I.
Adaptive CFAR Processor
For Nonhomogeneous
Environments
MOHAMMAD ALI KHALIGHI
MOHAMMAD HASAN BASTANI
Sharif University of Technology
A new constant false alarm rate (CFAR) processor is
presented, that exhibits a noticeable detection performance in the
presence of interfering targets, as well as an excellent false alarm
rate (FAR) regulation at the clutter power transition regions.
The presented CFAR processor, which is designed to work on the
logarithmic amplified video signals, can also easily adapt itself to
new environmental conditions. Furthermore, in the steady state,
its performance does not depend on the background noise power.
Simulation results show the obvious preference of the presented
processor to the conventional GOCA-LOGICFAR, regarding FAR
regulation at the clutter power transition regions. Noncoherent
integration of radar pulses is considered in the analyses.
Manuscript received November 21, 1998; revised September 2,
1999; released for publication February 23, 2000.
IEEE Log NO. T-AES136I3l01801.
Refereeing of this contribution was handled by W. D. Blair.
Authors’ current addresses: M. A. Khalighi, Laboratoire des
Images et des Signaux (LIS), ENSIEG, Domaine Universitaire,
BP-46, 38402, Saint-Martin-d’Hbres Cedex, France, E-mail:
(Ali.Khalighi@inpg.fr); M. H. Bastani, Dept. of Electrical,
Engineering, Sharif University of Technology, P.O.Box
11365-9363, Tehran, Iran, E-mail: (Bastani@sina.sharif.ac.ir).
0018-9251/00/$10.00 @ 2000 IEEE
INTRODUCTION
Use of a fixed threshold for the detection of radar
signals results either in a degradation in the detection
performance or an excessive increase in the FAR
(false alarm rate), as a consequence of the variation
in the background noise power. Therefore, using an
adaptive threshold with the CFAR (constant FAR)
property is unavoidable in most radar and automatic
detection systems. Up to now, numerous CFAR
processors are presented in the literature, each one
designed to work in some particular environmental
conditions.
The most primitive CFAR processor is CA (cell
averaging) or MLD (mean level detector). This
processor has a poor performance in nonhomogeneous
environments, i.e., in the presence of interfering
targets or clutter power transition regions.
Various modifications have been applied to CA
processor to improve its detection performance
in the presence of interferers. Some examples are
the censoring technique as in CCA (censored CA)
or CMLD (censored MLD) [l], and the excising
technique as in Ex (excision) detector [2, 31. The OS
(ordered statistics) CFAR detectors [4-61 also exhibit
a good performance in such conditions.
On the other hand, in order to preserve the CFAR
property at the clutter power transition regions,
the GOCA (greater-of CA) is proposed [5, 71. In
GOCA, the greater average of the leading and lagging
subwindows’ samples is used instead of the ensemble
average as in CA. This processor can regulate, to
some extent, the FAR at the clutter power transition
regions, while suffering a little more CFAR loss,
compared with CA [8].
The greater-of selection may be combined with
other techniques to provide a good performance in
the presence of both interferers and clutter regions.
Furthermore, many other CFAR processors have
been designed to work in some special environmental
conditions; most of them use combinations of the
already mentioned techniques.
Considerable particular conditions in our work
are the probable presence of interferers and clutter
regions. To work under such circumstances, some
CFAR processors such as CGO (censored greater-of)
[9, lo], ACMLD (automatic censored MLD) [ l l ] ,
OSGO (ordered statistics greater-of) [12, 131, and
ExGO (excision greater-of) [ 141 have already been
presented. However, each one has some disadvantages.
Performances of CGO, ACMLD, and ExGO detectors
at the clutter power transition regions are almost
similar to the performance of GOCA. However,
for CGO, if the number of interferers exceeds the
preassumed limit, its detection performance degrades
severely. Also, use of ACMLD would necessitate
much computation time, because of its relatively,
complex algorithm. Although ExGO processor
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 36, NO. 3 JULY 2000
889
Target
Logrrithmic
Amplifiw
Present
Processor
Fig. 1. Block diagram of receiver.
is very suitable from this point of view, it cannot
appropriately confront interfering targets and clutter
regions at the same time (as to be explained relatively
in Section IV). The OSGO processor requires much
computation time to extract the ranks of the two
reference subwindows. Furthermore, it suffers from
more CFAR loss, compared with the other processors
stated before.
