Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Long-range and high-precision localization method for underwater bionic positioning system based on joint active-passive electrolocation

Active electrolocation organ of weakly electric fish act as a proximity detection system with high accuracy in recognizing object parameters such as size and shape. In contrast, some fish with passive electrolocation organ are able to detect objects at a greater range. This paper proposes a joint active-passive electrolocation algorithm for long-range and high-precision underwater localization, inspired by the active and passive electroreceptive organs of fish. The study begins by designing a large experimental platform for the underwater localization system to investigate the response of underwater objects to active and passive electric fields. Based on the response, the paper proposes separate underwater active and passive electrolocation algorithms, which are then combined to form a joint algorithm. Experimental results demonstrate that the proposed algorithm achieves high localization accuracy and long detection distance. The joint active-passive electrolocation algorithm has potential applications in submarine resource exploration, underwater robotics, and maritime military projects, while also providing new ideas for future research on long-range underwater object detection and identification based on electrolocation.

Neural-Network-Based Localization Method for Wi-Fi Fingerprint Indoor Localization

Despite the high demand for Internet location service applications, Wi-Fi indoor localization often suffers from time- and labor-intensive data collection processes. This study proposes a novel indoor localization model that utilizes fingerprinting technology based on a convolutional neural network to address this issue. The aim is to enhance Wi-Fi indoor localization by streamlining the data collection process. The proposed indoor localization model leverages a 3D ray-tracing technique to simulate the wireless received signal strength intensity (RSSI) across the field. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of Wi-Fi indoor localization. In addition, an RSSI heatmap fingerprint dataset generated from the ray-tracing simulation is trained on the proposed indoor localization model. To optimize and evaluate the model's performance in real-world scenarios, experiments were conducted using simulated datasets obtained from the publicly available databases of UJIIndoorLoc and Wireless InSite. The results show that the new approach solves the problem of resource limitation while achieving a verification accuracy of up to 99.09%.

Convolutional Model with a Time Series Feature Based on RSSI Analysis with the Markov Transition Field for Enhancement of Location Recognition

Although numerous schemes, including learning-based approaches, have attempted to determine a solution for location recognition in indoor environments using RSSI, they suffer from the severe instability of RSSI. Compared with the solutions obtained by recurrent-approached neural networks, various state-of-the-art solutions have been obtained using the convolutional neural network (CNN) approach based on feature extraction considering indoor conditions. Complying with such a stream, this study presents the image transformation scheme for the reasonable outcomes in CNN, obtained from practical RSSI with artificial Gaussian noise injection. Additionally, it presents an appropriate learning model with consideration of the characteristics of time series data. For the evaluation, a testbed is constructed, the practical raw RSSI is applied after the learning process, and the performance is evaluated with results of about 46.2% enhancement compared to the method employing only CNN.

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.

A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-Concept

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct activities of daily living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with different activities to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADLs while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as the lack of functionalities to detect falls and indoor locations in a simultaneous manner, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an ambient assisted living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real-world. Proof-of-concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparative studies with prior works in this field are also presented to uphold the novelty of this work. The first comparative study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparative study shows that the cost of the development of this system is the lowest as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.

Harris hawks optimization algorithm and BP neural network for ultra-wideband indoor positioning

Traditional back propagation neural networks (BPNNs) for ultrawideband (UWB) indoor localization can effectively improve localization accuracy, although there is high likelihood of becoming trapped in nearby minima. To solve this problem, the random weights and thresholds of the BPNN are optimized using the Harris Hawks optimization algorithm (HHO) to obtain the optimal global solution to enhance the UWB indoor positioning accuracy and NLOS resistance. The results show that the predicted trajectory of the HHO and BPNN hybrid algorithm (HHO-BP) matches the actual position in the two-dimensional localization scenario with four base stations; the optimized average positioning error is effectively reduced in both indoor LOS and NLOS environments. In the LOS environment, the total mean error of the traditional BPNN algorithm is 6.52 cm, which is 26.99% better than the UWB measurement error; in the NLOS environment, the total mean error of the conventional BPNN is 14.82 cm, which is 50.08% better than the UWB measurement error. The HHO-BP algorithm is further optimized on this basis, and the total mean error in the LOS environment is 4.50 cm, which is 22.57% better than the conventional BPNN algorithm; in the NLOS environment, the total mean error is 9.56 cm, which is 17.54% better than the conventional BPNN algorithm. The experimental findings suggest that the approach has greater calibration accuracy and stability than BPNN, making it a viable choice for scenarios requiring high positional precision.

