Detection of snow clutter in ATC ground radarsignal

Laura Pierucci,Leonardo BocchiDepartment of Electronics and Telecommunications

University of Florence - Florence,ItalyEmail: laura.pierucci, leonardo.bocchi @unifi.it

Giuseppe Anania, Dionisio AcciaiGalileo Avionica - Finmeccanica

Firenze, ItalyEmail: giuseppe.anania, dionisio.acciai @galileoavionica.it

Abstract—The use of adaptive techniques may prove usefulin the processing of radar signals. The proposed radar clutterclassificator is aimed to improve the detection of snow clutterpresence in data acquired by a ground radar system in an airtraffic control environment.

The classifier receives as input a set of features which describethe appearance of the same plot in two consecutive scans of theground radar. The feature set includes two group of parameters,which respectively represent the shape of the plot in each scan,and the displacement of the center of mass of the plot betweenthe two scans.

The data set used in simulations has been extracted from ameasurement campaign carried out in presence of snow in Italianairports, with the help on an expert operator who manuallyclassified a set of plots as target, or clutter.

The performance of the classifier, a multilayer perceptrontrained with the backpropagation rule, indicates a correct clas-sification rate of about 98%.

Index Terms—clutter detection, neural network, shape analysis

I. INTRODUCTION

In the near future, Knowledge-Based systems for adaptiveradar (KB) or Cognitive Radar can intelligently improve theperformance of surveillance radar. The radar environmentis usually nonstationary and an efficient adaptation requiresreal-time exploitation of a priori knowledge concerning theenvironemnt surrounding the radar. Intelligent and adaptivedata processing is necessary for filtering, detection,trackingstages. In the new concept of cognitive radar the adaptivity isextended at the transmitter too: the radar continuously learnsfrom the surrounding enviroment changes and by using afeedback loop the transmitter can adjust its illumination ofthe environment in intelligent and robust manner [1].

However adaptive techniques for detection, tracking andclassification of possible targets are always necessary. NeuralNetworks (NN) and Genetic Algorithms have been success-fully used [2], [3], [4], [5], [6], [7] as radar signal processingrequires a high capacity to adapt to a changing environmentalsituation. Moreover, the self-adaptation of a NN allows todevelop a processing system without building a decision rule,starting from a set of labelled samples.

The reduction of the effect of clutter in ground radar isperformed, in conventional systems, by algorithms as MTI and

This work was supported by the SESM Center funds.

MTD, which are designed to remove unwanted clutter data, aswell as informations concerning its surrounding.

In literature only few papers report the use of NeuralNetwork as a detector and classifier for clutter data. Forinstance, Haykin [8] focused on the detection and classificationof three different clutter sources: birds, weather (rain andturbolence) and ground clutter. In this case, data was acquiredusing a L band radar for air traffic control. Available data isrelated to the amplitude and phase of the received signal andthe paper analyzes mainly the extration of a set of suitablefeatures which can discriminate among different clutter types.If a feature can provide a large separation among differentclasses in the data set, better will be the classifier output,allowing to reduce the intervention of the radar operator tovisually identify the object. In the paper, eleven features havebeen proposed to describe the properties of the received signal.The features were mainly derived by second order moments ofthe signal, and were fed to a four layer perceptron.The systemwas able to correctly classify, on average, 90% of the testset, with a correct classification rate, for each class, rangingfrom 94%, obtained in the target identification, to about 85%,concerning the detection of birds in the radar field.

A method for detecting small targets embedded in seaclutter is presented in [9]. The algorithm models the seaclutter through a Radial Basis Function (RBF) network and isdeveloped and tested on a data set acquired in the east coastof Canada. A correct model of the sea clutter is a key point, asit may give, depending on the application, informations aboutthe surface or appear as noise to be rejected. The effect of seacan be represented as a set of independent scatterers whichappear as a Gaussian distribution. The dynamic behaviour ofthe clutter has been described using a chaotic system [10],[11]. The RBF network can be effectively used to build apredictor of the clutter signal x(t):

x(t) = f (x(t − 1), x(t − 2), ..., x(t − k)) (1)

where the prediction is performed from the previous k samplesof the input signal {x(t − 1), x(t − 2), ..x(t − k)} using anunknown function f(·). The RBF is trained to obtain anapproximation of the funcion f from a dataset composed ofclutter data, where no target is present. After the training, inpresence of clutter, the prediction error is small and distributedaround zero, while the effect of an embedded target is to

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Fig. 1. Architecture of the system

produce a relatively large prediction error, which can be easilydetected by a thresholding method.