In this work, we present a new CFAR processor
that exhibits an excellent FAR regulation at the clutter
edges, while possessing a good robustness against
interfering signals. Noncoherent integration of radar
pulses is considered. Moreover, it is assumed that a
logarithmic video amplifier is used to increase the
input dynamic range of the receiver.
Note that most of the CFAR algorithms proposed
for the linear detection case are also applicable for the
logarithmic detection case, with a little modification.
For example, for the OS processor, it is not important
to work on linear or logarithmic amplified samples.
For the CA family CFAR processors, logarithmic
amplified samples can be processed in a similar way.
For these processors, use of logarithmic data results
in an increased robustness against interferers [ 15,
161. Nevertheless, the logarithmic processing leads
to some additional CFAR loss [15, 171. Moreover,
the performance of the processor in regulating the
FAR at the clutter power transition regions would be
degraded. This concept is investigated in [16], where
a comparison is made between the performances of
GOCA and GOCA-LOG CFAR processors. Use of
logarithmic amplification also causes an integration
loss, compared with the linear case [18].
The processor which we present here is based
on the excision technique [2, 31, and uses the idea
of AMEx (adaptive modified Ex) and AMEx-LOG
processors [ 19-21], which have been designed for
ESM (electronic support measures) systems. In this
design, a primary threshold adapted to the background
noiseklutter power, is used to remove the probable
interfering samples from the reference cells. Next, the,
remaining samples of each reference subwindow are
averaged. Greater-of selection is used to enable the
processor to prevent from the excessive FAR increase
in the clutter power transition regions. Due to the
feedback loop used in the processor structure, it can
adapt itself quickly to the new background conditions,
e.g. when the reference window is moved from a clear
to a clutter region.
Organization is as follows. The assumptions on the
statistical models are given in Section 11. In Section
890
111, Ex-LOGKFAR processor is introduced and its
performance is discussed briefly. Next, ExGO-LOG
processor is considered in Section IV and its
disadvantages to work in clutter regions is described.
The modification of ExGO-LOG to AExGO-LOG
(adaptive ExGO-LOG) processor to work under
the desired environmental conditions is discussed
in Section V. The method of determination of the
proposed processor's design parameters is considered
in Section VI. In Section VII, simulation results
concerning the performance of the proposed CFAR
processor are given. At last, a general conclusion of
the obtained results is presented in Section VIII.
II. ASSUMPTIONS AND STATISTICAL MODELS
It is assumed that a square law envelope detector
is used in the receiver, followed by a logarithmic
video amplifier. Fig. 1 shows the block diagram of
the receiver. A narrowband matched filter is used at
the IF section. Therefore, the Gaussian distribution
can be considered for the noise samples in its output.
With the assumption of IID (independent identically
distributed) samples in the detector input, the pdf
(probability distribution function) of noise samples
at the detector output is
x>o
where u2 is the variance of noise samples. Similarly,
assuming a Gaussian distribution for the background
clutter, the pdf of the clutter return samples at the
detector output is [5]
1
+
fex(x) = G2(1 CNR) exp
(-
X
W(1
+ CNR)
x20.
(2)
Here, CNR (clutter-to-noise ratio) denotes the relative
power of the clutter.
The video logarithmic amplifier characteristic
equation is considered as in (3) where x and z are the
input and output signals of the amplifier, respectively.
The analyses performed in this paper assume a Z 0.8
and b Z 3.5
z = aloglox+ b.
(3)
The pdf of the samples at the output of the
logarithmic am,plifier can be obtained using the
following equation
In 10
&(z) = -f
a
(lO'z-b'/").
x
(4)
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 36, NO. 3 JULY 2000
Video Amp.
