Convex Clustering for Multistatic Active Sensing via Optimal Mass Transport

Filip Elvander, Johan Karlsson, Toon van Waterschoot

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

Abstrakti

In multistatic active sensing, such as active sonar, exploiting the spatial diversity provided by the use of several transmitting and receiving units can lead to increased resolution and target localization performance. For multi-target scenarios, the success of the task depends on correctly identifying the subsets of measurements associated with each target, a problem of combinatorial nature. In this paper, we propose to address this problem using a convex relaxation. In particular, we propose a method inspired by the concept of optimal mass transport capable of jointly performing the measurement clustering and target localization. We show that this method may be interpreted as maximum likelihood estimation on a grid, allowing for local post-processing achieving statistical efficiency. The behavior of the proposed method is illustrated using simulated 2D examples.
AlkuperäiskieliEnglanti
Otsikko2021 29th European Signal Processing Conference (EUSIPCO)
KustantajaIEEE
Sivut1730-1734
Sivumäärä5
ISBN (elektroninen)978-9-0827-9706-0
ISBN (painettu)978-1-6654-0900-1
DOI - pysyväislinkit
TilaJulkaistu - 27 elok. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEuropean Signal Processing Conference - Dublin, Ireland, Dublin, Irlanti
Kesto: 23 elok. 202127 elok. 2021
Konferenssinumero: 29

Julkaisusarja

NimiIEEE International Conference on Acoustics, Speech and Signal Processing
ISSN (painettu)1520-6149
ISSN (elektroninen)2379-190X

Conference

ConferenceEuropean Signal Processing Conference
LyhennettäEUSIPCO
Maa/AlueIrlanti
KaupunkiDublin
Ajanjakso23/08/202127/08/2021

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