Design and Experimental Validation of Knowledge ... - IEEE Xplore

Design and Experimental Validation of Knowledge-Based CFAR Detectors A. De Maio

A. Farina

G. Foglia

Universit`a degli Studi di Napoli “Federico II” Via Claudio 21, 80125 Napoli, Italy Email: [email protected]

Selex - Sistemi Integrati Via Tiburtina Km.12.4, 00131 Roma, Italy Email: [email protected]

Elettronica S.p.A. Via Tiburtina Km.13.7, 00131 Roma, Italy Email: [email protected]

Abstract— This paper deals with the design and the analysis of Constant False Alarm Rate (CFAR) detectors exploiting Knowledge-Based (KB) processing techniques. The proposed algorithms are composed of two stages. The former is a KB data selector which, exploiting the a-priori information provided by a Geographic Information System (GIS), chooses the training samples for threshold adaptation. The latter stage is a conventional CFAR processor. The performance of the new schemes are analysed in the presence of real radar data, collected by the McMaster IPIX radar, also in comparison with other common CFAR detectors. The results show that noticeable performance improvements can be obtained suitably exploiting the a-priori information available about the sensed environment.

I. I NTRODUCTION The detection of a radar target is impaired by the presence of clutter returns due to reflections from buildings, trees, ground, sea etc. Since the clutter power is usually unknown, detection schemes with fixed threshold may result in an excessive number of false alarms (FAs) and/or in poor target detectability. A possible way to circumvent this drawback relies on the use of processing devices with an adaptive threshold capable of ensuring the CFAR property. Toward this goal several strategies have been proposed in open literature. Among them we mention the classical Cell Averaging CFAR (CA-CFAR) detector [1], [2] that resorts to secondary (or training) data from range cells in close proximity to the Cell Under Test (CUT) in order to perform the threshold adaptation. However, training data are often contaminated by power variations over range (in addition to the radar range equation effect), clutter discretes, and other outliers. Moreover the strength of the clutter also fluctuates with terrain type, elevation, ground cover and the presence of man-made structures. In these situations, training data may not be representative of the disturbance in the CUT, and the CA-CFAR exhibits strong degradations both in the detection performance as well as in the CFAR behaviour [3]. This is especially true in regions containing varying ground cover such as regions with land and sea. Several modifications of the CA-CFAR detection scheme have been proposed during the last three decades in order to reduce the impact of a nonhomogeneous secondary data window. The Greatest Of CFAR (GO-CFAR) algorithm, devised 0-7803-9497-6/06/$20.00 © 2006 IEEE.

in [4], tries to mitigate the impact of clutter discontinuities by appropriately choosing the reference window. As a result the algorithm shows a CFAR behaviour stronger than the CA-CFAR, but a detection probability (Pd ) worse than the counterpart when interfering targets, and in general outliers, are present in the training window (masking effect). In [5] the Smallest Of CFAR (SO-CFAR) processor is introduced; it reduces the masking effect but sacrifices the CFAR behaviour in nonhomogeneous clutter environments. A strong robustness can be obtained exploiting Order Statistic CFAR (OS-CFAR) schemes [6], which rely on the power ranking of the reference window samples. Nevertheless the OS-CFAR processor is unable to prevent an excessive FA rate in clutter transition regions [7]. Other algorithms, based on the excision of a predetermined number of reference cells and on clutter maps have also been proposed and assessed [8], [9], [10], [11]. A different philosophy which can aid the selection of training samples might be the real-time exploitation of apriori knowledge concerning the environment surrounding the radar [12], [13], [14].In fact, the environmental context is the key to efficient adaptation: sensors like humans might benefit from the context. Examples of a-priori knowledge are Digital Terrain Elevation Models (DTEM’s), previous look data, GIS’s, roadway maps (to highlight sectors of surveillance where moving cars or vehicles might be present), background of air/surface traffic, system calibration information, etc. The ultimate goal is to make the radar an intelligent device, such that it is capable of developing cognition of the surrounding environment. Otherwise stated, the environment in which the radar system operates acts as a teacher, and the radar becomes more expert with time by learning from the environment. This is basically the concept of KB or cognitive radar, known to the radar community since the pioneering papers of Vannicola [15], [16] and Haykin [17]. Recent advances in environmental measurements, DTEM, future information quality and accessibility, digital processing, mass, and randomaccess memory technologies have opened many possibilities, unthinkable in the past, for radar systems to improve their on-line performance. New real-time processing techniques are required to take advantages of these new opportunities, to bring radar performance back to optimum under difficult operation conditions such as littorals that include mixed sea and variable

