Road roughness significantly affects vehicle vibrations and ride quality. We introduce a Kalman filter (KF)-based method for estimating road roughness in terms of the international roughness index (IRI) by fusing inertial and speed measurements, offering a cost-effective solution for pavement monitoring. The method involves system identification on a physical vehicle to estimate realistic model parameters, followed by KF-based reconstruction of the longitudinal road profile to compute IRI values. It explores IRI estimation using vertical and lateral vibrations, the latter more common in modern vehicles. Validation on 230 km of real-world data shows promising results, with IRI estimation errors ranging from 1% to 10% of the reference values. However, accuracy deteriorates significantly when using only lateral vibrations, highlighting their limitations. These findings demonstrate the potential of KF-based estimation for efficient road roughness monitoring.

This paper investigates time synchronization in distributed sensor networks using the primary synchronization signal (PSS) in Long-Term Evolution (LTE). Two LTE-based time synchronization methods with receiver-to-receiver characteristics have been evaluated in simulations, the passive Scalable Wireless Network Synchronization (SWINS) and the active Reference Broadcast Synchronization (RBS). In addition, small scale hardware experiments were conducted for SWINS. Time synchronization is crucial for many applications, such as power grid monitoring, communication systems, and sensor data fusion. Global Navigation Satellite Systems (GNSS) are currently the state of the art for time synchronization in distributed wireless sensor networks. However, GNSS is vulnerable to jamming and spoofing, which requires alternative methods, e.g., using signals of opportunity. Both evaluated methods achieve accuracy comparable to GNSS in Matlab simulations with high SNR. SWINS performs better in synchronized LTE networks while RBS is superior in unsynchronized networks, which means that the LTE base station transmissions are not synchronous. The disturbance and sensitivity analysis indicates that joint clock offset and position estimation is preferable to sole clock offset estimation when the receiver position uncertainty exceeds 5 and 7 meters for SWINS and RBS respectively. The hardware experiments, using real experimental data, verify the simulation results by showing promising results and potential for real-world application.

Accurate positioning, navigation and timing (PNT) is important for military and civilian applications alike. In recent years navigation using signals of opportunity (SOPs) has received increased interest as a complement or alternative to Global Navigation Satellite Systems (GNSS). We previously proposed a localization framework using opportunistic digital television signals. In it, a navigator localizes itself aided by a stationary base station at a known location, using two time difference of arrival (TDOA) measurements and one twoway ranging (TWR) measurement. In this work we implement and evaluate the proposed framework using real measurements, and based on this propose an extended model which includes the difference in clock bias between the navigator and the base station. The experimental results show that the framework can achieve a root mean square error (RMSE) in absolute position of less than 50 m provided that at least three TDOA measurements are used (with or without TWR), and sometimes if only two TDOA measurements are used in combination with a TWR measurement. We also show experimentally that the transmitter clocks are stable enough for the navigator to extract the TDOA measurements relying only on its own current measurements and previously collected data by the base station. Thus, by replacing the TWR measurement with a third TDOA measurement, the base station need not be active or communicate with the navigator during the localization. In scenarios where communication is undesirable or impossible, this can be advantageous.

Due to recent events that have demonstrated the vulnerabilities of global navigation satellite systems (GNSS) there has been an increased interest in alternative methods for localization. One traditional alternative method is terrain-aided navigation (TAN), where a platform localizes itself by measuring the terrain elevation and comparing it to a digital elevation map (DEM). While single-agent TAN has been extensively studied, multi-agent TAN remains less explored. This paper addresses the multi-agent TAN problem with a focus on its properties. We formulate a weighted least squares (WLS) estimator for computing a snapshot solution to the problem and formulate a CramérRao Lower Bound (CRLB) to evaluate it. Using the expressions for the estimator and the CRLB we are able to highlight some insightful properties of the problem. The findings are verified in a simulation study where we evaluate the performance with respect to the altitude sensor accuracy, the group formation accuracy, the number of agents and their formation. Notably, we observe that the solution is relatively insensitive to errors in agent position, suggesting that low-accuracy inertial navigation systems and distance sensors are sufficient for determining their positions. Increasing the number of agents beyond a few seems to have a large effect on both the efficiency and robustness of the estimator, which lessens as the number of agents increases. However, increasing the number of agents does not compensate for poor altitude sensor quality. Additionally, while spatial separation between agents is important for effective map utilization, further separation beyond a certain point does not enhance performance. These findings provide design guidelines for multi-agent TAN systems and identify areas for further research.

