Target Detection and Target Type & Motion Classification:Comparison of Feature Extraction Algorithms

Yue Li1 Asok Ray1 Thomas A. Wettergren2

Keywords: Feature extraction; Pattern classification; Sonar sensing; Surveillance in ocean environment

Abstract— This paper addresses sensor network-basedsurveillance of target detection and target type & motionclassification. The performance of target detection and clas-sification could be compromised (e.g., due to high rates offalse alarm and misclassification), because of inadequaciesof feature extraction from (possibly noisy) sensor data andsubsequent pattern classification over the network. A featureextraction algorithm, called symbolic dynamic filtering (SDF),is investigated for solving the target detection & classificationproblem. In this paper, the performance of SDF is comparedwith two commonly used feature extractors, namely, Cepstrumand principal component analysis (PCA)). Each of these threefeature extractors is executed in conjunction with three well-known pattern classifiers, namely, k-nearest neighbor (k-NN),support vector machine (SVM), and sparse representation clas-sification (SRC). Results of numerical simulation are presentedbased on a dynamic model of target maneuvering and passivesonar sensing in the ocean environment. These results showthat SDF has a consistently superior performance for all tasks– target detection and target type & motion classification.

I. INTRODUCTION

Major tools of target detection & classification includeadaptation of signal amplitude thresholds [1], Bayesian esti-mation [2], and pattern recognition [3]. Recently, there hasbeen much interest in classification of target type & motionover wireless sensor networks under uncertain environments.In the context of target detection & classification usingacoustic sensors in the ocean environment, Scrimger et al. [4]analyzed a set of 50 source spectra obtained from merchantships among passenger/ferries, cargo ships, and tankers,where both mean spectra and source-level histograms werelargely insensitive to the ship class. Rajagopal et al. [5]reported target classification of different vessels with passivesonar sensors that were subjected to multiple sources of thenoise radiated from surface ships. Gorman [6] developeda neural network learning procedure to classify the sonarsignals between two undersea targets. Azimi et al. [7] in-troduced wavelet packets into the neural network learningalgorithm for target classification at low SNR situation.Kang et al. [8] analyzed the frequency spectrum signature ofpassive sonars from the perspectives of pattern recognitionand compared back propagation neural network (BPNN) and

1Y. Li and A. Ray are with the Department of Mechanical Engineer-ing, Pennsylvania State University, University Park, PA 16802-1412, USAemail: yueli.psu@gmail.com, axr2@psu.edu

2T.A. Wettergren is with the Naval Undersea Warfare Center (NUWC),Newport, RI 02841-1708, USA and also with Department of MechanicalEngineering, Pennsylvania State University, University Park, PA 16802-1412, USA email: thomas.wettergren@navy.mil

k-nearest neighbor (k-NN) methods on the features extractedfrom the Welch power spectral densities for multiple targettypes.

Jin et al. [3] investigated target detection followed bytarget type classification within a hierarchical structure ofunattended ground sensors (UGS), based on symbolic dy-namic filtering (SDF ) [9]. In SDF, the sensor time seriesdata are symbolized to construct probabilistic finite stateautomata (PFSA) and low-dimensional feature vectors areobtained from PFSA. Mallapragada et al. [10] also usedSDF for feature extraction to develop a language measure-theoretic tool of pattern classification to identify the type andmotion of mobile robots.

Following the work of Bahrampour et al. [11], this papermakes a comparative evaluation of three tools, namely,Cepstrum-based, PCA-based, and SDF-based, of featureextraction for target detection & classification in oceanenvironments by using passive sonar sensing systems. Toevaluate the performance of feature extraction algorithms, allthree feature extractors have been tested for the same sets ofdata and the extracted features are used in conjunction withthree pattern classifier tools, namely, k-nearest neighbor (k-NN) [12], support vector machines (SVM) [12], and sparserepresentation classification (SRC) [13].

The paper is organized into four main sections includingthe present section. Section II describes the dynamic modelof moving targets used for numerical simulation. Section IIIpresents the algorithms of different feature extraction meth-ods. Section IV presents the results of target detection &classification based on an ensemble of time series generatedby the dynamic model introduced in Section II. Section Vsummarizes and concludes this paper with recommendationsfor future research.

