location information of WiFi channel state information (CSI) and multi-directional visual images respectively. Then, the low-rank fusion module efficiently aggregates the specific actions and interactions of the two feature vectors while maintaining low computational complexity. The fusion features obtained are used for position estimation; In addition, for the CSI feature extraction subnetwork, we designed a novel construction method of CSI time-frequency characteristic map and a double-branch CNN structure to extract features. LRF-WiVi jointly learns the parameters of each module under the guidance of the same loss function, making the whole model more consistent with the goal of fusion localization. Extensive experiments are conducted in a complex laboratory and an open hall to verify the superior performance of LRF-WiVi in utilizing WiFi and visual signal complementarity. The results show that our method achieves more advanced positioning performance than other methods in both scenarios.

Hybrid Wi-Fi and BLE Fingerprinting Dataset for Multi-Floor Indoor Environments with Different Layouts

Indoor positioning has garnered significant interest over the last decade due to the rapidly growing demand for location-based services. As a result, a multitude of techniques has been proposed to localize objects and devices in indoor environments. Wireless fingerprinting, which leverages machine learning, has emerged as one of the most popular positioning approaches due to its low implementation cost. The prevailing fingerprinting-based positioning mainly utilizes wireless fidelity (Wi-Fi) and Bluetooth low energy (BLE) signals. However, the RSS of Wi-Fi and BLE signals are very sensitive to the layout of the indoor environment. Thus, any change in the indoor layout could potentially lead to severe degradation in terms of localization performance. To foster the development of new positioning methods, several open-source location fingerprinting datasets have been made available to the research community. Unfortunately, none of these public datasets provides the received signal strength (RSS) measurements for indoor environments with different layouts. To fill this gap, this paper presents a new hybrid Wi-Fi and BLE fingerprinting dataset for multi-floor indoor environments with different layouts to facilitate the future development of new fingerprinting-based positioning systems that can provide adaptive positioning performance in dynamic indoor environments. Additionally, the effects of indoor layout change on the location fingerprint and localization performance are also investigated.

Chirp-Loc: Multi-factor authentication via acoustically-generated location signatures

Context-based authentication has been proposed as a way to enable secure authentication with minimal or even no user interaction requirements by using sensor data to ensure the device being authenticated is in possession of the person initiating the authentication request. A key limitation of practically all context-based authentication systems is that they are vulnerable to context manipulation attacks where an attacker manipulates the environment to create a desired response in the sensor data. We contribute Chirp-Loc as a system that has been designed to improve the robustness of context-based authentication solutions against context-manipulation attacks. Chirp-Loc integrates an innovative approach based on room impulse response (RIR) to establish a location fingerprint that characterizes the physical environment instead of the ambient environment. We describe the design and development of an Android prototype of Chirp-Loc. We also conduct extensive accuracy and security analysis of Chirp-Loc by considering a multi-factor authentication solution that uses Chirp-Loc to verify the proximity of an authentication token, such as a smartphone. Through extensive experiments, we demonstrate that Chirp-Loc offers high degree of security and usability. Our work paves the way for improving the resilience of context-based authentication against attackers that manipulate the context information and offers a way to implement authentication systems that minimize user interaction demands while offering a high degree of security.

Robust fingerprint localization based on massive MIMO systems

Location-based services have become an important part of the daily life. Fingerprint localization has been put forward to overcome the shortcomings of the traditional positioning algorithms in indoor scenario and rich scattering environment. In this paper, a single-site multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is modeled, from which an angle delay channel power matrix (ADCPM) is extracted. Considering the changing environment, auto encoders are used to generate new fingerprints based on ADCPM fingerprints to improve the robustness of the fingerprints. When the scattering environment has changed beyond a certain extent, the robustness will not be able to make up for the positioning error. Under this circumstance, an updating of the fingerprint database is imperative. A new fingerprint database updating algorithm which combines a new clustering method and an updating rule based on probability is proposed. Simulation results show the desirable performance of the proposed methods.

