Action recognition using skeleton data (3D coordinates of human joints) is an attractive topic due to its robustness to the actor's appearance, camera's viewpoint, illumination, and other environmental conditions. However, skeleton data must be measured by a depth sensor or extracted from video data using an estimation algorithm, and doing so risks extraction errors and noise. In this work, for robust skeleton-based action recognition, we propose a deep state-space model (DSSM). The DSSM is a deep generative model of the underlying dynamics of an observable sequence. We applied the proposed DSSM to skeleton data, and the results demonstrate that it improves the classification performance of a baseline method. Moreover, we confirm that feature extraction with the proposed DSSM renders subsequent classifications robust to noise and missing values. In such experimental settings, the proposed DSSM outperforms a state-of-the-art method.

An adaptive per-survivor processing maximum likelihood sequence estimation (PSP-MLSE) using state-space based recursive least-squares (RLS) is proposed for rapidly time varying multi-path fading channels. Unlike PSP-MLSE using Kalman filtering, it does not require the knowledge of model statistics, and with an aid of state-space modeling, it has a robust performance to the fade rate, compared to PSP-MLSE using conventional RLS.

In this paper, a new description of a separable-denominator (S-D) two-dimensional (2-D) transfer matrix is proposed, and its realization is considered. Some of this problem had been considered for the transfer matrices whose elements are two-variables rational functions. We shall propose a 2-D transfer matrix whose inputs-outputs relation is represented by a ratio of two-variables polynomial matrices, and present an algorithm to obtain a 2-D state-space model from it. Next, it is shown that the description proposed in this paper is always minimally realizable. And, we shall present a method of obtaining the description proposed in this paper from a S-D 2-D rational transfer matrix.