Passive bistatic radar (PBR) is a cost-effective choice for detection and tracking of aircraft. In this paper we present how the Poisson multi-Bernoulli mixture (PMBM) filter is applied in a multi-target tracking application with multistatic PBR. To handle the PBR measurements, it is proposed that a Gaussian mixture target spatial density is used to represent the target state. A state dependent probability of detection model for PBR is presented and how it is used to design the target birth model. Simulated and experimental data are used to evaluate the performance of the described approaches.

As human settlement expands into the natural habitats of wild animals, the conflicts between humans and wildlife increases. The human-elephant conflict causes a tremendous amount of damage, often to poor villages close to the savannah. In this paper, we continue our earlier reported research on a geophone network aimed for elephant localisation by focusing on the detection challenge. We have now collected larger sets of seismic data with footsteps from both elephants and other big animals including humans. To detect the footsteps, a method is developed that analyses features of the geophone signal, which are then compared to those of an elephant footstep. The method detects $54 \%$ of the footsteps and has a classification accuracy of $89 \%$. Subsequently, the detected elephant footstep is used to calculate the direction of arrival (DOA) angle using a delay-andsum beamformer. The direction to an elephant is estimated with good precision on distances ranging from 8 to 30 meters. This research, not only, showcases a practical solution for mitigating human-elephant conflicts, but also underscores the potential of seismic technology in wildlife management and conservation efforts.

Due to vulnerabilities of Global Navigation Satellite Systems (GNSS) there is an increased interest in alternative navigation solutions, such as using signals of opportunity (SOPs). We propose a system where a mobile navigator localizes itself using two-way ranging (TWR) measurements to a stationary base station at a known location as well as time difference of arrival (TDOA) measurements from two terrestrial digital television transmitters. We investigate the feasibility of such a system by deriving the Cramér-Rao Lower Bound (CRLB) for varying noise levels and optimizing the placement of the base station, using the real-life positions of transmitters in the area around LinkÖping, Sweden. We simulate measurements and compute snapshot estimates, verifying that root mean square errors similar to the CRLB can be obtained. The results indicate that for the investigated levels of TWR noise, as long as the TDOA noise is sufficiently low it could be possible to achieve errors of a few tens of meters.

It is shown how dense optical flow obtained using deep learning can be used to provide high quality visual odometry. The obtained odometric information can be utilized as a component to reduce the inherent drift of inertial navigation systems (INS). This could be a key component to provide autonomous system with robust localization capability in GNSS denied environments. The method leverages the power of estimating optical flow from neural networks, which can provide reliable results even when feature based optical flow fails. Comparison of different methods to decide which points of the dense optical flow that should be used to provide as good visual odometry as possible has been performed. Furthermore, it is exemplified how the methodology can help limit the drift in an INs.

High-definition map with accurate lane-level information is crucial for autonomous driving, but the creation of these maps is a resource-intensive process. To this end, we present a cost-effective solution to create lane-level roadmaps using only the global navigation satellite system (GNSS) and a camera on customer vehicles. Our proposed solution utilizes a prior standard-definition (SD) map, GNSS measurements, visual odometry, and lane marking edge detection points, to simultaneously estimate the vehicle’s 6 D pose, its position within a SD map, and also the 3D geometry of traffic lines. This is achieved using a Bayesian simultaneous localization and multi-object tracking filter, where the estimation of traffic lines is formulated as a multiple extended object tracking problem, solved using a trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. In TPMBM filtering, traffic lines are modeled using B-spline trajectories, and each trajectory is parameterized by a sequence of control points. The proposed solution has been evaluated using experimental data collected by a test vehicle driving on highway. Preliminary results show that the traffic line estimates, overlaid on the satellite image, generally align with the lane markings up to some lateral offsets.

