Memory-efficient particle filter recurrent neural network for object localization
This study proposes a novel memory-efficient recurrent neural network (RNN) architecture specified to solve the object localization problem. This problem is to recover the object states along with its movement in a noisy environment. We take the idea of the classical particle filter and combine it w...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
02.10.2023
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Subjects | |
Online Access | Get full text |
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Summary: | This study proposes a novel memory-efficient recurrent neural network (RNN)
architecture specified to solve the object localization problem. This problem
is to recover the object states along with its movement in a noisy environment.
We take the idea of the classical particle filter and combine it with GRU RNN
architecture. The key feature of the resulting memory-efficient particle filter
RNN model (mePFRNN) is that it requires the same number of parameters to
process environments of different sizes. Thus, the proposed mePFRNN
architecture consumes less memory to store parameters compared to the
previously proposed PFRNN model. To demonstrate the performance of our model,
we test it on symmetric and noisy environments that are incredibly challenging
for filtering algorithms. In our experiments, the mePFRNN model provides more
precise localization than the considered competitors and requires fewer trained
parameters. |
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DOI: | 10.48550/arxiv.2310.01595 |