Measured values obtained in a measuring machine to be estimated are provided to estimate errors in the measured values. Based on the estimated errors in the measured values, a covariance matrix or correlation matrix of measured values is derived. The covariance matrix or correlation matrix is then subjected to eigenvalue decomposition to derive eigenvalues and eigenvectors. A normal random number with an expected value of 0 and a variance equal to an eigenvalue corresponding to the eigenvalue is generated as a coefficient of coupling for each eigenvector, and all eigenvectors are linearly coupled to generate pseudo-measured values of the measuring machine. The generated pseudo-measured values are subjected to statistic processing to estimate uncertainty of the measuring machine.

Disclosed is a technique for obtaining an estimate and variance of each variable based on a constraint manifold. Particles (or samples) are sampled in order to filter and fuse ambiguous data or information on at least one state variable of a system using the particles. The sampling is carried out in consideration of an influence which non-linearity of the constraint manifold of a system model, an observation model or another system model exerts on a probability distribution of the state variable. With this construction, it is possible to reduce decrease of fusion and filtering performance, decrease a Gaussian approximation error, and detect mismatched information.

An improved covariance matrix encoding scheme wherein a covariance matrix is described in terms of Euler angles and eigenvalues. A covariance matrix, P, is decomposed into its eigenvalues and eigenvectors. The eigenvalues and their corresponding eigenvectors are arranged starting with the smallest value, and the next two ordered such that, the eigenvector set comprises a right-handed coordinate system. Each eigenvalue is then encoded with a logarithmic or other compression scheme. Euler angles are calculated and angle is compressed and an offset is added to each angle. The covariance matrix is then reconstructed from the encoded values to test if the encoded matrix completely covers the original matrix. If necessary, a scale factor is applied to all reconstructed eigenvalues and the scaled versions are then re-encoded as described above. The scaling and re-encoding process ensures that the encoded matrix covers the original matrix.

A multiple model (MM) radar tracking filter which controls the weighting applied to outputs of first and second model functions responsive to non-Markovian switching logic, includes the first and second model functions, switching logic receiving unweighted outputs from the first and second model functions and generating first and second weighting signals, first and second multipliers generating respective first and second weighted output signals responsive to received ones of the unweighted outputs of the first and second model functions and the first and second weighting signals, and a feed back loop for providing a feedback signal to respective inputs of the first and second model functions responsive to the weighted outputs of the first and second multipliers. If desired, the MM radar tracking filter may also include a summer for generating a signal output responsive to the weighted outputs of the first and second multipliers. A method for controlling the MM radar tracking filter employing alternatives (non-Markov) switching logic is also described.

In accordance with an implementation of the present technique, a method for processing data is disclosed. The method involves analyzing consistency of sensor readings obtained from a plurality of sensors monitoring a device, where each sensor reading is indicative of at least one operational parameter of the device. The method also involves assigning a confidence value to each of the sensor readings, where the confidence value is indicative of an operational condition of one of the plurality of sensors that provided the sensor reading. The method also includes weighting each sensor reading based on the confidence value assigned to the sensor reading to determine an acceptance or a rejection of the sensor reading and fusing the sensor readings that are accepted to obtain a fused sensor reading corresponding to the operational parameter measured by the sensors on the device.