Estimating the Motion of a Maneuvering Targets Using Passive Measurements.
Abstract
The problem of estimating the trajectory of a maneuvering target using passive measurements obtained from an array of stationary sensors is investigated. The formulation is considered in a two-dimensional rectangular coordinate system. The unknown acceleration components are modelled as Brownian motion processes and consequently the dynamic model is linear. The types of measurements used in the estimation process are the frequency and the bearing angle of some are nonlinear functions of the state vector which consists of a reference frequency and the components of position, velocity and acceleration. Computation algorithms for Extended Kalman Filter and 'batch-sequential' filter are presented. Equations for including the effects of process noise on the batch solution are derived and the computation algorithm is also given. The performance of these filters is compared using noisy measurements simulated for two different scenarios with typical target maneuvers and sensor locations. Extended Kalman Filter is found to be the best in terms of computation time and accuracy of the estimated trajectory. Sensors located as far apart as feasible yield better results than those which are closer to each other. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1980
- Accession Number
- ADA095104
Entities
People
- B. D. Tapley
- B. E. Schutz
- P. A. M. Abusali
Organizations
- University of Texas at Austin