128

rK

r4

Fig. 1.

r2

rcut

r1

r3

rK−1

Diagram showing the reference window including the CUT.

odd i

terrain. In this paper we design CFAR processors capable of exploiting a-priori information, provided by a GIS, about the observed radar scene. The proposed schemes are composed of two stages. The former is a KB data selector which suitably chooses the reference samples exploiting a-priori information. The latter stage is a standard CFAR processor (in the sequel we use the CA-CFAR but another system could also be exploited). At the analysis stage we study the performance gain achievable with the aid of KB techniques. Precisely we compare, on real radar data, the performance of the new KB algorithms with the classical CFAR receivers (CA-CFAR, GO-CFAR, SO-CFAR and OS-CFAR). II. C LASSICAL CFAR D ETECTION S CHEMES The aim of this section is to quickly revisit some classical CFAR detection schemes, proposed during the last three decades in open literature, which will be used in the subsequent analysis for comparison purposes. With reference to a radar sweep, let rcut denote the sample from the CUT and r1 , . . . , rK the samples form the training cells (see Figure 1), namely returns from K adjacent range cells1 , symmetrically located with respect to the CUT. CA-CFAR. The CA-CFAR exploits the samples of the reference window for estimating, through the arithmetic mean of their squared modulus, the local clutter power. Then it evaluates the decision statistic normalizing the squared modulus of the return from the CUT to the aforementioned power estimate. Otherwise stated it implements the following decision procedure H |rcut |2 >1 < TCA , K H 0 |ri |2

(1)

i=1

where | · | denotes the modulus of a complex number and TCA is the detection threshold, set according to the desired false alarm Probability (Pf a ). GO-CFAR and SO-CFAR. A major assumption of the CA-CFAR is the equality of the interference statistics in the K reference cells as well as in the CUT. Unfortunately this assumption is often violated in many situations. For example, when the reference window contains a clutter edge or when returns from undesired targets are present in the training samples. A possible solution to this problem relies on dividing the reference window into two parts: the left window (data 1 For

simplicity K is assumed an even integer

characterised by even indexes, see Figure 1) and the right one (data characterised by odd indexes). From this partition, two estimates of the local clutter power can be constructed, i.e. 2 2 2 2 |ri |2 |ri |2 . (2) σ σ L = R = K K even i The GO-CFAR [4] normalizes the squared modulus of the primary sample to the maximum of (2), namely it operates as follows H1 |rcut |2 > T , (3) < GO K 2 2 max σL , σU H0 2 where TGO is the detection threshold, set according to the desired Pf a . The SO-CFAR [5] normalizes the squared modulus of the primary sample to the minimum of the two local power estimates, namely it operates as follows H1 |rcut |2 > (4) < TSO , K 2,σ 2 min σ H0 L U 2 is the detection threshold, set according to the

where TSO desired Pf a . OS-CFAR. Rather than estimating the local clutter power through an arithmetic average, the OS-CFAR processor [6] orders the squared modulus of the reference samples in increasing order. Then, denoting by |r(1) |2 ≤ |r(2) |2 ≤ . . . ≤ |r(K) |2 , the ordered statistics, the detector chooses the M -th one as an estimate of the local clutter power. In other words it works as follows H |rcut |2 >1 TOS , |r(M ) |2 < H0

