# Passive Localization of 3D Near-Field Cyclostationary Sources Using Parallel Factor Analysis

Authors: Jian Chen; Guohong Liu; Xiaoying Sun
Journal: International Journal of Antennas and Propagation
 (2013)

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## Abstract

By exploiting favorable characteristics of a uniform cross-array, a passive localization algorithm of narrowband cyclostationary sources in the spherical coordinates (azimuth, elevation, and range) is proposed. Firstly, we construct a parallel factor (PARAFAC) analysis model by computing the third-order cyclic moment matrices of the properly chosen sensor outputs. Then, we analyze the uniqueness of the constructed model and obtain three-dimensional (3D) near-field parameters via trilinear alternating least squares regression (TALS). The investigated algorithm is well suitable for the localization of the near-field cyclostationary sources. In addition, it avoids the multidimensional search and pairing parameters. Results of computer simulations are carried out to confirm the satisfactory performance of the proposed method.


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## Body

## 1. Introduction

There has been considerable interest in bearing estimation for radar, sonar, communication, and electronic surveillance [1]. Various high-resolution algorithms, such as MUSIC [2] and ESPRIT [3], have been proposed to obtain the direction-of-arrival estimation of the far-field sources. In addition, when the sources are localized at the Fresnel region [4] of the array aperture, both the azimuth and range should be estimated. Recently, a significant amount attention has been paid to this issue and several near-field sources localization algorithms [5–7] are also available. However, all these methods as mentioned above only address the 2D problem of estimating azimuth and range and rely on the assumption of the stationary sources.In recent years, several 3D near-field sources localization methods have been developed to obtain azimuth, elevation, and range. Meraim and Hua [8] proposed a second-order based method to cope with this issue. By translating the 1D uniform linear array of near field into a virtual rectangular array of virtual far field, Challa and Shamsunder [9] developed a fourth-order cumulants based Unitary-ESPRIT method. Moreover, [10, 11] also introduced efficient localization methods for 3-D near-field non-Gaussian stationary sources. It is obviously seen that all these methods still require that the incoming signals should be stationary ones; in addition, a multidimensional search or pairing parameters is also failed to avoid.Cyclostationarity, which is a statistical property processed by most man-made communication signals, is related to the underlying periodicity arising from cyclic frequency of based rates. Besson et al. [12] introduced an original far-field approximation [13] based method to deal with the bearings estimation of near-field cyclostationary sources. Due to the fact that the effect caused by the mismatch between the actual spherical wavefront phase vector and assumed planar wavefront vector was alleviated, the proposed technique showed a satisfactory performance for the near-field sources far away from the sensor array. The estimations for elevation and range, however, have not been well considered.In this paper, we consider the problem of jointly estimating elevation, azimuth, and range of the near-field cyclostationary sources; what is more, a two-stage passive localization method has been proposed. In the first stage, several third-order cyclic moment matrices of a cross-array observations data are computed, and a parallel factor analysis model in the cyclic statistic domain is constructed. In the second stage, the uniqueness of the constructed model is proved; in addition, the 3-D localizing parameters for the near-field sources have also been obtained through TALS. The algorithm developed in this paper would be well suitable for near-field cyclostationary sources, and it does not require multidimensional search or pairing parameters; in addition, it also can effectively alleviates the array aperture loss.The rest of this paper is organized as follows. Section2 introduces the signal model of near-field localization based on cross-array. Section 3 develops a joint estimation algorithm of three parameters in near-field. Section 4 shows simulation results. Section 5 presents the conclusion of the whole paper.


