Passive Position Location Estimation Using the Extended Kalman Filter

Several papers have been published recently using the method ofleast squares for passive position location estimation. While the Kalman filter is mentioned as an alternative approach in most ofthese papers, none of the papers actually compare the performanceof the Kalman filter with the method of least squares. In this paper,the performances of the extended Kalman filter and the iteratedextended Kalman filter are compared with the method of leastsquares. Monte Carlo results are given showing how the a prioricovariance matrix influences the accuracy of the extended Kalmanfilter.

[1]  W. Denham,et al.  Sequential estimation when measurement function nonlinearity is comparable to measurement error. , 1966 .

[2]  Norman Morrison,et al.  Introduction to Sequential Smoothing and Prediction , 1969 .

[3]  K. Spingarn,et al.  The application of linear regression filtering to aircraft target tracking , 1971 .

[4]  K. Spingarn,et al.  Linear Regression Filtering and Prediction for Tracking Maneuvering Aircraft Targets , 1972, IEEE Transactions on Aerospace and Electronic Systems.

[5]  J.L. Poirot,et al.  Application of Linear Statistical Models to Radar Location Techniques , 1974, IEEE Transactions on Aerospace and Electronic Systems.

[6]  WADE FOY,et al.  Position-Location Solutions by Taylor-Series Estimation , 1976, IEEE Transactions on Aerospace and Electronic Systems.

[7]  A.H. Haddad,et al.  Applied optimal estimation , 1976, Proceedings of the IEEE.

[8]  James Poirot,et al.  Position Location: Triangulation Versus Circulation , 1978, IEEE Transactions on Aerospace and Electronic Systems.

[9]  M. Wax.  Position Location from Sensors with Position Uncertainty , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Don Torrieri,et al.  Statistical Theory of Passive Location Systems , 1984, IEEE Transactions on Aerospace and Electronic Systems.

[11]  H Kagiwada,et al.  Numerical Derivatives and Nonlinear Analysis , 1986 .