Online Adaptive Kalman Filter for Target Tracking with Unknown Noise Statistics

2021 ◽  
pp. 1-1
Author(s):  
Yuming Chen ◽  
Wei Li ◽  
Yuqiao Wang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Adaptive Kalman Filter ◽  
Noise Statistics

A New Adaptive Kalman Filter with Inaccurate Noise Statistics

2019 ◽  
Vol 38 (9) ◽  
pp. 4380-4404 ◽  
Author(s):  
Dingjie Xu ◽  
Zhemin Wu ◽  
Yulong Huang
Keyword(s):  
Kalman Filter ◽  
Adaptive Kalman Filter ◽  
Noise Statistics

scholarly journals A Constant Gain Kalman Filter for Wireless Sensor Network and Maneuvering Target Tracking

2021 ◽  
Author(s):  
Peeyush Awasthi ◽  
Ashwin Yadav ◽  
Naren Naik ◽  
Mudambi Ramaswamy Ananthasayanam
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Wireless Sensor Network ◽  
Target Tracking ◽  
Sensor Network ◽  
The State ◽  
Wireless Sensor ◽  
State Equations ◽  
Maneuvering Target Tracking ◽  
Noise Statistics

One of the well-known approaches to target tracking is the Kalman filter. The problem of applying the Kalman Filter in practice is that in the presence of unknown noise statistics, accurate results cannot be obtained. Hence the tuning of the noise covariances is of paramount importance in order to employ the filter. The difficulty involved with the tuning attracts the applicability of the concept of Constant Gain Kalman Filter (CGKF). It has been generally observed that after an initial transient the Kalman Filter gain and the State Error Covariance P settles down to steady state values. This encourages one to consider working directly with steady state or constant Kalman gain, rather than with error covariances in order to obtain efficient tracking. Since there are no covariances in CGKF, only the state equations need to be propagated and updated at a measurement, thus enormously reducing the computational load. The current work first applies the CGKF concept to heterogeneous sensor based wireless sensor network (WSN) target tracking problem. The paper considers the Standard EKF and CGKF for tracking various manoeuvring targets using nonlinear state and measurement models. Based on the numerical studies it is clearly seen that the CGKF out performs the Standard EKF. To the best of our knowledge, such a comprehensive study of the CGKF has not been carried out in its application to diverse target tracking scenarios and data fusion aspects.

Adaptive Kalman Filtering with Recursive Noise Estimator for Integrated SINS/DVL Systems

2014 ◽  
Vol 68 (1) ◽  
pp. 142-161 ◽  
Author(s):  
Wei Gao ◽  
Jingchun Li ◽  
Guangtao Zhou ◽  
Qian Li
Keyword(s):  
Kalman Filter ◽  
Estimation Accuracy ◽  
Doppler Velocity ◽  
Fading Memory ◽  
Time Varying ◽  
Adaptive Kalman Filter ◽  
Statistical Parameters ◽  
One Step ◽  
Noise Statistics ◽  
Rough Sea

This paper considers the estimation of the process state and noise parameters when the statistics of the process and measurement noise are unknown or time varying in the integration system. An adaptive Kalman Filter (AKF) with a recursive noise estimator that is based on maximum a posteriori estimation and one-step smoothing filtering is proposed, and the AKF can provide accurate noise statistical parameters for the Kalman filter in real-time. An exponentially weighted fading memory method is introduced to increase the weights of the recent innovations when the noise statistics are time varying. Also, the innovation covariances within a moving window are averaged to correct the noise statistics estimator. Experiments on the integrated Strapdown Inertial Navigation System (SINS)/ Doppler Velocity Log (DVL) system show that the proposed AKF improves the estimation accuracy effectively and the AKF is robust in the presence of vigorous-manoeuvres and rough sea conditions.

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