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.

该研究提出了一种新颖的内存高效的循环神经网络（RNN）架构，用于解决目标定位问题，并通过将经典的粒子滤波器与 GRU RNN 架构相结合，提出了构建粒子滤波器 RNN 模型（mePFRNN）的关键特性是在处理不同尺寸环境时所需的参数数目相同。因此，相比于先前提出的 PFRNN 模型，该提议的 mePFRNN 架构消耗更少的存储参数内存。通过在对称且嘈杂的环境中进行测试，以展示我们模型的性能，实验结果表明，相比于其他考虑的竞争者，mePFRNN 模型具有更精确的定位结果，并且需要更少的训练参数。