Data association and tracking using hidden Markov models and dynamic programming

Martinerie, F.; Forster, Philippe

doi:10.1109/icassp.1992.226023

Cited by 13 publications

(5 citation statements)

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“…Hidden Markov model (HMM) filters [30], [35], [36], [39] are an application of such approximate grid-based methods in a fixed-interval smoothing context and have been used extensively in speech processing. In HMM-based tracking, a common approach is to use the Viterbi algorithm [18] to calculate the maximum a posteriori estimate of the path through the trellis, that is, the sequence of discrete states that maximizes the probability of the state sequence given the data.…”

Section: B Approximate Grid-based Methodsmentioning

confidence: 99%

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Arulampalam

Maskell

Gordon

et al. 2002

IEEE Trans. Signal Process.

9,766

6,959

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Abstract-Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.

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Section: B Approximate Grid-based Methodsmentioning

confidence: 99%

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Arulampalam

Maskell

Gordon

et al. 2002

IEEE Trans. Signal Process.

9,766

6,959

View full text Add to dashboard Cite

show abstract

“…This is particularly problematic in the case of small process noise where all particles would collapse to a single point within a few iterations. Some methods to solve this issue can be named as resample-move algorithm [150] and regularization algorithm [151]. 3.…”

Section: Resamplingmentioning

confidence: 99%

Fault Diagnosis And Prognosis Of Satellite Attitude Control System With Reaction Wheels and Control Moment Gyros

Rahimi¹

2021

Preprint

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Condition-based maintenance (CBM) and prognostics and health management (PHM), as consisting parts of diagnosis, prognosis, and health monitoring (DPHM) framework, have developed over the past decades to remedy the limitations of the traditional maintenance practices for complex systems. In space, where mass and power budget are restricted, application of CBM and PHM has become more vital to the success of a mission. Reaction wheels (RW) and Control Moment Gyros (CMG), as the most commonly used actuators onboard satellites, are prone to faults and failures. The ability to detect faults, isolate their location and severity, and estimate the remaining useful life (RUL) of the faulty unit can enhance mission success rate and reduce maintenance and damage costs extensively. Therefore, in this thesis, a model-based DPHM framework is developed and evaluated. Firstly, a novel fault detection algorithm is proposed, using Unscented Kalman filters (UKF) in conjunction with residual and innovation sequences, for detecting agile faults in RW/CMG onboard satellites. Secondly, a novel fault isolation algorithm is proposed, using UKF, Bayes’ probability and interacting multiple models (IMM), to isolate the location of the fault and its severity. Finally, a new fault prognosis approach is proposed, using UKF and particle filters (PF) to estimate the RUL of a faulty unit. Extensive simulations were conducted for each phase of the DPHM to verify advantages of the proposed techniques over the available methods in the literature. Extensive simulations were conducted to evaluate the performance of the proposed methods in each module of the framework. Regarding the proposed fault detection scheme, results showed superior performance of the proposed adaptation technique compared to the original UKF and a previously developed AUKF. The proposed fault isolation scheme was able to successfully isolate the faulty unit at multiple levels of isolation including formation level, system level, and actuator level with over 99% success rate for formation level, over 99% success rate for the RW assembly and for up to 90% success rate for the CMG assembly in the system level. For the CMG assembly, due to direct estimation of the fault parameters, it was possible to determine the severity of the faults as well as their location. Finally, the proposed fault prognosis approach provided RUL estimates with errors as low as 1.5% compared to the actual remaining useful life. Overall, the proposed framework can be regarded as a promising tool for fault detection, isolation and identification, and prognosis of the complex nonlinear systems. Furthermore, the proposed framework can be extended to other complex systems in space including multi-agent formation systems and other areas where the model of the system under study is available.

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“…For the expanded state-space model (20) and (21), the EKF can be applied straightforwardly. Unlike the recursive EKF, the iterative EFIR filter [24] utilizes measurements z n available on an interval of N past neighboring points from m = n − N + 1 to n. The EFIR filtering algorithm listed in Table I has the following specifics.…”

Section: Extended Ufir Filteringmentioning

confidence: 99%

“…The unscented Kalman filter (UKF) [19] demonstrates better performance when the state-space models is highly nonlinear. The hidden Markov model (HMM) filters [20] have appeared to be more useful for tracking [21]. A sequential Monte Carlo (SMC) method also known as a particle filter (PF) [22] was developed to estimate Bayesian models associated with Markov chains in discrete-time domain be especially useful for robot self-localization [23].…”

Section: Introductionmentioning

confidence: 99%

Triangulation-based indoor robot localization using extended FIR/Kalman filtering

Granados-Cruz

Pomarico-Franquiz

Shmaliy

et al. 2014

2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

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A combined extended finite impulse response (EFIR) and Kalman (EFIR/Kalman) algorithm is proposed for mobile robot localization via triangulation. A distinctive advantage of the EFIR algorithm is that it completely ignores the noise statistics which are typically not well known to the engineer. Instead, it requires an optimal averaging interval of Nopt points. To run this algorithm, several initial Kalman estimates are used for the roughly set noise covariances. We consider a mobile robot travelling on an indoor floorspace and localized via triangulation with three nodes in a view. We show that the EFIR/Kalman filter is more accurate than the extended Kalman filter under the uncertain noise statistics and initial state.

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Data association and tracking using hidden Markov models and dynamic programming

Cited by 13 publications

References 0 publications

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Fault Diagnosis And Prognosis Of Satellite Attitude Control System With Reaction Wheels and Control Moment Gyros

Triangulation-based indoor robot localization using extended FIR/Kalman filtering

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