SOFAR TRACKING EXPERIMENT EONS PHASE I (EXPERIMENTAL OCEAN NAVIGATION SYSTEM).

Abstract

On 25, 26 May 1966, a total of 115 Mk59-1 SOFAR bombs were dropped from a P3A aircraft flying at altitudes of 5,000 to 1,000 ft in the region northwest of Kauai in the Hawaiian Island chain. Arrival times of the axial SOFAR ray for the explosive signals were measured at a fixed network of hydrophones in the mid-Pacific and west coast of the United States. The coordinates and epicentral time of each explosion were then obtained on a digital computer using a least squares technique. To furnish a comparison standard for the SOFAR fixes, precise radar measurements of the aircraft position were made for eight drops at an altitude of 5,000 feet and the water impact point calculated from the equations of a freely falling body. These radar fixes are conservatively estimated to be accurate within 0.1 naut. mile. The comparison standard for the remainder of the shots was provided by LORAN-A equipment carried aboard the aircraft now flying at an altitude of 1,000 feet. The mean discrepancy between radar and SOFAR fixes is 0.618 + or - 0.287 naut. mile, while the mean discrepancy between LORAN-A and SOFAR fixes is 2.516 + or - 1.873 naut. mile. Since the SOFAR data show a high degree of internal consistency, it is inferred that, for this experiment, SOFAR furnished a more accurate fix than the LORAN-A.

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1966
Accession Number
AD0805419

Entities

People

  • Rex P. Brumbach
  • Scott C. Daubin

Tags

Communities of Interest

  • Air Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acoustic Equipment
  • Aircrafts
  • Altitude
  • Bodies
  • Computers
  • Consistency
  • Coordinate Systems
  • Digital Computers
  • Equations
  • Explosions
  • Explosives
  • Falling Bodies
  • Hydrophones
  • Impact Point
  • Mechanical Equipment
  • Standards
  • United States

Readers

  • Aerospace Test and Evaluation
  • Oceanography.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference