Information space of multi-sensor networks

Multi-sensor networks (MSN) have become very popular in the two past decades [56,30,16], with applications found in many different fields including wireless sensor networks [44,12,28], and the system of command, control, communications, computers, intelligence, surveillance and reconnaissance (C4ISR). It can significantly enrich the information and enhance performance in many tasks, such as noncooperative target tracking [55], and environmental sensing [30]. However, it is difficult to explore the capability of multi-sensor networks with the network architecture becoming more complex. Furthermore, the intrinsic structures and the capability of multi-sensor networks are unknown. Therefore, the identity of underlying information space across modalities becomes increasingly crucial in various applications. A variety of algorithms for understanding the structures and capability analyzing of multi-sensor networks have been It is a challenging problem to explore the capability of multi-sensor networks due to the identity of the underlying information space across modalities. In this paper, the information space for multi-sensor networks is developed from information geometry. The relationship between information space and the performance of multi-sensor networks is investigated. Different sensor information obtained by multi-sensor networks is represented, analyzed and fused concisely. The structure of the information space is studied such as geodesic, Ricci tensor and the information metric matrix. The structural properties of the information space are introduced: i) the symmetry; ii) the connection between information space's curvature and Einstein's field equation; iii) noise essence conjecture. The proposed analysis techniques are validated in many scenarios. The theoretical demonstration and numerical results indicate that the information described in different coordinate systems is equivalent. (c) 2021 Elsevier Inc. All rights reserved.