A Novel Localization Algorithm Based on RSSI and Multilateration for Indoor Environments

Indoor localization algorithms based on the received signal strength indicator (RSSI) in wireless sensor networks (WSNs) have higher localization accuracy than other range-free methods. This paper considers indoor localization based on multilateration and averaged received signal strength indicator (RSSI). We propose an approach called weighted three minimum distances method (WTM) to deal with the poor accuracy of distances deduced from RSSI. Using a practical localization system, an experimental channel model is deduced to assess the performance of the proposed localization algorithm in realistic conditions. Both simulated data and measured data are used to verify the proposed method. Compared with nonlinear least squares (NLS), Levenberg–Marquardt algorithm (LM) and semidefinite programming method (SDP), simulations show that the proposed method exhibits better localization accuracy but consumes more calculation time.

A Survey of Recent Indoor Localization Scenarios and Methodologies

Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks

Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose an end-to-end hybrid multimodal deep neural network localization system, MM-Loc, relying on zero hand-engineered features, but learning automatically from data instead. This is achieved by using modality-specific neural networks to extract preliminary features from each sensing modality, which are then combined by cross-modality neural structures. We show that our choice of modality-specific neural architectures can estimate the location independently. But for better accuracy, a multimodal neural network that fuses the features of early modality-specific representations is a better proposition. Our proposed MM-Loc system is tested on cross-modality samples characterised by different sampling rate and data representation (inertial sensors, magnetic and WiFi signals), outperforming traditional approaches for location estimation. MM-Loc elegantly trains directly from data unlike conventional indoor positioning systems, which rely on human intuition.

Access-Point Centered Window-Based Radio-Map Generation Network

Fingerprinting is the term used to describe a common indoor radio-mapping positioning technology that tracks moving objects in real time. To use this, a substantial number of measurement processes and workflows are needed to generate a radio-map. Accordingly, to minimize costs and increase the usability of such radio-maps, this study proposes an access-point (AP)-centered window (APCW) radio-map generation network (RGN). The proposed technique extracts parts of a radio-map in the form of a window based on AP floor plan coordinates to shorten the training time while enhancing radio-map prediction accuracy. To provide robustness against changes in the location of the APs and to enhance the utilization of similar structures, the proposed RGN, which employs an adversarial learning method and uses the APCW as input, learns the indoor space in partitions and combines the radio-maps of each AP to generate a complete map. By comparing four learning models that use different data structures as input based on an actual building, the proposed radio-map learning model (i.e., APCW-based RGN) obtains the highest accuracy among all models tested, yielding a root-mean-square error value of 4.01 dBm.

Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments

This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living (AAL), with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-based mathematical approach to model all possible forms of user interactions for any activity arising from user diversity of multiple users in such environments. Second, it presents a system that uses this approach with a machine learning method to model individual user-profiles and user-specific user interactions for detecting the dynamic indoor location of each specific user. Third, to address the need to develop highly accurate indoor localization systems for increased trust, reliance, and seamless user acceptance, the framework introduces a novel methodology where two boosting approaches—Gradient Boosting and the AdaBoost algorithm are integrated and used on a decision tree-based learning model to perform indoor localization. Fourth, the framework introduces two novel functionalities to provide semantic context to indoor localization in terms of detecting each user’s floor-specific location as well as tracking whether a specific user was located inside or outside a given spatial region in a multi-floor-based indoor setting. These novel functionalities of the proposed framework were tested on a dataset of localization-related Big Data collected from 18 different users who navigated in 3 buildings consisting of 5 floors and 254 indoor spatial regions, with an to address the limitation in prior works in this field centered around the lack of training data from diverse users. The results show that this approach of indoor localization for personalized AAL that models each specific user always achieves higher accuracy as compared to the traditional approach of modeling an average user. The results further demonstrate that the proposed framework outperforms all prior works in this field in terms of functionalities, performance characteristics, and operational features.

