Network dynamics based multidomain sensor data processing and fusion for multidomain operations

Bingcheng Li

doi:10.1117/12.2559990

21 April 2020 Network dynamics based multidomain sensor data processing and fusion for multidomain operations

Bingcheng Li

Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 1141309 (2020) https://doi.org/10.1117/12.2559990
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Multidomain sensor data processing and fusion provide reliable ways for situational awareness in multidomain operations and receive great attention in both industry and academia. However, these data processing and fusion are complicated in implementation due to various modalities and high complexity of sensor data. In network dynamics, graph theory is used to represent complex data and extract information, and graph evolution is applied to analyze network dynamics. In this paper, combining the technologies of these two different domains, we propose using network dynamics to process and fuse multidomain sensor data for multidomain operations. First, we propose a graph-theory based framework for multidomain sensor data processing and fusion. Then we apply this general framework to multidomain sensor data processing. Using one-dimensional radio frequency (RF) signal processing, two-dimensional image processing and three-dimensional light detection and ranging (LIDAR) data analytics as examples, we demonstrate that with the proposed method, the same architecture can be used to extract critical features for these three types of sensor data. Furthermore, experiments also show that the proposed method creates higher performance than traditional methods.

Conference Presentation

Citation Download Citation

Bingcheng Li "Network dynamics based multidomain sensor data processing and fusion for multidomain operations", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 1141309 (21 April 2020); https://doi.org/10.1117/12.2559990

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