Navigation Parameter Estimation Research Articles

Estimation of aircraft navigation parameters by a coherent radio altimeter taking into account errors in spectral processing of the echo signal

Estimation of aircraft navigation parameters by a coherent radio altimeter taking into account errors in spectral processing of the echo signal

Adaptive estimation of UVs navigation parameters by irregular inertial-satellite measurements

PurposeThere are shortcomings of modern methods of ensuring the stability of Kalman filtration in unmanned vehicles’ (UVs) navigation systems under the condition of a priori uncertainty of the dispersion matrix of measurement interference. First, it is the absence of strict criteria for the selection of adaptation coefficients in the calculation of the a posteriori covariance matrix. Secondly, it is the impossibility of adaptive estimation in real time from the condition of minimum covariance of the updating sequence due to the necessity of its preliminary calculation.Design/methodology/approachThis paper considers a new approach to the construction of the Kalman filter adaptation algorithm. The algorithm implements the possibility of obtaining an accurate adaptive estimation of navigation parameters for integrated UVs inertial-satellite navigation systems, using the correction of non-periodic and unstable inertial estimates by high-precision satellite measurements. The problem of adaptive estimation of the noise dispersion matrix of the meter in the Kalman filter can be solved analytically using matrix methods of linear algebra. A numerical example illustrates the effectiveness of the procedure for estimating the state vector of the UVs’ navigation systems.FindingsAdaptive estimation errors are sharply reduced in comparison with the traditional scheme to the range from 2 to 7 m in latitude and from 1.5 to 4 m in longitude.Originality/valueThe simplicity and accuracy of the proposed algorithm provide the possibility of its effective application to the widest class of UVs’ navigation systems.

An Improved Method Using SINS/PPP Method for Land Vehicle Gravimetry

Differential GNSS has been successfully applied in the SINS/GNSS vehicle gravity measurement method over the past few decades. However, GNSS error source has the spatio-temporal correlation in the differential mode. The navigation resource utilization efficiency is degraded due to the limitation of the baseline distance and the satellite common view. Using only one satellite receiver with high precision satellite orbit parameters and clock parameters, establishing navigation error models and parameter estimation methods, and obtaining high-precision receiver dynamic positioning, Precise Point Positioning (PPP) is a promising non-differential mode GNSS technique. The advantages of PPP are that it is simple to operate, low in cost, and not limited by the requirements for establishing a base station. This paper presents an SINS/PPP method for land vehicle gravimetry. Based on the strapdown gravimeter SGA-WZ02 developed by the National University of Defense Technology (NUDT), a land vehicle gravimetry test was carried out on a road about 36 kilometres long. The test consists of four measuring lines back and forth in the north-south direction with an average speed of 40km/h. Using this method, the internal accuracy of four repeated lines is 0.52mGal/0.9km. Compared with the results obtained by traditional SINS/GNSS method, the internal accuracy is 0.51mGal/0.9km, which shows that the accuracy calculated by SINS/PPP method is comparable to the results calculated by SINS/DGNSS method. Practical gravimetry results indicate that the proposed SINS/PPP method in this paper can meet the requirements of vehicle gravimetry under ideal GNSS observation conditions, and the feasibility of PPP technology for vehicle gravimetry is verified. Finally, some discussions and suggestions about the applicability of the method and its further improvement are put forward.

Novel EKF-Based Vision/Inertial System Integration for Improved Navigation

With advances in computing power, stereo vision has become an essential part of navigation applications. However, there may be instances wherein insufficient image data precludes the estimation of navigation parameters. Earlier, a novel vision-based velocity estimation method was developed by the authors, which suffered from the aforementioned drawback. In this paper, the vision-based navigation method has been integrated with a unique low-cost reduced inertial sensor system to bridge the navigation gap using one gyroscope and two accelerometers along with the inputs from wheel speed sensors. The integrated system is based on the extended Kalman filter and was tested on three trajectories with the introduction of vision data gaps. The system showed promising results for autonomous land vehicle applications.

Distributed navigation system for uniaxial wheeled modules

The possibility of the development of a distributed navigation system for the efficient estimation of the navigation parameters of a group of ground mobile modules is considered. A distinctive feature of the proposed system is that only a small part of the modules is equipped with precise navigation equipment. The problem is solved in a nonlinear formulation for nonholonomic wheeled modules using the Bayesian filter in the form of a particle filter. The consistency of the estimation of navigation parameters in the distributed system of modules is provided by the covariance intersection algorithm.

