scholarly journals Sequential linear filtering with non‐linear position and Doppler measurements for target tracking

2021 ◽  
Author(s):  
Ting Cheng ◽  
Lifu Li
Keyword(s):  
Target Tracking ◽  
Linear Filtering ◽  
Non Linear

Locality-Sensitive Non-Linear Kalman Filter for Target Tracking

2021 ◽  
Vol 13 (1) ◽  
pp. 36-57
Author(s):  
Ben-Bright Benuwa ◽  
Benjamin Ghansah
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Traffic Control ◽  
Unscented Kalman Filter ◽  
Research Area ◽  
Linear Filtering ◽  
Tracking Accuracy ◽  
Propagation Of Uncertainty ◽  
Non Linear ◽  
Marine Engineering

Target tracking (TT) with non-linear kalman filtering (NLKF) has recently become a very popular research area, particularly in the field of marine engineering and air traffic control. Contemporary NLKF algorithms have been very effective, in particular, with extensions and merging with a reduced root mean square error (RMSE) value. However, there are a number of issues that confront NLKF approaches, notably weakness in robustness, convergence speed, and tracking accuracy due to large initial error and weak observability. Furthermore, NLKF algorithms significantly results in error for high non-linear systems (NLS) because of the propagation of uncertainty. Again, there is a problem of estimating future states as a result of white noise. To handle these issues, the authors propose a novel non-linear filtering algorithm, called locality-sensitive NLKF (LSNLKF) that incorporates locality-sensitive adaptors into the structure of an integrated NLKF. They are the extended kalman filter (EKF) and the unscented kalman filter (UKF) for TT.

Embedding non-linear filtering in autonomous underwater vehicle dynamics via the Kolmogorov backward equation

2021 ◽  
pp. 014233122110196
Author(s):  
Ravish H. Hirpara ◽  
Shambhu N. Sharma
Keyword(s):  
State Vector ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Linear Filtering ◽  
Homogeneous Markov Process ◽  
Non Linear ◽  
Backward Equation ◽  
Noise Correction ◽  
Ito Process ◽  
Correction Terms

This paper revisits the state vector of an autonomous underwater vehicle (AUV) dynamics coupled with the underwater Markovian stochasticity in the ‘non-linear filtering’ context. The underwater stochasticity is attributed to atmospheric turbulence, planetary interactions, sea surface conditions and astronomical phenomena. In this paper, we adopt the Itô process, a homogeneous Markov process, to describe the AUV state vector evolution equation. This paper accounts for the process noise as well as observation noise correction terms by considering the underwater filtering model. The non-linear filtering of the paper is achieved using the Kolmogorov backward equation and the evolution of the conditional characteristic function. The non-linear filtering equation is the cornerstone formalism of stochastic optimal control systems. Most notably, this paper introduces the non-linear filtering theory into an underwater vehicle stochastic system by constructing a lemma and a theorem for the underwater vehicle stochastic differential equation that were not available in the literature.

Non-linear filtering with discontinuous observations and applications to life sciences

1983 ◽  
Vol 45 (4) ◽  
pp. 571-577 ◽  
Author(s):  
Giovanni B. Di Masi ◽  
Wolfgang J. Runggaldier
Keyword(s):  
Life Sciences ◽  
Linear Filtering ◽  
Non Linear
Sign in / Sign up

Export Citation Format

Share Document