Indoor Localization With Wireless Heterogeneous Devices by Composite Fingerprint Sets and Hybrid Classification

Wi-Fi fingerprint-based indoor localization has recently attracted significant research interest since Wi-Fi devices are widely deployed and practical, and no additional infrastructure is required. However, the Received Signal Strength (RSS) is significantly different on heterogeneous devices, and this difference has a negative impact on localization results. In this paper, we propose a multi-fingerprint and multi-classifier fusion (MFMCF) localization method to improve the localization accuracy and solve the problem of heterogeneous hardware. First, the individual feature set of original RSS fingerprint, signal strength difference (SSD) fingerprint and hyperbolic location fingerprint (HLF) are fused as a composite fingerprint set (CFS), and then the data dimension is reduced by linear discriminant analysis (LDA). Second, three representative machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF) are selected and trained to construct an integrated fusion model. Finally, in order to get more accurate predictions, in the online phase, a selective strategy based on entropy is proposed by calculating the entropy of each classifier’s prediction result. Experiments show that MFMCF is an effective scheme to solve the localization problem of heterogeneous devices and improve the localization accuracy.

Wireless communication indoor positioning method in 5G sub-station using deep neural network and location fingerprint algorithm

The use of outside positioning systems based on Global Navigation Satellite System (GNSS) for inside location is impractical due to a number of drawbacks which makes the use of machine learning for its solution. Location fingerprinting has historically relied on shallow learning methods. This research proposes novel technique in indoor positioning technique for 5 G substation by deep learning techniques to analyse the indoor positioning using deep neural networks using 5 G NR signal based weighted cross correlation kernel support vector machine. The experimental results shows the comparative analysis between proposed as well as existing technique in terms of SNR, positioning accuracy, positioning error, average relative error, spectrum efficiency which attained SINR of 88 %, positioning accuracy of 96 %, Positioning error of 65 %, average relative error of 56 %, spectral efficiency of 98 % and computational complexity of 51 %. It requires one base station and works with the majority of wireless networks. Its application potential is greater and its computational complexity is less than other indoor localization techniques now in use.

Photostable aggregation-induced emission of iridium(III) complex realizing robust and high-resolution imaging of latent fingerprints

Preparation of AIEgens with excellent photostability is of vital importance to acquire the optimal performance of aggregation-induced emission (AIE) and remains challenging. In this paper, a handy structural design strategy based on iridium(III) complexes for the creation of photostable AIEgens is reported. The synergistic effect of phenyl and carbazole improves the rotational activity of substituents, then activates the AIE property of iridium(III) complexes with considerably improved photostability. In addition, prompt and high-resolution imaging of latent fingerprints on tinfoil, glass, plastic, and steel is presented by immersion in an aqueous solution of IrCzPh for 60 s. With the aid of photostable AIE, legible and intact fingerprint images demonstrate ultrahigh definition, which can display down to level 3 of fingerprint details and accurately authenticate individual characteristics. Meanwhile, outstanding anti-aging performance of fingerprint images is emerged with no contrast attenuation under 365 nm irradiation for 60 min or aged in natural light for 7 days. Noteworthily, ultrahigh-contrast dynamic imaging of local fingerprint details is achieved.

A K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold

There are two problems with traditional indoor fingerprint location methods. First, irrelevant fingerprints in a fingerprint database interfere with the matching phase, which leads to poor positioning accuracy and stability of positioning results, and second, there is a large amount of computational overhead in the matching phase. Therefore, this paper proposes a K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold. In this method, a circular domain is formed in a coarse positioning process to narrow the positioning range. It solves the problem of the interference of irrelevant fingerprints. At the same time, a fault-tolerant mechanism is introduced to adjust the circular domain dynamically to ensure that the coarse positioning circular domain contains high similarity reference points and improve the fault tolerance of the coarse positioning. This method consists of offline and online phases. In the offline phase, the values of the received signal strength from Bluetooth low energy are preprocessed using a Gaussian filter to construct a fingerprint database. In the online phase, irrelevant fingerprints are filtered out by using the coarse positioning method. The filtered fingerprints are then matched with a testing point by the K-nearest neighbor algorithm, and the weighted centroids of the nearest reference points are solved. Finally, the coordinate of the testing point is obtained. The experimental results show that this method can effectively improve indoor positioning accuracy when compared with the traditional K-nearest neighbor. The average positioning error of the proposed method is 0.844 m.

