Kalman Filter Parameters Research Articles

Consideration of time-correlated errors in a Kalman filter applicable to GNSS

An algorithm for considering time-correlated errors in a Kalman filter is presented. The algorithm differs from previous implementations in that it does not suffer from numerical problems; does not contain inherent time latency or require reinterpretation of Kalman filter parameters, and gives full consideration to additive white noise that is often still present but ignored in previous implementations. Simulation results indicate that the application of the new algorithm yields more realistic and therefore useful state and covariance information than the standard implementation. Results from a field test of the algorithm applied to the problem of kinematic differential GPS demonstrate that the algorithm provides slightly pessimistic covariance estimates whereas the standard Kalman filter provides optimistic covariance estimates.

Video object tracking using adaptive Kalman filter

In this paper, a new video moving object tracking method is proposed. In initialization, a moving object selected by the user is segmented and the dominant color is extracted from the segmented target. In tracking step, a motion model is constructed to set the system model of adaptive Kalman filter firstly. Then, the dominant color of the moving object in HSI color space will be used as feature to detect the moving object in the consecutive video frames. The detected result is fed back as the measurement of adaptive Kalman filter and the estimate parameters of adaptive Kalman filter are adjusted by occlusion ratio adaptively. The proposed method has the robust ability to track the moving object in the consecutive frames under some kinds of real-world complex situations such as the moving object disappearing totally or partially due to occlusion by other ones, fast moving object, changing lighting, changing the direction and orientation of the moving object, and changing the velocity of moving object suddenly. The proposed method is an efficient video object tracking algorithm.

Enhancements to GPS operations and clock evaluations using a "total" Hadamard deviation

We describe a method based on the total deviation approach whereby we improve the confidence of the estimation of the Hadamard deviation that is used primarily in global positioning system (GPS) operations. The Hadamard-total deviation described in this paper provides a significant improvement in confidence indicated by an increase of 1.3 to 3.4 times the one degree of freedom of the plain Hadamard deviation at the longest averaging time. The new Hadamard-total deviation is slightly negatively biased with respect to the usual Hadamard deviation, and tau values are restricted to less than or equal to T/3, to be consistent with the usual Hadamard's definition. We give a method of automatically removing bias by a power-law detection scheme. We review the relationship between Kalman filter parameters and the Hadamard and Allan variances, illustrate the operational problems associated with estimating these parameters, and discuss how the Hadamard-total variance can improve management of present and future GPS satellite clocks.

Maximum entropy Kalman filter for image reconstruction and compression

Adaptive processes often attempt to minimize the mean square error (MSE) to filter a partially observed digital signal. While mathematically tractable, the MSE criterion often causes oversmoothing of the filtered signal. In this paper, we propose using maximum entropy (ME) as the optimization criterion to avoid the oversmoothing of signals. This criterion is motivated by the fact that ME methods make no assumptions regarding the unobserved data, aside from explicitly stated ones. The maximum entropy Kalman filter presented in this paper employs ME as its optimization criterion to explicitly identify the appropriate parameters of the standard Kalman filter, for the purpose of image compression and reconstruction.

Adaptive model estimation of machine-tool thermal errors based on recursive dynamic modeling strategy

A systematic model adaptation methodology is developed in order to continuously update the thermal-error model under varying manufacturing conditions. Process-intermittent probing and adaptive system identification techniques are integrated to monitor and estimate machine-tool thermal errors and recursively modify model coefficients as manufacturing process proceeds. The recursive model adaptation, which is based on the Kalman filter parameter estimation technique and multiple-sampling horizons, minimizes intrusion to production while maintaining good model adaptation capability. The experimental results proved that the proposed methodology has good model accuracy and robustness to frequently changing working conditions.

The application of soft computing techniques to an integrated navigation system of an AUV

This paper describes the implementation of a soft computing method based on fuzzy logic and multiobjective genetic algorithm (MOGA) techniques to adapt the parameters of an error-state complementary Kalman filter (ESCKF) to enhance the accuracy of an autonomous underwater vehicle (AUV) integrated navigation system. In the ESCKF, inertially-derived quantities from an inertial navigation system (INS) sensor are combined with direct measurements of the same quantities by use of the global positioning system (GPS) when the vehicle is on the surface and the velocity estimator output from a visual navigation system (VNS) based on laser stripe illumination methodology when the vehicle is in close contact to the bottom of the sea whilst performing an underwater mission. This strategy will alleviate the need for frequent excursions to the surface to obtain a GPS fix to reset the navigation solution produced by the INS that tends to drift after a certain period of time. This technique exploits the complementary error characteristics in such a way so as to produce optimal estimates in terms of minimum variance. To this end, errors of the sensors are modelled using first-order Markov processes and error data analysis is undertaken to determine the respective time constants and variances. To maintain the stability and performance of the ESCKF, which is likely to deteriorate when the assumed error and noise characteristics do not reflect the true ones, a fuzzy logic based scheme is used to make these values adaptive. The choice of fuzzy membership functions for this scheme is first carried out using a heuristic approach and further refined using a MOGA method.

A soft computing method for an AUV navigation system with pseudo-real-time applicability

This paper describes the implementation of a soft computing method based on fuzzy logic and multiobjective genetic algorithm techniques to adapt the parameters of an error-state complementary Kalman filter (ESCKF) to enhance the accuracy of an autonomous underwater vehicle (AUV) navigation system. In the ESCKF, inertially-derived quantities from an inertial navigation system (INS) sensor are combined with direct measurements of the same quantities by use of the global positioning system (GPS) and other aiding sensors. The backlash of the integration processes however, is that errors can grow rapidly and the values obtained therein can drift off the true value significantly. By contrast, the directly-measured data contain high frequency noise with bounded error. This instinctively suggests integrating the two sets of quantities, which is exactly what the ESCKF does. To maintain the stability and performance of the ESCKF, which is likely to deteriorate when the assumed error and noise characteristics do not reflect the true ones, a fuzzy logic based scheme is used to make these values adaptive. The choice of fuzzy membership functions for this scheme is first carried out using a heuristic approach and further refined using a multiobjective genetic algorithm method.