Maintaining consistent uncertainty estimates in localization systems is crucial as the perceived uncertainty commonly affects high-level system components, such as control or decision processes. A method for constructing an observability-constrained magnetic field-aided inertial navigation system is proposed to address the issue of erroneous yaw observability, which leads to inconsistent estimates of yaw uncertainty. The proposed method builds upon the previously proposed observability-constrained extended Kalman filter and extends it to work with a magnetic field-based odometry-aided inertial navigation system. The proposed method is evaluated using simulation and real-world data, showing that (i) the system observability properties are preserved, (ii) the estimation accuracy increases, and (iii) the perceived uncertainty calculated by the EKF is more consistent with the true uncertainty of the filter estimates.

Basis Function (BF) expansions are a cornerstone of any engineer's toolbox for computational function approximation which shares connections with both neural networks and Gaussian processes. Even though BF expansions are an intuitive and straightforward model to use, they suffer from quadratic computational complexity in the number of BFs if the predictive variance is to be computed. We develop a method to automatically select the most important BFs for prediction in a sub-domain of the model domain. This significantly reduces the computational complexity of computing predictions while maintaining predictive accuracy. The proposed method is demonstrated using two numerical examples, where reductions up to 50–75% are possible without significantly reducing the predictive accuracy.

The Particle filter can in theory estimate the state of any nonlinear system, but in practice it suffers from an exponential complexity in terms of the number of particles as the dimension of the state increases. The marginalized particle filter can potentially reduce this problem by improving the estimates, particularly for lower number of particles. However, it turns out that for certain systems, it does not provide any improvement in the accuracy of the estimate. The core cause of degeneracy is linked to when the uncertainty of the linear state conditioned on the nonlinear state is 0. Conditions for determining when this occurs are presented and applied to common constant velocity, constant acceleration and constant jerk models with various sampling methods. Interestingly, some combinations are useful while others should be avoided. These findings are supported using simulated systems.

Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Previous work on target tracking in this subfield has focused on fusion aspects only and derived optimal ways of reducing dimensionality based on fusion performance. In this work we propose a novel problem formalization where estimates are reduced based on association performance. The problem is analyzed theoretically and problem properties are derived. The theoretical analysis leads to an optimization strategy that can be used to partly preserve association quality when reducing the dimensionality of communicated estimates. The applicability of the suggested optimization strategy is demonstrated numerically in a multitarget scenario.

A new class of iterated linearization-based nonlinear filters, dubbed dynamically iterated filters, is presented. Contrary to regular iterated filters such as the iterated extended Kalman filter (IEKF), iterated unscented Kalman filter (IUKF) and iterated posterior linearization filter (IPLF), dynamically iterated filters also take nonlinearities in the transition model into account. The general filtering algorithm is shown to essentially be a (locally over one time step) iterated Rauch-Tung-Striebel smoother. Three distinct versions of the dynamically iterated filters are especially investigated: analogues to the IEKF, IUKF and IPLF. The developed algorithms are evaluated on 25 different noise configurations of a tracking problem with a nonlinear transition model and linear measurement model, a scenario where conventional iterated filters are not useful. Even in this “simple” scenario, the dynamically iterated filters are shown to have superior root mean-squared error performance as compared with their respective baselines, the EKF and UKF. Particularly, even though the EKF diverges in 22 out of 25 configurations, the dynamically iterated EKF remains stable in 20 out of 25 scenarios, only diverging under high noise.

Human-wildlife conflicts are a global problem which is central to the Global Goal 15 (life on land). One particular case is elephants, that can cause harm to both people, property and crops. An early warning system that can detect and warn people in time would allow effective mitigation measures. The proposed method is based on a small local network of geophones that sense the seismic waves of elephant footsteps. It is known that elephant footsteps induce low frequency ground waves that can be picked up by geophones in the ground. First, a method is described that detect the particular signature of such footsteps, and then the detections are used to estimate the direction of arrival (DOA). Finally, a Kalman filter is applied to the measurements in order to track the elephant. Field tests performed at a local zoo shows promising results with accurate DOA estimates at 15 meters distance and acceptable accuracy at 40 meters.

