Open AccessBearing-only Trajectory Estimation for Ballistic Target Using Sparse Representation
Author(s) -
Pu Wang,
Yun Cheng,
Qinghong Yin
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/887/1/012045
Subject(s) - observability , sparse approximation , trajectory , representation (politics) , estimator , bearing (navigation) , computer science , algorithm , trajectory of a projectile , motion (physics) , control theory (sociology) , artificial intelligence , mathematics , physics , statistics , control (management) , astronomy , politics , political science , law
This paper discusses the problem of estimating the trajectory of a ballistic target using bearing measurements from one passive sensor. The major challenge of this problem is the ill-conditioning of the estimation problem due to poor observability of the target motion via bearing measurements. We present an estimator based on the sparse representation algorithm for one sensor scenario. The sparse representation can dramatically reduce the amount of data needed to be estimated. With sparse representation, the trajectory of the ballistic target can be estimated in poor observability conditions. Besides, we introduce some complementary constraints to improve the observability of target motion. Simulation results indicate that the proposed estimator is effective.