This paper aims to provide the reader with a review of the main technologies explored in the literature to solve the indoor localization issue. Furthermore, some systems that use these enabling technologies in real-world scenarios are presented and discussed. This could deliver a better understanding of the state-of-the-art and motivate new research efforts in this promising field. Finally, focusing on one of the major challenges in the indoor localization field, i.e., the indoor animal tracking, existing indoor tracking systems have been reviewed and compared by analyzing advantages and drawbacks.

A survey on indoor positioning systems

One of the most important tasks in integration of GPS/INS is to choose the realistic dynamic model covariance matrix Q and measurement noise covariance matrix R for use in the Kalman filter. The performance of the methods to estimate both of these matrices depends entirely on the minimization of dynamic and measurement update errors that lead the filter to converge. This paper evaluates the performances of adaptive Kalman filter methods with different adaptations. Innovation and residual based adaptive Kalman filters were employed for adapting R and Q. These methods were implemented in a loose GPS/INS integration system and tested using real data sets. Their performances have been evaluated and compared. Their limitations in real-life engineering applications are discussed.

Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration

Accurate estimation of the dynamic states of a synchronous machine (e.g., rotor's angle and speed) is essential in monitoring and controlling transient stability of a power system. It is well known that the covariance matrixes of process noise (Q) and measurement noise (R) have a significant impact on the Kalman filter's performance in estimating dynamic states. The conventional ad-hoc approaches for estimating the covariance matrixes are not adequate in achieving the best filtering performance. To address this problem, this paper proposes an adaptive filtering approach to adaptively estimate Q and R based on innovation and residual to improve the dynamic state estimation accuracy of the extended Kalman filter (EKF). It is shown through the simulation on the two-area model that the proposed estimation method is more robust against the initial errors in Q and R than the conventional method in estimating the dynamic states of a synchronous machine.

Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation

To solve the problem of unknown noise covariance matrices inherent in the cooperative localization of autonomous underwater vehicles, a new adaptive extended Kalman filter is proposed. The predicted error covariance matrix and measurement noise covariance matrix are adaptively estimated based on an online expectation-maximization approach. Experimental results illustrate that, under the circumstances that are detailed in the paper, the proposed algorithm has better localization accuracy than existing state-of-the-art algorithms.

A New Adaptive Extended Kalman Filter for Cooperative Localization

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

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A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

The central task of GPS/INS integration is to effectively blend GPS and INS data together to generate an optimal solution. The present data fusion algorithms, which are mostly based on Kalman filtering (KF), have several limitations. One of those limitations is the stringent requirement on precise a priori knowledge of the system models and noise properties. Uncertainty in the covariance parameters of the process noise (Q) and the observation errors (R) may significantly degrade the filtering performance. The conventional way of determining Q and R relies on intensive analysis of empirical data. However, the noise levels may change in different applications. Over the past few decades adaptive KF algorithms have been intensively investigated with a view to reducing the influence of the Q and R definition errors. The covariance matching method has been shown to be one of the most promising techniques. This paper first investigates the utilization of an online stochastic modelling algorithm with regards to its parameter estimation stability, convergence, optimal window size, and the interaction between Q and R estimations. Then a new adaptive process noise scaling algorithm is proposed. Without artificial or empirical parameters being used, the proposed adaptive mechanism has demonstrated the capability of autonomously tuning the process noise covariance to the optimal magnitude, and hence improving the overall filtering performance.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0373463307004316

Improving Adaptive Kalman Estimation in GPS/INS Integration

A scrutiny of the existing time differencing carrier phase (TDCP) velocity estimation algorithms has revealed several shortcomings that could be further improved. One of these is that the velocity estimation at epoch t would require the receiver positions at epoch t+Δt or more are available. Another is the usage of satellite velocities in the calculations which would increase the receiver computational load and degrade the accuracy. In this paper, an improved TDCP velocity estimation approach has been proposed and tested. The new approach depends only on receiver position at epoch t and satellite positions at epoch t and t+Δt. Satellite velocities are not required in this calculation. This proposed algorithm has been validated using static and kinematic field test data, showing that equivalent velocity accuracy achievable by using differential GPS techniques can be made possible with the proposed standalone GPS method.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0373463310000482

Precise Velocity Estimation with a Stand-Alone GPS Receiver

The necessity for the precise time synchronization of measurement data from multiple sensors is widely recognized in the field of global positioning system/inertial navigation system (GPS/INS) integration. Having precise time synchronization is critical for achieving high data fusion performance. The limitations and advantages of various time synchronization scenarios and existing solutions are investigated in this paper. A criterion for evaluating synchronization accuracy requirements is derived on the basis of a comparison of the Kalman filter innovation series and the platform dynamics. An innovative time synchronization solution using a counter and two latching registers is proposed. The proposed solution has been implemented with off-the-shelf components and tested. The resolution and accuracy analysis shows that the proposed solution can achieve a time synchronization accuracy of 0.1 ms if INS can provide a hard-wired timing signal. A synchronization accuracy of 2 ms was achieved when the test system was used to synchronize a low-grade micro-electromechanical inertial measurement unit (IMU), which has only an RS-232 data output interface.

https://onlinelibrary.wiley.com/doi/pdf/10.4218/etrij.08.0106.0306

Time Synchronization Error and Calibration in Integrated GPS/INS Systems

This paper describes the design of a tightly coupled GPS/INS integration system based on nonlinear Kalman filtering methods. The traditional methods include linearization of the system around a nominal trajectory, and the extended Kalman filtering (EKF) method which linearizes the system around the previous estimate, or the predication, whichever is available. The recently proposed sigma-point Kalman filtering (SPKF) method uses a set of weighted samples (sigma points) to completely capture the first and second order moments of the prior random variable. In contrast to the EKF, the SPKF has a simpler implementation as it does not require the Jacobian matrices – the computation of which may lead to analytical or computational problems in some applications. This research is conducted under the Australian Cooperative Research Centre (CRC) for Spatial Information (CRC-SI) project 1.3 'Integrated Positioning and Geo-referencing Platform'. The project aims to develop a generic hardware/software platform for positioning and imaging sensor integration. The current work focuses on development of software and algorithms, and a field programmable gate arrays (FPGA) based GPS/INS data logging system. In the current development phase, a tightly coupled GPS/INS integration system based on a linearization around the INS solution has been designed and implemented. The system uses the GPS pseudorange and Doppler measurements to estimate the INS errors. This paper describes further developments of the integration filter design based on the EKF and SPKF methodologies, in order to compare the performance of nonlinear filtering approaches. Experimental results are presented and further planned developments are outlined.

Weidong Ding

Papers

Improving Adaptive Kalman Estimation in GPS/INS Integration

Evaluating the Performances of Adaptive Kalman Filter Methods in GPS/INS Integration

Precise Velocity Estimation with a Stand-Alone GPS Receiver

Time Synchronization Error and Calibration in Integrated GPS/INS Systems

Low-cost Tightly Coupled GPS/INS Integration Based on a Nonlinear Kalman Filtering Design