Track Detection of Low Observable Targets Using a Motion Model

Abstract

A method for detecting a low observable target track using an acceleration-based overall motion model is proposed. Unlike the existing track-before-detect methods that are based on sequential state updates, this method computes integrated echo energy for the entire hypothesized motion. The detection and the estimation of the track are made simultaneously using the batch processing approach. A comparison of track detection probability shows higher performance against low observable targets. Using a motion similarity metric and motion model homogeneity, a performance prediction model is derived and compared with the simulation results.

Keywords

• Tracking
• Track before detect
• Detection
• Batch processing
• Hidden Markov model
• Motion model