Aircraft guidance law identification using interactive multiple model estimation

Li, Sai; Fang, Huajing

doi:10.1109/ccdc.2018.8407656

Cited by 1 publication

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that in IMMM, the weights of regimes change constantly during the simulation, while the weighted sum of them, i.e., the identification results, remain stable. This is different from IMM or MMAE, in which the weight of the true situation converges to nearly 100% at the end of the simualation [3], [11].…”

Section: Sample Runmentioning

confidence: 57%

“…In [6], an adaptive receding horizon controller based on Bayesian inference is presented to identify the guidance law of a missile with perfect information on it. Most previous studies only focused on identifying the guidance laws using extended Kalman filters (EKFs) [3], [7], [8], [9], [10], [11] or unscented Kalman filters (UKFs) [5], [12], [13], [14] under the assumption that the first-order lateral time constant is a known constant. However, there are several drawbacks of using a KF-based estimation method to solve this problem:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter Identification of a PN-Guided Incoming Missile Using an Improved Multiple-Model Mechanism

Wang¹,

Wang²,

Fan³

2022

Preprint

View full text Add to dashboard Cite

An active defense against an incoming missile requires information of it, including a guidance law parameter and a first-order lateral time constant. To this end, assuming that a missile with a proportional navigation (PN) guidance law attempts to attack an aerial target with bang-bang evasive maneuvers, a parameter identification model based on the gated recurrent unit (GRU) neural network is built in this paper. The analytic identification solutions for the guidance law parameter and the first-order lateral time constant are derived.The inputs of the identification model are available kinematic information between the aircraft and the missile, while the outputs contain the regression results of missile parameters. To increase the training speed and the identification accuracy of the model, an output processing method called improved multiplemodel mechanism (IMMM) is proposed in this paper. The effectiveness of IMMM and the performance of the established model are demonstrated through numerical simulations under various engagement scenarios.

show abstract

Section: Sample Runmentioning

confidence: 57%