2002Unpublished venueOpen access

Measures of effectiveness for multiple ROTHR track data fusion (MRTDF)

W.J. Yssel, William C. Torrez, R.A. Lematta

Open full text 3 citations

Abstract

Relocatable Over-the-Horizon Radar (ROTHR) is a long range, bi-static, high frequency radar surveillance system tasked with tracking air targets over large land and ocean areas. The long ranges, together with the relatively low altitudes of the targets, require that the radar look beyond the line-of-sight, in other words, over-the-horizon (OTH). This is accomplished by refracting the signal off the ionosphere to points beyond the horizon. In order to quantify the performance of the data fusion of multiple OTH radar tracks, several measures of effectiveness (MOEs) have been developed which correspond to the unique technical challenges facing a single OTH radar tracking system. This paper describes appropriate MOEs arising from OTH technical issues relating to ionospheric mode identification, crossing targets, and low Doppler targets. These MOEs were chosen to be sensitive to the performance that would be expected by fusing data from two OTH radar systems having overlapping coverage.

About this research paper

What this paper is about

Relocatable Over-the-Horizon Radar (ROTHR) is a long range, bi-static, high frequency radar surveillance system tasked with tracking air targets over large land and ocean areas. The long ranges, together with the relatively low altitudes of the targets, require that the radar look beyond the line-of-sight, in other words, over-the-horizon (OTH). This is accomplished by refracting the signal off the ionosphere to points beyond the horizon. In order to quantify the performance of the data fusion of multiple OTH radar tracks, several measures of effectiveness (MOEs) have been developed which correspond to the unique technical challenges facing a single OTH radar tracking system. This paper describes appropriate MOEs arising from OTH technical issues relating to ionospheric mode identification, crossing targets, and low Doppler targets. These MOEs were chosen to be sensitive to the performance that would be expected by fusing data from two OTH radar systems having overlapping coverage.

Why it matters

OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Relocatable Over-the-Horizon Radar (ROTHR) is a long range, bi-static, high frequency radar surveillance system tasked with tracking air targets over large land and ocean areas. The long ranges, together with the relatively low altitudes of the targets, require that the radar look beyond the line-of-sight, in other words, over-the-horizon (OTH). This is accomplished by refracting the signal off the ionosphere to points beyond the horizon. In order to quantify the performance of the data fusion of multiple OTH radar tracks, several measures of effectiveness (MOEs) have been developed which correspond to the unique technical challenges facing a single OTH radar tracking system. This paper describes appropriate MOEs arising from OTH technical issues relating to ionospheric mode identification, crossing targets, and low Doppler targets. These MOEs were chosen to be sensitive to the performance that would be expected by fusing data from two OTH radar systems having overlapping coverage.

Key concepts: Over-the-horizon radar, Radar, Radar tracker, Computer science, 3D radar, Sensor fusion, Remote sensing, Fire-control radar

Related papers

Back to paper searchBrowse research topicsOriginal source
Measures of effectiveness for multiple ROTHR track data fusion (MRTDF) — Research Paper | ScholarLens