Hybrid Filter Scheme for Optimizing Indoor Mobile Cooperative Tracking System

The precise indoor tracking system using Xbee signal strength protocol has become a potential research to the WSN applications. The main aspects for the success tracking system is accuracy performance based on location estimation. The improvement of location estimation is complicated issue, especially using RSSI with low accuracy due to the signal attenuation from multipath effect at indoor propagation. Hence, many existing research typically focused on specific methods for providing improvement schemes at tracking system area. Then, we propose hybrid filter schemes, including extended gradient filter (EGF) for filtering noise signal based distance modification, and modified extended Kalman filter (MIEKF) will be combined with trilateration for filtering the error position estimation. Using mobile cooperative tracking scenario refers to our previous work, the proposed hybrid filter scheme which is called modified iterated extended gradient Kalman filter (MIEGKF) can optimize the error estimation around 41.28% reduction with 0.63 meters MSE (mean square error) value

A New Approach to Linear/Nonlinear Distributed Fusion Estimation Problem

Disturbance noises are always bounded in a practical system, while fusion estimation is to best utilize multiple sensor data containing noises for the purpose of estimating a quantity--a parameter or process. However, few results are focused on the information fusion estimation problem under bounded noises. In this paper, we study the distributed fusion estimation problem for linear time-varying systems and nonlinear systems with bounded noises, where the addressed noises do not provide any statistical information, and are unknown but bounded. When considering linear time-varying fusion systems with bounded noises, a new local Kalman-like estimator is designed such that the square error of the estimator is bounded as time goes to $\infty$. A novel constructive method is proposed to find an upper bound of fusion estimation error, then a convex optimization problem on the design of an optimal weighting fusion criterion is established in terms of linear matrix inequalities, which can be solved by standard software packages. Furthermore, according to the design method of linear time-varying fusion systems, each local nonlinear estimator is derived for nonlinear systems with bounded noises by using Taylor series expansion, and a corresponding distributed fusion criterion is obtained by solving a convex optimization problem. Finally, target tracking system and localization of a mobile robot are given to show the advantages and effectiveness of the proposed methods.Comment: 9 pages, 3 figure

On improving the performance of the Gauss-Newton filter

Includes abstract.Includes bibliographical references.The Gauss-Newton filter is a tracking filter developed by Norman Morrison around the same time as the celebrated Kalman filter. It received little attention, primarily due to the computation requirements at the time. Today computers have vast processing capacity and computation is no-longer an issue. The filter finite memory length is identified as the key element in the Gauss-Newton filter adaptability and robustness. This thesis focuses on improving the performance of the Gauss-Newton. We incorporate the process noise statistics into the filter algorithm to obtain a filter which explains the error covariance inconsistency of the Kalaman filter. In addition, a biased version of the linear Gauss-Newton filter, with lower mean squared error than the unbiased filter, is proposed. Furthermore the Gauss-Newton filter is adapted using the Levenberg Marquardt method for improved convergence. In order to improve the computation requirements, a recursive version of the filter is obtained

Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

© 2016 World Scientific Publishing Company. System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input-output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input-output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and mobile node, has been discussed in this work, interested to the indoor positioning applications. Developing an indoor location tracking system based on received signal strength indicator (RSSI) of WSN is considered cost effective and the simplest method. The suitable technique for estimating position out of RSSI measurements is the extended Kalman filter (EKF) which is especially used for non linear data as RSSI. In order to reduce the estimated errors from EKF algorithm, this work adopted forward data processing of the EKF algorithm to improve the accuracy of the filtering output, its called iterated extended Kalman filter (IEKF). However, using IEKF algorithm should know the stopping criterion value that is influenced to the maximum number iterations of this system. The number of iterations performed will be affected to the computation time although it can improve the estimation position. In this paper, we propose modified IEKF for mobile cooperative tracking system within only 4 iterations number. The ilustrated results using RSSI measurements and simulation in MATLAB show that our propose method have capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with conventional IEKF algorithm with MSE 1.09 m. The time computation perfomance of our propose method achived in 3.55 seconds which is better than adding more iteration process.     

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity

Active Classification for POMDPs: a Kalman-like State Estimator

The problem of state tracking with active observation control is considered for a system modeled by a discrete-time, finite-state Markov chain observed through conditionally Gaussian measurement vectors. The measurement model statistics are shaped by the underlying state and an exogenous control input, which influence the observations' quality. Exploiting an innovations approach, an approximate minimum mean-squared error (MMSE) filter is derived to estimate the Markov chain system state. To optimize the control strategy, the associated mean-squared error is used as an optimization criterion in a partially observable Markov decision process formulation. A stochastic dynamic programming algorithm is proposed to solve for the optimal solution. To enhance the quality of system state estimates, approximate MMSE smoothing estimators are also derived. Finally, the performance of the proposed framework is illustrated on the problem of physical activity detection in wireless body sensing networks. The power of the proposed framework lies within its ability to accommodate a broad spectrum of active classification applications including sensor management for object classification and tracking, estimation of sparse signals and radar scheduling.Comment: 38 pages, 6 figure