II. DYNAMIC MODEL OF MOVING TARGETS

Passive sonar sensors (e.g., an array of hydrophones)record changes in the ambient acoustic pressure around thedevice. Since this pressure field is usually analyzed spec-trally, commonly used modeling approaches in both militaryapplications [14] and laboratory studies [15] consider theacoustic field as an ensemble of its spectral components.Although this approach is useful for short-duration signalsas well as for signals that are comprised of a simple singletone, it may not be effective for capturing the intermittentlyoccurring subtle changes in the pressure levels that areattributable to target motion, especially for maneuvering

2014 American Control Conference (ACC)June 4-6, 2014. Portland, Oregon, USA

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targets (i.e., targets that change their speed and/or direction).This paper assumes that the targets are moving in the oceanenvironment and that a typical target emits a statisticallystationary noise signature. A sensor, located at a positionx, receives an acoustic signature ytgt(x, t) from the target,Then, the received signal at the sensor site is obtained as

yrec(x, t) = ytgt(x, t) + n(t) (1)

where n(t) that is the ambient noise. The rationale forthe assumption of additive noise in Eq. (1) is that, overa given region of interest, the noise in the received signalis independent of the sensor location x. Furthermore, thenoise is assumed to be uniform over a frequency band [15].However, the signature ytgt(x, t) of a target signal is afunction of the time-varying distance dtgt(x, t) between thesensor and the moving target, where the target position xT (t)is a known function of time. Then, the distance to the targetis simply given by

dtgt(x, t) = d(x,xT (t))

where d(·, ·) is the standard Euclidean distance function.For generating the time series ytgt(x, t) over a finite set

of time steps in the time interval of length T = nδt, whichis discretized as

t ∈ Td , {t0, t0 + δt, t0 + 2δt, . . . , t0 + nδt}

Assuming that the acoustic signature being radiated bythe target is statistically stationary over the time intervalof length T , the source signature is converted into aneffective temporal realization via a standard inverse fastFourier transform. For each time step in the desired discretetime interval Td, the distance from the source to the targetis computed as dtgt(x, t). There are two dominant effectsfrom this distance on the received signature. The first effectaccrues from the fact that the source level is attenuated bythe acoustic medium, where the attenuation loss is assumedto follow the inverse-square law due to spherical spreadingas it is dominant at the short ranges of interest [16]. Thesecond effect accrues from the fact that there is a timedelay that varies as a function of the distance due to thetime of propagation. It is necessary to compute the receiveddiscrete-time interval Tr that may not be identical to asimple translation of the source time interval (i.e., Tr 6=Td+a constant). The sampling time intervals of the receivedsignal are computed as

Tr , {(t0 + dtgt(x, t0)/c) , (2)

(t0 + δt+ dtgt(x, t0 + δt)/c) , . . . ,

(t0 + nδt+ dtgt(x, t0 + nδt)/c)}

where c is the nominal speed of sound propagation in themedium at the point of interest (e.g., the default value isc = 1500 m/s). The effects of the signal attenuation arerealized by computing the source signal degradation at eachtime step in Td as

ytgt(x, t) = S(t)L(dtgt(x, t)) (3)

where S(t) is the source energy level of the target (i.e.,inverse FFT of the source spectra) and L(d) is the sphericalspreading loss for distance d.

The resulting degraded signal levels in ytgt(x, t) are thenmapped to the corresponding time in Tr. Finally, a cubicspline interpolation of the resulting signal levels is performedat the nonuniform time steps in Tr to a set of uniform timesteps over the same interval. Then, a random realization ofthe noise component is generated numerically for additionto the resulting signature to create the modeled receivedacoustic signal.

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Fig. 1. Acoustic Signal for a Single Target

The time series of acoustic noise is generated accordingto its power spectral density characteristics, where differenttypes of targets have distinguishable noise spectra fromeach other. In this paper, there are two types of targetsthat emit acoustic noise that are distinguishable based ontheir power/density distributions. An example of the receivedacoustic signal for a single target passing by a sensor site ispresented in Fig. 1.

III. FEATURE EXTRACTORS

This section introduces three different feature extractionalgorithms, namely, Cepstrum [13], PCA [12], and SDF [9].Although the details of these algorithms have been wellexplained in earlier publications, this paper focuses on theunderlying concepts of each feature extraction method fortime series data recorded by passive sonar sensors.

A. Cepstrum for Feature Extraction

Cepstrum-based feature extraction has been widely usedin speech recognition and acoustic signal classification [17].The advantage of Cepstrum is to equalize the importanceof different frequency ranges for sensor signal by takinglogarithm in the frequency scale instead of the original linearscale [18].

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In this paper, all small values of frequency coefficientsare discarded before taking the inverse Fourier transform.The first Nc components are used to generate Cepstrumfeatures for classification. The goal here is to enhance thecomputation efficiency at each sonar sensor node, whileretaining the critical frequency components of the signal.

