  In non-linear filtering, it is traditional to compare non-linear
architectures such as neural networks to the standard linear Kalman Filter
(KF). We observe that this mixes the evaluation of two separate components: the
non-linear architecture, and the parameters optimization method. In particular,
the non-linear model is often optimized, whereas the reference KF model is not.
We argue that both should be optimized similarly, and to that end present the
Optimized KF (OKF). We demonstrate that the KF may become competitive to neural
models - if optimized using OKF. This implies that experimental conclusions of
certain previous studies were derived from a flawed process. The advantage of
OKF over the standard KF is further studied theoretically and empirically, in a
variety of problems. Conveniently, OKF can replace the KF in real-world systems
by merely updating the parameters.