(5)

with TOS the detection threshold, set according to the desired Pf a . In the performance analysis of Section 4 the OS-CFAR has been used with M = 0.75K. III. KB-CFAR D ETECTION S CHEMES This section is devoted to the design of two data selectors which, exploiting the a-priori information provided by a GIS, are capable of discarding nonhomogeneous samples from the available secondary data set. We just recall that a GIS is a software technology which represents a geographic site through layers, namely geometrically and semantically uniform sets, characterized by an attribute describing their content. The higher the number of attributes, the more detailed the representation. From a practical viewpoint, there exist two different ways of representing a given layer, usually referred to as the raster and the vectorial models. The former provides a regular quantization of a given area through rectangular cells (raster cells) whose greater side coincides with the spatial resolution of the representation and whose topographical content is described by an attribute. The latter, instead, provides a finer quantization of the scene, employing points, lines, and

129

K cells) and the K − KS − i=1 D(i) indices of the nonhomogeneous cells closest to the CUT. After the SKB training selection procedure, a CA-CFAR detector is employed to perform the final decision about the target presence. The cascade of the SKB training selection algorithm and the CA-CFAR detector will be denoted as SKBCFAR. B. Dynamic KB Selector

Fig. 2. Example of GIS raster representation. Attribute 0 denotes terrain. Attribute 1 denotes lake.

polygons in place of the raster cells. In the sequel we focus on the raster model and highlight that the idea we introduce can also be extended to the case of a vectorial representation. According to the raster model a given scene is represented by an array of rectangular cells each of them containing a numerical value which labels the corresponding attribute. For example in Figure 2a we have a scene composed of two lakes and homogeneous terrain. The corresponding raster representation is displayed in Figure 2b where the number 0 indicates the attribute terrain and the number 1 denotes the attribute lake. The training selection algorithms we propose assume that the size of the radar cell is equal to the size of the raster cell, and resort to the GIS information to select the most homogeneous reference samples. The basic assumption is that homogeneous secondary samples come from cells which have the ”same” terrain as the CUT. A. Static KB Selector

The main difference between the static training selection algorithm of the previous subsection and the dynamic one relies on the fact that the former excises a fixed number of cells while the latter chooses dynamically the quoted number. In other words the a-priori information is further used to select the number of cells to be censored. Let KM AX and Kmin be the maximum and minimum number of data exploited for the threshold adaptation process. The set Ω containing the indexes of the selected secondary data can be obtained as follows. K • if i=1 D(i) ≥ KM AX , then Ω is the set of the KM AX smallest indexes such that D(i) = 1. This is tantamount to select as training data the KM AX returns from the homogeneous spatially closest to the CUT. cells K • if Kmin ≤ D(i) ≤ KM AX , then Ω is the set of i=1 all indexes such that D(i) = 1. Otherwise stated all the returns from training cells homogeneous to the CUT are exploited K for setting the threshold. • if i=1 D(i) < Kmin , then Ω is composed of the indexes such that D(i) = 1 (indexes of the homogeK neous cells) and the Kmin − i=1 D(i) indexes of the nonhomogeneous cells closest to the CUT. The two-stage KB detectors, which exploit one of the proposed data selectors as the first stage and the CA-CFAR as the second stage will be referred to SKB-CFAR and DKBCFAR processors. IV. P ERFORMANCE A NALYSIS

The first training selection algorithm devised in this paper, referred to as Static KB (SKB) selector, excises a fixed number KS of data from the training set and works as follows. Exploiting the information provided by the GIS, assign to the CUT and to the K reference cells the corresponding attributes Xcut and Xi , i = 1, . . . , K. Moreover, for all i ∈ {1, . . . , K} evaluate the homogeneity indicator  if Xi = Xcut  0 (6) D(i) =  1 if Xi = Xcut

This section is devoted to the performance assessment of the new algorithms in the presence of real data (X-band sea clutter), collected by the McMaster IPIX radar in November 1993. First of all the description of the real dataset and the a-priori information employed for the analysis are described. Then the performance, in terms of CFAR behaviour and Pd , is evaluated also in comparison with the classical CFAR schemes of Section 2.