Algorithm 1 for Cepstrum-based feature extraction ispresented below.

Algorithm 1 Cepstrum for feature extraction

Input: Time series data sets x ∈ R1×N ; Cut-off sample Nf (where Nf ≤

N ); and dimension of the Cepstrum feature Nc (where Nc ≤ Nf ).Output: Extracted Cepstrum-based feature p ∈ R

1×Nc of the time-seriesx

1: Compute the magnitude of FFT |F (ω)| of the given time series whereω = 1, . . . , N

2: Store the first Nf frequency components and discard the rest3: Compute fc(t) = ℜ

(

F−1 (log |F (ω)|))

, t = 1, . . . , Nf ,whereF (ω) is the Fourier transform of the signal f(t); the operator F−1

is the inverse Fourier transform; and ℜ(z) indicates the real part of acomplex scalar z.

4: Compute the feature p = [fc(1) fc(1) . . . fc(Nc)]

B. Principal Component Analysis for Feature Extraction

The principal component analysis (PCA) for feature ex-traction has been widely used in diverse applications [12].Compared to Cepstrum that only takes advantage of imbed-ded information in a time series, PCA is extracts the signalinformation by projecting the training data into the low-dimensional feature space [12].

Algorithm 2 for PCA-based feature extraction is presentedbelow.

Algorithm 2 Principal component analysis for feature extraction

Input: Training time series data sets xj ∈ R1×N , j = 1, . . . ,M ;

Tolerance η ∈ (0, 1); and test time series data set x ∈ R1×N

Output: Extracted feature vector p ∈ R1×m for the time-series x

1: Construct the “centered version” training data matrix X ∈ RM×N ,

where each row xj has zero mean.2: Compute the matrix S = (1/M)XX

T

3: Compute the normalized eigenvectors {vi} of S with their correspond-ing eigenvalues {λi} in the decreasing order of magnitude

4: Compute the normalized eigenvectors ui =1√Mλi

(

X)T

vi

5: Find the smallest m ≤ M such that∑m

i=1λi > η

∑Mi=1

λi

6: Construct (N ×m) projection matrix W = [u1,u2, ...,um]7: Generate (1×m) reference patterns p = xW

C. Symbolic Dynamic Filtering (SDF)

This subsection presents pertinent information regardinga recently developed pattern recognition tool, called sym-bolic dynamic filtering (SDF) [9], which has been used fortarget detection & classification in a variety of physicalapplications. In SDF, a time series of sensor signals isfirst converted into a symbol sequence that, in turn, leadsto the construction of a probabilistic finite state automaton(PFSA) [19][20][21][22]. Figure 2 illustrates the concept ofconstructing (deterministic) finite state automata (FSA) from(possibly preprocessed) time series.

… ! ""#"" !…

Symbol Sequence

Finite State Machine

Partitioning of Pre-

processed Sensor Data

!

"

#

#

"

!

"!

A B

C D

"

"

##

!

!

#

Alphabet

$={0,1,2,3}

States

Q={A,B,C,D}

Fig. 2. Underlying concept of finite state automata (FSA)

1) Symbolization of Time Series: This step requires parti-tioning (also known as quantization) of the time series dataof the measured signal. The signal space is partitioned intoa finite number of cells that are labeled as symbols, i.e.,the number of cells is identically equal to the cardinality|Σ| of the (symbol) alphabet Σ. As an example for theone-dimensional time series in Fig. 2, the alphabet Σ ={α, β, γ, δ}, i.e., |Σ| = 4, and three partitioning lines dividethe ordinate (i.e., y-axis) of the time series profile into fourmutually exclusive and exhaustive regions. Considerationsfor the choice of alphabet size |Σ| include the maximumdiscrimination capability of a symbol sequence and theassociated computational complexity.

For partitioning tool for the ensemble of time seriesdata, this paper makes use of maximum entropy partitioning(MEP) that maximizes the entropy of the generated symbols.In MEP, the information-rich cells of a data set are parti-tioned finer and those with sparse information are partitionedcoarser, i.e., each cell contains (approximately) equal numberof data points under MEP.

