Indoor geolocation based on earth magnetic field

Pedestrian dead reckoning (PDR) is widely used in handheld indoor positioning systems. However, low-cost inertial sensors built into smartphones provide poor-quality measurements, resulting in cumulative error which consists of heading estimation error caused by gyroscope and step length estimation error caused by an accelerometer. Learning more motion features through limited measurements is important to improve positioning accuracy. This paper proposes an improved PDR system using smartphone sensors. Using gyroscope, two motion patterns, walking straight or turning, can be recognised based on dynamic time warp (DTW) and thus improve heading estimation from an extended Kalman filter (EKF). Joint quasi-static field (JQSF) detection is used to avoid bad magnetic measurements due to magnetic disturbances in an indoor environment. In terms of periodicity of angular rate while walking, peak–valley angular velocity detection and zero-cross detection is combined to detect steps. A step-length estimation method based on deep belief network (DBN) is proposed. Experimental results demonstrate that the proposed PDR system can achieve more accurate indoor positioning.

Pedestrian dead reckoning (PDR) is the critical component in indoor pedestrian tracking and navigation services. While most of the recent PDR solutions exploit in-built inertial sensors in smartphones for next step estimation, due to measurement errors and sensing drift, the accuracy of walking direction, step detection, and step length estimation cannot be guaranteed, leading to large accumulative tracking errors. In this paper, we propose a radar-assisted PDR scheme, called RadarPDR, which integrates a frequency-modulation continuous-wave (FMCW) radar to assist the inertial sensors-based PDR. We first establish a segmented wall distance calibration model to deal with the radar ranging noise caused by irregular indoor building layouts and fuse wall distance estimation with acceleration and azimuth signals measured by the inertial sensors of a smartphone. We also propose a hierarchical particle filter(PF) together with an extended Kalman filter for position and trajectory adjustment. Experiments have been conducted in practical indoor scenarios. Results demonstrate that the proposed RadarPDR is efficient and stable and outperforms the widely used inertial sensors-based PDR scheme.

Robust attitude and heading estimation with respect to a known reference is an essential component for indoor localization in robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using 9-axis solid-state MEMS-based sensors. The accuracy of heading estimation on such a system depends on the Earth's magnetic field measurement accuracy. The measurement of the Earth's magnetic field using MEMS-based magnetometer sensors in an indoor environment, however, is strongly affected by external magnetic perturbations. This paper presents a novel approach for robust indoor heading estimation based on skewed-redundant magnetometer fusion. A tetrahedron platform based on Hall-effect magnetic sensors is designed to determine the Earth's magnetic field with the ability to compensate for external magnetic field anomalies. Additionally, a correlation-based fusion technique is introduced for perturbation mitigation using the proposed skewed-redundant configuration. The proposed fusion technique uses a correlation coefficient analysis for determining the distorted axis and extracts the perturbation-free Earth's magnetic field vector from the redundant magnetic measurement. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the magnetic field measurement and estimates the Earth's true magnetic field. Using the proposed platform, we achieve a Root Mean Square Error of 12.74° for indoor heading estimation without using an additional gyroscope.

Systems combining inertial measurement unit and magnetic field information have been extensively studied for indoor positioning and navigation. However, existing research predominantly addresses static magnetic field distributions, with limited attention to user-environment interactions. Establishing the correlation between magnetic field changes and user behavior presents new challenges for enhancing spatial structure perception. This paper proposes an indoor positioning algorithm based on magnetic semantic extended Kalman filtering (EKF) for inertial navigation system. We analyze the projection characteristics of magnetic field vectors in the carrier coordinate system, revealing the inherent relationship between abrupt changes in magnetic field components and user's dynamic behavior. Based on this, we introduce fuzzy logic methods to model the relationship between abrupt changes in the magnetic field and angular velocity fluctuations, thereby accurately extracting the semantic information of the magnetic field environment. To fully utilize this semantic information, we design an EKF algorithm that integrates magnetic semantic perception information to assist the INS in state updating and error correction. The proposed algorithm not only improves positioning accuracy but also enhances the system's adaptability to dynamic environmental changes. The experimental results indicate that the maximum error of our indoor positioning system is 0.93 meters and the error is controlled within 1 meter.

Location based service (LBS) cannot be realized unless the location of the user is available. For indoor LBS, indoor positioning must be utilized and many researchers have been working on indoor positioning and tracking. For example, extended Kalman filter (EKF) was exploited in Bluetooth based indoor positioning. Nowadays, WLAN (wireless local area network) is available virtually everywhere. Thus, WLAN based indoor positioning and tracking is more economical than Bluetooth based ones. The main purpose of this paper is to propose a new WLAN based EKF indoor tracking method by extending existing Bluetooth based EKF positioning method. Experimental results comparing our method with other previous methods are discussed.