## 2. Near-Field Signal Model Based Cross-Array

### 2.1. Near-Field Signal Model

We consider a near-field scenario ofM uncorrelated narrowband signals impinging on a cross-array signed with the x and y axes (Figure 1), which consists of L=2N+3 sensors with element spacing d.Figure 1
Sensor-source configuration for the near-field problem.Let the array center be the phase reference point, and the signals received by the(l,0)th and the (l,0)th can be, respectively, expressed as
(1)xl,0(t)=∑m=1Msm(t)exp(j(γxml+φxml2))+nl,0(t),x0,l(t)=∑m=1Msm(t)exp(j(γjml+φjml2))+n0,l(t),
where n(t) donates the sensor additive noise, and
(2)γxm=-2πdλsinαmcosθm,φxm=πd2λrm(1-sin2αmcos2θm),γym=-2πdλsinαmsinθm,φym=πd2λrm(1-sin2αmsin2θm),
where αm, θm, and rm indicate elevation, azimuth, and range of mth signal, respectively, and λ is wavelength of source signal.Themth source signal with amplitude zm(t) can be modeled as
(3)sm(t)=zm(t)exp(jωt)m=1,2,…,M,
where ω~ is the center frequency.


### 2.2. Assumption of Signal Model

The main problem addressed in this paper is to jointly estimate the sets of parameters(αm,θm,andrm), and then the following assumptions are assumed to hold:(1)
the envelopezm(t) is non-Gaussian stationary random process with zero mean and nonzero skewness;(2)
the sensor noise is the additive stationary one and independent from the source signals;(3)
the sensor array is a uniform linear array with element spacingd≤λ/4; in addition, the source number M is not more than sensor number N.


### 2.3. Parallel Factor Analysis

We need to introduce the following notation that will be used in the sequel.Definition 1 (see [14]).
Letxk,n,p stand for the (k,n,p) element of a three-dimensional tensor X, if
(4)xk,n,p=∑m=1Mak,mbn,mcp,m,whereak,m denotes the (k,m) element of matrix A and similarly for the others. Equation (4) indicates xk,n.p as a sum of triple products, which is variably known as the trilinear model, trilinear decomposition, triple product decomposition, canonical decomposition, or parallel factor (PARAFAC) analysis.Definition 2 (see [14]).
For a matrixB∈CI×J, if all I<J columns of B are linearly independent, but there exists a collection of I+1 linearly dependent columns of B, then it has Kruskal-rank (k-rank) kB=I.Theorem 3 (see [15]).
Consider a three dimensional tensorX as defined in (4), and M represents the common dimension, if
(5)kA+kB+kCT≥2M+2,
then A, B, and C are unique up to permutation and (complex) scaling of columns.


## 2.1. Near-Field Signal Model

We consider a near-field scenario ofM uncorrelated narrowband signals impinging on a cross-array signed with the x and y axes (Figure 1), which consists of L=2N+3 sensors with element spacing d.Figure 1
Sensor-source configuration for the near-field problem.Let the array center be the phase reference point, and the signals received by the(l,0)th and the (l,0)th can be, respectively, expressed as
(1)xl,0(t)=∑m=1Msm(t)exp(j(γxml+φxml2))+nl,0(t),x0,l(t)=∑m=1Msm(t)exp(j(γjml+φjml2))+n0,l(t),
where n(t) donates the sensor additive noise, and
(2)γxm=-2πdλsinαmcosθm,φxm=πd2λrm(1-sin2αmcos2θm),γym=-2πdλsinαmsinθm,φym=πd2λrm(1-sin2αmsin2θm),
where αm, θm, and rm indicate elevation, azimuth, and range of mth signal, respectively, and λ is wavelength of source signal.Themth source signal with amplitude zm(t) can be modeled as
(3)sm(t)=zm(t)exp(jωt)m=1,2,…,M,
where ω~ is the center frequency.


## 2.2. Assumption of Signal Model

The main problem addressed in this paper is to jointly estimate the sets of parameters(αm,θm,andrm), and then the following assumptions are assumed to hold:(1)
the envelopezm(t) is non-Gaussian stationary random process with zero mean and nonzero skewness;(2)
the sensor noise is the additive stationary one and independent from the source signals;(3)
the sensor array is a uniform linear array with element spacingd≤λ/4; in addition, the source number M is not more than sensor number N.


