3 results about "Covariance matrix" patented technology

The invention discloses a GNSS troposphere wet delay estimation method and system, equipment and a medium, and the method comprises the following steps: obtaining observation values of different frequency points, and forming an ionosphere-free combination observation equation in each epoch; performing Kalman filtering state updating and observation updating according to the prior ZWD random walk parameters to obtain an unknown variable forecast value and a variance covariance matrix shown in the description of the unknown variable forecast value; calculating mathematical expectations when given priori ZWD random walk noise and observation noise; using the mathematical expectation as a pseudo observation value, and estimating a new ZWD random walk parameter by using data in a sliding window; and updating the priori ZWD random walk parameter by using the new ZWD random walk parameter. According to the method, random model parameters changing along with time can be estimated in real time, and the troposphere wet delay estimation value can be improved.

The invention relates to an airborne early warning radar sea surface maneuvering ship target adaptive tracking method comprising the following steps: 1, converting measurement trace point data detected by an airborne early warning radar to a tracking coordinate system; 2, transmitting the processing result of the previous smooth interval to the current smooth interval; 3, according to a processing result of a previous smooth interval, solving posterior distribution of a measurement noise covariance matrix by using variational Bayesian learning; 4, solving the prediction distribution of the measurement noise covariance matrix in the step 3 in the current smooth interval by adopting a blanking model; 5, carrying out forward filtering processing by using the measurement trace point data output in the step 1 and the processing result in the step 4 and using an interactive multi-model algorithm; step 6, carrying out reverse smoothing processing by adopting an RTS-IMM algorithm according to the processing results of the step 4 and the step 5; and 7, solving a fusion estimation value through moment matching processing according to a processing result of the step 6, and outputting a track.