In other words, BE can be calculated using the
following equation
Sampler
BE = ul0g,o(2a2) + b - 0 . 2 5 0 7 ~+ a.
(9)
The performance of Ex-LOG processor is similar to
Ex-CFAR which is discussed extensively in [2, 31,
except that it suffers from more CFAR loss due to
t
the use of logarithmic amplification [20, 211. In the
presence of interfering targets, assuming constant y,,
Fig. 2. Block diagram of Ex-LOG/CFAR processor.
any increase in the number of interferers results in
an increase in p f , (false alarm probability). However,
assuming constant F& (correcting -yD with respect to
In this paper we consider the non-fluctuating model
the number of interferers), any increase in the number
(Swerling case 5) for targets. Therefore, for a
of interferers results in a decrease in Pd (detection
received pulse with the amplitude A, the pdf of
probability), due to the increased CFAR loss.
signal-plus-noise samples at the detector output will
Regarding the probable environmental conditions,
be [22]
specially the maximum probable number of interfering
samples, the width of the reference window can be
1
x+A2
AJ
determined appropriately, so that the least possible
f,(x) = -exp -2a2
202 )IO(*)’
degradation occurs in the presence of interferers.
BE (or equivalently a ) is an important design
x10
(5)
parameter of the processor. If BE is chosen too low,
where Zo(.) is the modified Bessel function of the
most of the noise samples will be excised. Thus, a
first kind and zero order. The pdf of signal-plus-noise
great CFAR loss will result. On the other hand, if BE
samples at the output of logarithmic amplifier can be
is chosen too high, the excisor will fail in removing
obtained using (4) and (5).
the probable interference samples, resulting in the
The number of cells of each reference subwindow
degradation of the detection performance. In other
and the number of integrated radar pulses are
words, a wide IZ (ineffectiveness zone [2]) results.
indicated as K and M ,respectively.
Excisur
(
IV.
Ill.
Ex-LOC/CFAR PROCESSOR
The block diagram of the Ex-LOGKFAR
processor is shown in Fig. 2 [20, 211. This processor
is the modified version of Ex-CFAR for the case of
logarithmic amplification. As can be seen, at first
a primary threshold, called B E , is used to excise
the probable interferer samples. Next, the remained
samples are averaged, and after the summation of the
average with a threshold parameter y, (as in CA-LOG
processor), the final threshold B, is obtained.
Obviously, when BE -+CO, the processor reduces to
CA-LOG. The important feature of this processor is
its simple structure and the required low computation
time for calculating the detection threshold. Similar to
the case of Ex-CFAR, an important parameter, called
excisor coefficient, is defined for the processor as
a = BE - E { Z }
As stated previously, the greater-of technique is
usually used in order to avoid excessive FAR increase
at the clutter power transition regions. Application
of this technique in the Ex-CFAR structure has been
already considered in [ 141. The resulting performance,
apart from the interferers problem, is similar to
that of GOCA processor. However, this design (or
equivalently ExGO-LOG), has an obvious drawback
that is not considered in the paper. Taking a wide
range of variations for the variance of background
samples (noise or clutter), necessitates the choice
of a relatively high B E . Therefore, in,the noise-only
regions, the processor exhibits a very wide IZ and
the excisor would fail in excising the interference
samples. In this case, for high values of samples
variance (i.e., in clutter regions), the FAR regulation
and the IZ characteristics may be suitable.
(6)
where
V.
E { Z } = uloglo(2a2)+ b - aylogloe
(7)
with y as the Euler constant defined as
y = -Lme-~1nxdx~0.57721.
ExCO-LOCKFAR PROCESSOR
(8)
MODIFYING THE EXGO-LOGPROCESSOR’S
ST RUCT URE
In the previous section it was explained that
applying both excision and greater-of techniques does
not result in a good performance, from the points of
view of FAR regulation at the clutter power transition
KHALIGHI & BASTANI: ADAPTIVE CFAR PROCESSOR FOR NONHOMOGENEOUS ENVIRONMENTS
89 1
Druiaiuo
YU
(0
Fig. 3. Block diagram of AExGO-L0GKFA.R processor.
regions and the suppression of the effect of interferers
on the detection performance.