Conventional direction of arrival (DOA) estimators are based on array processing using either time differences or beam-forming. The proposed approach is based on the received power at each microphone, which enables simple hardware, low sampling frequency and small arrays. The problem is recast into a linear regression framework where the least squares method applies, and the main drawback is that different sound sources are not readily separable.Our proposed approach is based on a training phase where the directional sensitivity of each microphone element is estimated. This model is then used as a fingerprint of the observed power vector in a real-time estimator. The learned power vector is here modeled by a Fourier series expansion, which enables Cramér-Rao lower bound computations. We demonstrate the performance using a circular array with eight microphones with promising results.

A framework for tightly integrated motion mode classification and state estimation in motion-constrained inertial navigation systems is presented. The framework uses a jump Markov model to describe the navigation system’s motion mode and navigation state dynamics with a single model. A bank of Kalman filters is then used for joint inference of the navigation state and the motion mode. A method for learning unknown parameters in the jump Markov model, such as the motion mode transition probabilities, is also presented. The application of the proposed framework is illustrated via two examples. The first example is a foot-mounted navigation system that adapts its behavior to different gait speeds. The second example is a foot-mounted navigation system that detects when the user walks on flat ground and locks the vertical position estimate accordingly. Both examples show that the proposed framework provides significantly better position accuracy than a standard zero-velocity aided inertial navigation system. More importantly, the examples show that the proposed framework provides a theoretically well-grounded approach for developing new motion-constrained inertial navigation systems that can learn different motion patterns.

This letter investigates relationships between iterated filtering algorithms based on statistical linearization, such as the iterated unscented Kalman filter (iukf), and filtering algorithms based on quasi–Newton (qn) methods, such as theqniterated extended Kalman filter (qn–iekf). Firstly, it is shown that theiukfand the iterated posterior linearization filter (iplf) can be viewed asqnalgorithms, by finding a Hessian correction in theqn–iekfsuch that theiplfiterate updates are identical to that of theqn–iekf. Secondly, it is shown that theiplf/iukfupdate can be rewritten such that it is approximately identical to theqn–iekf, albeit for an additional correction term. This enables a richer understanding of the properties of iterated filtering algorithms based on statistical linearization.

Robust and highly accurate position estimation in underground mines is investigated by considering a vehicle equipped with 2D laser scanners. A survey of available methods to process data from such sensors is performed with focus on feature extraction methods. Pros and cons of the usage of different methods for the positioning application with 2D laser data are highlighted, and suitable methods are identified. Three state-of-the-art feature extraction methods are adapted to the scenario of positioning in a predefined map and the methods are evaluated through experiments conducted in a simulated underground mine environment. Results indicate that feature extraction methods perform in parity with the method of matching each ray individually to the map, and better than the point cloud scan matching method of a pure ICP, assuming a highly accurate map is available. Furthermore, experiments show that feature extraction methods more robustly handle imperfections or regions of errors in the map by automatically disregarding these regions. Note to Practitioners—Robust positioning in GNSS denied environments is a complex and real problem experienced by practitioners in many fields. The focus of this paper is underground mining; however, the findings and discussions have bearing on many other applications where reliable GNSS is unavailable and lidar is used for positioning in unstructured environments. An important take home message, of interest in any application with dynamically changing environments, is that by using feature extraction map errors are automatically handled as features are simply not matched in erroneous regions. The experiments in this paper are performed with a realistic laser simulation model trained on real data; hence the gap between simulation and reality is relatively small ensuring the results are relevant for practical purposes. In general, the feature extraction methods are sensitive to the parameter settings, and would have to be properly tuned for the specific application. Furthermore, the computational complexity, which is only mentioned briefly in this paper, varies a lot between the methods and has to be investigated further to ensure meeting real-time requirements. Since the feature extraction methods robustly handle errors in the map, future research will explore how this can be used to enable automatic updates of the map.

