Industry Tip: Picking the Minimum Process Noise Variance for Your NCV Track Filter

A kind of adaptive filter algorithm based on the estimation of the unknown input is proposed for studying the adaptive adjustment of process noise variance of boost phase trajectory. Polynomial model is used as the motion model of the boost trajectory, truncation error is regarded as an equivalent to the process noise and the unknown input and process noise variance matrix is constructed from the estimation value of unknown input according to the quantitative relationship among the unknown input, the state estimation error, and optimal process noise variance. The simulation results show that in the absence of prior information, the unknown input is estimated effectively in terms of magnitude, a positive definite matrix of process noise covariance which is close to the optimal value is constructed real-timely, and the state estimation error approximates the error lower bound of the optimal estimation. The estimation accuracy of the proposed algorithm is similar to that of the current statistical model algorithm using accurate prior information.

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Robust Multipath-Assisted SLAM with Unknown Process Noise and Clutter Intensity

Remote Sensing ◽

10.3390/rs13091625 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1625

Author(s):

Zesheng Dan ◽

Baowang Lian ◽

Chengkai Tang

Keyword(s):

Simultaneous Localization And Mapping ◽

User Equipment ◽

Factor Graph ◽

Model Parameters ◽

Noise Variance ◽

Parameter Uncertainties ◽

Process Noise ◽

Environmental Features ◽

Localization And Mapping ◽

Slam Algorithm

In multipath-assisted simultaneous localization and mapping (SLAM), the geometric association of specular multipath components based on radio signals with environmental features is used to simultaneously localize user equipment and map the environment. We must contend with two notable model parameter uncertainties in multipath-assisted SLAM: process noise and clutter intensity. Knowledge of these two parameters is critically important to multipath-assisted SLAM, the uncertainty of which will seriously affect the SLAM accuracy. Conventional multipath-assisted SLAM algorithms generally regard these model parameters as fixed and known, which cannot meet the challenges presented in complicated environments. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing a robust multipath-assisted SLAM algorithm that can accommodate model mismatch in process noise and clutter intensity. Specifically, we describe the evolution of the process noise variance and clutter intensity via Markov chain models and integrate them into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the user equipment, environmental features and unknown model parameters to achieve the goals of simultaneous localization and mapping, as well as adaptively learning the process noise variance and clutter intensity. Finally, the simulation results demonstrate that the proposed approach is robust against the uncertainty of the process noise and clutter intensity and shows excellent performances in challenging indoor environments.

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Performance Analysis and Comparison for High Maneuver Target Track Based on Different Jerk Models

Journal of Control Science and Engineering ◽

10.1155/2018/6432485 ◽

2018 ◽

Vol 2018 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Qinghai Meng ◽

Bowen Hou ◽

Dong Li ◽

Zhangming He ◽

Jiongqi Wang

Keyword(s):

Integration Method ◽

State Variables ◽

Noise Variance ◽

Process Noise ◽

Practical Applications ◽

Maneuvering Target ◽

Maneuvering Targets ◽

Mathematical Simulations ◽

Target Track ◽

Adaptive Noise

The Jerk model is widely used for the track of the maneuvering targets. Different Jerk model has its own state expression and is suitable to different track situation. In this paper, four Jerk models commonly used in the maneuvering target track are advanced. The performances of different Jerk models for target track with the state variables and the characters are compared. The corresponding limit conditions in the practical applications are also analyzed. Besides, the filter track is designed with UKF algorithm based on the four different models for the high-maneuvering target. The simplified dynamic model is used to gain the standard trajectory with Runge-Kutta numerical integration method. The mathematical simulations show that Jerk model with self-adaptive noise variance has the best robustness while other models may diverge when the initial error is much larger. If the process noise level is much lower, the track accuracy for four Jerk models is similar and stationary in the steady track situation, but it will be descended greatly in the much highly maneuvering situation.

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Improving Navigational Accuracy for AUVs Based on an Adaptive Modified Square-Root Cubature Kalman Filter

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.945-949.1430 ◽

2014 ◽

Vol 945-949 ◽

pp. 1430-1434

Author(s):

Chen Sun ◽

Jian Long Li

Keyword(s):

Kalman Filter ◽

Autonomous Underwater Vehicles ◽

Linear Measurement ◽

Square Root ◽

Noise Variance ◽

Process Noise ◽

Cubature Kalman Filter ◽

Measurement Function ◽

Computational Errors ◽

Noise Statistics

This paper addresses an adaptive modified square-root cubature Kalman filter for the navigation of autonomous underwater vehicles (AUVs). The standard square-root cubature Kalman filter (SCKF) implements the CKF using square-root filtering to reduce computational errors. It can be modified due to the nonlinear system with a linear measurement function. The modification leads to a decrease computational complexity. Sage-Husa noise statistics estimator is combined with the Modified SCKF to estimate the unknown and changing system process noise variance. The experimental results show that compared with the MSCKF and the EKF algorithm, the adaptive MSCKF show the best accuracy for a real system with unknown process noise variance.

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