The set Ω containing the indexes of the selected secondary data can be obtained as follows. K • if i=1 D(i) ≥ K −KS , then Ω is the set of the K −KS smallest indexes such that D(i) = 1. This is tantamount to selecting as training data the K − KS returns from the homogeneous cells spatially closest to the CUT. K • if D(i) < K − KS , then Ω is composed of the i=1 indexes such that D(i) = 1 (indexes of the homogeneous

Radar measurements were collected in November 1993 using the McMaster IPIX radar from a site in Dartmouth [18], Nova Scotia, on the East Coast of Canada (see Figure 3a). The radar was mounted on a cliff facing the Atlantic Ocean, at a height of 100 feet above the mean sea level, and scans the site over 370 Deg in 10 seconds in a continuous azimuth scan mode. More details on the experiment can be found in [18], [19]. The illuminated area (Dataset13) ranges from the

A. Real Data and GIS Map

130

Atlantic Ocean, to the shoreline of Cow Bay, and to the lands near Halifax. For the analysis we select the first Ns = 100 range cells and Nt = 1800 temporal/azimuth samples ranging from 220 Deg to 303 Deg, namely an open view of 83 Deg over the region that includes sea, land and the heterogeneous clutter composed by the shoreline of Cow Bay. We assess the performance of the algorithms in correspondence of the most heterogeneous range cells of the dataset, in order to analyze the worst conditions for the CFAR detectors. The 2-D clutter intensity field is plotted in Figure 3b: the strongest return are plotted in red while the weaker returns are plotter in blue. We do not possess a GIS representation for the region of Figure 3a. Nevertheless, in order to prove the effectiveness of the proposed KB receivers on real data, we have constructed with the aid of the geographic map of Figure 3a, a GIS database assuming that the size of the radar cell coincides with the size of the raster cell. By doing so we come up with the representation depicted in Figure 3c where there are three different attributes: the sea, marked by the blue raster cells, the homogeneous land, plotted in orange, and the heterogeneous clutter (red-colored, corresponding to the shoreline of Cow Bay). From the synthetic GIS map of Figure 3c the distribution of the different attributes along the analysed region can be observed • Land: for all the analysed azimuths there are areas of homogenous land, especially in correspondence of range cells far from the radar site. • Sea: there is the presence of a sea area in correspondence of range cells close to the radar site. This region is delimited by the Cow Bay beach which divides the sea from the Cow Bay lake, as shown in Figure 3a. • Mixed Land and Sea: between the Cow Bay sea and the homogeneous land region, there is the presence of a highly nonhomogeneous area including the Cow Bay lake, the Mceas island, and all the areas between the shoreline of Cow Bay and the lands near Halifax.

b) 303°

100

Range Cells 1

220°

c) 303° 100

Range Cells 1

B. Number of FAs This subsection is devoted to a CFAR analysis of the considered detectors. It is assumed that the size of the reference window is K = 32 and that the index of the CUT varies in the set [17, 84]. The overall data window is slid in space from range bin to range bin and in azimuth from 220 Deg to 303 Deg. By doing so the total number of trials available for estimating the actual number of FAs is Ntrials = Nt × (Ns − K) = 122400 .

(7)

The SKB-CFAR detector assumes KS = 16 whereas the DKB-CFAR processor considers Kmin = 4 and KM AX = 16. In the sequel Pf a is fixed to 10−6 and, exploiting the theoretical CFAR property, the thresholds of the receivers are set under the hypothesis of spatially homogeneous Gaussian clutter. This is tantamount to assuming that the K + 1 returns of the complete data window (CUT plus training cells) are

220°

Fig. 3. Subplot a) Geographic map of the experiment site: the red line delineates the selected area. Subplot b) 2-D intensity field of the mixed land and sea clutter live data: the strongest return are plotted in red while the weaker returns are in blue. Subplot c) GIS representation of the considered dataset: blue raster cells represent sea, orange raster cells represent homogeneous land and red raster cells represent mixed land and sea.