Smartphone-based indoor localization has attracted considerable attentions in both research and industrial areas. However, the localization accuracy and robustness are still challenging problems due to low-cost noisy devices, especially in those complicated localization environments. Considering that pedestrian dead-reckoning (PDR) devices are widely equipped in recent smartphones, we propose a novel indoor localization fusing algorithm that integrates both wireless fidelity (WiFi) features and PDR features. By formulating the fusing indoor localization as a recursive function approximation problem, a sliding-window-based displacement scheme is designed to generate a time-series-based feature data set. We further apply the long short-term memory (LSTM) network for data fusion and localization on this data set by taking advantage of its benefits in time-series prediction and characterization. To evaluate the performance of the proposed algorithm, we compare it with state-of-the-art filter-based localization algorithms in three typical movements and three postures of holding smartphones. Extensive experiment results demonstrate the accuracy and robustness of the proposed algorithm in indoor localization, even in some extreme environments.

<div class="section abstract"><div class="htmlview paragraph">The attitude of autonomous marine, land, air or space vehicles can be determined from the state estimation by means of mathematical tools such as Extended Kalman filter. In the process of calculation of this value, three-axis inertial and non-inertial sensors are used. From the knowledge of the orientation of the main axis, it is implemented the automatic control of the vehicle.</div><div class="htmlview paragraph">The solid-state magnetometer is a non-inertial sensor with three axes that provides both the direction and the magnitude of the magnetic field vector which it is being submitted. However, measurements of the Earth's magnetic field vector, obtained using the three-axis magnetometer, are not accurate. In the vehicle itself, the presence of ferrous materials creates disturbances in the Earth's magnetic field, distorting the measured field. The accuracy of the magnetometer can be improved by means of various calibration methods, which, in summary, calculate the vector components of the bias, the systematic error, which can be modeled as a magnetic field that overlaps and distorts the actual terrestrial magnetic field. More than 90% of the measured geomagnetic field is generated in the earth's outer core and can be described by mathematical models that serve as reference to some of the magnetometer calibration algorithms.</div></div>

Detecting a weak physical signal immersed in overwhelming noises entails separating the two, a task for which machine learning is naturally suited. In principle, such a signal is generated by a nonlinear dynamical system of intrinsically high dimension for which a mathematical model is not available, rendering unsuitable traditional linear or nonlinear state-estimation methods that require an accurate system model (e.g., extended Kalman filters). We exploit the architectures of reservoir computing and feed-forward neural networks (FNNs) with time-delayed inputs to solve the weak-signal-detection problem. As a prototypical example, we apply the machine-learning schemes to Earth's magnetic anomaly field-based navigation. In particular, the time series are collected from the interior of the cockpit of a flying aircraft during different maneuvering phases, where the overwhelmingly strong noise background is the result of other components of Earth's magnetic field and the fields generated by the electronic devices in the cockpit. We demonstrate that, when combined with the traditional Tolles-Lawson model for Earth's magnetic field, the articulated machine-learning schemes are effective for accurately detecting the weak anomaly field from the noisy time series. The schemes can be applied to detecting weak signals in other domains of science and engineering.

Positioning systems are used in a wide range of applications which require determining the position of an object in space, such as locating and tracking assets, people and goods; assisting navigation systems; and mapping. Indoor Positioning Systems (IPSs) are used where satellite and other outdoor positioning technologies lack precision or fail. Ultra-WideBand (UWB) technology is especially suitable for an IPS, as it operates under high data transfer rates over short distances and at low power densities, although signals tend to be disrupted by various objects. This paper presents a comprehensive study of the precision, failure, and accuracy of 2D IPSs based on UWB technology and a pseudo-range multilateration algorithm using Time Difference of Arrival (TDoA) signals. As a case study, the positioning of a area, four anchors (transceivers), and one tag (receiver) are considered using bitcraze’s Loco Positioning System. A Cramér–Rao Lower Bound analysis identifies the convex hull of the anchors as the region with highest precision, taking into account the anisotropic radiation pattern of the anchors’ antennas as opposed to ideal signal distributions, while bifurcation envelopes containing the anchors are defined to bound the regions in which the IPS is predicted to fail. This allows the formulation of a so-called flyable area, defined as the intersection between the convex hull and the region outside the bifurcation envelopes. Finally, the static bias is measured after applying a built-in Extended Kalman Filter (EKF) and mapped using a Radial Basis Function Network (RBFN). A debiasing filter is then developed to improve the accuracy. Findings and developments are experimentally validated, with the IPS observed to fail near the anchors, precision around , and accuracy improved by about for static and for dynamic measurements, on average.

