Monopulse Radar Air-ti-Ground Ranging

The use of a two-lobe monopulse radar for measuring slant range to the surface of the earth in the absence of discrete targets is analyzed. It is shown that tracking dispersion can be considered as the resultant of two components. One component is independent of range and results from the finite pulse length and gate length and the random nature of the return signals. The other component is due to receiver noise and increases as the signal-to-noise ratio decreases. The dispersion component independent of range is shown to be proportional to the pulse length and tracking gate length. The variable dispersion is shown to be proportional to the five halves power of the range and the three halves power of the cotangent of the depression angle of the antenna boresight axis. Performance calculations for a specific radar are carried out and compared with experimental data.     

Target tracking in maneuver-centered coordinates

Aircraft targets normally maneuver on circular paths, which has led to tracking filters based on circular turns. A coordinate system to track circular maneuvers with a simple Kalman filter is introduced. This system is a polar coordinate system located at the center of the maneuver. It leads to a tracking filter with range, angle, and angular velocity in the state vector. Simulation results are presented, showing that the algorithm displays improved performance over methods based on constant x-y acceleration when tracking circular turns     

Comparison of two-sensor tracking methods based on state vectorfusion and measurement fusion

There are two approaches to the two-sensor track-fusion problem. Y Bar-Shalom and L. Campo (ibid., vol.AES-22, 803-5, Nov. 1986) presented the state vector fusion method, which combines state vectors from the two sensors to form a new estimate while taking into account the correlated process noise. The measurement fusion method or data compression of D. Willner et al. (1976) combines the measurements from the two sensors first and then uses this fused measurement to estimate the state vector. The two methods are compared and an example shows the amount of improvement in the uncertainty of the resulting estimate of the state vector with the measurement fusion method