131

303°

84

303°

84

Range CUT

Range CUT 17

17

220°

220°

Fig. 4. PPI of the CA-CFAR detector for azimuth samples ranging from 220 Deg to 303 Deg and range cells from 17 to 84: the total number of FA is 621.

303°

Fig. 6. PPI of the SO-CFAR detector for azimuth samples ranging from 220 Deg to 303 Deg and range cells from 17 to 84: the total number of FA is 5957.

84

303°

84 Range CUT

Range CUT

17

17

220°

220°

Fig. 5. PPI of the GO-CFAR detector for azimuth samples ranging from 220 Deg to 303 Deg and range cells from 17 to 84: the total number of FA is 387.

Fig. 7. PPI of the OS-CFAR detector for azimuth samples ranging from 220 Deg to 303 Deg and range cells from 17 to 84: the total number of FA is 4078.

independent and identically distributed, zero-mean, complex, Gaussian random variables. Under this simulation setup, the actual Number of FAs, Nf a , for each analysed system, is evaluated. The results can be summarized as follows.

In Figure 8 the PPI of the SKB-CFAR is shown. It indicates that, resorting to the SKB selection strategy, the total number of FAs can be reduced. Indeed, in this case, Nf a = 203 highlighting the effectiveness of the KB pre-processing. • In Figure 9 the PPI of the DKB-CFAR is shown. This receiver exhibits the lowest number of FAs, i.e. Nf a = 82. In other words, in the presence of strong nonhomogeneous scenarios, adaptivity on the number of cells to be censored significantly helps the adaptive threshold setting. The previous results, summarized in Table I, leads to the conclusion that all the considered systems are not able to maintain rigorously the theoretical FA rate when operate in the presence of heterogeneous measured clutter data. Nevertheless the use of a-priori knowledge can noticeably improve the performance. In fact the new processors exhibit a number of FAs smaller than the classical CFAR schemes.

•

•

•

•

In Figure 4 the PPI (Plan Position Indicator) of the CACFAR detector is shown. The black areas correspond to regions where the decision statistic is below the threshold, whereas the dots correspond to range and azimuth positions where the decision statistic crosses the threshold. For the quoted receiver Nf a = 621. In Figure 5 the PPI of the GO-CFAR is shown. In this case the total number of FAs is Nf a = 387, which, as expected, is lower than the number exhibited by the CACFAR. In Figure 6 the PPI of the SO-CFAR is shown. The total number of FAs is 5957. This number is greater than the one corresponding to the CA-CFAR and the GO-CFAR due to the lower robustness of the Smallest Of training strategy with respect to clutter power mismatches. In Figure 7 the PPI of the OS-CFAR is shown. The total number of FAs is 4078, namely the OS-CFAR performs (in terms of FAs) better than the SO-CFAR but worse than CA-CFAR and the OS-CFAR.

•

C. Detection Performance This subsection is devoted to the analysis of the considered detection schemes in the presence of synthetic targets injected into the real dataset. To this end, cell 65 is chosen as the CUT and the surrounding cells ([49, 64] ∪ [66, 81]) as training

132

303°

84 Range CUT

30 17 20

Test Statistic (dB)

10

220°

0 −10 −20 −30 −40 −50 220

Fig. 8. PPI of the SKB-CFAR detector for azimuth samples ranging from 220 Deg to 303 Deg and range cells from 17 to 84: the total number of FA is 203.

303°

84

230

240

250

260 270 Azimuth (Deg)

280

290

300

Fig. 10. Test Statistic versus the azimuth for the CA-CFAR (solid line). Starmarked values are in correspondence of the positions where the useful targets, with SCR = 15 dB, are present. The dotted line represents the theoretical detection threshold for Pf a = 10−6 .

Range CUT 17

30 20

Test Statistic (dB)

10 220°

Fig. 9. PPI of the DKB-CFAR detector for azimuth samples ranging from 220 Deg to 303 Deg and range cells from 17 to 84: the total number of FA is 82.