Multiple Fingerprinting Localization by an Artificial Neural Network.

Fingerprinting localization is a promising indoor positioning methods thanks to its advantage of using preinstalled infrastructure. For example, WiFi signal strength can be measured by pre-existing WiFi routers. In the offline phase, the fingerprinting localization method first stores of position and RSSI measurement pairs in a dataset. Second, it predicts a target’s location by comparing the stored fingerprint database to the current measurement. The database size is normally huge, and data patterns are complicated; thus, an artificial neural network is used to model the relationship of fingerprints and locations. The existing fingerprinting locations, however, have been developed to predict only single locations. In practice, many users may require positioning services, and as such, the core algorithm should be capable of multiple localizations, which is the main contribution of this paper. In this paper, multiple fingerprinting localization is developed based on an artificial neural network and an analysis of the number of targets that can be estimated without loss of accuracy is conducted by experiments.

An Adaptive User Tracking Algorithm Using Irregular Data Frames for Passive Fingerprint Positioning.

Wi-Fi fingerprinting is the most popular indoor positioning method today, representing received signal strength (RSS) values as vector-type fingerprints. Passive fingerprinting, unlike the active fingerprinting method, has the advantage of being able to track location without user participation by utilizing the signals that are naturally emitted from the user’s smartphone. However, since signals are generated depending on the user’s network usage patterns, there is a problem in that data are irregularly collected according to the patterns. Therefore, this paper proposes an adaptive algorithm that shows stable tracking performances for fingerprints generated at irregular time intervals. The accuracy and stability of the proposed tracking method were verified by experiments conducted in three scenarios. Through the proposed method, it is expected that the stability of indoor positioning and the quality of location-based services will improve.

LoRaWAN‐implemented node localisation based on received signal strength indicator

AbstractLong Range Wireless Area Network (LoRaWAN) provides desirable solutions for Internet of Things (IoT) applications that require hundreds or thousands of actively connected devices (nodes) to monitor the environment or processes. In most cases, the location information of the devices arguably plays a critical role and is desirable. In this regard, the physical characteristics of the communication channel can be leveraged to provide a feasible and affordable node localisation solution. This paper presents an evaluation of the performance of LoRaWAN Received Signal Strength Indicator (RSSI)‐based node localisation in a sandstorm environment. The authors employ machine learning algorithms, Support Vector Regression and Gaussian Process Regression, which turn the high variance of RSSI due to frequency hopping feature of LoRaWAN to advantage, creating unique signatures representing different locations. In this work, the RSSI features are used as input location fingerprints into the machine learning models. The proposed method reduces node localisation complexity when compared to GPS‐based approaches whilst provisioning more extensive connection paths. Furthermore, the impact of LoRa spreading factor and kernel function on the performance of the developed models have been studied. Experimental results show that the SVR‐enhanced fingerprint yields the most significant improvement in node localisation performance.

Connecting food supply chains

Connecting food supply chains

Optimization and Evaluation of Multidetector Deep Neural Network for High-Accuracy Wi-Fi Fingerprint Positioning

To fulfill the need for high-accuracy indoor positioning in many location-based services (LBSs) and the emerging Internet of Things (IoT) applications, in this article, we propose a novel scene-analysis positioning solution of the multidetector deep neural network (DNN) architecture, with preprocessing steps, model optimization techniques, and variance estimation methods. During the offline site-surveying phase in our approach, fingerprint databases are created by purposely built robotic surveying devices traversing the target site to gather perceivable Wi-Fi and other signals including to create spatial positioning models for further use in the online positioning phase. The intricate nonlinear relationship between fingerprints and spatial positions are thus resolved by the multidetector DNN in our approach. Hyperparameter analyses were conducted to further optimize our proposed multidetector model in terms of complexity, achieving at least 6.7 times of parameter complexity reduction while retaining < 1% degradation of 0.9-m (3 ft) positioning accuracy level.

