There is a simple, straightforward derivation that starts with the assumptions of the Kalman filter and requires a little Algebra to arrive at the update and extrapolation equations as well as some properties regarding the measurement residuals (difference between the predicted state and the measurement). To start, the Kalman Filter is a linear, unbiased estimator that uses a predictor/corrector process to estimate the state given a sequence of measurements. This means that the general process involves predicting the state and then correcting the state based upon the difference between that prediction and the observed measurement (also known as the residual). The question becomes how to update the state prediction with the observed measurement such that the resulting state estimate is: (1) a linear combination of the predicted state "x" and the observed measurement "z" and (2) has an error with zero mean (unbiased). Base upon these assumptions, the Kalman Filter can be derived.
State and Measurement Model Notation and Assumptions
The state dynamics model for the state vector $\bar x_k$ at time $k$ is given by the state transition matrix $F_{k-1}$ and the state vector $\bar x_{k-1}$ at a previous time $k-1$. The state dynamics model also includes process noise given by $\bar v_{k-1}$ at time $k-1$.
The measurement model for the measurement vector $\bar z_k$ at time $k$ is given by the observation matrix $H_k$ and the state vector $\bar x_k$ at time $k$. The measurement model also includes measurement noise given by $\bar w_k$ at time $k$.
The Kalman Filter derivation is easier if we make the Linear Gaussian assumptions and assume that the measurement noise and process noises are statistically independent (uncorrelated):
State Estimation and Error Notiations
Now, we wish to find the state estimate $\hat x$ given a time series of measurements and define the following notation:
$\hat x_{k|k}$ is the state estimate at time $k$ after updating the Kalman Filter with all measurements through time $k$. That is, it is the updated/filtered state estimate.
$\hat x_{k|k-1}$ is the state estimate at time $k$ after updating the Kalman Filter with all but the most recent measurement. That is, it is the predicted state estimate.
$\tilde x_{j|k}$ is the estimation error in the state, which is given by:
$\tilde x_{j|k} = x_j - \hat x_{j|k}$
$P_{k|k}$ is the state estimate error covariance matrix at time $k$ after updating the Kalman Filter with all measurements through time $k$. That is, it is the error covariance for the updated/filtered state estimate.
$P_{k|k-1}$ is the state estimate at time $k$ after updating the Kalman Filter with all but the most recent measurement. That is, it is the error covariance for the predicted state estimate.
$P_{j|k}$ is the state estimate error covariance matrix, which is given by:
$P_{j|k} = E[\tilde x_{j|k} \tilde x_{j|k}^{\prime}]$
The predicted measurement that is predicted by the Kalman Filter is found by taking the expectation of the measurement model with the zero mean measurement noise assumption:
$\hat z_{k|k-1} = E[\bar z_k] = E[H_k \bar x_k + \bar w_k] = H_k E[\bar x_k] + E[\bar w_k] = H_k \hat x_{k|k-1}$
Finally, the residual vector is the difference between the observed measurement $z_k$ at time $k$ and the predicted measurement:
$\eta_k = z_k - \hat z_{k|k-1} = H_k \hat x_{k|k-1}$
Kalman Filter Derivation
We assume that the updated state estimate is a linear combination of the predicted state estimate and the observed measurement as:
and we wish to find the weights (gains) $K^{\prime}_k$ and $K_k$ that produce an unbiased estimate with a minimum state estimate error covariance.
Unbiased Estimate Assumption
Applying the unbiased estimation error assumption, we have that:
and with $E[\tilde x_{k|k}] = 0$, this results in:
which results in:
Substituting this relationship between $K^{\prime}_k$ and $K_k$ back into the linear combination assumption, we have:
where $K_k$ is called the Kalman Gain.
Minimizing the State Estimate Error Covariance
We start by computing the algebraic form of the updated covariance matrix:
We then compute the trace of the error covariance $Tr[P_{k|k}]$ and minimize it by: (1) computing the matrix derivative with respect to the Kalman Gain $K_k$ and (2) setting this matrix equation to zero. The solution for the Kalman Gain $K_k$ is given by:
$\frac{\partial Tr[P_{k|k}]}{\partial K_k}$ = 0
results in:
Kalman Update
From the above derivation, the Kalman Update equations are given as:
where
Kalman Extrapolation
The extrapolation equations are simply a result of applying the system dynamics model and applying the definition of the error covariance matrix:
Residual Covariance
The residual covariance is given by applying the formal definition of the expectation of the quadratic form of the residual vector $\eta_k$:
Best Answer
It is actually neither of these covariance matrices. The covariance "S" in the Mahalanobis distance is the covariance of the residual vector (difference between the predicted Kalman measurement "z_hat" and the measurement "z" you are attempting to associate). Thus, S = errorCovPre + R where R is the measurement covariance matrix, which is often specified as a diagonal matrix containing the variance of each measurement dimension contained in the measurement vector "z". It is also important to remember that z_hat = H * x_predicted where x_predicted is the predicted state of the Kalman filter and "H" is the observation matrix that maps the state space into the measurement space.