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Since the innovation of the ubiquitous Kalman filter more than five decadesback it is well known that to obtain the best possible estimates the tuning ofits statistics $X_0$, $P_0$, $\Theta$, $R$ and $Q$ namely initial state andcovariance, unknown parameters, and the measurement and state noise covariancesis very crucial. The earlier tweaking and other systematic approaches arereviewed but none has reached a simple and easily implementable approach forany application. The present reference recursive recipe based on multiplefilter passes through the data leads to a converged `statistical equilibrium'solution. It utilizes the pre, post, and smoothed state estimates and theircorresponding measurements and the actual measurements as well as theircovariances to balance the state and measurement equations and form generalizedcost functions. The filter covariance at the end of each pass is heuristicallyscaled up by the number of data points and further trimmed to provide the $P_0$for subsequent passes. A simultaneous and proper choice for $Q$ and $R$ basedon the filter sample statistics and certain other covariances leads to a stablefilter operation providing the results after few iterations. When only $R$ ispresent in the data by minimizing the `innovation' cost function using the nonfilter based Newton Raphson optimization results served as an anchor formatching and tuning the filter statistics. When both $R$ and $Q$ are present inthe data the consistency between the injected noise sequences and theirstatistics provided a simple route and confidence in the present approach. Atypical simulation study of a spring, mass, damper system with a weak nonlinear spring constant shows the present approach out performs earliertechniques. The Part-2 of the paper further consolidates the present approachbased on an analysis of real airplane flight test data.

A Heuristic Reference Recursive Recipe for the Menacing Problem of  Adaptively Tuning the Kalman Filter Statistics. Part-1. Formulation and  Simulation Studies