## 2.3. Parallel Factor Analysis

We need to introduce the following notation that will be used in the sequel.Definition 1 (see [14]).
Letxk,n,p stand for the (k,n,p) element of a three-dimensional tensor X, if
(4)xk,n,p=∑m=1Mak,mbn,mcp,m,whereak,m denotes the (k,m) element of matrix A and similarly for the others. Equation (4) indicates xk,n.p as a sum of triple products, which is variably known as the trilinear model, trilinear decomposition, triple product decomposition, canonical decomposition, or parallel factor (PARAFAC) analysis.Definition 2 (see [14]).
For a matrixB∈CI×J, if all I<J columns of B are linearly independent, but there exists a collection of I+1 linearly dependent columns of B, then it has Kruskal-rank (k-rank) kB=I.Theorem 3 (see [15]).
Consider a three dimensional tensorX as defined in (4), and M represents the common dimension, if
(5)kA+kB+kCT≥2M+2,
then A, B, and C are unique up to permutation and (complex) scaling of columns.


## 3. PARAFAC Based 3D Near-Field Sources Localization

### 3.1. Compute the Third-Order Cyclic Moments Matrices

LetM3,xa(0,-l-1,-l) denote the third-order cyclic moment with cyclic frequency α defined as
(6)M3,xa(0,-l-1,-l)=limT→∞1T∑t=1TE{x0,0(t)x-l-1,0(t)x-l,0*(t)}exp(-jαt),
where the superscript “*” denotes the complex conjugate operation; in addition, E{x0,0(t)x-l-1,0(t)x-l,0*(t)} can be given by
(7)E{x0,0(t)x-l-1,0(t)x-l,0*(t)}=∑m=1Mm3,zmexp(j(-γxm+ϕxm))exp(j2ϕxml)exp(jωt)+m1,n∑m=1Mm2,zmexp(j[γxm(-l-1)+ϕxm(-l-1)2])×exp(j2ωt)+m1,n∑m=1Mm2,zmexp(-j[γxm(-l)+ϕxm(-l)2])+m2,n∑m=1Mm1,zmexp(jωt)+m1,n∑m=1Mm2,zmexp(j(-γxm+ϕxm))exp(j2ϕxml)+m2,n∑m=1Mm1,zmexp(j[γxm(-l-1)+ϕxm(-l-1)2])×exp(jωt)+m2,n∑m=1Mm1,zmexp(-j[γxm(-l)+ϕxm(-l)2])×exp(jωt)+m3,n,
where m1,zm, m2,zm, and m3,zm denote mean, variance, and the third-order moment of zm(t); in addition, m1,n, m2,n, and m3,n represent mean, variance, and the third-order cyclic moment of n(t), respectively.With assumption being considered, we further obtain(8)M3,xa(0,-l-1,-l)=∑m=1Mm3,zmexp(j(-γxm+ϕxm))exp(j2ϕxml)δ(ω-α)+m1,n∑m=1Mm2,zmexp(j[γxm(-l-1)+ϕxm(-l-1)2])×δ(2ω-α)+m1,n∑m=1Mm2,zmexp(-j[γxm(-l)+ϕxm(-l)2])δ(α)+m1,n∑m=1Mm2,zmexp(j(-γxm+ϕxm))exp(j2ϕxml)δ(α)+m3,nδ(α),
where δ is impulse function. Letting a=ω~ and substituting (8) into (6) yield
(9)M3,xa(0,-l-1,-l)=∑m=1Mm3,zmexp(j(-γxm+φxm))exp(j2lφxm).Based on (9), we reconstruct the spatial third-order cyclic moment matrix M1a, in which the (k,q)th element can be expressed as follows:
(10)M1a(k,q)=M3,xa(0,q-k-1,q-k)=∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j2(k-q)φxm).In a matrix form, (10) can be written as
(11)M1a=AMzaΩ1*Φ1AH,
where the superscript “H” represents conjugate transpose operation, Mza denotes the third-order moment matrix of source signals, and
(12)A=[a1,a2,…,aM],am=[1,exp(j2φxm),…,exp(j2(N-1)φxm)],Ω1=diag(exp(jγx1),exp(jγx2),…,exp(jγxM)),Φ1=diag(exp(jφx1),exp(jφx2),…,exp(jφxM)).On the other hand, following the same process described above, we can easily obtain(13)M3,xa(0,l+1,l)=m3,u∑m=1Mm3,zmexp(j(γxm+φxm))exp(j2lφxm),M3,xa(1,-l-1,-l)=m3,u∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j(γym+φym))exp(j2lφxm),M3,xa(-1,-l-1,-l)=m3,u∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j(-γym+φym))exp(j2lφxm).And the elements of the other three matrices can be expressed as(14)M2a(k,q)=M3,xa(0,k-q+1,k-q)=∑m=1Mm3,zmexp(j(γxm+φxm))exp(j2(k-q)φxm),M3a(k,q)=M3,xa(1,q-k-1,q-k)=∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j(γym+φym))exp(j2(k-q)φxm),M4a(k,q)=M3,xa(-1,q-k-1,q-k)=∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j(-γym+φym))exp(j2(k-q)φxm).Finally, we have(15)M2a=AMzaΩ1Φ1AH,M3a=AMzaΩ1*Φ1Ω2Φ2AH,M4a=AMzaΩ1*Φ1Ω2Φ2AH
with
(16)Ω2=diag(exp(jγy1),exp(jγy2),…,exp(jγyM)),Φ2=diag(exp(jφy1),exp(jφy2),…,exp(jφyM)).


