An Improved of Extended Kalman Filtering Method on Tracking Accuracy of Bistatic Sonar System

2014 ◽  
Vol 596 ◽  
pp. 494-497
Author(s):  
Yong Sun ◽  
Jun Wei Zhao
Keyword(s):  
Kalman Filtering ◽  
Tracking Accuracy ◽  
Extended Kalman Filtering ◽  
Filtering Method ◽  
Filter Methods ◽  
Sonar System ◽  
Tracking Process ◽  
Measured Information ◽  
Tracing Algorithm ◽  
Simulation Results

The Extended Kalman Filter methods were widely used in the estimation of tracing situation in military fields. In this paper, we proposed a method of using multiple iteration of the observation and covariance matrix in the measuring equations during the tracking process in bistatic sonar system. Therefore the iterating extended kalman filtering (IEKF) algorithm was emerged at this situation. The simulation results show that the proposed tracing algorithm exhibits higher accuracy compared with the EKF algorithm. This new method can take full application of the measured information to improved the tracing accuracy in the whole controlled area. Keywords: bistatic sonar; tracing accuracy; IEKF algorithm; target moving analysis

An Algorithm of Improving Least-Square Method on Tracing Accuracy of Bistatic Sonar System

2014 ◽  
Vol 568-570 ◽  
pp. 168-171 ◽  
Author(s):  
Yong Sun ◽  
Jun Wei Zhao
Keyword(s):  
Full Rank ◽  
Least Square Method ◽  
Singular Value ◽  
Least Square ◽  
Sonar System ◽  
Rank Matrix ◽  
Measured Information ◽  
Tracing Algorithm ◽  
Simulation Results ◽  
Value Decomposition

The least square algorithms were widely used in the estimation of localizing and tracing situation in military fields. In this paper, we proposed a method of using a full rank matrix instead of using singular value decomposition to solving the non-full rank matrix. Therefore the improving least square (ILS) algorithm was emerged at this situation. The simulation results show that the proposed tracing algorithm exhibits higher accuracy compared with the least square algorithm. This new method can take full application of the measured information to improved the tracing accuracy in the whole controlled area.

A Research of Improving Data Fusing Algorithm Localization in Multistaic Sonar

2014 ◽  
Vol 530-531 ◽  
pp. 240-244
Author(s):  
Yong Sun ◽  
Jun Wei Zhao
Keyword(s):  
New Method ◽  
Localization Algorithm ◽  
Distance Measurements ◽  
Localization Accuracy ◽  
Sonar System ◽  
Measured Information ◽  
Simulation Results ◽  
Data Fusing

For the purpose of improving the localization accuracy of bistatic sonar in baseline districts and side districts, the most effective method is to increase the number of transmitting and receiving stations, which forms a multistatic sonar system. The mature algorithm of multistatic sonar system which contains three distance measurements volume in one subset, calls the multistatic time-only localization (TOL) algorithm. This paper proposes a new algorithm which merges the TOL algorithm and IBOL algorithm. of improving the bearing-only localization algorithm. The simulation results show that the proposed localization algorithm exhibits higher accuracy compared with the TOL algorithm and IBOL algorithm. This new method can take full application of the measured information to improved the localization accuracy in the whole controlled area.

Evaluation of spectral modulated interferograms by the extended Kalman filtering method

2005 ◽  
Author(s):  
Igor Gurov ◽  
Petr Hlubina ◽  
Mikhail Taratin ◽  
Alexey Zakharov
Keyword(s):  
Kalman Filtering ◽  
Extended Kalman Filtering ◽  
Filtering Method

scholarly journals Modified Extended Kalman Filtering for Tracking with Insufficient and Intermittent Observations

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Pengpeng Chen ◽  
Honglu Ma ◽  
Shouwan Gao ◽  
Yan Huang
Keyword(s):  
Kalman Filtering ◽  
Multiplicative Noise ◽  
Filter Design ◽  
Tracking System ◽  
Simulation Studies ◽  
Tracking Accuracy ◽  
Extended Kalman Filtering ◽  
Single Target ◽  
Fixed Topology ◽  
Filtering Problem

This paper is concerned with the Kalman filtering problem for tracking a single target on the fixed-topology wireless sensor networks (WSNs). Both the insufficient anchor coverage and the packet dropouts have been taken into consideration in the filter design. The resulting tracking system is modeled as a multichannel nonlinear system with multiplicative noise. Noting that the channels may be correlated with each other, we use a general matrix to express the multiplicative noise. Then, a modified extended Kalman filtering algorithm is presented based on the obtained model to achieve high tracking accuracy. In particular, we evaluate the effect of various parameters on the tracking performance through simulation studies.

A GEO-pivoted adaptive extended Kalman filtering method in low-latitude dense building environment

Measurement ◽  
2020 ◽  
pp. 108657
Author(s):  
Xiao Yin ◽  
Hongzhou Chai ◽  
Minzhi Xiang ◽  
Zhenqiang Du ◽  
Xiangyu Tian
Keyword(s):  
Kalman Filtering ◽  
Extended Kalman Filtering ◽  
Low Latitude ◽  
Filtering Method ◽  
Building Environment
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