A certain problem exists in radar tracking. Usually the detections provide noisy measurements on a target's position in radar coordinates (range and angle) at discrete times, while the positions and motion are to be determined in Euclidean space (rectangular coordinates), and an estimator such as the Kalman filter is used. In which case the track is biased and has an inconsistent error covariance matrix. Of course, over the years many techniques have been proposed for "debiasing" such estimates. Here it is shown that they generally do not make the tracks more accurate and in some cases their estimates are worse. Fortunately, a simple method exists whereby an extended Kalman filter can be nearly optimal - unbiased, less noisy, with a more consistent covariance matrix. Nowadays that is dubbed the Extended Preferred Ordering Theorem. In an earlier work by this author it was shown that the angle measurements should be used first, the range measurement last. Others have shown its efficacy in a variety of real-world radar tracking applications. In this work that method is further extended so that the measurement components can be used in any order with virtually the same results, and the extended Kalman filter is made to be stable, in addition to having its bias reduced and covariance matrix improved. The book also presents several of the more commonly used tracking methods and simple examples are used throughout, whereby the reader may gain a deeper understanding of the radar tracking metric accuracy problem.