In fact, if the processor has the property that it
can adapt itself to the new background conditions,
its performance (in the steady state) would be
independent of the background noise power. So,
its parameters could be determined without any
anxiety about the noise power variation limits. In
other words, in the steady state, the IZ characteristics
will be insensitive to the noise power. The idea
of adaptation is deduced from the fact that for the
Ex-LOG processor, it can be shown that [20, 211
E { V } = BE + Const.
(10)
So, if the variance of V can be somehow neglected,
BE can be obtained via the addition of V with a
constant parameter. The same idea is used in the
design of AMEx-LOGKFAR processor [20, 211.
The more the number of reference cells, the more
reasonable the assumption will be. The resulted
structure is named AEx-LOGKFAR. However, in our
application the greater-of selection is used at the same
time, and hence, the feedback should be taken from
the greater average (called %):
BE=V,+CO
where CO serves as a feedback coefficient.
Regarding noise-only conditions, the reasonability
of the design is the same as AEx-LOG. The
block diagram of the resulted processor, named
AExGO-LOG/CFAR, is shown in Fig. 3. As in the
case of AMEx-LOG processor, the performance
of the introduced algorithm is independent of the
variance of background noise, and this is a very
892
(1 1)
important characteristic of the processor. Therefore,
in the clutter regions, the same IZ characteristics of
noise-only regions results.
A small modification is made to the CFAR
algorithm in the case of removal of all samples of a
subwindow 'by the excisor. In this case, the ordinary
procedure of the algorithm is abandoned, such that
the previous computed threshold (last B,) is used,
and in the next cycle, the excisor is made ineffective.
Thereafter, the normal CFAR algorithm is used
again. This concept is explained in more detail in
Section VII. It is seen that the processor exhibits
an excellent FAR regulation, confronting the clutter
power transii.ion regions.
Considering the feedback loop (from
to BE),
it should be noticed that the determination of CO is
independent of the value of 7, (it was not the case for
AMEx-LOG [20, 211). Due to the complexity of the
structure of the processor, its analysis is very difficult
and so, in most cases the simulation results are used
to study its performance.
Apart from the problem of clutter, the processor
follows a transient behavior as a result of variations
in the background noise power. The extreme case
happens when noise jammers are present in the
combat envirfonment.Note that the additive broadband
noise jamming can be modeled as Gaussian noise
[2]. The transient behavior is due to the feedback
loop in the structure of the processor. The results
presented in the following sections are obtained
in the steady state of the algorithm. The transient
behavior of the algorithm resulted from the variations
in the noise power is similar to the case of AMEx
and AMEx-LOG processors. This is extensively
investigated in [ 19-21 J and is not discussed here.
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 36, NO. 3 JULY 2000
4.8
loJ
..............
4.6
10''
4.4
IOS
4.2
4
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Fig. 4.
VI.
DETERMINATION OF AExGO-LOG DESIGN
PARAMETERS
Three important parameters of the processor
are K (the width of each reference subwindow), 7,
(the threshold parameter), and CO (the feedback
coefficient).
A.
Determination of Width of Reference Subwindows
The determination of K should be performed
considering the endured CFAR loss, the homogeneity
of the reference window, and the maximum probable
number of interferers. In our application, since we
profit the integration of radar pulses, the effective
number of reference samples is 2 K M , where M is
the number of integrated radar pulses. This value
can be large enough and hence, the problem of
CFAR loss does not impose a serious limitation.
Therefore, in order to reduce the required threshold
computation time, K can be chosen small. In this way,
the condition of the homogeneity of the reference
window will be well satisfied too. However, K should
be taken large enough, so that in the worst case
conditions, the appearance of the interferer samples
in the reference window does not result in the excision
of the majority of samples in a subwindow.
