scholarly journals The Robust Passive Location Algorithm for Maneuvering Target Tracking

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Xiaojun Yang ◽  
Gang Liu ◽  
Jinku Guo ◽  
Hongqiao Wang ◽  
Bing He
Keyword(s):  
Kalman Filter ◽  
Fuzzy Filter ◽  
Passive Location ◽  
Maneuvering Target Tracking ◽  
New Approach ◽  
Maneuvering Target ◽  
Change Of State ◽  
Location Algorithm ◽  
Filter Divergence ◽  
Location Methods

With the advantages such as high security and far responding distance, the passive location has a broad application in military and civil domains such as radar and aerospace. However, most of the current passive location methods are based on the framework of the probability theory and cannot be used to deal with fuzzy uncertainty in the passive location systems. Though the fuzzy Kalman filter can be used in the uncertainty systems, it could not deal with the abrupt change of state like the maneuvering target which will lead to the filter divergence. Therefore, in order to track the maneuvering target in the fuzzy passive system, we proposed a robust fuzzy extended Kalman filter based on the orthogonality principle and the fuzzy filter in the paper. Conclusion can be made based on the simulation result that this new approach is more precise and more robust than the fuzzy filter.

Vision-based terrain referenced navigation of aerial vehicles using an adaptive extended Kalman filter

2017 ◽  
Vol 232 (8) ◽  
pp. 1584-1597 ◽  
Author(s):  
Sung-Hoon Mok ◽  
Youngjoo Kim ◽  
Hyochoong Bang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Least Squares Method ◽  
New Approach ◽  
Terrain Referenced Navigation ◽  
Digital Terrain ◽  
Highly Nonlinear ◽  
Noise Covariance ◽  
Adaptive Extended Kalman Filter ◽  
Filter Divergence

This paper addresses a vision-based terrain referenced navigation of an aircraft. A digital terrain map, in the surroundings of the aircraft, is compared with the camera measurements to estimate the aircraft position. Generally, the measurement equation in the terrain referenced navigation is highly nonlinear due to the sharp changes of terrain. Thus, the conventional extended Kalman filter could lead to unstable navigation solutions. In this paper, a new approach using an adaptive extended Kalman filter is proposed to cope up with the nonlinearity problem. A least squares method is utilized to derive the linearized measurement equations. The Jacobian matrix and sensor noise covariance are modified as a means of smoothing the sharp changes of terrain. Monte Carlo simulations verify that the proposed filter gives the stable navigation solutions, even when there is a large initial error, which is the primary reason for the filter divergence. Moreover, the proposed adaptation barely requires additional computational burden, whereas the high-order filters such as particle filter generally needs higher computational power.

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