A formulation of the adversarial risk for multiobject filtering
A formulation of the adversarial risk for multiobject filtering
This article is focused on estimating a quantity of interest in the context of military impact assessment that we shall call adversarial risk. We formulate the adversarial risk as a function of the multiobject state describing a group of weapons, and propose two approaches to estimate it using multiobject filters. The first, optimal, approach is tailored to filters for point processes, and produces the mean estimate of the adversarial risk and its variance. The second, naïve, approach is applicable to any filter producing point estimates of the multiobject state, yet it is not capable of equipping a risk estimate with an indicator of its quality. We develop an implementation of the optimal approach for a particular multiobject filter and compare it to the naïve approach.
Adversarial risk, Impact assessment, Multiobject filtering
2082-2092
Narykov, Alexey
0bae82a8-2e38-4cc8-8237-1bdf7fe512a8
Delande, Emmanuel
e17b3b32-0949-4914-801e-c9386bce39a5
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
1 August 2021
Narykov, Alexey
0bae82a8-2e38-4cc8-8237-1bdf7fe512a8
Delande, Emmanuel
e17b3b32-0949-4914-801e-c9386bce39a5
Clark, Daniel E.
537f80e8-cbe6-41eb-b1d4-31af1f0e6393
Narykov, Alexey, Delande, Emmanuel and Clark, Daniel E.
(2021)
A formulation of the adversarial risk for multiobject filtering.
IEEE Transactions on Aerospace and Electronic Systems, 57 (4), .
(doi:10.1109/TAES.2021.3098130).
Abstract
This article is focused on estimating a quantity of interest in the context of military impact assessment that we shall call adversarial risk. We formulate the adversarial risk as a function of the multiobject state describing a group of weapons, and propose two approaches to estimate it using multiobject filters. The first, optimal, approach is tailored to filters for point processes, and produces the mean estimate of the adversarial risk and its variance. The second, naïve, approach is applicable to any filter producing point estimates of the multiobject state, yet it is not capable of equipping a risk estimate with an indicator of its quality. We develop an implementation of the optimal approach for a particular multiobject filter and compare it to the naïve approach.
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Published date: 1 August 2021
Additional Information:
Funding Information:
This work was supported in part by the Joint AFRL-Dstl Basic-Research Grant in Autonomous Signal Processing (AFOSR grant FA9550-19-1-7008, and Dstl Task No. 1000133068). The work of A. Narykov was supported by the James Watt scholarship from Heriot-Watt University.
Publisher Copyright:
© 2021 IEEE.
Keywords:
Adversarial risk, Impact assessment, Multiobject filtering
Identifiers
Local EPrints ID: 475495
URI: http://eprints.soton.ac.uk/id/eprint/475495
ISSN: 0018-9251
PURE UUID: 08a658a3-94d2-4238-b919-752e8fdb9a3c
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Date deposited: 20 Mar 2023 17:43
Last modified: 17 Mar 2024 13:11
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Contributors
Author:
Alexey Narykov
Author:
Emmanuel Delande
Author:
Daniel E. Clark
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