How to account for multiple signals in the input to a Kalman filter
The kinematic filter (which is mathematically a polynomial Kalman filter) has the ability to track any signal as long as the sampling rate is high enough, because when it's so, the complex motion of the target becomes linearized and fits into the quadratic kinematic model being employed by the Kalman filter dynamic model but the disadvantage is the processing cost due to high sampling rate.

Also the polynomial (kinematic) Kalman filter will not distinguish between two sinusoids but instead track their sum ignoring the noise only, unlike a specifically designed sinusoidal filter which tracks the chosen sinusoid and ignores all the rest as disturbance. However the model needs to be exact and known in advance as its fundamental disadvantage. The figure below shows a simulation example of a sinusoidal Kalman filter with two sinusoidal signals added together plus uncorrelated zero mean white noise as the measurement signal. It can be seen that the sinusoidal Kalman filter tracks the chosen sine wave (which is used in deriving the system dynamics matrix) and ignores all the rest as disturbance including the added white noise and the other sinewave which was at an harmonic multiple of the tracked sinusoidal frequency.



The extended kalman filter is a compromise, it has some additional computational burden, specifically the per sample update of the linearized derivatives, but a compromise can be found by lowering the sampling rate compared to a kinematic filter's requirement.