Optimizing Anchor Node Deployment for Fingerprint Localization With Low-Cost and Coarse-Grained Communication Chips

A part of off-the-shelf wireless communication chips can only provide very coarse-grained information of the distance range between the transmitter and receiver. Exploiting such chips for precise wireless indoor positioning (WIP) becomes very valuable since their costs are comparatively low. For the WIP systems built on the coarse-grained communication chips, this article discusses the optimal anchor node deployment problem for fingerprint localization. The problem is very challenging since it implies three particularly important requirements of adaptive localization precision, unique fingerprint, and a minimum number of anchor nodes. To minimize the required number of anchor nodes while satisfying the three requirements, we propose two anchor node deployment algorithms, namely, ANDAs and WANDA, both of which are developed for mediate and large-area deployment, respectively. We first prove the effectiveness of the chip in maintaining robust fingerprints and formulate the deployment optimization problem as a minimum attribute reduction problem. Then, to deal with the high computational complexity involved, ANDA is proposed by significantly reducing the search space of the optimization problem. To practically address the combination explosion problem for large-area deployment, the idea of area partitioning is adopted. Investigating anchor nodes at different grid vertices in subareas that may have different sharing degrees, WANDA is proposed to achieve an overall anchor node deployment optimization. Simulation and experimental results demonstrate the validity and the superiority of ANDA and WANDA over counterparts. For large-area deployment, compared to ANDA, WANDA can further decrease the total amount of anchor nodes by 17%.

Indoor Fingerprinting Positioning System Using Deep Learning with Data Augmentation

Indoor Fingerprinting Positioning System Using Deep Learning with Data Augmentation

Research on Fingerprint and Hyperbolic Fusion Positioning Algorithm Based on 5G Technology

With the development of the Internet of Things technology, higher requirements are put forward for the positioning accuracy of objects. This paper presents an indoor fusion positioning algorithm based on the 5th Generation Mobile Communication Technology (5G), which effectively solves two problems. The first is that fingerprint positioning is susceptible to environmental changes and results in inaccurate fingerprint matching. The second is the problem of the hyperbolic positioning algorithm based on the line-of-sight fluctuating too much in complex indoor environments. This paper uses a 5G flexible subcarrier interval of Orthogonal Frequency Division Multiplexing (OFDM) to significantly reduce the time delay error of Time Of Arrival (TOA), and an improved genetic algorithm and the weighted hyperbolic algorithm are used to estimate the optimal position coordinates. In the offline database establishment stage of fingerprint positioning, the Channel State Information-reference signal (CSI-RS) of multiple-beam sets provides high-dimensional information for subsequent training and prediction. The online stage cooperates with the improved residual network model to make predictions. Finally, the positioning information and the error distribution function generated by the two positioning processes are simultaneously used as the input of the Kalman filter to obtain the precise position coordinates. The simulation results show that in complex indoor scenes where line-of-sight propagation and non-line-of-sight propagation paths are mixed, the accuracy of this method can reach below 0.82 m. Thus, the positioning accuracy is significantly improved compared with other methods, which can meet most indoor scene positioning needs.

