Square-root second-order extended Kalman filter and its application in target motion analysis

2010 ◽  
Vol 4 (3) ◽  
pp. 329 ◽  
Author(s):  
F. Daowang ◽  
L. Teng ◽  
H.Z. Tao
Keyword(s):  
Kalman Filter ◽  
Motion Analysis ◽  
Extended Kalman Filter ◽  
Second Order ◽  
Target Motion ◽  
Square Root ◽  
Target Motion Analysis

scholarly journals Extended Kalman Filter for Bearings-Only Tracking

2019 ◽  
Vol 8 (6) ◽  
pp. 637-640 ◽  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Constant Velocity ◽  
Target Motion ◽  
Passive Mode ◽  
Crucial Issue ◽  
Straight Line ◽  
Optimal Linear ◽  
Target Motion Analysis ◽  
Line Path

Target tracking using bearings-only measurements in passive mode operation of sonar is a crucial issue of underwater tracking. Target motion in underwater scenario is analyzed using bearings-only measurements and calculating parameters like range, course and speed of the target. This is called Target Motion Analysis (TMA). TMA process is highly non-linear as the measurements chosen are nonlinearly related to the selected target state vector and the traditional, optimal linear Kalman filter will not be appropriate to use. It is presumed that the target is moving in straight line path with constant velocity, so Extended Kalman Filter (EKF) is proposed in this paper. The algorithm is simulated for several scenarios using MATLAB. Monte-Carlo runs are performed to evaluate the capability of the algorithm.

A New Method for Target Motion Analysis

2013 ◽  
Vol 336-338 ◽  
pp. 2354-2358
Author(s):  
Chao Ma ◽  
Chun Jie Qiao ◽  
Yue Ke Wang ◽  
Shen Zhao
Keyword(s):  
Kalman Filter ◽  
Motion Analysis ◽  
Relative Error ◽  
Target Motion ◽  
New Method ◽  
Range Estimation ◽  
Target Motion Analysis

The paper proposes a new method for a targets trajectory, assumed to be linear and uniform, based on the observation of its speed and bearings. After introducing the new method based on assumed model, the paper analyzes the relative error of range estimation caused by speed and bearings estimations relative error; The results of target motion analysis (TMA) is optimized by linearizing the model and using kalman filter. Pond test shows that relative error of range estimation calculated by this method is less than 10-1.

scholarly journals Robust target motion analysis using the possibility particle filter

2019 ◽  
Vol 13 (1) ◽  
pp. 18-22 ◽  
Author(s):  
Branko Ristic ◽  
Jeremie Houssineau ◽  
Sanjeev Arulampalam
Keyword(s):  
Particle Filter ◽  
Motion Analysis ◽  
Target Motion ◽  
Target Motion Analysis
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