A Tightly-Coupled Positioning System of Online Calibrated RGB-D Camera and Wheel Odometry Based on SE(2) Plane Constraints

The emergence of Automated Guided Vehicle (AGV) has greatly increased the efficiency of the transportation industry, which put forward the urgent requirement for the accuracy and ease of use of 2D planar motion robot positioning. Multi-sensor fusion positioning has gradually become an important technical route to improve overall efficiency when dealing with AGV positioning. As a sensor directly acquiring depth, the RGB-D camera has received extensive attention in indoor positioning in recent years, while wheel odometry is the sensor that comes with most two-dimensional planar motion robots, and its parameters will not change over time. Both the RGB-D camera and the wheel odometry are commonly used sensors for indoor robot positioning, but the existing research on the fusion of RGB-D and wheel odometry is limited based on classic filtering algorithms; few fusion solutions based on optimization algorithm of them are available at present. To ensure the practicability and greatly improve the accuracy of RGB-D and odometry fusion positioning scheme, this paper proposed a tightly-coupled positioning scheme of online calibrated RGB-D camera and wheel odometry based on SE(2) plane constraints. Experiments have proved that the angle accuracy of the extrinsic parameter in the calibration part is less than 0.5 degrees, and the displacement of the extrinsic parameter reaches the millimeter level. The field-test positioning accuracy of the positioning system we proposed having reached centimeter-level on the dataset without pre-calibration, which is better than ORB-SLAM2 relying solely on RGB-D cameras. The experimental results verify the excellent performance of the frame in positioning accuracy and ease of use and prove that it can be a potential promising technical solution in the field of two-dimensional AGV positioning.

JLGBMLoc-A Novel High-Precision Indoor Localization Method Based on LightGBM

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, with the localization system based on received signal strength (RSS), is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm-named joint denoising auto-encoder (JDAE)-which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on the UJIIndoorLoc dataset and the Tampere dataset, the experimental results show that the proposed model increases the positioning accuracy dramatically compared with other existing methods.

Combining RSSI and Accelerometer Features for Room-Level Localization

The continuing advancements in technology have resulted in an explosion in the use of interconnected devices and sensors. Internet-of-Things (IoT) systems are used to provide remote solutions in different domains, like healthcare and security. A common service offered by IoT systems is the estimation of a person’s position in indoor spaces, which is quite often achieved with the exploitation of the Received Signal Strength Indication (RSSI). Localization tasks with the goal to locate the room are actually classification problems. Motivated by a current project, where there is the need to locate a missing child in crowded spaces, we intend to test the added value of using an accelerometer along with RSSI for room-level localization and assess the performance of ensemble learning methods. We present here the results of this preliminary approach of the early and late fusion of RSSI and accelerometer features in room-level localization. We further test the performance of the feature extraction from RSSI values. The classification algorithms and the fusion methods used to predict the room were evaluated using different protocols applied to a public dataset. The experimental results revealed better performance of the RSSI extracted features, while the accelerometer’s individual performance was poor and subsequently affected the fusion results.

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user’s indoor location in a specific ‘activity-based zone’ during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the ‘zone-based’ indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user’s indoor position that outperforms all similar works in this field, as per the associated root mean squared error—one of the performance evaluation metrics in ISO/IEC18305:2016—an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.