Optimal sonar tactics over uncertain sediments

Tactical patterns for monostatic sensors were developed during the Cold War for deep, uniform underwater environments, where a simple median detection range defined a fixed spacing between search ladder legs. Acoustic conditions in littoral environments are so complex that spatial variability of bot...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:The Journal of the Acoustical Society of America 2005-09, Vol.118 (3_Supplement), p.1934-1934
Hauptverfasser: DelBalzo, Donald R., Powers, William J., Cole, Bernie F.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Tactical patterns for monostatic sensors were developed during the Cold War for deep, uniform underwater environments, where a simple median detection range defined a fixed spacing between search ladder legs. Acoustic conditions in littoral environments are so complex that spatial variability of bottom sediment properties destroys the simple homogeneous assumption associated with standard tactical search concepts. Genetic algorithms (GAs) have been applied to this problem to produce near-optimal, non-standard search tracks for monostatic mobile sensors that maximize probability of detection in such inhomogeneous environments. The present work describes a new capability called SPEAR (search planning with environmentally adaptive response) that adds tactical adaptation to search paths in a complex, littoral environment, as new in situ backscattering and bottom loss information becomes available. This presentation reviews the GA approach and discusses tactical adaptation to uncertain bottom sediment properties. The results show that easily implemented dynamic changes in active pulse depression angles and frequencies can produce significant improvement in detection performance in a complex littoral area. [Work supported by NAVSEA.]
ISSN:0001-4966
1520-8524
DOI:10.1121/1.4780894