Location information is one of the most vital information required to achieve intelligence and context-awareness for Internet of Things applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on the use global navigation satellite system (GNSS) for autonomous navigation and related applications, it is important to provide robust navigation solutions. Radio frequency interference, either intentional or unintentional, has a direct impact on GNSS navigation performance related to observability and accuracy. In terms of security, spoofing is the major issue of concern. This paper focuses on multiantenna GNSS and inertial navigation system (INS)-odometer integration to improve robustness, security, and privacy of navigation solutions. Multiantenna GNSS provides robustness against different interference sources and integration with INS provides continuous navigation solutions during short-term signal outages. Performance of the proposed architecture is evaluated using different user scenarios in the presence of spoofing and interference signals in real vehicular environments.

GNSS-dependent positioning, navigation, and timing synchronization procedures have a significant impact on everyday life. Thus, such an extensively used system progressively become an attractive target for illegal exploitation and attacks. Position and timing solutions provided by GNSS receivers can be threatened by structural interference such as spoofing threats. This paper provides an overview of recent research work on GNSS signal authentication utilizing spatial processing methods. Different spatial processing approaches for spoofing detection, classification and mitigation are characterized and compared. Three different processing methods, namely antenna array processing, moving receiver and cloud based spoofing countermeasure are analyzed in details. The benefits and disadvantages of each are discussed.

Intermittent tracking or cyclic tracking is a common method used in modern Global Navigation Satellite System (GNSS) receivers to conserve power. It is critical for receivers in battery-operated devices like wearables, smartphones and handheld devices. These devices are often used in degraded or weak signal environments such as indoors. This paper discusses the challenges for intermittent signal tracking in weak signal scenarios and quantifies the achievable position accuracy. Issues for cyclic tracking in signal challenged environments are shown in the test results. Finally, a possible solution is proposed that can alleviate these effects.

With advances in microelectromechanical system (MEMS) technology, many modern personal navigation devices incorporate measurements from various low-cost sensors alongside Global Navigation Satellite Systems (GNSS) receivers. However, both GNSS and other low-cost sensors are prone to the occurrence of faults that are either un-modeled or poorly modeled. This affects the usability of such personal navigation devices in some applications where reliability is a critical parameter. This paper thus presents several algorithms to either detect and remove such faults or model them properly in order to improve performance, especially reliability, for low cost multi-sensor integrated navigation systems. The algorithms presented in this paper can be broadly categorized into two parts. The first part focuses on optimizing the use of GNSS measurements in harsh environments. This is done by replacing the assumption of normal distribution of GNSS measurements with that of a heavy-tailed distribution. Moreover, the covariance of such distribution is also adapted to match the true error characteristics of the surrounding environment with the aid of inertial units. The second part of the algorithm detects possible faults arising in various sensors. Based on the type of sensor fault, the algorithm either rejects some of the measurements before they enter the integration filter, issues a warning signal to indicate lack of reliability information or deems the navigation solution unusable. The analyses of the proposed algorithms showed that faults were detected successfully and that the performance of the navigation system was improved in terms of both reliability and accuracy.

New location algorithms using Wi-Fi or/and magnetometer sensors are proposed considering the orientation impact on the measurements. The feasibility of magnetometer alone fingerprint positioning and orientation inference is also assessed with real indoor data. Android smart devices with low cost sensors are used to build up database along with a Gaussian Processes Regression (GPR) model and to collect independent track test to validate results. The corresponding performance of various solutions such as Wi-Fi alone, magnetometer alone and the magnetometer-aided Wi-Fi are compared. The effects of user's orientation on Wi-Fi signal strength, sensed magnetic fields and overall positioning results in real indoor office-like scenarios are also assessed and investigated.

This paper discusses the fusion schemes of multiple sensors including accelerometer, gyro, magnetometer and pressure sensor for improved indoor positioning accuracy. The main focus is to make fully use of complementary features in sensors (such as gyro and magnetometer) and navigation structure (such as PDR and strapdown INS). A centralized Kalman filter is designed and implemented for this integration. Tests are conducted at indoor without using external wireless signal aiding. Positioning performance is evaluated among different integration strategies by different sensors and different complementary features.