The increase in perception capabilities of connected mobile sensor platforms (e.g., self-driving vehicles, drones, and robots) leads to an extensive surge of sensed features at various temporal and spatial scales. Beyond their traditional use for safe operation, available observations could enable to see how and where people move on sidewalks and cycle paths, to eventually obtain a complete microscopic and macroscopic picture of the traffic flows in a larger area. This paper proposes a new method for advanced traffic applications, tracking an unknown and varying number of moving targets (e.g., pedestrians or cyclists) constrained by a road network, using mobile (e.g., vehicles) spatially distributed sensor platforms. The key contribution in this paper is to introduce the concept of network bound targets into the multi-target tracking problem, and hence to derive a network-constrained multi-hypotheses tracker (NC-MHT) to fully utilize the available road information. This is done by introducing a target representation, comprising a traditional target tracking representation and a discrete component placing the target on a given segment in the network. A simulation study shows that the method performs well in comparison to the standard MHT filter in free space. Results particularly highlight network-constraint effects for more efficient target predictions over extended periods of time, and in the simplification of the measurement association process, as compared to not utilizing a network structure. This theoretical work also directs attention to latent privacy concerns for potential applications.

An optical flow inspired magnetic-field based odometry estimation process is presented. The estimation process is based upon taking "image" like measurements of the magnetic-field using a magnetometer array. From the measurements a model of the local field is learned. Using the learned model the pose change that gives the smallest prediction error of the measurement at the next time instant is calculated. Two models for describing the magnetic-field are presented, and the performance of the odometry estimation process when using the two models is evaluated. The evaluation shows that at a high signal-to-noise ratio the pose change can be estimated with an error of only a few percentage of the true pose change. Further, the evaluation shows that the uncertainty of the estimate can be consistently estimated. Thus, the proposed odometry estimation process can be used to reduce the navigation error growth rate of, for example, inertial navigation systems by providing reliable odometry information when passing by magnetized objects.

We consider a decentralized sensor network of multiple nodes with limited communication capability where the cross-correlations between local estimates are unknown. To reduce the bandwidth the individual nodes determine which subset of local information is the most valuable from a global perspective. Three information selection methods (ISM) are derived. The proposed ISM require no other information than the communicated estimates. The simulation evaluation shows that by using the proposed ISM it is possible to determine which subset of local information is globally most valuable such that both reduced bandwidth and high performance are achieved.

An inference method for Gaussian process augmented state-space models are presented. This class of grey-box models enables domain knowledge to be incorporated in the inference process to guarantee a minimum of performance, still they are flexible enough to permit learning of partially unknown model dynamics and inputs. To facilitate online (recursive) inference of the model a sparse approximation of the Gaussian process based upon inducing points is presented. To illustrate the application of the model and the inference method, an example where it is used to track the position and learn the behavior of a set of cars passing through an intersection, is presented. Compared to the case when only the state-space model is used, the use of the augmented state-space model gives both a reduced estimation error and bias.

Lidar-based positioning in a 2D map is analyzed as a method to provide a robust, high accuracy, and infrastructure-free positioning system required by the automation development in underground mining. Expressions are derived that highlight separate information contributions to the obtained position accuracy. This is used to develop two new methods that efficiently select which subset of available lidar rays to use to reduce the computational complexity and allow for online processing with minimal loss of accuracy. The results are verified in simulations of a mid-articulated underground loader in a mine. The methods are shown to be able to reduce the number of rays needed without considerably affecting the performance, and to be competitive with currently used methods. Furthermore, simulations highlight the effects of errors in the map and other map properties, and how imperfect maps degrades the performance of different selection strategies.

An approach for belief space planning is presented, where knowledge about the landmark density is used as prior, instead of explicit landmark positions. Having detailed maps of landmark positions in a previously unvisited environment is considered unlikely in practice. Instead, it is argued that landmark densities should be used, as they could be estimated from other sources, such as ordinary maps or aerial imagery. It is shown that it is possible to use virtual landmarks to approximate the landmark density to solve the presented problem. This approximation is also shown to give small errors during evaluation. The approach is tested in a simulated environment, in conjunction with an extended information filter (EIF), where the computed path is shown to be superior compared to other alternative paths used as benchmarks.

