By Charles K. Chui, Guanrong Chen
A evaluate of powerful radar monitoring clear out tools and their linked electronic filtering algorithms. It examines newly built platforms for disposing of the real-time execution of entire recursive Kalman filtering matrix equations that decrease monitoring and replace time. It additionally makes a speciality of the position of monitoring filters in operations of radar info processors for satellites, missiles, airplane, ships, submarines and RPVs.
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Additional info for Kalman Filtering Techniques for Radar Tracking
906-91 1, November 1973. Discrete-Time One-Dimensional Filters 2. 3. 4. 39 K. V. Ramachandra, Estimation of optimum steady state position, velocity and acceleration using noisy sampled position data. Electro Technology (India), vol. 23, pp. 53-59, September 1979. K. V. Ramachandra, Optimum steady state position, velocity and acceleration estimation using noisy sampled position data. IEEE Transactions on Aerospace and Electronic System AES-23, pp. 705-708, September 1987. A. W. Bridgewater, Analysis of second and third order steady state tracking filters.
The steady state filter characteristics of the two-dimensional trackers are analytically determined making use of the properties of the uncoupled one-dimensional trackers discussed in Chapter 2. These results are of practical interest in developing trackers for tracking aircraft and similar vehicles. These results also eliminate the real time execution of the complete Kalman filter matrix equations, providing a significant reduction in tracking and updating time. This is illustrated in the extension of the one-dimensional Friedland’s model and Ramachandra’s model I to two dimensions.
From Ref. 10 Velocity accuracy before and after position determination. 11 Acceleration accuracy before and after position determination. 13 Normalized acceleration gain K3T2 as a function of I’. I’. 19) recursively to the steady state, we get the same values as given above. 9, it is seen that k11 5 I , whereas PI1 can be larger or smaller than unity depending upon the level of plant noise. 62. 103) as the suitable sampling time which would keep the position error in a sampled data system below the inherent sensor error .
Kalman Filtering Techniques for Radar Tracking by Charles K. Chui, Guanrong Chen