Several terrestrial communication systems use the same frequency spectrum as Global Navigation Satellite Systems (GNSS) or bands close to it. Pseudolites constitute one such system that generally uses the GNSS spectrum for signal propagation. The signals from pseudolites in the operating region play a crucial role in augmenting satellite based user navigation. These signal transmitters are deployed at ground stations for the majority of their applications. In the near region, pseudolites are overpowered and cause impediments to normal receiver operation due to an increase in the noise floor. Interference and jammers form another source of GNSS disruption. Interference could result from intentional jammers or unintentional signal disturbances from in-band or out-of-band high power sources. A GNSS user can experience interference from near region pseudolites as well as from other interferers. These interference sources may have directional coexistence with pseudolites or GNSS satellites. Such disruptions might completely block signal acquisition and lead to processing failure. The capability of antenna array space-time processing to counter the above mentioned scenario is demonstrated herein. In addition to interference mitigation, pseudolite signal recovery in the near region and subsequent enhancements are shown. Results that demonstrate improvement in measurement geometry for various antenna array configurations and different processing modes are also shown.

Foot mounted navigation systems can be deployed for tracking military personnel, first responders and offenders. Determining the activity and environment of individuals can provide valuable information to those monitoring these individuals. This paper provides activity and environment classification for a foot mounted device that uses an IMU and GPS receiver. Using information from the navigation filter (e.g. velocity), GPS signal tracking parameters, and IMU measurements this paper presents an algorithm that classifies the following activities: indoor, outdoor, stationary, crawling, walking, running, biking, moving in vehicle, level, up or down elevator and up or down stairs. Multiple probability density functions that map each feature (i.e. metric) to an activity are provided. Then a naive Bayesian probabilistic model is used to determine the probability of an activity. To improve reliability and accuracy of the classification several conditions are added. The algorithm shows excellent results for activity classification, however environment classification is less reliable due to variations in GPS tracking abilities as a function of the environment. Results are shown with images from the data collection.

Accurate positioning of first responders, electronic monitoring, and military personnel is often critical in GNSS denied environments. In such environments, inertial navigation systems (INS) are typically the preferred tool to be used for navigation. However, the gyros suffer from errors including biases, scale factors and g-dependent errors being the most significant ones. In order to sustain an accurate navigation solution for long durations, the gyroscope errors have to be measured and mitigated. Ideally, this calibration is done in situ. The attitude obtained using visual information is independent of the errors affecting the gyroscope. Human-made environments are commonly full of straight and parallel lines found in orthogonal directions. Perspective projection mapping transforms three-dimensional scenes into two-dimensional images. The process maintains the straight lines but modifies their parallelism resulting in an apparent point intersection of the lines. This point is called the vanishing point. Lines in three orthogonal directions constitute three vanishing points. The vanishing point locations are dependent on the camera rotation, but not camera translation. By monitoring the motion of the vanishing point locations in consecutive images, the relative roll, heading and pitch attitudes may be obtained. The absolute attitude, known from some a priori knowledge of the building layout, is then used to update the inertial navigation filter. Over time the visual measurements mitigate the cumulative errors of the gyro bias, scale factor and g-dependent bias. The performance of the vision-aided INS based navigation approach is evaluated herein. A camera is attached to a backpack and foot of a user moving through typical pedestrian based environments. The case of a foot-mounted camera is unique because of the high accelerations experienced during the human gait. The visual-aiding correction is found to significantly improve the attitude accuracy, especially the heading. Using the body solution, namely the camera and INS attached to a backpack, the vision-aiding yielded a 93 % improvement in the heading error during evaluation tests. With a foot-mounted solution, namely the INS and camera attached to the ankle of the user, the horizontal position error decreased by 34 %.

Heading estimation plays an important role in pedestrian navigation applications. With the advent of smart-phones equipped with MEMS sensors, it has become possible to utilize ones orientation information along with location. This combination has allowed researchers to investigate provisioning users with orientation aware location based services as well as seamless navigation in different environments using Pedestrian Dead Reckoning (PDR) techniques. Although gyroscopes are considered to be the primary sensors for orientation estimation, the errors associated with these sensors require periodic updates from other sources. In case of small hand held devices, these other sources are accelerometers for roll and pitch estimates and magnetic field sensors for the heading. In order to utilize the magnetic field sensors for heading estimation with respect to some known reference, it is desirable to measure only the Earth's magnetic field components. Although this is achievable in the outdoors, presence of manmade infrastructure in all kinds of urban environments makes it impossible to sense only the Earth's magnetic field at all times. These manmade magnetic anomalies caused by electronic devices, ferrous materials, mechanical and electrical infrastructures among others are the main culprits contaminating the magnetic field information. Therefore it is desirable to investigate how good one can estimate the heading using magnetic field alone in different pedestrian navigation environments by isolating the perturbed regions from the clean ones. In this paper, a detector is proposed that can identify the magnetic field measurements that can be used for estimating heading with adequate accuracy. The expected errors in the heading estimates are also output based on the test statistics, which allow the proposed detector to be utilized for sensor fusion and estimation of errors associated with gyroscopes. Real world data is acquired using a custom designed consumer grade sensor platform and a high accuracy reference system. Theoretical analysis and experimental results show that the proposed detector is capable of identifying the effects of perturbations on the Earth's magnetic field, which provides users with a better estimate of magnetic heading in different pedestrian navigation environments.

