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.

翻译：本研究提出了一种新型内存高效的递归神经网络（RNN）架构，专门用于解决目标定位问题。该问题旨在从噪声环境中恢复运动目标的状态。我们借鉴经典粒子滤波的思想，并将其与GRU RNN架构相结合。由此产生的内存高效粒子滤波RNN模型（mePFRNN）的关键特征在于：处理不同规模的环境时，所需参数数量相同。因此，与先前提出的PFRNN模型相比，所提出的mePFRNN架构在存储参数时消耗更少的内存。为展示模型性能，我们在对称且噪声密集的极端挑战性滤波算法环境中进行了测试。实验结果表明，mePFRNN模型比所对比的竞争模型提供更精确的定位，且所需训练参数更少。