Studie av CFAR-algoritmer i Weibull-fördelat
Report UPTEC 97121E, 51 pages,
The use of five different constant
false-alarm rate (CFAR) algorithms for target
detection in Weibull-clutter has been studied.
The algorithms considered were Cell Averaging
(CA), Censored CA (CCA), Maximum-Likelihood (ML),
Censored ML (CML) and Order Statistic (OS).
The investigation has been carried out both by
simulations and by applying the
algorithms on physical data.
Two different ML algorithms for estimation of
the shape parameter of the Weibull distribution,
have also been studied. The shape parameter
estimation was used to improve the
robustness of the algorithms.
The investigation shows that unbiased estimation
of the shape parameter can be performed
with sufficiently low variance,
based on a small number of samples.
Investigations also show that the CA and
ML algorithms respond poorly to a multiple-target
situation. The censored algorithms perform much better.
In the tests with physical data, the
false-alarm rate was much higher than expected.
The reason for this is that the probability of
clutter samples with high power is higher than permitted
by a Weibull-distribution.
- Communicator C3 Consult
- Thesis Advisor:
- Mikael Sternad