0 −10 −20 −30 −40 −50 220

Detector CA-CFAR GO-CFAR SO-CFAR OS-CFAR SKB-CFAR DKB-CFAR

Nf a 621 387 5957 4078 203 82

TABLE I Nf a OVER Ntrials = 122400 FOR THE ANALYZED CFAR DETECTORS .

240

250


280

290

300

Fig. 11. Test Statistic versus the azimuth for the GO-CFAR (solid line). Starmarked values are in correspondence of the positions where the useful targets, with SCR = 15 dB, are present. The dotted line represents the theoretical detection threshold for Pf a = 10−6 .

30 20 10 Test Statistic (dB)

data (i.e. K = 32). As to the KB processors they assume respectively KS = 16, Kmin = 4, and KM AX = 16. Under this simulation setup, the decision statistic of the analysed systems is plotted versus the azimuth together with the theoretical detection threshold ensuring Pf a = 10−6 . By doing so the total number of tests is 1800 referring to the azimuth positions between 220 Deg and 303 Deg. Finally, three synthetic non-fluctuating targets, with a Signal to Clutter power Ratio (SCR) equal to 15 dB, are injected at the azimuth positions 247 Deg (target 1), 266 Deg (target 2) and 290 Deg (target 3). The results of the analysis can be summarized as follows

230

0 −10 −20 −30 −40 −50 220

230

240

250


280

290

300

Fig. 12. Test Statistic versus the azimuth for the SO-CFAR (solid line). Starmarked values are in correspondence of the positions where the useful targets, with SCR = 15 dB, are present. The dotted line represents the theoretical detection threshold for Pf a = 10−6 .

133

•

30 20

Test Statistic (dB)

10 0 −10 −20 −30 −40 −50 220

• 230

240

250


280

290

300

Fig. 13. Test Statistic versus the azimuth for the OS-CFAR (solid line). Starmarked values are in correspondence of the positions where the useful targets, with SCR = 15 dB, are present. The dotted line represents the theoretical detection threshold for Pf a = 10−6 .

•

30

•

20

Test Statistic (dB)

10 0 −10 −20

•

−30 −40 −50 220

230

240

250


280

290

300

Fig. 14. Test Statistic versus the azimuth for the SKB-CFAR (solid line). Star-marked values are in correspondence of the positions where the useful targets, with SCR = 15 dB, are present. The dotted line represents the theoretical detection threshold for Pf a = 10−6 .

•

In Figure 10 the decision statistic of the CA-CFAR is plotted versus the azimuth. The stars indicate the azimuth positions where the useful targets are present. The plot shows that only target 3 is over the threshold and is thus correctly detected. Moreover, around 280 Deg there is a great concentration of FAs. Finally, in correspondence of the azimuth interval centred at 260 Deg, the decision statistic assumes very low values (about −40 dB); this is due to the bad training which causes an over-nulling effect of the clutter in the CUT. In Figure 11 the decision statistic of the GO-CFAR is plotted versus the azimuth showing that, even in this case, only the target 3 crosses the threshold. Again, around 280 Deg, the decision statistic is often over the detection threshold. Finally the over-nulling effect of the clutter in the CUT is still markedly present. In Figure 12 the decision statistic of the SO-CFAR is plotted versus the azimuth showing all the targets are over the threshold. However, as observed in Subsection 4.2, the main drawback of the quoted detector is the number of FAs. Actually a significant number of threshold crossings are present in absence of useful target echos. In Figure 13 the decision statistic of the OS-CFAR is plotted versus the azimuth: target 2 and target 3 are detected but the decision statistic is over the threshold several times also in absence of useful target, especially around 280 Deg and 293 Deg. In Figure 14 the decision statistic of the SKB-CFAR detector is plotted versus the azimuth: target 1 is still below the threshold, while target 2 and target 3 are correctly detected. The figure also indicates that number of FAs around the azimuth 280 Deg significantly reduces as well as the over-nulling effect around 260 Deg. In Figure 15 the decision statistic of the DKB-CFAR detector is plotted versus the azimuth. In this case, due to the adaptive number of censored returns, the detection threshold is no longer constant. Actually the threshold value is ruled by the expression −