Diagnosis techniques for sensor faults of industrial processes

A model-based procedure exploiting analytical redundancy for the detection and isolation of faults in input-output control sensors of a dynamic system is presented. The diagnosis system is based on state estimators, namely dynamic observers or Kalman filters designed in deterministic and stochastic environments, respectively, and uses residual analysis and statistical tests for fault detection and isolation. The state estimators are obtained from an input-output data process and standard identification techniques based on ARX or errors-in-variables models, depending on signal to noise ratio. In the latter case the Kalman filter parameters, i.e., the model parameters and input-output noise variances, are obtained by processing the noisy data according to the Frisch scheme rules. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine model. Results from simulation show that minimum detectable faults are perfectly compatible with the industrial target of this application.

An efficient implementation of Reid's multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking

An efficient implementation of Reid's multiple hypothesis tracking (MHT) algorithm is presented in which the k-best hypotheses are determined in polynomial time using an algorithm due to Murly (1968). The MHT algorithm is then applied to several motion sequences. The MHT capabilities of track initiation, termination, and continuation are demonstrated together with the latter's capability to provide low level support of temporary occlusion of tracks. Between 50 and 150 corner features are simultaneously tracked in the image plane over a sequence of up to 51 frames. Each corner is tracked using a simple linear Kalman filter and any data association uncertainty is resolved by the MHT. Kalman filter parameter estimation is discussed, and experimental results show that the algorithm is robust to errors in the motion model. An investigation of the performance of the algorithm as a function of look-ahead (tree depth) indicates that high accuracy can be obtained for tree depths as shallow as three. Experimental results suggest that a real-time MHT solution to the motion correspondence problem is possible for certain classes of scenes.

Autoregressive and adaptive estimation with an application to hurricane track prediction

The key result of this paper is that following a change in a parameter of AR (p), an autoregressive process of order p, the innovations sequence of the Kalman filter parameters will follow an autoregressive moving-average model in addition to a transient function. Furthermore, it is also shown that the first p values of the innovations' sample autocorrelation can be used to form a sufficient statistic to detect if at least one of the parameters in the AR (p) model did change at an unknown point in time. Following a parameter change detection process, improved estimates and noise statistics can be determined and implemented to modify the Kalman filter. The revised model will thus be more consistent with the most recent process behaviour. To motivate the reader, a simulation exercise was conducted to validate the on-line change detector and adaptive estimation algorithm. The proposed algorithm was used to predict hurricane movements with real data provided by the National Hurricane Center.

A Kalman filter-based architecture for interference excision

This paper presents a novel Kalman filtering approach to the suppression of narrowband interference from direct sequence spread spectrum communications systems. The approach is based on the digital phase-locked loop Kalman filter. Because the interference is assumed to be much stronger than either the signal or noise, the Kalman filter locks onto a function related to the interference. The net result is an estimate of the phase of the interference and its envelope. Though more optimum for interferer's of constant envelope, the algorithm is characterized as a function of the Kalman filter parameters through computer simulation for narrowband Gaussian noise interference. Examples of the phase- and envelope-tracking capabilities of the algorithm are presented, followed by bit error rate curves for interference bandwidths ranging from 0.5% to 5.0% of the chip rate of the spread spectrum signal.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Model reference adaptive control of time varying and stochastic systems

The adaptive control of general delay, linear time-varying systems is addressed. A simple model reference adaptive control law is devised. It does not require a priori knowledge of the sign of the high-frequency gain. This control law coupled with the parameter estimation equations allows a simple representation of the closed-loop system. This greatly eases the stability and performance analysis, and has potential for generalization to adaptive pole placement and other control laws suitable for nonminimum-phase systems. For the Kalman filter parameter estimator coupled with this control law, stability is obtained without persistence of excitation or knowledge of the sign of the high frequency gain. Performance results for the extended-least-squares algorithm are described. >

Studies of distributed parameter systems: Decoupling the state-parameter estimation problem

The problem of state-parameter estimation is considered in terms of decoupling the estimation procedure. First, the theoretical preliminaries necessary for the mathematical statement of the problem are defined. Then using the extended Kalman filter (EKF) approach, the state and parameter are estimated by applying the solution techniques to a distributed parameter system. Next, the state estimation problem is decoupled from the parameter estimation problem and by using a numerical example, the advantage of this decoupling procedure is demonstrated. The numerical results show that convergence can be improved when this decoupling procedure is employed. The effect of the location of the measurements on the estimation problem is also analysed in this work. The results show that the convergence of the problem depends on the location as well as the number of measurements.

Functional Separation of EMG Signals via ARMA Identification Methods for Prosthesis Control Purposes

Multifunctional control of artificial limbs via electromyographic (EMG) actuation requires means for reliably recognizing or distinguishing between the various functions on the basis of the recorded EMG data. Furthermore, constraints of weight, cost, and computation time on practical prosthesis application must be satisfied. An approach to the aforementioned recognition problem is given in terms of deriving a fast parametric-recognition algorithm whereby the autoregressive-moving-average (ARMA) parameters and the Kalman filter parameters of the EMG time series are identified. It is shown that the resulting identified parameters yield sufficient information to discriminate between a small number of upper extremity functions. Problems involved in practical prosthesis control via the present approach and problems of hardware realization are discussed to illustrate the validity of the approach.