Comparison of Single-point and Two-point Difference Track Initiation Algorithms Using Position Measurements
-
摘要: 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.Abstract: 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.
-
Key words:
- Track initiation /
- Kalman filter /
- unbiased estimator /
- minimum mean square error
计量
- 文章访问数: 3136
- HTML全文浏览量: 19
- PDF下载量: 1649
- 被引次数: 0