1

TCA = Pf aK − 1, 30 20

Test Statistic (dB)

10 0 −10 −20 −30 −40 −50 220

230

240

250


280

290

300

Fig. 15. Test Statistic versus the azimuth for the DKB-CFAR detector (solid line). Star-marked values are in correspondence of the positions where the useful targets, with SCR = 15 dB, are present. The dotted line represents the theoretical detection threshold for Pf a = 10−6 .

which clearly indicates that the higher the number of training data, exploited for evaluating the normalization factor in (1), the smaller the threshold. For the case at hand, it ranges between 1.3 dB (corresponding to homogeneous regions, i.e. KM AX = 16) and 14.9 dB (in correspondence of very nonhomogeneous regions, where the DKB algorithm decides to use Kmin = 4 training cells). The plot indicates that all the targets are over the threshold and thus correctly detected. Moreover the FAs around 280 Deg are now absent, as the detection threshold increases in correspondence with the quoted azimuth position. Finally, the very flat behaviour of the decision statistic indicates that the over-nulling effect of the clutter within the CUT is significantly reduced. In the last part of this subsection we evaluate the Pd versus the SCR, setting the detection threshold of the receivers in

134

training selection strategies to Doppler processing and/or space time adaptive processing.

1 0.9 0.8

ACKNOWLEDGMENT

0.7

The authors are deeply indebted to Prof. S. Haykin and Dr. B. Currie of the McMaster University (Canada) who have kindly provided the IPIX data. We also thank Dr. V. Vannicola for providing reference [16], Dr. G. Capraro and Dr. M. Wicks for several interesting discussions concerning KB signal processing.

Pd

0.6 0.5 0.4 0.3 0.2 0.1 0 −10

−5

0

5

10 SCR (dB)

15

20

25

30

R EFERENCES

Fig. 16. Pd versus SCR of the CA-CFAR (solid curve), GO-CFAR (dashdot curve), SO-CFAR (dotted curve), OS-CFAR (dashed curve), SKB-CFAR (dot-marked curve), and DKB-CFAR (plus-marked curve). The thresholds of all the receivers are set in order to guarantee Nf a = 82.

order to guarantee the same Nf a = 82. The total number of trials available for evaluating Pd , for a given SCR value, is Ntrials = 122400. In Figure 16 the Pd of the analysed detectors is compared. The plots highlight that the OS-CFAR and SO-CFAR exhibit the worst performance. For low/medium values of Pd . the CA-CFAR and GO-CFAR achieve almost the same performance, but for high Pd ’s, the former has a slight performance advantage over the latter. The DKB-CFAR achieves a better performance than the SKB-CFAR and both uniformly outperform the classical CFAR detection schemes. For Pd = 0.5 the gain of the DKB-CFAR is 5.6 dB over the SKB-CFAR, 11.6 dB over the CA-CFAR, 12.0 dB over the GO-CFAR, 14.2 dB over the OS-CFAR, and 14.3 dB over the SO-CFAR. In conclusion, the analysis has clearly highlighted the advantages of KB training selection strategies, which not only keep the number of FAs as low as possible, but also improve the detection performance of classical CFAR processors. V. C ONCLUSION In this paper we have introduced and assessed two KBCFAR detectors which exploit the a-priori information provided by a GIS about the topology of the illuminated environment. They are composed of two stages. The former is a KB data selector which chooses the most homogeneous returns exploiting the GIS information. The latter is a classical CFAR detector. At the analysis stage we have compared, on real radar data, the performance of the new detectors with that of conventional CFAR schemes. The results have highlighted that, even with few attributes and a low resolution GIS map, the a-priori knowledge can lead to significant performance improvements. Actually the introduced detectors ensure a CFAR behaviour and a detection performance better than the classical CFAR schemes. Future research tracks might concern the study of the proposed KB-CFAR detectors when a high resolution GIS map, with a high number of attributes, is available. Finally it might be of interest the application of the proposed KB

