Memoryless Polynomial LMS Adaptive Filter for Orbit Object Tracking
Rongtai Cai, Qingxiang Wu, Ping Wang, Mingjia Wang, Yuanhao Wu · 2009
In order to find a fast and effective solution to orbit object tracking, a memoryless polynomial adaptive filter is proposed in this paper. Unlike Volterra adaptive filter, the proposed filter is composed of polynomials in different orders, which can fit normal orbit trajectory well. A memoryless polynomial filter (MLF) is designed first. The designed memoryless polynomial filter can be separated into a linearization filter and a FIR filter. Analogous to linear LMS adaptive filter, a LMS adaptive algorithm is derived for the memoryless polynomial filter, which is called Memoryless polynomial LMS adaptive filter (MLPLMS adaptive filter). Experiments show that the proposed filter has better performance than that of a normal LMS filter on orbit tracking.