As a special case to be considered, assuming
M = 8 and the possibility of the presence of at most
one interferer sample in each subwindow, K = 4
is chosen. So, the minimum effective number of
reference noise samples in each subwindow (under
the conditions of maximum possible interference), will
be 24, which is a suitable value.
B.
Determination of Threshold Parameter
7, is determined considering the values of K ,
CO, and the design false alarm probability (Pfa-d).
For instance, the simulation results in Fig. 4 show the
.IL
10.~I
1.5
2
2.5
3
3.5
CO
Fig. 5. AExGO-LOG processor, probability of excising three
noise samples, M = 8.
curves of 7, versus CO for three different values of
for the case of K = 4 and M = 8.
C.
Determination of the Feedback Coefficient
CO is an important design parameter of the
processor which in fact determines B E . Excessive
increase in CO results in the failure of the excisor
in removing probable interferer samples. On the
other hand, an excessive decrease in CO leads to the
increase in CFAR loss or even to probable divergence
of the algorithm. This happens because of successive
decrease in the values of BE and B, (and as a result,
BE -+ 0).
Considering the case of K = 4 and M = 8, the
method of determination of CO is as follows.
1) The first criterion to be considered, is that the
excision of all samples of a subwindow should occur
with a very low probability. According to the previous
assumptions that the presence of at most one interferer
sample in each subwindow is probable, the probability
of removing 3 ( K - 1) noise samples by the excisor
is our criterion. This probability which is defined as
is the probability of
equals &, where
excising one sample (IID samples have already been
versus
considered). Fig. 5 depicts the curve of
CO. The interval of CO > 2.00 which is equivalent to
< 3 x lop5 seems to be suitable. Note that for this
case, the values of CO < 0.8 lead to the divergence of
the algorithm. Furthermore, values of 0.8 < CO < 1.5
leading to too much excision of noise samples is not
shown in the figure.
2) The next criterion is the detection performance,
from the points of view of CFAR loss and the
suppression of the effect of interfering signals.
Simulation results show that the endured CFAR
loss has a very poor sensitivity to CO for 1.5 <
CO < 2.00, and is nearly insensitive to CO for larger
values. Thus, almost no restrictions arise from this
point of view. On the other hand, Fig. 6 shows the
curves of Pd versus CO, while the presence of one
KHALIGHI & BASTANI: ADAPTIVE CFAR PROCESSOR FOR NONHOMOGENEOUS ENVIRONMENTS
893
1
1
0.8
0.8
0.6
0.6
5 dB
Pd
p,
0.4
04
0.2
0.2
0
1.5
2
2.5
3
4
35
4.5
5
5.5
6
0
LO4
CO
Fig. 6. AExGO-LOG processor, effect of interfering signals
(INR = 2 dB, completely integrated) on detection performance of
main signals; K = 4, M = 8, one interferer sample in each
subwindow.
,
Pfzl
Fig. 7. AExGO-LOG processor, ROC characteristic curves for
case of K = 4 and M = 8.
interferer sample with the relative power of INR
(interference-to-noise ratio) is considered in each
subwindow. Each interferer sample is obtained
from the noncoherent integration of M returns of an
interfering target with the relative power of INR (it is
denoted as completely integrated interferer). The effect
of interference on pd (resulting from unsuccessful
action of the excisor) is evident for large values of
CO. Note that all values of SNR and INR indicated in
this paper, refer to the values before the integration
of radar-pulses. According to Fig. 6, the values of
CO < 2.4 seem to be suitable.
3) The last criterion in the determination of CO
is the transient response of the processor confronting
clutter power transition regions. Simulation results
confirm that for values of CO > 2.00, the transient
speed of the algorithm is almost independent of CO.
In summary, we chose CO = 2.25 for the special case
of K = 4 and M = 8.
Consider another special case of K = 10 and
M = 16. In this case, the presence of at most 7
interferer samples in each subwindow is assumed to
be probable. Taking into account the above mentioned
criteria, CO = 3.00 is chosen. For this value, F& =
2x
Obviously,
does not depend on K ,but it
is sensitive to the value of M . For larger values of M ,
the variance of the integrated samples will be larger,
and hence, the probability of excising for a distinct
CO will increase.
ex
VII.