Indoor localization using the magnetic fingerprint collected by the embedded sensors of smartphone has been under constant improvement with the widespread popularity of the smartphone. Most of the current systems rely on the Particle Filter (PF) or the Kalman Filter (KF) to combine the Pedestrian Dead Reckoning (PDR) with the magnetic or WiFi fingerprint for improving their accuracy. In this paper, a system using the PDR and the magnetic fingerprint is proposed for indoor localization, which is based on the combination of the Particle Filter (PF) and the Extend Kalman Filter (EKF). The system includes the Pedestrian Dead Reckoning (PDR) module and the magnetic fingerprint matching module. In particular, the hybrid indoor positioning algorithm which combines the Particle Filter (PF) and the Extend Kalman Filter (EKF) is proposed in the magnetic fingerprint matching module for the fusion of the results of the Pedestrian Dead Reckoning module and the magnetic fingerprint. This hybrid indoor positioning algorithm is the key component which can reduce the computation of the Particle Filter effectively and solve the inherent blindness and particles degeneration problem. The obtained results in the real scenarios show that our fusion system achieves better results than the widely adopted system in which the Particle Filter (PF) or the Kalman Filter (KF) is used. The evaluation shows the system achieves a localization accuracy about 1-2m on average in a large building.

Inspired by the indoor localization technology of mobile robots, a novel Electronic Travel Aids (ETA) is developed based on multi-sensor fusion using an extended Kalman filter (EKF). A wearable sensor system is designed to estimate the real-time posture of user's lower limb. This system can be used both for monitoring the user's walking status and for realizing the relative localization. A belt of multiple sonar sensors worn by the user can detect the obstacle of unknown environment. Simultaneously, this sonar belt can also be used as the absolute localization device in a similar way as the mobile robot. An EKF is applied to realize the data fusion of different sensor systems. The abilities of obstacle avoidance and indoor localization of the proposed ETA are both verified through experiments.

In this thesis, we design and build a system for unmanned aerial vehicles (UAVs). We combine, adjust and improve existing open-source algorithms for localization tasks in an indoor and outdoor environment with window detection for transitioning between the two environments. For outdoor localization, we mainly use GPS, while for indoor localization, we use Extended Kalman Filter (EKF). However, for the transitioning area where the GPS is unreliable and the environment has too few structures for EKf, we use an opening such as a window or a door for localization with a stereo camera. We compare our technique with GPS only, EKF only, manually estimated path, and ground truth from a ZED2 camera.

Kalman filtering represents formidable computational linear algebra requirements for each new input measurement vector. The air-to-air missile guidance problem is addressed for which an extended Kalman filter (EKF) is required because the measurements are nonlinear in Cartesian coordinates. An explicit formulation is used. At the outset, we discretize the system dynamics and measurement model and incorporate a discrete-time EKF. A factorized L D LT algorithm is used to propagate the covariance matrices between sample times. A simulation analysis of the number of data bits required in the computations is provided. Comparison with other EKF algorithms shows that this method requires only 18 bit accuracy (compared to 32-40 bits for other methods). Quantitative position, velocity and acceleration estimates obtained for a highly maneuverable target are presented. A high-accuracy floating point optical processor is presented that is capable of computing the full EKF to allow a new measurement update each msec.

Indoor positioning systems have already been introduced in commercial facilities. Since, the signals transmitted from GPS satellites do not penetrate inside buildings, Wifi, Zigbee and Bluetooth are used for indoor position estimation. In this work, we only focus on the use of Bluetooth due to its advantages such as low power consumption, wide signal range and inexpensive. However, the accuracy of positioning is not sufficient in current technologies. Therefore, this paper proposes an indoor positioning method for improving accuracy, using compass and walking speed with an extended Kalman filter. Preliminary experimental results improves accuracy up to 21.2%.

Recently various indoor positioning techniques have been developed based on smartphone. However, most of them need external signals. In this paper a self-contained approach relying on built-in inertial sensors is implemented. Taking advantage of Pedestrian Dead Reckoning, it updates the current position by measuring the length and the heading of each step. Foremost the whole walking process is divided into segments, in which only straight walking is involved. After that the Feature Vectors are extracted for step detection. Specially, to cope with the instabilities caused by gait change, an equivalent Model Wave is created to substitute the original data. Finally, Particle Filter is employed for map matching. According to a group of experiments, our approach is as accurate as traditional positioning technique but shows more robustness.