Due to the automatic knowledge acquisition and continuoussystem refinement capabilities of neural networks, they havebeen successfully used in many target detection applications. Anumber of neural architectures have been proposed includingbut not limited to the multi-layer perceptron (MLP), self-organizing feature maps (SOFMs), Hopfield networks: how-ever among these architectures, MLP is one of the simplestand most widely used neural network models. In many prac-tical applications, a neural network trained on an extremelyhigh-dimensional space easily becomes overparameterized,resulting in a large difference between training and testingperformance and poor estimates of a posteriori probabilities.To reduce the input dimensionality, while maximizing theseparability between different classes, the data should betransformed into a small set of features that could representthe classes distinctively. These features extraction is thereforean important and critical task. In this work we evaluate the useof a neural network to improve the detection and the labellingof clutter in radar images. The analysis has been carried outusing a set of acquisition campaign carried out on a set ofground radars, installed in italian airports, acquired in presenceof snow in the airport. The aim of this work is to detecta ground clutter signal produced by remains of snow afterthe snowplows went to clean the runways and the main areasof the airport. Each plot which is extracted by the existingprocessing chain is analyzed and a suitabe feature vector isused to describe its characteristics. A neural network has beentrained to discriminate clutter from target, having as input thefeature vector. The system has been realized mainly usingMatlab, as concerns the acquisition module and the featureextraction module. The classifiers were tested using the Wekapackage [12].

II. SYSTEM ARCHITECTURE

The system architecture is described in the block diagramreported in figure 1. In the top line, it is shown a schematicof a conventional processing chain of an existing radar. Thesignal received from the antenna is sampled, and a map ofthe ground clutter is adaptively obtained (for instance, usinga long-time averaging). The ground clutter map is used toremove from the radar screen the presence of buildings and

fixed targets in the surroundings of the airport. Then, aftera Cfar, a plot extractor detects and segments out from thewhole images regions composed of connected pixels havingan over-threshold power. The information correlated to thoseplots is afterward used to localize and detect target by couplingplots over time. The proposed system is depicted in the bottomline, and it operates directly after the plot extractor of theexisting processing chain. The basic processing element usedin the proposed system is therefore a single plot detected by thechain. A simple tracking system has been realized to associatecorresponding plots in two consecutive scans of the radar.

The system samples a small Region of Interest (ROI) aroundthe center of the plot, and forwards it to the feature extractionblock. The extracted features are used to feed a neural network,which discriminates plots containing a snow clutter from thoserelated to the targets.

A. Data set

The system has been developed and tested on data acquiredfrom a set of ground radar systems. The data has been acquiredduring a measurement campaign carried out on winter, 2005in order to collect a suitable set of measurement during andafter snowfall.

A reduced test set has been obtained fro the first set ofacquisition by visually sampling a few clutter plots and aboutthe same number of targets (vehicles moving in the airport).Then, after all the measurements were collected, an extendeddata set has been extracted, with the collaboration of an expertradar operator. From a series of consecutive sweeps acquired inthree different sessions, spaced about thirty minutes each other,the operator manually selected all plots which representedsnow clutter. At the same time, he selected a set of targetswhich were present in the same sweeps. A sample of thedifferent classes is reported in Fig. 2. The dataset contains, atthis point, about 3000 samples of snow clutter, and about 1000sample of targets. In order to improve the quality of the testset, a bootstrapping method has been used: a simple classifierhas been trained on the data set, and tested on the samesweeps used to extract the traning set. About 1000 additionalplots were selected among those plots which were classifiedas clutter by the network, but which were classified as eithertarget or suspect by the operator. The plots which have beenselected using this procedure have been added to the training

Fig. 2. Example of the manually selected plots, representing clutter andtarget.

set and labelled as target. The resulting data set is thereforecomposed of about 5000 sample, 3000 of which are labelledas clutter, and 2000 of which belong to the class “target”.

