Based on the transformer model, a deep data association and track filtering network (DeepAF) was constructed in this paper to achieve the function of data association and end-to-end track filtering. Combined with the existing track initiation methods, DeepAF can be used to track multiple targets in clutter environments. Experimental results show that DeepAF can stably and effectively track targets moving in different models such as constant velocity, constant acceleration, and constant turn rate. Compared with the probability hypothesis density filter and the probabilistic data association method, which were set with different state transition matrices manually to match with the actual target motion models, DeepAF has similar estimation accuracy in respect of target velocity and better estimation accuracy in respect of target position with less time consumption. For position estimation, compared with PHD, DeepAF can reduce the estimation error by 49.978, 49.263, and 2.706 m in the CV, CA, and CT motion models. Compared with PDA, DeepAF can reduce the estimation error by 13.465, 23.98, and 4.716 m in CV, CA, and CT motion models. For time consumption, compared with PHD, DeepAF can reduce the time by 991.2, 982.3, and 979.5 s in CV, CA, and CT motion models. Compared with PDA, DeepAF can reduce the time by 61.6, 60.5, and 61.4 s in CV, CA, and CT motion models.

With the proliferation of smart devices, the Internet of Things (IoT) is rapidly expanding. This study proposes a miniaturized controllable metamaterial with low control voltage for achieving low-power and compact designs in IoT node devices. Operating at a target frequency of 2.4 GHz, the proposed metamaterial requires only a 3.3 V control voltage and occupies approximately one-third of the wavelength in size. Experimental validation demonstrates its excellent reflective control performance, positioning it as an ideal choice for low-power IoT systems, particularly in the context of miniaturized and low-power IoT node applications.

This paper considers the reduced-order H∞ filtering problem for singular Markovian jump systems (SMJSs) with incomplete transition rates (ITRs) by using augmented system method. The considered conditions in this paper are necessary and sufficient (NS), whereas the existing conditions are mainly sufficient. To be concrete, by extracting system matrices in the considered system from augmented system, NS condition for the existence of the full-order H∞ filtering is provided in terms of linear matrix inequalities (LMIs). However, it is hard to extend the condition to the existence of the reduced-order H∞ filtering. Thus, by fixing augmented system matrices, NS condition for the existence of the reduced-order one is presented to guarantee the desired filtering error system to be stochastically admissible with H∞ performance level. Furthermore, there are neither complicated matrix transformation nor equality/rank constraints in this paper. One numerical and one practical examples are illustrated to demonstrate the effectiveness of the achieved results.