Modern maritime navigation is heavily dependent on satellite systems. Availability of an accurate position is critical for safe operations, but satellite-based navigation systems are vulnerable to interference, jamming, and spoofing. In this work, we propose a method for maritime navigation independent of GNSS, able to provide absolute positioning of the vessel based on marine radar scans. A measurement model is presented where a Digital Elevation Model is used to predict the output of a marine radar, given a hypothetical position. The model, as used by an on-line particle filter, is used to track the movements of a ship from real recorded data. This demonstrates the feasibility of this method for robust positioning, without the need of external positioning signals, in a maritime environment. The tracking only uses sensors commonly available on maritime vessels, and demonstrates its application using freely available elevation data.

A wearable microphone array platform is used to localize stationary sound sources and amplify the sound in the desired directions using several beamforming methods. The platform is equipped with inertial sensors and a magnetometer allowing predictions of source locations during orientation changes and compensation for the displacement in the array configuration. The platform is modular, open and 3D printed to allow for easy reconfiguration of the array and for reuse in other applications, e.g., mobile robotics. The software components are based on open source. A new method for source localization and signal reconstruction using Taylor expansion of the signals is proposed. This and various standard and non-standard Direction of Arrival (DOA) methods are evaluated in simulation and experiments with the platform to track and reconstruct multiple and single sources. Results show that sound sources can be localized and tracked robustly and accurately while rotating the platform and that the proposed method outperforms standard methods at reconstructing the signals.

The number of sensors used in tracking scenarios is constantly increasing, this puts high demands on the tracking methods to handle these data streams. Central processing (ideally optimal) puts high demands on the central node, is sensitive to inaccurate sensor parameters, and suffers from the single point of failure problem. Decentralizing the tracking can improve this, but may give considerable performance loss. The newly presented inverse covariance intersection method, proven to be consistent, even under unknown track cross-correlations, is benchmarked against alternatives. Different track-to-track methods, including smoothed association over a window, are compared. A scenario with objects tracked in multiple cameras, not necessarily optimized for tracking, are used to give realism to the evaluations.

A model-based method to perform odometry using an array of magnetometers that sense variations in a local magnetic field is presented. The method requires no prior knowledge of the magnetic field, nor does it compile any map of it. Assuming that the local variations in the magnetic field can be described by a curl and divergence free polynomial model, a maximum likelihood estimator is derived. To gain insight into the array design criteria and the achievable estimation performance, the identifiability conditions of the estimation problem are analyzed and the Cramér-Rao bound for the one-dimensional case is derived. The analysis shows that with a second-order model it is sufficient to have six magnetometer triads in a plane to obtain local identifiability. Further, the Cramér-Rao bound shows that the estimation error is inversely proportional to the ratio between the rate of change of the magnetic field and the noise variance, as well as the length scale of the array. The performance of the proposed estimator is evaluated using real-world data. The results show that, when there are sufficient variations in the magnetic field, the estimation error is of the order of a few percent of the displacement. The method also outperforms current state-of-the-art method for magnetic odometry.

Acoustic frequency tracking of a harmonic signal with continuously varying frequency is considered. The Rao-Blackwellized point mass filter (RBPMF), previously proposed by the authors for mechanical vibration tracking, is applied to the problem. The RBPMF is compared with two periodogram-based methods, and the similarities and differences between them are explained. Both experimental and simulation results in a Doppler frequency tracking scenario are presented, and the results show that the RBPMF can have significantly less estimation error than the competing methods.

In polar region operations, drift ice positioning and tracking is useful for both scientific and safety reasons. At its core is a Multi-Target Tracking (MTT) problem in which currents and winds make motion modeling difficult. One recent algorithm in the MTT field, employed in this paper, is the Labeled Multi-Bernoulli (LMB) filter. In particular, a proposed reformulation of the LMB equations exposes a structure which is exploited to propose a compact algorithm for the generation of the filter's posterior distribution. Further, spatial indexing is applied to the clustering process of the filter, allowing efficient separation of the filter into smaller, independent parts with lesser total complexity than that of an unclustered filter. Many types of sensors can be employed to generate detections of sea ice, and in this paper a recorded dataset from a Terrestrial Radar Interferometer (TRI) is used to demonstrate the application of the Spatially Indexed Labeled Multi-Bernoulli filter to estimate the currents of an observed area in Kongsfjorden, Svalbard.

