Reinforcement learning-based waveform optimization for MIMO multi-target detection

Li Wang; Stefano Fortunati; Maria S. Greco; Fulvio Gini

doi:10.1109/ACSSC.2018.8645304

Reinforcement learning-based waveform optimization for MIMO multi-target detection

Li Wang, Stefano Fortunati, Maria S. Greco, Fulvio Gini

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A cognitive beamforming algorithm for colocated MIMO radars, based on Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based optimization protocol that allows the MIMO radar, i.e. the agent, to iteratively sense the unknown environment, i.e. the radar scene involving an unknown number of targets at unknown angular positions, and consequently, to synthesize a set of transmitted waveforms whose related beam patter is tailored on the acquired knowledge. The performance of the proposed RL-based beamforming algorithm is assessed through numerical simulations in terms of Probability of Detection (P-{D}).

Original language	English
Title of host publication	Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1329-1333
Number of pages	5
ISBN (Electronic)	9781538692189
DOIs	https://doi.org/10.1109/ACSSC.2018.8645304
Publication status	Published - 2 Jul 2018
Externally published	Yes
Event	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States Duration: 28 Oct 2018 → 31 Oct 2018

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2018-October
ISSN (Print)	1058-6393

Conference

Conference	52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/Territory	United States
City	Pacific Grove
Period	28/10/18 → 31/10/18

Access to Document

10.1109/ACSSC.2018.8645304

Cite this

Wang, L., Fortunati, S., Greco, M. S., & Gini, F. (2018). Reinforcement learning-based waveform optimization for MIMO multi-target detection. In M. B. Matthews (Ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 (pp. 1329-1333). Article 8645304 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2018.8645304

Wang, Li ; Fortunati, Stefano ; Greco, Maria S. et al. / Reinforcement learning-based waveform optimization for MIMO multi-target detection. Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. editor / Michael B. Matthews. IEEE Computer Society, 2018. pp. 1329-1333 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

@inproceedings{a13fcf92e84b4623933725e5828e3cb0,

title = "Reinforcement learning-based waveform optimization for MIMO multi-target detection",

abstract = "A cognitive beamforming algorithm for colocated MIMO radars, based on Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based optimization protocol that allows the MIMO radar, i.e. the agent, to iteratively sense the unknown environment, i.e. the radar scene involving an unknown number of targets at unknown angular positions, and consequently, to synthesize a set of transmitted waveforms whose related beam patter is tailored on the acquired knowledge. The performance of the proposed RL-based beamforming algorithm is assessed through numerical simulations in terms of Probability of Detection (P-\{D\}).",

author = "Li Wang and Stefano Fortunati and Greco, \{Maria S.\} and Fulvio Gini",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 ; Conference date: 28-10-2018 Through 31-10-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/ACSSC.2018.8645304",

language = "English",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "1329--1333",

editor = "Matthews, \{Michael B.\}",

booktitle = "Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018",

}

Wang, L, Fortunati, S, Greco, MS & Gini, F 2018, Reinforcement learning-based waveform optimization for MIMO multi-target detection. in MB Matthews (ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018., 8645304, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2018-October, IEEE Computer Society, pp. 1329-1333, 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018, Pacific Grove, United States, 28/10/18. https://doi.org/10.1109/ACSSC.2018.8645304

Reinforcement learning-based waveform optimization for MIMO multi-target detection. / Wang, Li; Fortunati, Stefano; Greco, Maria S. et al.
Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. ed. / Michael B. Matthews. IEEE Computer Society, 2018. p. 1329-1333 8645304 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Gini, Fulvio

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Y1 - 2018/7/2

N2 - A cognitive beamforming algorithm for colocated MIMO radars, based on Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based optimization protocol that allows the MIMO radar, i.e. the agent, to iteratively sense the unknown environment, i.e. the radar scene involving an unknown number of targets at unknown angular positions, and consequently, to synthesize a set of transmitted waveforms whose related beam patter is tailored on the acquired knowledge. The performance of the proposed RL-based beamforming algorithm is assessed through numerical simulations in terms of Probability of Detection (P-{D}).

AB - A cognitive beamforming algorithm for colocated MIMO radars, based on Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based optimization protocol that allows the MIMO radar, i.e. the agent, to iteratively sense the unknown environment, i.e. the radar scene involving an unknown number of targets at unknown angular positions, and consequently, to synthesize a set of transmitted waveforms whose related beam patter is tailored on the acquired knowledge. The performance of the proposed RL-based beamforming algorithm is assessed through numerical simulations in terms of Probability of Detection (P-{D}).

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M3 - Conference contribution

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Wang L, Fortunati S, Greco MS, Gini F. Reinforcement learning-based waveform optimization for MIMO multi-target detection. In Matthews MB, editor, Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. IEEE Computer Society. 2018. p. 1329-1333. 8645304. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2018.8645304

Reinforcement learning-based waveform optimization for MIMO multi-target detection

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