  This note reveals an explicit relationship between two representative finite
impulse response (FIR) filters, i.e. the newly derived and popularized
Kalman-Like unbiased FIR filter (UFIR) and the receding horizon Kalman FIR
filter (RHKF). It is pointed out that the only difference of the two algorithms
lies in the noise statistics ignorance and appropriate initial condition
construction strategy in UFIR. The revelation can benefit the performance
improvement of one by drawing lessons from the other. Some interesting
conclusions have also been drawn and discussed from this revelation.