'
I
6 dB
Z I
0.8
0.6
I
R dB
SNRPSdB.
F
F
-6
-I
-2
2
U
4
2 dB
6
U
IO
INR (dB)
(4
8 dB
I
SNR = S dB
4
4 dB
Pd
0.2
2 dB
SIMULATION RESULTS
At first, we consider the special case of K = 4 and
M = 8. Fig. 7 depicts the ROC (receiver operating
characteristics) curves of the processor. Besides, IZ
characteristic curves are shown in Fig. 8, where
one interferer sample is considered in each
subwindow.
894
10"
10.'
In Fig. 8(a:) the interferer samples are considered
as completely integrated. The narrow IZ results
from the good performance of the excisor as well
as the logarithmic detection that reduces the effect
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS VOL. 36, NO. 3 JULY 2000
1.1~;
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Relathe position of range cells
,ytmr-,
200
,
150
......... : ........ :... ................................
100
-. . . . . . . .:. ...... .:. . . . . . .:. . . . . . . .:. .
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50
0
(a)
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...................
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.......................
40
Relative position ofrnnge cells
(b)
Fig. 9. Comparison between transient behaviors of AExGO-LOG
and GOCA-LOG processors confronting a relatively strong clutter
region, CNR E 5 dB, K = 4, M = 8, Pfa-d = w4.
(a) p f , curves.
(b) Standard deviation of background samples.
of interference. It can be seen that relatively large
values of INR have almost no effect on Pd, because
the interferers are successfully removed by the
excisor. However, the presence of strong interfering
targets near the main target in azimuth can cause a
degradation on its detection. As an example, this case
is considered in Fig. 8(b) where each interferer sample
is obtained from the integration of 2 returns of an
interfering target and 6 noise samples (it is denoted
as partially integrated interference).
The transient behavior of the algorithm
confronting a relatively strong clutter with CNR S
5 dB, is shown in Fig. 9. As a worst case, the
clutter is assumed to have a rather sharp edge. In
order to perform a comparison, the behavior of the
GOCA-LOG/CFAR processor is also shown in Fig. 9.
The excellent FAR regulation of the AExGO-LOG
processor is obvious. This performance is specially
noticeable at the front edge of the clutter region. The
function of the processor is described in more details
as follows.
Suppose that the test cell advances gradually
from the clear region towards the clutter region. The
relatively strong clutter return samples which fall in
the leading reference subwindow, are removed by
the excisor and have almost no effect on FAR. At
the time when the test cell reaches just behind the
clutter edge, all samples of the leading subwindow
are excised with a high probability. As explained
previously, in the case of the excision of all samples
of a subwindow (that is only probable for the leading
one), the regular threshold computing procedure is
abandoned, and the last computed threshold is used
by the detection system. Then, for the next threshold
computing cycle (test cell in clutter), the excisor is
made ineffective and the simple averages of two
subwindows is calculated as V, and V, (see Fig. 3).
I
0.8
0.6
Pd
0.4
0.2
0
Fig. 11. AExGO-LOG processor, ROC characteristic curves for
case of K = 10 and M = 16.
Hereafter, the CFAR processor will follow its regular
algorithm.
The resulted FAR regulation at the clutter edge is
specially desirable for tracking the targets near coast
areas.
Even if the clutter is not strong and the excision
of clutter return samples does not happen with a high
probability, the performance of the algorithm will still
be preferable to that of GOCA-LOG. This is shown
in Fig. 10, where a relatively weak clutter power
transition region with CNR Z 3 dB is considered.
