![](bg7.png)
demonstrated using a well known object tracking
problem.
As the non adaptive GHF reportedly excels other
non adaptive sigma point filters like UKF and CDF,
the performance of the proposed adaptive GHF has
been compared with its non adaptive version.
In the absence of analytical proof of
convergence, each adaptive nonlinear filter,
including the proposed one, are to be thoroughly
evaluated with the help of extensive simulation
studies or real time experiments in several fields of
application before such filtering techniques may be
widely applied in practice with confidence.
However, the proposed filter may be
recommended for state and parameter estimation of
nonlinear systems because of its improved
estimation performance, good convergence, simple
adaptation rule, capacity to accommodate wide
uncertainty in the initial choice of measurement
noise covariance.
ACKNOWLEDGEMENTS
The first two authors acknowledge Council of
Scientific and Industrial Research, New Delhi, India
for financial support and express their gratitude to
the Centre for Knowledge Based System, Jadavpur
University, Kolkata, India for infrastructural
support.
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