Phased Array Radar Resource Management Using Continuous Double Auction

2023-06-12

Alexander Charlish, Karl Woodbridge, Hugh Griffiths [CWG15].

Bottom Line Up Front

Run a quick auction to adjust the sizes of tasks, then dump the resulting tasks into earliest deadline first. It works better than randomly dropping tasks, it costs less computationally than Q-RAM.

Summary

• multiplexed in time and angle
• time/power budget

• multidimensional parameter selection problem

  • task revisit interval
  • task dwell duration

Part 2 of CDAPS (Continuous Double Action Param Selection) paper has good problem formulation.

• In this formulation, task attributes are not handed down from god but instead are what is being adjusted.
• Maximize utility (sum of task utilities) constrained by time
• Proves some optimal solution exists.

Market equilibrium, like a multiagent system. Tasks are agents, along with “auctioneer” agent. Agents announce bids or sales of struct Trade{s: quantity, p: price, $kappa_k$: agent identifier}. Framing auctioneer as an agent is awkward, but whatever.

Simulation:

• 200 targets require active tracking.
• Each task has a coherent dwell duration in $\left[0.1\text{ms},10\text{ms}\right]$, revisit interval in $\left[100\text{ms},3500\text{ms}\right]$.
• Randomly evolving Singer trajectory.

Okay so splits up task selection and task scheduling again in a way that confuses me. After this exotic auction (and I guess the expensive Q-RAM as well?) or just randomly throwing tasks off the boat, it just jams them in earliest deadline first order and executes them.

Basically CDAPS is Q-RAM but computationally cheaper.

Asserts in conclusion that it adapts quickly to environmental changes but I don’t see this being evaluated. I guess it’s just a “low compute is low compute” argument?