利用位置测量的单点和两点差分跟踪起始算法的比较研究

We consider the problem of initializing the tracking filter of a target moving with nearly constant velocity when position-only (1D, 2D, or 3D) measurements are available. It is known that the Kalman filter is optimal for such a problem, provided it is correctly initialized. We compare a single-point and the well-known two-point difference track initialization algorithms. We analytically show that if the process noise approaches zero and the maximum speed of a target used to initialize the velocity variance approaches infinity, then the single-point algorithm reduces to the two-point difference algorithm. We present numerical results that show that the single-point algorithm performs consistently better than the two-point difference algorithm in the mean square error sense. We also present analytical results that support the conjecture that this is true in general.

Comparison of Single-point and Two-point Difference Track Initiation Algorithms Using Position Measurements

We consider the problem of initializing the tracking filter of a target moving with nearly constant velocity when position- only(1D,2D,or 3D)measurements are available.It is known that the Kalman filter is optimal for such a problem,provided it is correctly initialized.We compare a single-point and the well-known two-point difference track initialization algorithms.We analytically show that if the process noise approaches zero and the maximum speed of a target used to initialize the velocity variance approaches infinity,then the single-point algorithm reduces to the two-point difference algorithm.We present numerical results that show that the single-point algorithm performs consistently better than the two-point difference algorithm in the mean square error sense.We also present analytical results that support the conjecture that this is true in general.