Abstract
In this work, we present an optical space imaging dataset using a range of event-based neuromorphic vision sensors. The unique method of operation of event-based sensors makes them ideal for space situational awareness (SSA) applications due to the sparseness inherent in space imaging data. These sensors offer significantly lower bandwidth and power requirements making them particularly well suited for use in remote locations and space-based platforms. We present the first publicly-accessible event-based space imaging dataset including recordings using sensors from multiple providers, greatly lowering the barrier to entry for other researchers given the scarcity of such sensors and the expertise required to operate them for SSA applications. The dataset contains both day time and night time recordings, including simultaneous co-collections from different event-based sensors. Recorded at a remote site, and containing 572 labeled targets with a wide range of sizes, trajectories, and signal-to-noise ratios, this real-world event-based dataset represents a challenging detection and tracking task that is not readily solved using previously proposed methods. We propose a highly optimized and robust feature-based detection and tracking method, designed specifically for SSA applications, and implemented via a cascade of increasingly selective event filters. These filters rapidly isolate events associated with space objects, maintaining the high temporal resolution of the sensors. The results from this simple yet highly optimized algorithm on the space imaging dataset demonstrate robust high-speed event-based detection and tracking which can readily be implemented on sensor platforms in space as well as terrestrial environments.
Highlights
We demonstrated the ability to detect a resident space object in orbits ranging from low-earth orbit (LEO) to geosynchronous orbits (GEO) [12]
The bottom row (d) shows how, at each stage of processing, the event density of the recordings is reduced into an ever more efficient representation of the data. Together these results demonstrate that over the wide range of heterogeneous input event streams, the proposed algorithm generates a sparse yet informative output event stream. (b) Shows the per recording specificity distribution is shifted from a mean of 0.63 for the raw events to 0.98 and 0.99 for the detection and tracking events with most results at 1
When the sparser detection event stream is interpolated via the tracker, the sensitivity rises above the raw events
Summary
O UR increasing reliance on space-based technologies for communication, navigation and security tasks as well as the recent dramatic drop in the cost of space launches has created an immediate need for better methods for detecting and tracking objects in orbit around the earth [1]. The cost of collisions in space poses a significant risk to both our space infrastructure and future space missions. Space Situational Awareness (SSA), and Space Traffic Management (STM) — its civilian counterpart — are critical tasks for regulation and enforcement of the use of space, and to prevent a future catastrophic. Date of publication July 16, 2020; date of current version November 18, 2020. The associate editor coordinating the review of this article and approving it for publication was Dr Chirasree Roychaudhuri.
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