2 edition of Multiple-model tracking with fixed-lag smoothing using imprecise information found in the catalog.
Multiple-model tracking with fixed-lag smoothing using imprecise information
Thesis (Ph.D) - University of Birmingham, School of Engineering, Department of Electronic, Electrical and Computer Engineering, 2003.
|Statement||by Zhufang Yang.|
|The Physical Object|
|Pagination||xv, 232p. ;|
|Number of Pages||232|
This paper analyzes the feasibility of using ultrasonic sensors for low cost vehicle-positioning and tracking in the lane adjacent to the host vehicle in order to identify free areas around the vehicle and provide information to an automatic avoidance collision system that can perform autonomous braking and lane change manoeuvres. In the clutter environment, radar/infrared sensors are used to track the background of maneuvering targets. Aiming at the shortcomings of traditional probabilistic data association theory in solving multiple target echoes and numbers in measurement, it is presented by combining interactive multiple model (IMM) and multiple detection probabilistic data association filter (MDPDAF) in a multi.
Flight Control Design using Incremental Nonlinear Dynamic Inversion with Fixed-lag Smoothing Estimation. 14 April | International Journal of Aeronautical and Space Sciences, Vol. 15 Helicopter tracking control using direct neural dynamic programming. Multiple model . Interacting multiple model integrated probabilistic data association filters (IMM-IPDAF) for track formation on maneuvering targets in cluttered environments Author(s): .
Target tracking is an element of systems that performs tasks such as surveillance, navigation, aviation and obstacle avoidance. It is generally difficult to represent different behavioural aspects of the motion of a manœuvring target with a single model. Therefore multiple model-based approaches are usually required when seeking solutions for manœuvring target tracking problems, which are. This study is concerned with risk-sensitive filtering and smoothing for a class of discrete-time jump Markov non-linear systems. Using the so-called reference probability method, the authors present a general theoretical framework to yield recursions for deriving filtered and smoothed estimates through identifying the approximations made by the interacting multiple model (IMM) estimation approach.
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New Multiple-Model Tracking Filter With Fixed-Lag Smoothing 4 Overview Of The Thesis 7 CHAPTER 2 PROBLEMS IN TRACKING WITH IMPRECISE INFORMATION Introduction 9 Problems In Singer’s Tracking Filter 17 Problems In Input Estimation Tracking Multiple-model tracking with fixed-lag smoothing using imprecise information.
By Zhufang Yang. Get PDF (3 MB) Abstract. A new multiple-model filter for target tracking has been developed and performed well in this thesis.
The procedure of the new multiple model (MM) filter has no compromises between non-manoeuvre and manoeuvre, and between Author: Zhufang Yang. Further improvement for multiple-model tracking is provided by using the fixed-lag smoothing technique. In comparison with the multiple-model filter alone, the fixed-lag smoothing multiple-model filter provides much better performance (even with fixed lag d=1), and can be implemented in a real time at the costs of a small delay and slight Author: Zhufang Yang.
MULTIPLE-MODEL TRACKING WITH FIXED- LAG SMOOTHING USING IMPRECISE INFORMATION. This book contains the latest developments in the implementation and.
The fixed-lag smoothing algorithm is developed by applying the basic interacting multiple model (IMM) approach and the probabilistic data association (PDA) technique to a state-augmented system.
The Interacting Multiple Model (IMM) estimator has been proven to be effective in tracking agile targets. Smoothing or retrodiction, which uses measurements beyond the current estimation time.
Zhugang Yang, Multiple –“Model Tracking with Fixed – Lag smoothing Using Imprecise Information”, School of Engineering (Elect ronic, Electrical & Computer engineering) The University of.
The smoothing window can be of any length N. The proposed method to smooth the target hybrid state at fixed lag is also applied to the enhanced multiple model (EMM) tracking algorithm. Simulation results indicate that the performance of fixed lag smoothing GMM-ITS significantly improves false track discrimination and root mean square errors.
Interacting multiple model-probabilistic data association (IMM-PDA) fixed-lag smoothing algorithm provides an efficient solution to track a maneuvering target in a cluttered environment. Whereas, the smoothing lag of each model in a model set is a fixed constant in traditional algorithms.
