The maximum entropy method (MEM) for spectral analysis was suggested by Burg (1967). Its mathematical properties have been discussed in detail by Lacoss (1971), Burg (1972), and Ulrych (1972b) who found that the MEM in general is superior to the more conventional methods of spectral estimation. It has, for example, better resolution and gives more realistic power estimates, especially for short data records. However, the application of the method in the analysis of more complicated geophysical data series is reported in a surprisingly small number of papers. Ulrych (1972a) successfully used the MEM for the analysis of data on long period geomagnetic reversals.

In many practical systems there is a delay in some of the sensor devices, for instance vision measurements that m a y have a long processing time. How to fuse these measurements in a Kalman filter is not a trivial problem if the computational delay is critical. Depending on how much time there is at hand, the designer has to make trade offs between optimality and computational burden of the filter. In this paper various methods in the literature along with a new method proposed by the authors will be presented and compared. The nem method is based on "extrapolaiing" the measurem.ent to present time using past and present estimates of the Kalman filter and calculating an optimal gain for this extrapolated m.easurement.

A conceptually simple method for power estimation in maximum entropy spectral analysis, based on evaluation of complex residues of the spectral density estimator, is suggested. Numerical integration of the peaks of the power density function is thus avoided. The agreement in simple cases with conventional estimates is demonstrated, and the explicit performance is analyzed in detail in a series of examples. The close connection between the residue power estimate and the estimate proposed recently

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E m a i l tdlOiau.dtn.dk mX: +45 to make an accurate dynamical model of the robot contemplating all the nonlinearities caused by for instance friction forces, is not a trivial task and is hardly ever seen in the literature (one example though is found in[l]). The problem (besides the noulinearities) is that a lot of parameters that change with for instance time and temperature are required to be known quite precisely.