Event-Based Sensor Noise Modeling for Space-Based Space Domain Awareness

Abstract Building off the foundation of a physics-based end-to-end model for event-based vision sensors (EVS) observing resident space objects (RSOs), we apply new techniques to model realistic low-light sensor noise. While previous approaches simulate memorized current leakage and apply temporal noise models, our methods improve on these approaches and additionally account for current-following noise as an event source. These improvements are key components for accurate event-generating simulators which can advise requirements and concepts of operations for dedicated event-based Space Domain Awareness (SDA) architectures. The EVS pixel’s independent and asynchronous recording of changes in photocurrent produces data with high temporal resolution and dynamic range making it an attractive technology for SDA. EVS are particularly appealing as a space-based payload because of their sparse data output reducing the need for downlink, computational, and power resources. However, truth data is limited for space-based architectures. Therefore, simulation of the underlying physics is particularly important. Our physics-based end-to-end model now includes noise based on induced photocurrent to generate simulated events closer to known truth. We introduce a new Poisson-based method to model the noise generated by the temporal variation of the dark current and a method to tune high-frequency white noise on the induced photocurrent to model the noise on signals above the dark current. These techniques demonstrably improve EVS noise modeling by closely matching observed event rate and polarity behavior, moving EVS one step closer to operational space-based SDA usage.