Showing 2 results for Cfar
M. R. Moniri, M. M. Nayebi, A. Sheikhi,
Volume 4, Issue 4 (12-2008)
Abstract
A detector for the case of a radar target with known Doppler and unknown
complex amplitude in complex Gaussian noise with unknown parameters has been derived.
The detector assumes that the noise is an Auto-Regressive (AR) process with Gaussian
autocorrelation function which is a suitable model for ground clutter in most scenarios
involving airborne radars. The detector estimates the unknown parameters by Maximum
Likelihood (ML) estimation for the use in the Generalized Likelihood Ratio Test (GLRT).
By computer simulations, it has been shown that for large data records, this detector is
Constant False Alarm Rate (CFAR) with respect to AR model driving noise variance. Also,
measurements show the detector excellent performance in a practical setting. The detector’s
performance in various simulated and actual conditions and the result of comparison with
Kelly’s GLR and AR-GLR detectors are also presented.
M. Alaee, M. Sepahvand, R. Amiri, M. Firoozmand,
Volume 6, Issue 3 (9-2010)
Abstract
In order to detect targets upon sea surface or near it, marine radars should be
capable of distinguishing signals of target reflections from the sea clutter. Our proposed
method in this paper relates to detection of dissimilar marine targets in an inhomogeneous
environment with clutter and non-stationary noises, and is based on adaptive thresholding
determination methods. The variance and the mean values of the noise level have been
estimated in this paper, based on non-stationary, statistical methods and thresholding has
been carried out using the suggested two-pole recursive filter. Making the rate of false
alarm constant, the concerned threshold resolves the hypothesis of existence or absence of
the target signal. Performance of the mentioned algorithm has been compared with the
well-known conventional method as CA-CFAR in terms of decreasing the losses and
increasing calculation speed. The algorithm provided for detection of signal has been
implemented as a part of signal-processing algorithms of some practical marine radar. The
results obtained from the algorithm performance in a real environment indicate appropriate
workability of this method in heterogeneous environment and non-stationary interference.
