A nonlinear voter-estimator for redundant systems

Broen, R. B.

doi:10.1109/cdc.1974.270532

Cited by 16 publications

(5 citation statements)

References 7 publications

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“…necessary precision for unambiguous isolation, and may take too long to detect and isolate failures. [78]) operates on the output of the Kalman filter and gently deweights a possible dissenter's contribution from overall navigation solution based on instantaneous degree-of-departure without breaking system structure or priority of data flow.…”

Section: Votingmentioning

confidence: 99%

Decentralized Filtering and Redundancy Management for Multisensor Navigation

Kerr¹

1987

IEEE Trans. Aerosp. Electron. Syst.

130

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Failure detection and redundancy management is discussed for avionics applications of integrated navigation involving coordinated use of multiple simultaneous sensor subsystems such as GPS, JTIDS, TACAN, VOR/DME, ILS, an inertial navigation system (INS), and possibly even Doppler AHRS. A brief high level survey is provided to assess the status of those techniques and methodologies advertized as already available for handling the challenging real-time failure detection, redundancy management, and Kalman filtering aspects of these systems with differing availabilities, differing reliabilities, differing accuracies, and differing information content/sampling rates.Following the status review, a new failure detection/redundancy management approach is developed based on voter/monitoring at both the raw data and at the filtered-data level, as well as using additional inputs from hardware built-in-testing (BIT) and from specialized tests for subsequent failure isolation in the case of ambiguous indications. The technique developed involves use of Gaussian confidence regions to reasonably account for the inherent differences in accuracy between the various sensor subsystems. Online estimates of covariances from the Kalman filter are to be used for this purpose (when available). A technique is provided for quantitatively evaluating both the probability of detecting failed component subsystems and the probability of false alarm to be incurred, which is then to be traded off as the basis for rational selection of the thresholds used in the automated decision process. Moreover, the redundancy management procedure is demonstrated to be amenable to pilot or navigation operator prompting and override, if necessary.A structure to accommodate differing rates of subsystem assessment and tally is developed and alternative designs for navigation architectures are offered based on likely subsystem utilization and newly emerging concepts in decentralized Kalman filtering. Many of the decentralized filtering concepts are only now economically feasible for real-time implementation due to recent availability of commercial parallel processing chips and/or VHSIC compatible systolic array versions of all the requisite algorithms and transformations necessary to support such Kalman filter mechanizations in a few chips.

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Section: Votingmentioning

confidence: 99%

Decentralized Filtering and Redundancy Management for Multisensor Navigation

Kerr¹

1987

IEEE Trans. Aerosp. Electron. Syst.

130

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“…At this time most of the work has been at a theoretical level with only a few real applications of techniques [6]- [9], [I 3], [3 I l, [36], [59]- [60]. Much work is yet to be done in the development of implementable systems complete with a variety of design tradeoffs.…”

Section: Discussionmentioning

confidence: 99%

“…Simple logic is then developed to detect failures and eliminate faulty instruments, for example, if one of the three redundant signals differs markedly from the other two, the differing signal is eliminated. Recently, Broen [9] has developed a class of voter-estimators that possesses advantages relative to standard voting techniques: Consider the dynamical system…”

Section: ~:(T) = -~Y(t) + Vbmentioning

confidence: 99%

A survey of design methods for failure detection in dynamic systems

Willsky¹

1977

Microelectronics Reliability

642

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Summary--In this paper we survey a number of methods for the detection of abrupt changes (such as failures) in stochastic dynamical systems. We concentrate on the class of linear systems, but the basic concepts, if not the detailed analyses, carry over to other classes of systems. The methods surveyed range from the design of specific failure-sensitive filters, to the use of statistical tests on filter innovations, to the development of jump process formulations. Tradeoffs in complexity vs performance are discussed.

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“…These efforts have been motivated by a wide variety of applications including the detection of sensor and actuator failures [1,2,4,19,[26][27][28][29][30][31][32][33][34][35] the tracking of maneuvering vehicles [20,21,23,25], and numerous signal analysis problems (electrocardiogram analysis [5,6], geophysical signal processing [7], edge detection in images [8,9], freeway monitoring [10,11],...).…”

Section: Introductionmentioning

confidence: 99%

“…There are several methods that have been developed which fit the general form of Figure 1. In particular, hard [33] and soft [34] voting systems can be interpreted in this fashion. Another example is the multiple observer design described in [36].…”

Section: Introductionmentioning

confidence: 99%

Detection of abrupt changes in dynamic systems

Willsky

Detection of Abrupt Changes in Signals and Dynamical Systems

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In this paper we present some of the basic ideas associated with the detection of abrupt changes in dynamic systems. Our presentation focuses on two classes of methods --multiple filter-based techniques and residual-based methods --and in far more detail on the multiple model and generalized likelihood ratio methods. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

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A nonlinear voter-estimator for redundant systems

Cited by 16 publications

References 7 publications

Decentralized Filtering and Redundancy Management for Multisensor Navigation

Decentralized Filtering and Redundancy Management for Multisensor Navigation

A survey of design methods for failure detection in dynamic systems

Detection of abrupt changes in dynamic systems

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