Agricultural machinery GNSS/IMU-integrated navigation based on fuzzy adaptive finite impulse response Kalman filtering algorithm

2021 ◽  
Vol 191 ◽  
pp. 106524
Author(s):  
Shichao Li ◽  
Man Zhang ◽  
Yuhan Ji ◽  
Zhenqian Zhang ◽  
Ruyue Cao ◽  
...  
Keyword(s):  
Impulse Response ◽  
Kalman Filtering ◽  
Finite Impulse Response ◽  
Integrated Navigation ◽  
Agricultural Machinery ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm ◽  
Fuzzy Adaptive

Application of Interval Kalman Filtering Algorithm in Integrated Navigation Data Fusion

2012 ◽  
Vol 433-440 ◽  
pp. 4059-4064
Author(s):  
Yun Feng Ma
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Interval Matrix ◽  
Uncertain Parameters ◽  
Integrated Navigation ◽  
System Matrix ◽  
Inversion Algorithm ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm ◽  
Time Accuracy

The traditional Kalman filter cannot be used directly when some system parameters such as certain elements of the system matrix are not precisely known or gradually change with time. Some uncertain parameters can be described as an interval model. An interval Kalman filtering algorithm is studied in this paper, which can be used to process a system with uncertain parameters. A simple inversion algorithm of interval matrix has been applied and its statistic performances and iterative form are similar to those of traditional Kalman filter. Simulation results show that such filtering algorithm can provide the real time accuracy error estimation and can be applied to such kind of low-cost integrated navigation system.

A Measurement-Based Robust Adaptive Kalman Filtering Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 3773-3779 ◽  
Author(s):  
Yan Hong Chang ◽  
Hai Zhang ◽  
Qi Fan Zhou
Keyword(s):  
Kalman Filtering ◽  
Measurement Noise ◽  
Statistical Characteristics ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Measurement Systems ◽  
Filtering Algorithm ◽  
Gps Measurement ◽  
Kalman Filtering Algorithm ◽  
Robust Adaptive

In the case that the accuracy of standard kalman filter (SKF) declines when the noise statistical characteristics are unknown or changing, a measurement-based adaptive kalman filtering algorithm (MAKF) is presented. Based on the contrastive analysis of measurement characteristics of different measurement systems, MAKF is put forward to estimate adaptively the measurement noise variance R by co-difference measurement sequences. Simulation is performed by applying this algorithm to the GPS/INS integrated navigation system, the results show that MAKF can track the GPS measurement noise in real time on condition that the GPS measurement noise is unknown or changing, and the filtering accuracy and robustness are superior to those of SKF and an improved Sage-Husa adaptive kalman filtering algorithm.

Transmission rate conditions for distributed filtering in sensor networks against eavesdropper

2021 ◽  
pp. 014233122110056
Author(s):  
Bingya Zhao ◽  
Ya Zhang
Keyword(s):  
Sensor Networks ◽  
Topological Entropy ◽  
Kalman Filtering ◽  
Transmission Rate ◽  
Necessary Condition ◽  
Estimation Errors ◽  
Positive Role ◽  
Filtering Algorithm ◽  
Wiretap Channels ◽  
Kalman Filtering Algorithm

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.

scholarly journals Distributed Diffusion Unscented Kalman Filtering Algorithm with Application to Object Tracking

2020 ◽  
Vol 53 (2) ◽  
pp. 3577-3582
Author(s):  
Hao Chen ◽  
Jianan Wang ◽  
Chunyan Wang ◽  
Dandan Wang ◽  
Jiayuan Shan ◽  
...  
Keyword(s):  
Object Tracking ◽  
Kalman Filtering ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm
Sign in / Sign up

Export Citation Format

Share Document