Classification of motion mode (walking, running, standing still) and device mode (hand-held, in pocket, in backpack) is an enabler in personal navigation systems for the purpose of saving energy and design parameter settings and also for its own sake. Our main contribution is to publish one of the most extensive datasets for this problem, including inertial data from eight users, each one performing three pre-defined trajectories carrying four smartphones and seventeen inertial measurement units on the body. All kind of metadata is available such as the ground truth of all modes and position. A second contribution is the first study on a joint classifier of motion and device mode, respectively, where preliminary but promising results are presented.

Many positioning systems rely on accurate time of arrival measurements. In this paper, we address not only the accuracy but also the relevance of Time of Arrival (TOA) measurement error modeling. We discuss how better knowledge of these errors can improve relative distance estimation, and compare the impact of differently detailed measurement error information. These models are compared in simulations based on models derived from an Ultra Wideband (UWB) measurement campaign. The conclusion is that significant improvements can be made without providing detailed received signal information but with a generic and relevant measurement error model.

Narrowband Internet of Things (NB-IoT) is an emerging cellular technology designed to target low-cost devices, high coverage, long device battery life (more than ten years), and massive capacity. We investigate opportunities for device tracking in NB-IoT systems using Observed Time Difference of Arrival (OTDOA) measurements. Reference Signal Time Difference (RSTD) reports are simulated to be sent to the mobile location center periodically or on an on-demand basis. We investigate the possibility of optimizing the number of reports per minute budget on horizontal positioning accuracy using an on-demand reporting method based on the Signal to Noise Ratio (SNR) of the measured cells received by the User Equipment (UE). Wireless channels are modeled considering multipath fading propagation conditions. Extended Pedestrian A (EPA) and Extended Typical Urban (ETU) delay profiles corresponding to low and high delay spread environments, respectively, are simulated for this purpose. To increase the robustness of the filtering method, measurement noise outliers are detected using confidence bounds estimated from filter innovations.

The speed of a wheeled vehicle is usually estimated using wheel speed sensors (WSS) or GPS. If these signals are unavailable, other methods must be used. We propose a novel approach exploiting the fact that vibrations from rotating axles, with fundamental frequency proportional to vehicle speed, are transmitted via the vehicle chassis. Using an accelerometer, these vibrations can be tracked to estimate vehicle speed while other sources of vibrations act as disturbances. A state-space model for the dynamics of the harmonics is presented and formulated such that there is a conditional linear-Gaussian substructure, enabling efficient Rao-Blackwellized methods. A variant of the Rao-Blackwellized point-mass filter is derived, significantly reducing computational complexity, and reducing the memory requirements from quadratic to linear in the number of grid points. It is applied to experimental data from the sensor cluster of a car and validated using the rotational frequency from WSS data. The proposed method shows improved performance and robustness in comparison to a Rao-Blackwellized particle filter implementation and a frequency spectrum maximization method.

The commercial interest in proximity services is increasing. Application examples include location- based information and advertisements, logistics, social networking, file sharing, etc. In this paper, we consider network-based positioning based on times series of proximity reports from a mobile device, either only a proximity indicator, or a vector of RSS from observed nodes. Such positioning corresponds to a latent and nonlinear observation model. To address these problems, we combine two powerful tools, namely particle filtering and Gaussian process regression (GPR) for radio signal propagation modeling. The latter also provides some insights into the spatial correlation of the radio propagation in the considered area. Radio propagation modeling and positioning performance are evaluated in a typical office area with Bluetooth-Low-Energy (BLE) beacons deployed for proximity detection and reports. Results show that the positioning accuracy can be improved by using GPR.

This paper presents a method for global pose estimation using inertial sensors, monocular vision, and ultra wide band (UWB) sensors. It is demonstrated that the complementary characteristics of these sensors can be exploited to provide improved global pose estimates, without requiring the introduction of any visible infrastructure, such as fiducial markers. Instead, natural landmarks are jointly estimated with the pose of the platform using a simultaneous localization and mapping framework, supported by a small number of easy-to-hide UWB beacons with known positions. The method is evaluated with data from a controlled indoor experiment with high precision ground truth. The results show the benefit of the suggested sensor combination and suggest directions for further work.

