A framework integrates particle-based fixed-lag smoothing and semi-Markov maneuver modeling with a generalized guidance law to explicitly account for time-varying estimation delays in stochastic interception.
The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients
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A Bayesian decision theory framework modifies the DGL1 guidance law to incorporate estimation errors, yielding a stochastic law that complies with the generalized separation theorem and uses trajectory shaping for better estimation.
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Comprehensive Approach to Directly Addressing Estimation Delays in Stochastic Guidance
A framework integrates particle-based fixed-lag smoothing and semi-Markov maneuver modeling with a generalized guidance law to explicitly account for time-varying estimation delays in stochastic interception.
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Unified Estimation--Guidance Framework Based on Bayesian Decision Theory
A Bayesian decision theory framework modifies the DGL1 guidance law to incorporate estimation errors, yielding a stochastic law that complies with the generalized separation theorem and uses trajectory shaping for better estimation.