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NASA NTRS · 20220007373

Distributed Target Tracking With Optimal Data Migration

Vahram Stepanyan·Keerthana Kannan·Evan T Kawamura·Thomas Lombaerts·Corey Ippolito
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Abstract

The paper presents an Extended Kalman Filter based framework for airborne target tracking using dynamic information fusion from multi-modal sensors with geodiversity. First, the algorithm execution location is determined using an optimal data migration strategy, next the sensors information is dynamically fused at each estimation instance using validity flag for each sensor reading, finally the target estimation is updated based on the fused innovation vector. The approach is applied to synthetic data generated from the radar and camera models located on the ground for the simulated target flight in Reflection simulation environment.

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Vahram Stepanyan, Keerthana Kannan, Evan T Kawamura, Thomas Lombaerts, Corey Ippolito. Distributed Target Tracking With Optimal Data Migration. https://ntrs.nasa.gov/citations/20220007373

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The paper presents an Extended Kalman Filter based framework for airborne target tracking using adaptive information fusion from multi-modal multi-rate distributed sensors network. First, the tracking algorithm execution location is determined using an optimal data migration strategy, which also computes the associated delays for each sensor data to arrive at the computing location. Next, the fast (zero-delay) sensors information is dynamically fused in the filter correction procedure at the arrival instance of each valid sensor reading. Finally, the target estimation is updated based on the valid slow (delayed) data, which are grouped according to the delay-time steps before application of the Larsen's method. This approach is applied to the synthetic sensor data generated by means of the ground based radar and camera models for the simulated target flight in Reflection simulation environment.

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Target Tracking with Distributed Sensing and Optimal Data Migration

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