New CFAR algorithm and circuit development for radar receiver
Automatic target detection radar requires adaptive thresholding achieved by the Constant False Alarm Rate (CFAR) circuit to control the false alarm caused by variations in the background clutter. This thesis deal with the problem that happened when an abrupt variation in background clutter mer...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/4125/1/24p%20MUSTAFA%20SUBHI%20KAMAL.pdf http://eprints.uthm.edu.my/4125/2/MUSTAFA%20SUBHI%20KAMAL%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/4125/3/MUSTAFA%20SUBHI%20KAMAL%20WATERMARK.pdf http://eprints.uthm.edu.my/4125/ |
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| Summary: | Automatic target detection radar requires adaptive thresholding achieved by the
Constant False Alarm Rate (CFAR) circuit to control the false alarm caused by
variations in the background clutter. This thesis deal with the problem that happened
when an abrupt variation in background clutter merged with a multi-interfering
target, and when the clutter cloud itself centered with multi-interfering targets. To
detect targets in such environments, it needs a robust CFAR algorithm that excises
the target spikes and clutter edges from the CFAR window to give the best possible
estimation of the noise background. The Maximum Spike Subtraction MSS-CFAR
family that uses two lock circuits to find two maximum spikes in the CFAR window
that subtracted from sample summing to make better background noise estimation
that used to construct an adaptive threshold. The MSS-CFAR family is MSS-CA�CFAR, MSS-GO-CFAR, and MSS-SO-CFAR, MSS-CFAR family in addition to two
core algorithms were studied which are cell averaged CA-CFAR family that includes
the greatest of GO-CFAR and smallest of SO-CFAR and ordered statistics OS-CFAR
family that include greatest of ordered statistics OSGO-CFAR and the smallest of
ordered statistics OSSO-CFAR. All these algorithms are simulated using MATLAB
and applied them to three different clutter models that represent different
environment cases. The CA-CFAR family failed to handle models two and three also
OS-CFAR family except for OS-CFAR that handle all models successfully. For the
MSS-CFAR family, MSS-CA-CFAR could handle all models successfully, and
comparing with OS-CFAR, the MSS-CA-CFAR need less hardware and processing
time because it did not need a sorting process that is essential for OS-CFAR.
Therefore, the MSS-CA-CFAR is chosen to implement by practical digital circuit
and there is another important feature in the MSS-CFAR algorithm that is parallel
processing since the spike selection process is done at the same time with summing
of samples process that makes this algorithm much less in processing time from any
other algorithm using the same environment. The last MATLAB test for MSS-CA-
vi
CFAR with a spiky exponential model shown in Table 4.3 in chapter four shows
clearly that MSS-CA-CFAR detects nine targets from ten that means the efficiency
of detection of the proposed method is 90%. The field-programmable gate array
FPGA chip that is used to implement the MSS-CA-CFAR algorithm needs only three
signals from the radar receiver to match with the receiver circuit correctly which are
time base clock signal period reset trigger signal and the pulse duration time. |
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