This is an example of a bivariate HP filter. There's a common growth component for both series. Each series has its own intercept and loading on the growth component, that is, the model has
y1(t) = a1 + g1 G(t) + v1(t)
y2(t) = a2 + g2 G(t) + v2(t)
where G(t) is a standard local trend state space model. If the "SV" matrix is the identity, the series are given equal weight in determining G(t). Changing that to a non-identity will force G to fit better the series with the smaller value for SV.
The setup actually will fit two or more series; you just have to change n=2 and redo the frml yf line.