For the second special case (K = 10 and M = 16)
the ROC curves are shown in Fig. 11. Since K M
is relatively large, the increase pf K does not result
in a considerable improvement of the detection
performance. However, the increase of M results in
a greater integration gain, and hence, improves the
detection performance. On the other hand, the IZ
characteristic curves are shown in Fig. 12, where the
KHALIGHI & BASTANI: ADAPTIVE CFAR PROCESSOR FOR NONHOMOGENEOUS ENVIRONMENTS
895
1
5 dB
0.8
3 dB
0.6
SNR = 2 dB
4 dB
*d
0.4
1 dB
0.2
0 dB
0
-8
-.I
-6
-2
2
0
8
6
4
M R (dB)
Fig. 12. AExGO-LOG processor, IZ characteristic curves; 6
interferer samples in each subwindow, K = 10, M = 16,
= 10-4, completely integrated interference.
lo"
I
.....
p,
.....
lo"
lo"
0
20
IO
50
40
30
GO
70
80
90
.
.
.
100
Relative position of range cells
clutter regions and/or interfering targets. Besides its
excellent performance, another favorable advantage of
AExGO-LOIGprocessor is its simple structure which
makes it possible to be easily implemented in real
time. Use of the excising technique and the greater-of
selection, enables the processor to efficiently confront
the interfering targets, while exhibiting a noticeable
FAR regulation at the clutter power transition regions.
On the other hand, employing a feedback loop in the
processor stiructure, enables it to adapt itself easily to
new environmental conditions; so that its performance
and its characteristics become independent of the
background noise power. Moreover, in noise-only
conditions, the additional endured CFAR loss of
the processor, compared with GOCA-LOG is
negligible.
The method of determination of the processor
design pararneters was discussed, and its performance
analysis was performed in different situations.
Although the logarithmic amplification and the
integration ad radar pulses were considered in this
paper, the general function of the processor can be
used in the other cases, For example, for the linear
detection case (AExGO-CFAR), only CO and -yD
should be entered as multiplicand factors.
REFERENCES
.
150
-. . . . . .:.
.
.
.
.
.
.
.
.
.
.
.
.:.
. . . . ... . . . . .:. . . . . . . . . . . . ;. . . . . . . .. . . . . . . _
..
.
.
,
.
.
.
.
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.
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50
0
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IO
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Relatiye position ofrange cells
[2]
(b)
Fig. 13. Comparison between transient behaviors of
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strong clutter region, CNR '2 5 dB, K = 10, M = 16, Gad = W4.
(a)
curves. (b) Standard deviation of background samples.
ea
presence of 6 completely integrated interferer samples
is assumed in each reference subwindow. It can be
seen that for larger values of M , the effect of weak
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of AExGO-LOG and GOCA-LOG processors,
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performance is similar to the previous special case,
and again the preference of AExGO-LOG processor is
noticeable.
Simulation results show that even for the previous
case (K = 4 and M = 8), the difference between
the endured CFAR loss of AExGO-LOG and
GOCA-LOG algorithms is negligible.
VIII.
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CONCLUSION
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Mohammad A. Khalighi.was born in Kerman, Iran, on March 5, 1975. He
received his B.Sc. and M.Sc. degrees in electrical engineering from Sharif
University of Technology, Tehran, Iran, in 1995 and 1997, respectively. He is now
a Ph.D. student in INPG (Institut National Polytechnique de Grenoble), Grenoble,
France.
From 1997 to 1998 he has worked at the department of electrical engineering
of Sharif University as a design engineer. His main interests are in electronic
instrumentation, high speed analog and digital circuitry, and digital signal
processing with application to communication systems. His current research fields
include detection and synchronization in multiple antenna mobile radio systems.
Mohammad H. Bastani received his B.Sc. degree in electrical engineering
in 1979 from Sharif University of Technology, Tehran, Iran. He received
his “diplome d’ingenieur” and doctoral degrees from ENST (Ecole National
Superieur de Telecommunications), Paris, France, in electrical engineering, in
1981 and 1984, respectively.
He has been an Assistant Professor at the Department of Electrical
Engineering of Sharif University since 1984. His research interests are in
stochastic signal processing, data fusion, and radar design.
KHALIGHI & BASTANI: ADAPTIVE CFAR PROCESSOR FOR NONHOMOGENEOUS ENVIRONMENTS
897