B. Tracking module

The information concerning the temporal evolution of a plotis a crucial point in discriminating snow clutters from targets.A plot representing a target mantains a similar shape acrossconsecutive scans of the radar, possibly changing its positionif the target is moving. On the other hand, snow clutters aresupposed to remain in the same position. However, due to thelow signal level of the clutter itself, the plot detector may splitan elongated clutter, randomly, in several plots, causing andapparent movement of the resulting plots.

A sample of this situation is presented in Fig. 3, where theplots labelled “2D” and “2E”, which have been detected in thefirst scan appear as a single large plot, labelled “2E”, in thenext scan. In this case, plot “2D” disappears from one scanto the next one, while plot “2E” has shifted right and downduring the merging process.

A simple tracking module has been therefore developed todetect corresponing (i.e. referring to the same object) plotsin two consecutive scans of the same region. The trackingmodule pairs the plot in the two scans using a nearest-neighborapproach, by relating each plot in the first scan with the plots,belonging to the second scan, which is more closer to theposition of the first one. This approach is not the optimalone for the detection and tracking of moving targets, as itdoes not take into account the inferred detection of the targetspeed which may be used to improve the target localizationacross several scans. However, the nearest neighbor approachappears useful for the detection of clutters, as it focuses onthe behaviour of static objects.

C. Features

The selection of a proper feature set is a key point inthe design of a neural classifier. In order to obtain goodresults the network needs to be feeded with a pattern havinga small number of components, to prevent overfitting of the

Fig. 3. Apparent movement of the snow in consecutive radar scans: plots”2D” and “2E” in the first scan (top) have merged on the second scan (bottom).

network and to improve its generalization capabilities. On theother hand, reducing the number of components in the patternmay reduce the information available to perform the desiredclassification.

The proposed feature vector is composed of three parts: thefirst two describe the shape of the plot in the current scan andin the previous one, while the last part is the distance betweenthe plots, measured as the distance between the centre of mass.

The vector of shape descriptors, has been designed closelyfollowing the reasoning scheme of an expert radar operator indiscriminating snow clutter from possible targets.

The resulting feature vector is composed of six componentsvi, with i = 1, 2.., 6, defined as follows:

v1 = d2max/n (2)

v2 = std(pi) (3)v3 = avgb(di)/dmax (4)v4 = stdb(di)/dmax (5)v5 = daz/drange (6)v6 = dmax (7)

where pi is a generic pixel belonging to the plot and di is itsdistance from the center of mass of the plot. The maximumvalue of this distance is indicated as dmax, while daz anddrange are the distances measured in the azimuth directionand in the range direction, respectively. The operators avgb

and stdb indicate the average and the standard deviation of thepixels belonging to the boundary of the plot. The performanceof the feature vector, with respect to a set of geometricalfeatures, has been assessed in a previous work [13] for the

Fig. 4. Example distribution of one of the extracted features: histogram offeature v3 (average distance of the boundary from centre of mass) for classesclutter (red) and target (blue).

detection of clutter from a single radar scan.The physical meaning which may be associated to each of

the above defined features can be described as follows:• the value of v1 is related to the circularity of the plot.

This feature assumes its minimum value for a circularplot and increase with an elongated plot

• the standard deviation of the pixel values is used todiscriminate a non-uniform plot, which may hide a target

• the statistical parameters (average and standard deviation)of the distance of the boundary from the centre of massrelate to the irregularity of the shape of the plot

• the ratio between the dimensions measured in the rangeand in the azimut direction may help in discriminate theelongation of the plot related to the distance from thetransmitting antenna

• the maximum distance from the centre of mass indicatesthe overall size of the plot.

A visual description of the extracted features can be ob-tained from the figure 4, which shows the histogram of oneof the extracted features (feature v3, average distange of theboundary from the center of mass). The histogram show thereis a sensible difference in the distribution of the feature forthe two classes, althought the single feature is not sufficientto completely discriminate the two classes.

The overall size of the feature vector which has beenfed as input to the neural classifier is therefore equal to 13components (6 shape descriptors of the plot in the currentscan, 6 shape descriptorss of the plot in the previous scan,and the distance between their centre of mass).

