For understanding state space in general, I suggest Time Series Analysis by State Space Methods. For time series forecasting generally, check out Forecasting: Principals and Practice. For Kalman filtering, there’s Kalman and Bayesian Filters in Python.
For video resources, I like these two videos as an introduction and overview of structural time series modeling. I haven’t found a really high quality resource specifically on VAR modeling, but you get a lot of the necessary intuition from studying structural models generally.