2) Construction of probabilistic finite state automata(PFSA): D-Markov machines are models of probabilisticlanguages, where the future symbol is causally dependent onthe (most recently generated) finite set of (at most) D sym-bols. Thus, D-Markov machines form a proper subclass ofPFSA with applications in various fields of research such asanomaly detection [9] and robot motion classification [10]. AD-Markov chain is a statistically (quasi-)stationary stochasticprocess S = · · · s−1s0 · · · s1 · · · , where the probability ofoccurrence of a new symbol depends only on the last Dsymbols, i.e.,

P [sn | sn−1 · · · sn−D · · · ] = P [sn | sn−1 · · · sn−D]

Words of length D on a symbol string are treated as thestates of the D-Markov machine before any state-mergingis executed. As a consequence of having D = 1, thenumber of states is equal to the number of symbols, i.e.,|Q| = |Σ|, where the set of all possible states is denotedas Q = {q1, q2, . . . , q|Q|} and |Q| is the number of (finitelymany) states. The (estimated) state transition probabilitiesare defined as

p (qk | ql) ,N (ql, qk)

∑

i=1,2,...,|Q| N (ql, qi)∀qk, ql ∈ Q (4)

where N (ql, qk) is the total count of events when qk occursadjacent to ql in the direction of motion. Having computed all

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of these probabilities p (qk | ql)∀qk, ql ∈ Q, the (estimated)state transition probability matrix of the PFSA is given as

Π =

p (q1 | q1) . . . p(

q|Q| | ql)

.... . .

...p(

q1 | q|Q|

)

· · · p(

q|Q| | q|Q|

)

. (5)

By appropriate choice of partitioning, it is ensured that theresulting Markov chain model yields an irreducible stochasticmatrix Π. The rationale is that, under statistically stationaryconditions, the probability of every state being reachablefrom any other state within finitely many transitions mustbe strictly positive [23].

Algorithm 3 for SDF-based feature extraction is presentedbelow.

Algorithm 3 Symbolic dynamic filtering for feature extraction

Input: Training time series data sets xj ∈ R1×N , j = 1, . . . ,M , a test

time series data set x ∈ R1×N , and number of symbols |Σ|

Output: Extracted SDF-based feature vector p ∈ R1×|Σ| for the time-

series x1: Initialize y = ∅2: for j = 1 to M do3: y = y

⋃

xj4: end for5: Partition y using MEP to obtain the (common) partition vector ℘6: Use ℘ on the test data set x to obtain the symbol string s7: Construct the (irreducible) state transition probability matrix Π by using

Eqs. (4) and (5)

IV. RESULTS AND DISCUSSION

This section presents the results of target detection &classification based on the ensemble of (sonar sensor) timeseries generated from a dynamic model of moving targetsin a simulated ocean environment. First, the performanceof the three feature extraction algorithms is compared ondifferent binary classification problems for the single sensorcase. Then, the detection and classification capability of asingle passive sonar sensor is tested by numerical simulationon a sensor network for target detection, classification, andtracking.

A. Binary Classifications of Signal Sensor

This subsection discusses the target detection & classifi-cation problem for the signal sensor case. One passive sonarsensor is trained and tested for three binary classificationproblems: (i) detection of target presence, (ii) classificationof the target noise type (after it is detected), and (iii)classification of the target motion (in conjunction with (ii))from the convexity or concavity of the motion curve. Threedifferent commonly used classifiers have been applied fortarget detection & classification in conjunction with each ofall feature extractors described in last section.

1) Target Simulation and Generation of Data Sets: Insimulation, a passive sonar sensor is deployed at centralbottom position of a given surveillance area (500m×250m).Four target classes are generated by the dynamic modelintroduced in Section II. Each target class is a combination

of two distinct types of target noise and two differentinterpretations of target curved motion as depicted in Fig. 3.

Targets randomly travel inside the surveillance region atfixed speed of 3m/s (or 6 knots), while the passive sonarsensor records the time series of acoustic amplitude at asampling frequency of 100Hz for every case. Each targetclass contains data set of 100 simulation cases. There are also400 cases with no target presented (i.e., pure environmentnoise) are generated for target detection problem.

In this section, these three classification problems aretreated separately, because the main objective is to comparethe performance of Cepstrum, PCA and SDF as featureextractors. Thus, the detection & classification problems aretreated separately to yield an unambiguous comparison.

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(b) Target noise type II: Gaussianwith mean 22 Hz and standard de-viation 5 Hz

Fig. 4. Different noise types of moving targets

2) Classification Results of Single Sensor Case: A pas-sive sonar sensor is deployed to collect acoustic signalsfor detection and classification of moving targets. Featuresextracted from the acoustic signals by each of the PCA,Cepstrum, or SDF algorithm for each task are respectivelyassigned into training and test sets. The extracted features arethen tested with three classifiers: (i) support vector machines(SVM) [12], (ii) k-nearest neighbor (k-NN) [12], (iii) sparserepresentation classier (SRC) [24].