A new approach is developed in this paper. In target tracking of the behaviour of fish, the Segmenting Track Identifier, a non-Bayesian curve fitting and segmenting tracker, is shown to be most effective for tracking the unpredictable and complex horizontal motion of fish, while a Kalman fixed-interval and fixed-lag smoother using a constant-velocity model is shown to be most effective.
If we substitute into the fixed-lag smoothing algorithm of Theorem 1, we can calculate the fixed-lag smoothing estimate recursively.
Fig. 1 illustrates the signal z(t) and the fixed-lag smoothing estimate z ˆ (t, t) for the white Gaussian observation noise N(0, 2) by the RLS fixed-lag smoother in Theorem 1.
Fig. 2 illustrates the mean-square values (MSVs) of the fixed-lag smoothing. We consider the problem of tracking multiple maneuvering targets in clutter using switching multiple target motion models. A suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and the joint probabilistic data association (JPDA) technique to a Markovian switching system.
A new algorithm for the problem of tracking multiple maneuvering targets in the presence of clutter is proposed, through extending the IMM/Mscan-JPDA filtering algorithm, to the fixed-lag smoothing case.
A suboptimal fixed-lag smoothing algorithm is developed by applying the IMM and the multiscan JPDA approaches to a state augmented system. The proposed algorithm is illustrated via a. The Kalman filter model assumes the true state at time k is evolved from the state at (k − 1) according to = − + + where F k is the state transition model which is applied to the previous state x k−1;; B k is the control-input model which is applied to the control vector u k;; w k is the process noise which is assumed to be drawn from a zero mean multivariate normal distribution, with.
The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers.
The equivalent-noise approach converts the problem of maneuvering target tracking to that of state. Chakravorty, R. and Challa, S. () Fixed lag smoothing technique for track maintenance in clutter, in Proc.
Intelligent Sensors Sensor Networks and Information Processing, pp. – Chakravorty, R. and Challa, S. () Smoothing framework for automatic track initiation in clutter, in Proc. 8th International Conference on. Flight Control Design using Incremental Nonlinear Dynamic Inversion with Fixed-lag Smoothing Estimation 14 April | International Journal of Aeronautical and Space Sciences, Vol.
15 Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm. The smoothing is often used to improve the performance of filtering since more information is available.
The fixed-point smoothing, fixed-lag smoothing and fixed-interval smoothing are three types of smoothing. The fixed-lag smoothing seems more suitable for target tracking. Multiple-model tracking with fixed-lag smoothing using imprecise information.
University of Birmingham. Ph.D. Yin, Jianxin (). Monitoring of airborne particulate mass and number concentrations in the UK atmosphere. University of Birmingham. Ph.D. An Interacting Multiple Model (IMM) algorithm for manoeuvring target tracking in the presence of standoff jammer is proposed.
In the IMM, the conventional Gaussian likelihood is replaced with a Gaussian sum (GS) likelihood, derived from a sensor model accounting for both the measurements and jamming information.
Thus, the model-conditioned posterior probability density function of the state is. Multiple model method is the mainstream approach to maneuvering target tracking under motion mode uncertainty. , ,  A GM-PHD filter for jump Markov system (JMS) models is proposed for maneuvering target tracking.
4 A linear Gaussian JMS (LGJMS) multi-target model is modeled, which accommodates targets with switching linear dynamics.A Fixed Lag Smoothing IPDA Tracking in Clutter: Multi-target tracking: Shiyou Xu, Chaojing Tang, Peiliang Jing, and Zengping Chen Exploiting Imprecise Constraints in Particle Filtering Based Target Tracking: A multiple model PHD approach to tracking of cars under an assumed rectangular shape: Pietro Morerio, Lucio Marcenaro, and Carlo S.on road constrained tracking we furthermore highlight the book , which advocates the here applied particle methods, used in an interacting multiple model (IMM) framework (with the road segment as mode state) for such a problem.
Further Fixed lag smoothing distribution from ﬁlter output (FL).