### 3.2. Build the Parallel Factor Analysis Model

Considering the situation of limited samples, we build a parallel factor analysis model that uses the third-order cyclic moments as(17)M~a=[M~a(:,:,1)M~a(:,:,2)M~a(:,:,3)M~a(:,:,4)]=[M~1aM~2aM~3aM~4a]=[AMzaΩ1*Φ1AHAMzaΩ1Φ1AHAMzaΩ1*Φ1Ω2Φ2AHAMzaΩ1*Φ1Ω2*Φ2AH]+ν1
with C=A*, and the Khatri-Rao product [15] for (17) shows
(18)M~a=(D⊗A)CT+ν1,
where
(19)D=[g-1(MzaΩ1*Φ1)g-1(MzaΩ1Φ1)g-1(MzaΩ1*Φ1Ω2Φ2)g-1(MzaΩ1*Φ1Ω2*Φ2)],
with g-1(MzaΩ1*Φ1) denoting a row vector consisting of diagonal matrix MzaΩ1*Φ1.Similarly, (18) also yields
(20)X~=(A⊗C)DT+ν2,Y~=(C⊗D)AT+ν3.


### 3.3. Solve the Parallel Factor Analysis Model, and Estimate 3-D Parameters

As it stands,A and C are both Vandermonde matrices, and then they have Kruskal-rank (k-rank) kA=kCT=M. On the other hand, the k-rank of D will be kD=min(4,M). When the condition that the number of signals being M≥2 holds, then A, C, and D are unique up to permutation and scaling of columns. With trilinear alternating least squares regression, we obtain that
(21)C~T=argminCT∥M~α-(D⊗A)CT∥F2,D~T=argminDT∥X~-(A⊗C)DT∥F2,A~T=argminAT∥Y~-(C⊗D)AT∥F2.Then using these estimates, we can associate with each pair(γxm,γym,andφym) as follows:
(22)γ~xm=12arg(D~(2,m)D~(1,m)),γ~ym=12{arg(D~(3,m)D~(1,m))-arg(D~(4,m)D~(1,m))},φ~ym=12{arg(D~(3,m)D~(1,m))+arg(D~(4,m)D~(1,m))}.Finally, the sources parameters can be estimated as(23)α~m=asin(λ2πd(γ~xm2+γ~ym2)1/2),(24)θ~m=atan(γ~ymγ~xm),(25)r~m=πd2(1-sin2α~msin2θ~m)λφ~ym.


