An efficient radar tracking algorithm using multidimensional Gauss-Hermite quadratures
نویسندگان
چکیده
In radar tracking target motion is best modeled in Cartesian coordinates. Its position is however measured in polar coordinates (range and azimuth). Tracking in Cartesian coordinates with noisy polar measurements requires either converting the measurements to a Cartesian frame of reference and then applying the linear Kalman lter to the converted measurement [1] or using the extended Kalman lter (EKF) [2] in mixed coordinates. The rst approach is accurate only for moderate cross-range errors; the second approach is consistent only for small errors. A new e cient tracking algorithm using the multidimensional GaussHermite quadratures [3] to propagate the mean and the covariance of the conditional probability density function is presented. This method is compared with the EKF and the converted measurement Kalman lter (CMKF) and it is shown to be more accurate.
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