While SRC has been recently introduced as a pattern clas-sification tool [25], SVM and k-NN are well-known standardtools of pattern classification [3] [24]. Design parameters areoptimized for each of the three classifiers. A linear kernelwith a maximum of 2000 iterations is chosen for the SVMclassifier [12]; the neighborhood size k = 3 is chosen for thek-NN classifier [12]; and an upper bound of error ǫ = 0.1is chosen for the SRC classifier [24]. Decisions of targetdetection & classification are made as explained below.

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Fig. 5. Simulation results for target detection and classification in a sensor network

TABLE I

CONFUSION MATRICES FOR TARGET DETECTION

k-NN SVM SRC

Target Present No Target Target Present No Target Target Present No Target

Cepstrum Target Present 200 0 200 0 200 0No Target 0 200 0 200 0 200

PCA Target Present 200 0 200 0 0 200No Target 174 26 119 81 0 200

SDF Target Present 200 0 200 0 200 0No Target 0 200 0 200 0 200

TABLE II

CONFUSION MATRICES FOR TARGET NOISE TYPE CLASSIFICATION

k-NN SVM SRC

Type I Type II Type I Type II Type I Type II

Cepstrum Type I 100 0 100 0 100 0Type II 0 100 0 100 0 100

PCA Type I 85 15 53 47 56 44Type II 90 10 53 47 35 65

SDF Type I 93 7 100 0 100 0Type II 0 100 0 100 0 100

TABLE III

CONFUSION MATRICES FOR TARGET MOTION CLASSIFICATION

k-NN SVM SRC

Convex Concave Convex Concave Convex Concave

Cepstrum Convex 67 33 63 37 92 8Concave 21 79 20 80 23 77

PCA Convex 100 0 99 1 25 75Concave 100 0 43 57 0 100

SDF Convex 80 20 81 19 81 19Concave 3 97 12 88 5 95

Tables I, II and III present averaged confusion matricesrelated to the tasks of target detection, target type classifica-tion, and target motion classification, respectively. The resultsshow that SDF, as a feature extractor, clearly yields superiorperformance for all three tasks. While Cepstrum works wellfor target detection and target type classification, its perfor-

mance is slightly degraded for target motion classification. Inall three tasks, the PCA-based feature extraction appears tobe inadequate in terms of achieving high accuracy regardlessof the choice of a classifier.

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B. Application in sensor network for local target tracking

The results of target detection and target type & motionclassification are presented under simulated scenarios ofand multiple sensors. To generate these results, 12 passivesonar sensors are placed in the (300m× 300m) simulatedsurveillance region as displayed in Fig. 5. For each ofthe simulation runs, there is one target traveling acrosssurveillance region, as shown by blue curves in Fig. 5.Each sensor first performs target detection. If a sensor nodemakes a positive detection, then it conducts target noise typeclassification. In the simulation, all sensors records the resultsfor these two tasks of target detection and type classification.Subsequently, each sensor node reports the results on targetmotion classification. It is seen from the results of the threesimulation runs that the target trajectory information (e.g.,entry location, exit location, and motion curve pattern) canbe predicted from the reports generated by the sensor nodes.

V. SUMMARY, CONCLUSIONS, AND FUTURE WORK

This paper addresses sensor network-based surveillancein the ocean environment, and the objective is to achievehigh probabilities of correct decisions for target detectionand target type & motion classification with low false alarmrates. To this end, performance of three feature extraction al-gorithms, namely, Cepstrum, Principal Component Analysis(PCA) and Symbolic Dynamic Filtering (SDF) have beencompared. Each of these feature extraction algorithms hasbeen executed in conjunction with three classification algo-rithms, namely, k-Nearest Neighbor (k-NN), Support VectorMachines (SVM) and Sparse Representation Classification(SRC). The results show that the performance of SDF-basedfeature extraction is consistently superior to that of PCA-based feature extraction and is, on the average, better thanthat of Cepstrum in terms of successful detection, false alarm,and overall correct classification rates.

Much theoretical and experimental research is neededbefore the presented algorithms of target detection andclassification can be used in practice. In this regard, a fewkey topics of future research are delineated below.

(i) Comparison with additional methods of featureextraction (i.e., besides Cepstrum and PCA) in con-junction with other classification algorithms (i.e.,besides k-NN and SVM).

(ii) Development of a rigorous field test procedurefor validating robustness of SDF-based featureextraction under different conditions (e.g., sensorconfiguration and presence of multiple targets) anddata types (i.e., other than acoustic amplitude).

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