## 3.1. Compute the Third-Order Cyclic Moments Matrices

LetM3,xa(0,-l-1,-l) denote the third-order cyclic moment with cyclic frequency α defined as
(6)M3,xa(0,-l-1,-l)=limT→∞1T∑t=1TE{x0,0(t)x-l-1,0(t)x-l,0*(t)}exp(-jαt),
where the superscript “*” denotes the complex conjugate operation; in addition, E{x0,0(t)x-l-1,0(t)x-l,0*(t)} can be given by
(7)E{x0,0(t)x-l-1,0(t)x-l,0*(t)}=∑m=1Mm3,zmexp(j(-γxm+ϕxm))exp(j2ϕxml)exp(jωt)+m1,n∑m=1Mm2,zmexp(j[γxm(-l-1)+ϕxm(-l-1)2])×exp(j2ωt)+m1,n∑m=1Mm2,zmexp(-j[γxm(-l)+ϕxm(-l)2])+m2,n∑m=1Mm1,zmexp(jωt)+m1,n∑m=1Mm2,zmexp(j(-γxm+ϕxm))exp(j2ϕxml)+m2,n∑m=1Mm1,zmexp(j[γxm(-l-1)+ϕxm(-l-1)2])×exp(jωt)+m2,n∑m=1Mm1,zmexp(-j[γxm(-l)+ϕxm(-l)2])×exp(jωt)+m3,n,
where m1,zm, m2,zm, and m3,zm denote mean, variance, and the third-order moment of zm(t); in addition, m1,n, m2,n, and m3,n represent mean, variance, and the third-order cyclic moment of n(t), respectively.With assumption being considered, we further obtain(8)M3,xa(0,-l-1,-l)=∑m=1Mm3,zmexp(j(-γxm+ϕxm))exp(j2ϕxml)δ(ω-α)+m1,n∑m=1Mm2,zmexp(j[γxm(-l-1)+ϕxm(-l-1)2])×δ(2ω-α)+m1,n∑m=1Mm2,zmexp(-j[γxm(-l)+ϕxm(-l)2])δ(α)+m1,n∑m=1Mm2,zmexp(j(-γxm+ϕxm))exp(j2ϕxml)δ(α)+m3,nδ(α),
where δ is impulse function. Letting a=ω~ and substituting (8) into (6) yield
(9)M3,xa(0,-l-1,-l)=∑m=1Mm3,zmexp(j(-γxm+φxm))exp(j2lφxm).Based on (9), we reconstruct the spatial third-order cyclic moment matrix M1a, in which the (k,q)th element can be expressed as follows:
(10)M1a(k,q)=M3,xa(0,q-k-1,q-k)=∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j2(k-q)φxm).In a matrix form, (10) can be written as
(11)M1a=AMzaΩ1*Φ1AH,
where the superscript “H” represents conjugate transpose operation, Mza denotes the third-order moment matrix of source signals, and
(12)A=[a1,a2,…,aM],am=[1,exp(j2φxm),…,exp(j2(N-1)φxm)],Ω1=diag(exp(jγx1),exp(jγx2),…,exp(jγxM)),Φ1=diag(exp(jφx1),exp(jφx2),…,exp(jφxM)).On the other hand, following the same process described above, we can easily obtain(13)M3,xa(0,l+1,l)=m3,u∑m=1Mm3,zmexp(j(γxm+φxm))exp(j2lφxm),M3,xa(1,-l-1,-l)=m3,u∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j(γym+φym))exp(j2lφxm),M3,xa(-1,-l-1,-l)=m3,u∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j(-γym+φym))exp(j2lφxm).And the elements of the other three matrices can be expressed as(14)M2a(k,q)=M3,xa(0,k-q+1,k-q)=∑m=1Mm3,zmexp(j(γxm+φxm))exp(j2(k-q)φxm),M3a(k,q)=M3,xa(1,q-k-1,q-k)=∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j(γym+φym))exp(j2(k-q)φxm),M4a(k,q)=M3,xa(-1,q-k-1,q-k)=∑m=1Mm3,zmexp(j(-γxm+φxm))×exp(j(-γym+φym))exp(j2(k-q)φxm).Finally, we have(15)M2a=AMzaΩ1Φ1AH,M3a=AMzaΩ1*Φ1Ω2Φ2AH,M4a=AMzaΩ1*Φ1Ω2Φ2AH
with
(16)Ω2=diag(exp(jγy1),exp(jγy2),…,exp(jγyM)),Φ2=diag(exp(jφy1),exp(jφy2),…,exp(jφyM)).


