Neural-Network-Based Filtering with Encoding Mechanisms: Applications to Multiple Unmanned Aerial Vehicles

In this paper, the neural-network-based filtering problem is investigated for multi-sensor systems with dynamic encoding mechanisms. The sensor nodes and the remote filter are connected through bandwidth-constrained communication networks. To alleviate the communication burden, a novel dynamic encoding-based data compression-decompression mechanism is proposed so as to encode the data into a limited number of bits. Then, with the aid of the neural network learning method, a neural-network-based set-membership filter is developed for estimating the system states. Sufficient conditions are obtained to ensure that the filtering error remains within the bounded ellipsoidal set. In addition, the neural network tuning parameters and the filter gains are calculated by solving constrained optimization problems. Finally, the effectiveness of the proposed filtering algorithm is verified through a scenario of maneuvering target tracking using multiple unmanned aerial vehicles.