D. Neural classifier

Two different groups of classifiers have been used in thevarious phases of the work. A classic classifier, based on theBayesian approach, is used as a reference implementation forthe evaluation of the results. A Bayesian classifier assignseach input sample S to the class C which maximises theposterior probability p(C|S): given the input sample data ,

Fig. 5. Diagram of the feature extractor and the neural classifier

the classifer obtain an estimate for the probability it belongsto each of the possbile classes and assignes the sample tothe class having maximum probability. The estimation of theprobability is performed using the Bayes theorem:

p(C|S) =p(C)p(S|C)

p(S)(8)

The estimation of the conditional probablities p(S|C), whenthe dimensionality of the sample S is larger, is problematicbecause it requires a very large training set. The “naive”assumption allows to obtain a simple solution by assuming theindependence of different components of S, given the class C:

p(S = s|C) =n∏

i=1

p(Si = si|C) (9)

where Si are the components of the sample vector S.The second classifier is a multilayer perceptron, using a

network composed of an input layer, an hidden layer composedof units with sigmoidal activation, and two output units withsigmoidal activation, corresponding to the two desired classes.The units in the input layer correspond to the number ofinputs in the two groups of features. The selection of thenumber of hidden neurons has been performed experimentally,by training, using the same data set, a set of networks witha number of hidden neurons varying from 5 to 15. We selectto use two output units, instead of a single boolean output,in order to allow an easy extension of the architecture torecognize different classes of clutter. The diagram of thenetwork architecture is reported in figure 5.

Each network has been trained with the backpropagationrule, using momentum, and mean square error to evaluate theperformance. Both classifiers have been designed and testedusing the Weka system.

III. RESULTS

The system has been developed and tested using 10-foldcross validation on the available data set. Results report the

TABLE IPERFORMANCE (10 FOLDS CROSS-VALIDATION) OF DIFFERENT

CLASSIFIERS

Classifier Target Clutter ROC Area

Bayes 93.1% 91.3% 0.976MLP 5 units 98.3% 97.8% 0.997MLP 7 units 98.5% 98.3% 0.996MLP 9 units 98.7% 98.3% 0.996MLP 11 units 98.8% 98.6% 0.996MLP 13 units 98.5% 98.6% 0.996MLP 15 units 98.4% 98.5% 0.996

average performance over all the runs of the classifiers. In thefirst phase we build a Bayesian classifier, used as a referencesystem. The performance of this classifier, reported in Table I,indicate an acceptable behaviuor of the system, with a correctclassification rate of 93.1%.

In the second phase, we trained the set of multilayerperceptrons over the same data set. The results, reported inthe same table as above, indicate the neural classifiers havea correct classification rate of about 98%, with an area underthe ROC curve equal to about Az = 0.996. As it can beseen in the table, the behaviour of the different MLP neuralnetworks is comparable. The network having 5 units in thehidden layer exhibits the better overall performance, expressedby the area under the ROC curve. The working point has beenselected by locating the point in the ROC curve having themaximum classification accuracy, altought this may not bethe optimal choice. Indeed, in case the relative severity ofthe possible errors (a plot of clutter classified as target vs.a target misclassified as clutter) is different, it is possible toselect a different work point, either by using a different balanceof the number of examples belonging to the two classes, orby varying the thresholds used to compare the outputs of theunits.

The best performance, in the selected work point, is ob-tained using a network having 11 hidden neurons. Thisnetwork, indeed, has a correct classification rate of 98.7%,corresponding to 98.8% correct detections of targets and 98.6correct detections of clutter.

Figure 6 shows the resulting ROC curve, corresponding tothe network with 5 neurons. The plot shows only the upperleft corner of the curve.

IV. CONCLUSION

The reported results indicate that neural network, coupledwith an adequate feature set, may successfully identify snowclutter. The shape analysis, together with informations relatingto the temporal evolution of the plot, allow to obtain a highcorrect classification rate.

Future work will include the integration of the system withan analysis of the spatial distribution of the plots present inthe same scan, in order to include informations about spatialarrangement of plots in the classification process.

Fig. 6. ROC curve of the neural classifier (5 hidden neurons) - upper leftregion.

ACKNOWLEDGMENT

The authors are grateful to the italian SESMCenter in Romewhich funded this research.

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