## 3.2. Build the Parallel Factor Analysis Model

Considering the situation of limited samples, we build a parallel factor analysis model that uses the third-order cyclic moments as(17)M~a=[M~a(:,:,1)M~a(:,:,2)M~a(:,:,3)M~a(:,:,4)]=[M~1aM~2aM~3aM~4a]=[AMzaΩ1*Φ1AHAMzaΩ1Φ1AHAMzaΩ1*Φ1Ω2Φ2AHAMzaΩ1*Φ1Ω2*Φ2AH]+ν1
with C=A*, and the Khatri-Rao product [15] for (17) shows
(18)M~a=(D⊗A)CT+ν1,
where
(19)D=[g-1(MzaΩ1*Φ1)g-1(MzaΩ1Φ1)g-1(MzaΩ1*Φ1Ω2Φ2)g-1(MzaΩ1*Φ1Ω2*Φ2)],
with g-1(MzaΩ1*Φ1) denoting a row vector consisting of diagonal matrix MzaΩ1*Φ1.Similarly, (18) also yields
(20)X~=(A⊗C)DT+ν2,Y~=(C⊗D)AT+ν3.


## 3.3. Solve the Parallel Factor Analysis Model, and Estimate 3-D Parameters

As it stands,A and C are both Vandermonde matrices, and then they have Kruskal-rank (k-rank) kA=kCT=M. On the other hand, the k-rank of D will be kD=min(4,M). When the condition that the number of signals being M≥2 holds, then A, C, and D are unique up to permutation and scaling of columns. With trilinear alternating least squares regression, we obtain that
(21)C~T=argminCT∥M~α-(D⊗A)CT∥F2,D~T=argminDT∥X~-(A⊗C)DT∥F2,A~T=argminAT∥Y~-(C⊗D)AT∥F2.Then using these estimates, we can associate with each pair(γxm,γym,andφym) as follows:
(22)γ~xm=12arg(D~(2,m)D~(1,m)),γ~ym=12{arg(D~(3,m)D~(1,m))-arg(D~(4,m)D~(1,m))},φ~ym=12{arg(D~(3,m)D~(1,m))+arg(D~(4,m)D~(1,m))}.Finally, the sources parameters can be estimated as(23)α~m=asin(λ2πd(γ~xm2+γ~ym2)1/2),(24)θ~m=atan(γ~ymγ~xm),(25)r~m=πd2(1-sin2α~msin2θ~m)λφ~ym.