[1] H. M. Finn, “Adaptive Detection in Clutter,” Proceedings of the National Electronics Conference, Vol. 22, pp. 562-567, 1966. [2] B. O. Steenson, “Detection Performance of a Mean Level Threshold,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 4, No. 4, pp. 529534, July 1968. [3] R. Nitzberg, “Constant-False-Alarm-Rate Signal Processors for Several Types of Interference,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 8, No. 1, pp. 27-44, January 1972. [4] V. G. Hansen and J. H. Sawyers, “Detectability Loss Due to GreatestOf-Selection in a Cell Averaging CFAR,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 16, No. 1, pp. 115-118, January 1980. [5] G. V. Trunk, “Range Resolution of Targets Using Automatic Detectors,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 14, No. 5, pp. 750-755, September 1978. [6] H. Rohling, “Radar CFAR Thresholding in Clutter and Multiple Target Situations,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 19, No. 4, pp. 608-621, July 1983. [7] P. P. Gandhi and S. A. Kassam, “Analysis of CFAR Processors in Nonhomogeneous Background,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 24, No. 4, pp. 427-445, July 1988. [8] J. T. Rickard and G. M. Dillard, “Adaptive Detection Algorithms for Multiple Target Situations,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 13, No. 4, pp. 338-343, July 1977. [9] H. Goldman and I. Bar-David, “Analysis and Application of the Excision CFAR Detector,” IEE Proc. on Radar and Signal Processing, Vol. 135, No. 6, pp. 563-575, December 1988. [10] E. Conte, M. Longo, and M. Lops, “Two-Sided Censored Mean-Level Detector for CFAR in Multiple Target Situations and Clutter Edges,” Alta Frequenza, Vol. LVIII, No. 2, pp. 165-173, March-April 1989. [11] S. D. Himonas and M. Barkat, “Automatic Censored CFAR Detection for Nonhomogeneous Environments,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 28, No. 1, pp. 286-304, January 1992. [12] W. L. Melvin, M. Wicks, P. Antonik, Y. Salama, P. Li, and H. Schuman, “Knowledge-Based Space-Time Adaptive Processing for Airborne Early Warning Radar,” IEEE AES Systems Magazine, Vol. 13, No. 4, pp. 37-42, April 1998. [13] R. Adve, P. Antonik, W. Baldygo, C. Capraro, G. Capraro, T. Hale, R. Schneible, and M. Wicks, “Knowledge-base Application to Ground Moving Target Detection, ” AFRL-SN-TR-2001-185, In-House Technical Report, September 2001. [14] J. R. Guerci, “Knowledge-Aided Sensor Signal Processing and Expert Reasoning,” Proc. of the 2002 Workshop on Knowledge-Aided Sensor Signal Processing and Expert Reasoning (KASSPER), Washington D.C., April 2002, USA. [15] V. C. Vannicola and J. A. Mineo, “Applications of Kowledge-Based Systems to Surveillance,” Proc. of the 1988 IEEE National Radar Conference, pp. 157-164, April 1988. [16] V. C. Vannicola, L. K. Slaski, and G. J. Genello, “Knowledge Based Resource Allocation for Multifunction Radars,” Proc. of the 1993 SPIE Conference on Signal and Data Processing of Small Targets, Vol. 1954, pp. 410-425, April 1993. [17] S. Haykin, “Radar vision,” Proc. of the Second International Specialist Seminar on the Design and Application of Parallel Digital Processors, pp. 75-78, April 1991. [18] http://soma.crl.mcmaster.ca/ipix/ [19] A. Drosopoulos, “Description of the OHGR Database,” Tech. Report No. 94-14, Defence Research Establishment Ottawa, December 1994, Ottawa.

135