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Minimum Bitrate Neuromorphic Encoding for Continuous-Time Gauss-Markov Processes
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DOI：10.1109/TAC.2024.3419586.png)
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Abstract
En 中文
In this work, we study minimum data rate tracking of a dynamical system under a neuromorphic event-based sensing paradigm. We begin by bridging the gap between continuous-time (CT) system dynamics and information theory's causal rate distortion theory. We motivate the use of nonsingular source codes to quantify bitrates in event-based sampling schemes. This permits an analysis of minimum bitrate event-based tracking using tools already established in the control and information theory literature. We derive novel, nontrivial lower bounds to event-based sensing, and compare the lower bound with the performance of well-known schemes in the established literature.
Keywords:
Sensors
Bit rate
Real-time systems
Costs
Robot sensing systems
Distortion
Rate-distortion
Continuous-time (CT) systems
information theory
Kalman filters
networked control systems
optimal control
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W