Calibration and Stochastic Modelling of Inertial Navigation Sensor Erros

The integration of Global Positioning System (GPS) with an inertial measurement unit (IMU) has been widely used in many applications of positioning and orientation. The performance of a GPS-aided inertial integrated navigation system is mainly characterized by the ability of the IMU to bridge GPS outages. This basically depends on the inertial sensor errors that cause a rapid degradation in the integrated navigation solution during periods of GPS outages. The inertial sensor errors comprise systematic and random components. In general, systematic errors (deterministic) can be estimated by calibration and therefore they can be removed from the raw observations. Random errors can be studied by linear or high order nonlinear stochastic processes. These stochastic models can be utilized by a navigation filter such as, Kalman filter, to provide optimized estimation of navigation parameters. Traditionally, random constant (RC), random walk (RW), Gauss-Markov (GM), and autoregressive (AR) processes have been used to develop the stochastic model in the navigation filters. In this technical note, the inertial sensor errors are introduced and discussed. Subsequently, a six-position laboratory calibration test is described. Then, mathematical models for RC, RW, GM, and AR stochastic models with associated variances for gyros and accelerometer random errors are presented along with a discussion regarding ongoing research in this field. Also, the implementation of a stochastic model in a loosely coupled INS/GPS navigation filter is explained.

Deterministic particle filtering for GPS navigation in the presence of multipath

In GPS navigation, distortion of the delay and phase of the received signal due to multipath propagation can degrade seriously the position estimation. In some applications such as aircraft landing, detecting the occurrence of multipath can be as important as mitigating its effect on the pseudodistance. This paper proposes deterministic particle filtering for joint multipath detection and navigation parameter estimation. The efficiency of the proposed method is illustrated by numerical simulations.

Cellular Multiadaptive Analogic Architecture: A Computational Framework for UAV Applications

An efficient adaptive algorithm in real-time applications should make optimal use of the available computing power for reaching some specific design goals. Relying on appropriate strategies, the spatial resolution/temporal rate can be traded against computational complexity; and sensitivity traded against robustness, in an adaptive process. In this paper, we present an algorithmic framework where a spatial multigrid computing is placed within a temporal multirate structure, and at each spatial grid point, the computation is based on an adaptive multiscale approach. The algorithms utilize an analogic (analog and logic) architecture consisting of a high-resolution optical sensor, a low-resolution cellular sensor-processor and a digital signal processor. The proposed framework makes the acquisition of a spatio-temporally consistent image flow possible even in case of extreme variations (relative motion) in the environment. It ideally supports the handling of various difficult problems on a moving platform including terrain identification, navigation parameter estimation, and multitarget tracking. The proposed spatio-temporal adaptation relies on a feature-based optical-flow estimation that can be efficiently calculated on available cellular nonlinear network (CNN) chips. The quality of the adaptation is evaluated compared to nonadaptive spatio-temporal behavior where the input flow is oversampled, thus resulting in redundant data processing with an unnecessary waste of computing power. We also use a visual navigation example recovering the yaw-pitch-roll parameters from motion-field estimates in order to analyze the adaptive hierarchical algorithmic framework proposed and highlight the application potentials in the area of unmanned air vehicles.

Integrated position estimation using aerial image sequences

Presents an integrated system for navigation parameter estimation using sequential aerial images, where the navigation parameters represent the positional and velocity information of an aircraft for autonomous navigation. The proposed integrated system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values reduces the reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in a large positional error. Therefore, absolute position estimation is required to compensate for the positional error generated by the relative position estimation. Absolute position estimation algorithms using image matching and digital elevation model (DEM) matching are presented. In the image matching, a robust-oriented Hausdorff measure (ROHM) is employed, whereas in the DEM matching, an algorithm using multiple image pairs is used. Experiments with four real aerial image sequences show the effectiveness of the proposed integrated position estimation algorithm.

Hybrid estimation of navigation parameters from aerial image sequence

This work presents a hybrid method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the position and velocity information of an aircraft for autonomous navigation. The proposed hybrid system is composed of two parts: relative position estimation and absolute position estimation. Computer simulation with two different sets of real aerial image sequences shows the effectiveness of the proposed hybrid parameter estimation algorithm.

Optimal Estimation of the Parameters for Ship Navigation

This paper considers the optimal estimation of a ship's navigation parameters as a nonlinear stochastic state estimation problem. For the design of the selected estimation methods: the ext.ended Kalman filter and decoupled Kalman filter, an extended maneuvering ship mathematical model was used. Compensation of the effects in position measurement. was achieved using appropriate models of roll and pitch, based on the suitable second-order shaping filters. Performance evaluation of selected algoritluns with respect to estimation errors and computational requirements, was accomplished for the case of a representative ship maneuver and for different sensor configurations. For practical realization, a unique form of decoupled navigation estimator, proposed by the authors, is shown to be a good compromise between estimation accuracy and realization complexity.