This paper considers the problem of tracking and predicting the state of a dynamic system with stochastic dynamics and multiple modes of operation. A well-known approach to this problem is the "interacting multip...
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ISBN:
(纸本)9780738131269
This paper considers the problem of tracking and predicting the state of a dynamic system with stochastic dynamics and multiple modes of operation. A well-known approach to this problem is the "interacting multiple model" (IMM) estimator, which uses knowledge of the different modes of operation to update a bank of kalmanfilters (each optimal for a given mode of operation). The IMM combines estimates according to the posterior probability of the different modes. Despite their popularity, IMMs are known to sometimes be slow to detect mode switching, however, which can result in large state estimation errors. This paper addresses this problem by developing an autoencoder-Interacting Multiple Model (AEIMM) algorithm. The AEIMM effectively embeds an IMM within an autoencoder framework to create a hybrid approach using both deep learning and classical tracking frameworks. The motivation for this approach is that the neural network can perform nonlinear transformations on the measurements to help the IMM more quickly identify mode changes. The effectiveness of the AEIMM is demonstrated in a maneuvering target tracking scenario. Numerical results show that the AEIMM outperforms classical tracking techniques as well as hybrid techniques and a Long Short-Term Memory network in this scenario.
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