In this paper, an algorithm for the detection of quasi-static instants (QS) from handheld MEMS devices is presented. In order to tune the detector according to the variety of motions that the hand can perform, a decision tree classifier, able to recognize activities typical for mobile phone users, such as phoning, texting, walking with swinging hand or carrying the device in a bag, has been designed and implemented. Performances of the proposed detector of QS epochs and of the motion mode classifier are assessed with experimental data collected with several individuals. In addition, the relationship between QS instants and human gait is investigated. Specifically, the use of QS instants for the detection of the user's step is analyzed.

Due to the capability of GPS to provide an accurate and stable long-term reference, the use of a GPS receiver as an acceleration sensor has gained an increasing research interest. GPS-derived acceleration information is determined using Doppler rate measurements that are directly estimated from the output of a Frequency Lock Loop (FLL) or obtained by double-differentiating the carrier phase observations produced by a Phase Lock Loop (PLL). This paper presents a complete theoretical model allowing the evaluation of the Doppler rate accuracy and relating the variance of Doppler rate measurements to the Carrier-to-Noise density power ratio (C/N0) and the receiver architecture. Both standard and High Sensitivity (HS) receiver architectures are considered and the developed theoretical model is validated using live GPS data. Empirical results are compared against the developed theoretical model and, in all cases, a good agreement between empirical and theoretical results is observed.

This paper describes a hybrid AOA/TODA mobile station (MS) location estimation method based on the CDMA wireless communications signals. The method utilized estimates the angle of arrival (AOA) and time difference of arrival (TDOA) of downlink pilot channel. In this paper, signal parameter estimations including AOA and TDOA, and position estimation challenges in real wireless communications systems are denoted. For position estimation, the mathematical model and its linearized model for a hybrid TDOA/AOA method based on the Taylor series expansion are derived. The performance analysis of the high-resolution MUSIC angle and double delta delay estimators using field measurements from a typical outdoor IS-95 CDMA radio propagation channel are demonstrated. In this paper, practical considerations on the implementation of a standalone positioning system are described. The performance of TDOA and hybrid AOA/TDOA positioning system when the receiver has access to minimum LOS signals are compared.

In this paper, a new method for AOA estimation, based on the synthetic ESPRIT algorithm is described and tested with actual CDMA signals. Physical size constraints of handheld devices typically exclude the possibility of using multiple antennas. To overcome this limitation, antenna array can be synthesized by physically moving a single antenna in an arbitrary trajectory while collecting a snapshot of the received signal. By taking advantage of the rotational invariance of the ESPRIT algorithm, antenna array can be synthesized by moving a single doublet without using controllable moving motors or external aiding sensors. This configuration can be installed in any translationally rotational moving platforms such as vehicles. With this configuration, system complexity reduces considerably. Experimental and field test results of AOA estimation of synthetic ESPRIT algorithm based on actual CDMA IS-95 signals represents applicability of proposed method.

The accuracy of time of arrival (TOA) measurements is significantly compromised due to the multipath components which coexist with the desired line of sight (LOS) components in the received signal. Effective mitigation of multipath is offered by using an antenna array with beamforming capabilities. However, arrays are physically large and unwieldy from a signal processing perspective and hence not suitable for a small handheld device. In this paper, a receiver consisting of a single antenna that is moved arbitrarily in space forming a synthetic antenna is considered. Blind calibration of the resultant spatial array is achieved through the use of fourth order cummulants. As demonstrated in this paper, a synthetic array, based on this blind calibration method can practically enhance the LOS signal by suppressing multipath through spatial filtering and thereby improve the TOA performance. Monte Carlo simulations are performed to demonstrate the performance of the proposed method.

This paper introduces the main modulation improvements brought by the future global navigation satellite systems (GNSSs) compared to the currently available GPS C/A signal. Its impact on the code and phase tracking performance is illustrated and its implication on the design of these two tracking loop design is analyzed.

An enhanced two-step least squared (LS) approach is proposed here for hybrid time difference of arrival/angle of arrival (TDOA/AOA) wireless location. Compared to the original two-step LS algorithm, the method here can provide better performance, almost the same as that of a Taylor-series estimator. Compared to the Taylor-series solution, the method has many computational advantages, such as, light computational burden, no linearization, no initial point selection issue, and no convergence issue.