Micro-Doppler radar signatures have great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data need to be complemented with accurate simulated radar data to be able to successfully design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment, any desired scenario may be generated. Motion capture (MOCAP) has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of MOCAP data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source ofa prioriknowledge to improve the classification performance of real radar data, particularly in the case when the total amount of data is small.

This paper presents a system which combines a zero-velocity-update-(ZUPT-)aided inertial navigation system (INS), using a foot-mounted inertial measurement unit (IMU), with opportunistic use of multi-frequency received signal strength (RSS) measurements. The system does not rely on maps or pre-collected data from surveys of the radio-frequency (RF) environment. Instead it builds its own database of collected RSS measurements during the course of the operation. New RSS measurements are continuously compared with the stored values in the database, and when the user returns to a previously visited area this can thus be detected. This enables loop-closures to be detected online and used for error drift correction. The system utilises a distributed particle simultaneous localization and mapping (DP-SLAM) algorithm which provides a flexible 2D navigation platform that can be extended with more sensors. The experimental results presented in this paper indicates that the developed RSS SLAM algorithm can, in many cases, significantly improve the positioning performance of a foot-mounted INS.

In the context of a smart user assistance system for industrial manipulation tasks it is necessary to capture motions of the upper body and limbs of the worker in order to derive his or her interactions with the task space. While such capturing technology already exists, the novelty of the proposed work results from the strong requirements of the application context: The method should be flexible and use only on-body sensors, work accurately in industrial environments that suffer from severe magnetic disturbances, and enable consistent registration between the user body frame and the task space. Currently available systems cannot provide this. This paper suggests a novel egocentric solution for visual-inertial upper-body motion tracking based on recursive filtering and model-based sensor fusion. Visual detections of the wrists in the images of a chest-mounted camera are used as substitute for the commonly used magnetometer measurements. The on-body sensor network, the motion capturing system, and the required calibration procedure are described and successful operation is shown in a real industrial environment.

This paper addresses two fundamental requirements of full body motion monitoring: (a) the ability to sense the input of the user and (b) the means to interpret the captured input. Appropriate technology in both areas is required for an interactive virtual reality system to provide feedback in a useful and natural way. This paper combines technologies for both areas: It develops a sensor fusion approach for capturing user input based on miniature on-body inertial and magnetic motion sensors. Furthermore, it presents work in progress to automatically generate models for motion patterns from the captured input. The technology is then used and evaluated in the context of a personalized virtual rehabilitation trainer application.

Visual simultaneous localisation and mapping (SLAM) is since the last decades an often addressed problem. Online mapping enables tracking in unknown environments. However, it also suffers from high computational complexity and potential drift. Moreover, in augmented reality applications the map itself is often not needed and the target environment is partially known, e.g. in a few 3D anchor or marker points. In this paper, rather than using SLAM, measurements based on optical flow are introduced. With these measurements, a modified visual-inertial tracking method is derived, which in Monte Carlo simulations reduces the need for 3D points and allows tracking for extended periods of time without any 3D point registrations.

A class of nonlinear transformation-based filters (NLTF) for state estimation is proposed. The nonlinear transformations that can be used include first (TT1) and second (TT2) order Taylor expansions, the unscented transformation (UT), and the Monte Carlo transformation (MCT) approximation. The unscented Kalman filter (UKF) is by construction a special case, but also nonstandard implementations of the Kalman filter (KF) and the extended Kalman filter (EKF) are included, where there are no explicit Riccati equations. The theoretical properties of these mappings are important for the performance of the NLTF. TT2 does by definition take care of the bias and covariance of the second order term that is neglected in the TT1 based EKF. The UT computes this bias term accurately, but the covariance is correct only for scalar state vectors. This result is demonstrated with a simple example and a general theorem, which explicitly shows the difference between TT1, TT2, UT, and MCT.