## 4. Computer Simulation Results

In this section, we explicit some simulation results to evaluate the performance of proposed method. For all examples, a uniform linear array with number of 15 sensors and element spacing 0.25λ is displayed, where λ is the wavelength of the narrowband source signals. Two near-field or far-field equal power cyclostationary signals are impinging on the array with center frequency 0.25π, and their envelopes can be modeled as exponentially distributed. To be compared, we simultaneously execute the method of [12], which is suitable for the 3-D near-field non-Gaussian stationary sources localization. The presented results are evaluated by the estimated root mean square error (RMSE) from the averaged results of 200 independent Monte-Carlo simulations.In the first example, Two near-field sources are located at (35°, 46°, and 1/6λ) and (20°, 60°, and 2/5λ), respectively. The additive noise is time and spatially white Gaussian with zero mean and unit variance. In addition, the snapshot number is set equal to 1024. When SNR varies from 0dB to 25dB, the RMSE of the elevation, azimuth, and range estimations for two near-field cyclostationary sources can be shown in Figures 2, 3, and 4. For the comparison, the performance in the same situation for the fourth-order cumulants based method has also been displayed. From these three figures, it is obvious that the proposed method performs better in elevation, azimuth, and range estimation than the FFA based algorithm for all SNRs. What is more, the RMSE of range estimations for the first source that is closer to the array is less than the second one. This phenomenon is well agreement with the theoretical analysis that the sources closer to sensor array would hold a smaller standard deviation than the one far from the array [7].Figure 2
The RMSE of elevation estimations for two near-field sources using the proposed method and the cumulant based method versus SNR.Figure 3
RMSE of azimuth estimations for two near-field sources using the proposed method and the cumulant based method versus SNR.Figure 4
The RMSE of range estimations for two near-field sources using the proposed method and the cumulant based method versus SNR.In the second example, the simulation condition is similar to the first example, except that the SNR is set at 10 dB, and the snapshot number is varied from 400 to 2000. The RMSE of elevation, azimuth, and range estimations of two near-field cyclostationary sources obtained from the proposed method as well as the cumulant based method can be displayed in Figures5, 6, and 7. From these three figures, we can see that the proposed method still shows a more satisfactory accuracy than the cumulant based method in all available snapshots. In addition, the range estimations for the first sources are much better than the second one, while the elevation and azimuth estimations for both near-field sources are similar for the proposed method.Figure 5
The RMSE of elevation estimations for two near-field sources using the proposed method and the cumulant based method versus snapshot number.Figure 6
The RMSE of azimuth estimations for two near-field sources using the proposed method and the cumulant based method versus snapshot number.Figure 7
The RMSE of range estimations for two near-field sources using the proposed method and the cumulant based method versus snapshot number.In the third example, the additive noise is time and spatially white Gaussian random process and the colored noise from a second-order AR model with parameter (−1.8,0.9), respectively. In addition, the other simulation condition is similar to the first example, except that the SNR is set at 10 dB, and snapshot number is equal to 1024. The mean and variance of the elevation, azimuth, and range estimations for two near-field cyclostationary sources can be shown in Table1. From this table, we can easily see that the effectiveness of the proposed method is not little affected by the difference of the sensor noise.Table 1
RMSE of 3D parameters for near-field localization in the third example.
Example
Source
True
Mean
Variance
Source
True
Mean
Variance
White noise
α
1
35°
34.95°
0.1923
α
2
20°
20.03°
0.1647
θ
1
40°
39.99°
0.0419
θ
2
60°
59.99°
0.1000
r
1
λ/6
0.1669λ
3.5744 × 10−5
r
2
2λ/5
0.3984λ
8.2107 × 10−4
Colored noise
α
1
35°
35.02°
0.2146
α
2
20°
19.98°
0.0618
θ
1
40°
40.03°
0.0795
θ
2
60°
60.02°
0.1165
r
1
λ/6
0.1662λ
2.8455 × 10−5
r
2
2λ/5
0.4036λ
8.4173 × 10−4


## 5. Conclusion

This paper considers the problem of the passive localization of 3-D near-field cyclostationary sources and proposes an efficient third-order cyclic moment based algorithm. The construction of the parallel factor analysis model in the cyclic domain avoids the multidimensional search and pairing parameters. Moreover, the utilization of trilinear alternating least squares regression deals with the joint estimation of elevation, azimuth, and range. From the simulation results mentioned above, the proposed method outperforms the cumulant based method in locating near-field cyclostationary sources; in addition, the stationary noise has a little influence on the estimated accuracy of the proposed method.



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*Source: 657653-2013-06-16.xml*