The method for tracking based on Kalman Elter with debiased consistent converted 2D measurements was given and discussed in [l, 41. In this work explicit expressions for debiasing compensation terms and dehiased covariance statistics related to the 3D case are presented. The proposed procedure can be employed in active sonar systems or long range radar systems especially when the cross-range errors are significantly large relative to the range errors.

The continuous-time generalized predictive control (CGPC) using a long horizon cost function has superior robustness as compared to several other control strategies suitable for adaptive control. The main purpose of this paper is to put forward an analytical, explicit stable CGPC control design method for minimum-phase SISO systems that is based on a set of closed-loop characteristics with de® nite time-domain speci® cations. Explicit formulae for closed-loop characteristic polynomials are given and then the prototype design characteristic polynomials are catalogued that can serve as a basis for fully analytical design procedure assuring both the nominal stability and nominal performance speci® cations. A numerical example is given in order to illustrate the approach.

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SUMMARYThis paper addresses certain fundamental issues related to the discrete-time design problem of the deltadomain generalized predictive control (d-GPC) for both minimum phase and non-minimum phase linear SISO plants including nominal stability and nominal performance of the closed-loop system. The approach being presented is completely analytical, and the nominal performance of the control system is directly achieved by a prototype design of the closed-loop system characteristics resulting in definite time-domain specifications. Two design methods are offered in which a model-based prediction paradigm is applied to achieve the future output and the future filtered output trajectory of the plant. Prediction of the first type is based on suitable emulations of the output d-derivatives and is used in the GPC controller design for minimum-phase models of the plant. Prediction of the second type utilizes emulation of derivatives of the output filtered by the numerator polynomial of the transfer function of the controlled part of the plant. It can be employed both for minimum phase and non-minimum phase plants. A numerical example is given that illustrates the d-GPC method for controller design.