Multi-Level Federated Graph Learning and Self-Attention Based Personalized Wi-Fi Indoor Fingerprint Localization

Deep learning-based Wi-Fi indoor fingerprint localization, which requires a large received signal strength (RSS) dataset for training, has been widely studied. Federated learning (FL) is recently introduced into indoor localization in order to address the problem of data sharing without privacy disclosure. However, under the serious data heterogeneity of FL, the averaged model will perform worse for individual client. In this letter, a multi-level federated graph learning and self-attention based personalized indoor localization method is proposed to further capture the intrinsic features of RSS, and learn the aggregation manner of shared information uploaded by clients, with better personalization accuracy. The experimental results demonstrate that proposed methods achieve a higher personalized localization accuracy than compared personalized federated learning (PFL) methods, in most of the simulation settings.

CSWS—Suppress confusion subregion in indoor image fingerprint localization

AbstractImage data can provide rich content information, which has attracted a lot of attention in the field of indoor fingerprint positioning. However, indoor image information from different locations is characterized by high content repetition, and these repetitive regions can cause the problem of insufficient differentiation of adjacent image fingerprints or even fingerprint misclassification. To solve this problem, this article proposes a confusion subregion weighted suppression strategy in the image fingerprint database. First, duplicate regions (confusion subregions) are extracted from the fingerprint database using an appropriate salient region detection method. Then the similarity of these duplicate regions in Euclidean space is defined, and the degree of influence of these regions on the distinguishability of the fingerprint database is measured. Finally, the suppression of these duplicate regions is achieved in the original image fingerprint database by introducing weighted suppression coefficients. Experiments show that the algorithm proposed in this article can achieve significant results in the fingerprint localization task of real indoor scenes and effectively improve the localization accuracy.

The Time-of-Arrival Offset Estimation in Neural Network Atomic Denoising in Wireless Location.

With the increasing demand for wireless location services, it is of great interest to reduce the deployment cost of positioning systems. For this reason, indoor positioning based on WiFi has attracted great attention. Compared with the received signal strength indicator (RSSI), channel state information (CSI) captures the radio propagation environment more accurately. However, it is necessary to take signal bandwidth, interferences, noises, and other factors into account for accurate CSI-based positioning. In this paper, we propose a novel dictionary filtering method that uses the direct weight determination method of a neural network to denoise the dictionary and uses compressive sensing (CS) to extract the channel impulse response (CIR). A high-precision time-of-arrival (TOA) is then estimated by peak search. A median value filtering algorithm is used to locate target devices based on the time-difference-of-arrival (TDOA) technique. We demonstrate the superior performance of the proposed scheme experimentally, using data collected with a WiFi positioning testbed. Compared with the fingerprint location method, the proposed location method does not require a site survey in advance and therefore enables a fast system deployment.

CRCLoc: A Crowdsourcing-Based Radio Map Construction Method for WiFi Fingerprinting Localization

The WiFi-based fingerprint indoor-positioning system has attracted increasing interest from industry and academia, benefiting from the widespread deployment of the wireless local area network (WLAN) infrastructure. However, with the expansion of application scenarios, this system suffers from labor-intensive work for received signal strength (RSS) fingerprint construction. In this article, we propose a crowdsourcing-based radio map construction method and trajectory matching algorithm to solve the time-consuming preliminary fingerprint data collection in the offline phase, where the tedious collection work is replaced by the massive crowdsourcing sensor data. First, the latent information of the map is extracted by using some image processing methods, and all possible routes are obtained simultaneously with the depth-first traversal method. Then, considering the strict restrictions on walking in indoor environments, the crowdsourcing trajectories are produced by matching the result of pedestrian dead reckoning (PDR) with the candidate routes based on the Shape Context algorithm. Further, a crowdsourcing trajectory can be represented by uniformly distributed reference points based on step detection, and each point corresponds to a unique RSS of access points (APs). Since such a fingerprint construction method can be performed while smartphone holders are moving and not aware of their actual positions, it can be referred to the dynamic construction method of the radio map. The real scenario experiments reveal that the proposed solution can significantly reduce the time and manpower consumption to build the radio map. Moreover, compared with traditional schemes, our crowdsourcing-based radio map construction method for the WiFi fingerprinting localization (CRCLoc) system can improve the accuracy and robustness of the indoor-positioning results.