# Reduced-Complexity Direction of Arrival Estimation Using Real-Valued Computation with Arbitrary Array Configurations

Authors: Feng-Gang Yan; Jun Wang; Shuai Liu; Yi Shen; Ming Jin
Journal: International Journal of Antennas and Propagation
 (2018)

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## Abstract

A low-complexity algorithm is presented to dramatically reduce the complexity of the multiple signal classification (MUSIC) algorithm for direction of arrival (DOA) estimation, in which both tasks of eigenvalue decomposition (EVD) and spectral search are implemented with efficient real-valued computations, leading to about 75% complexity reduction as compared to the standard MUSIC. Furthermore, the proposed technique has no dependence on array configurations and is hence suitable for arbitrary array geometries, which shows a significant implementation advantage over most state-of-the-art unitary estimators including unitary MUSIC (U-MUSIC). Numerical simulations over a wide range of scenarios are conducted to show the performance of the new technique, which demonstrates that with a significantly reduced computational complexity, the new approach is able to provide a close accuracy to the standard MUSIC.


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## Body

## 1. Introduction

Direction-of-arrival (DOA) estimation is a fundamental array processing problem with many applications, for example, radar, sonar, wireless communication, and smart antenna design [1, 2]. This topic has been extensively studied during the past four decades, resulting in many efficient and accurate algorithms including multiple signal classification (MUSIC) [3], maximum likelihood (ML) [4], subspace fitting (SF) [5], minimum norm (MN) [6], and their derivations. Among those methods, the MUSIC algorithm is able to provide super resolution DOA estimation for closely spaced sources and has an easy implementation with arbitrary array configuration [7]. However, such advantages are archived at the expense of huge complexity which is mainly caused by an involved eigenvalue decomposition (EVD) step and a tremendous spectral search step, both are generally implemented with complex-valued computations. Therefore, the standard MUSIC is prohibitively expensive in scenarios where real-time processing is required [8].It is well known that the problem of DOA estimation can be simplified with some specified array structures, based on which a variety of low-complexity methods has been proposed for high computational efficiency. For example, when the array geometry satisfies the rotational invariant property, DOA can be immediately computed without spectral search by using the estimation of signal parameters via rotational invariance technique (ESPRIT) [9]. Exploiting the special geometry of a uniform linear array (ULA), one can also find source DOA by polynomial rooting with search-free computations using the well-known root-MUSIC algorithm [10]. Besides, numerous useful modifications of ESPRIT and root-MUSIC have been proposed with circular and other kinds of arrays [11, 12]. As the array structures used in these techniques are still rather specific, many promising attempts such as array interpolation (AI) [13], manifold separation technique (MST) [14], and Fourier-domain (FD) root-MUSIC [15] have been proposed recently to extend ESPRIT and root-MUSIC to more general classes of array configurations. Unfortunately, those approaches usually use a polynomial with a sufficiently high order to warrant that the truncation errors are small, and hence, the complexity for finding the roots of this polynomial may be higher than expected [16].Considering one multiplication between two complex variables generally require four times that between two real ones, algorithms with real-valued computation can reduce about 75% complexity as compared to their complex versions. Following this idea, Huarng and Yeh [17] introduced an outstanding unitary transformation for low-complexity DOA estimation with real-valued computation. Whereafter, Linebarger et al. [18] proposed a forward/backward (FB) averaging method which obtains real-valued arithmetic for fast DOA estimation as well. These techniques are firstly exploited by the unitary MUSIC (U-MUSIC) algorithm [19] and then extended to many other derivations including unitary root MUSIC (U-root-MUSIC) [20], unitary ESPRIT (U-ESPRIT) [21], unitary method of direction-of-arrival estimation (U-MODE) [22], and unitary matrix pencil (U-MP) [23]. Based on the special structure of a centrosymmetrical array (CSA), those algorithms transform the complex array covariance matrix into a real one. It has been proven that this real matrix is symmetrical, and hence, both matrix decomposition and spectral search can be implemented with real-valued computation. Besides, it has been found that real-valued algorithms also show improved accuracy as compared to complex-valued approaches. Despite their increased estimation accuracies with reduced costs, almost all of the state-of-the-art real-valued methods are only suitable for CSAs [24], which severely limits their applications [25, 26].As a further development of our previous work in [27], the objective of the present paper is to propose a new real-valued subspace-based algorithm for fast DOA estimation. However, unlike the abovementioned methods that attempted to overcome the high-complexity problem using special array structures or array mapping techniques [28], we show that exploiting only the real part of the array covariance matrix can lead to a real-valued version of the MUSIC algorithm, which has implementation advantages over most state-of-the-art real-valued techniques since it can be used with arbitrary arrays. Furthermore, we prove that the real part of the array covariance matrix can be equivalently reformulated as an entire array covariance matrix received by a virtual array with an extended array manifold which is real. Because the virtual array covariance matrix and the virtual array manifold are real, both tasks of matrix decomposition and spectral search can be implemented with efficient real-valued computations instead of complex-valued ones. Thanks to these merits, the new algorithm is able to reduce about 75% computational complexity as compared to the standard MUSIC. Finally, we provide in-depth insights into the new approach including the EVD of the real part of the array covariance matrix as well as performance simulation over a wide range of application scenarios, which demonstrate that with significantly reduced complexity, the proposed approach can still provide acceptable accuracy close to the standard MUSIC.


### 1.1. Mathematical Notations

Throughout the paper, matrices and vectors are denoted by upper and lower boldface letters, respectively. Complex and real vectors and matrices are denoted by single-bar- and double-bar upper boldface letters, respectively, and detailed mathematical notations are defined in Table1.Table 1
Mathematical notations.
σn2
Noise power
⋅T
Transpose
⋅∗
Conjugation
⋅H
Hermitian transpose
⋅
Round down to integer
⋅
Frobenius norm
E⋅
Mathematical expectation
Re⋅
Real part of the embraced element
Im⋅
Imaginary part of the embraced element
0
Zero matrix (vector)
IM
M×M identity matrix
δ⋅
Dirac delta function
rank⋅
Rank of the embraced matrix
span⋅
Column space of the embraced matrix
diag⋅
Diagonal matrix composed of the embraced elements


## 1.1. Mathematical Notations

Throughout the paper, matrices and vectors are denoted by upper and lower boldface letters, respectively. Complex and real vectors and matrices are denoted by single-bar- and double-bar upper boldface letters, respectively, and detailed mathematical notations are defined in Table1.Table 1
Mathematical notations.
σn2
Noise power
⋅T
Transpose
⋅∗
Conjugation
⋅H
Hermitian transpose
⋅
Round down to integer
⋅
Frobenius norm
E⋅
Mathematical expectation
Re⋅
Real part of the embraced element
Im⋅
Imaginary part of the embraced element
0
Zero matrix (vector)
IM
M×M identity matrix
δ⋅
Dirac delta function
rank⋅
Rank of the embraced matrix
span⋅
Column space of the embraced matrix
diag⋅
Diagonal matrix composed of the embraced elements


## 2. Signal Model and Standard MUSIC

### 2.1. Signal Model and Basic Assumptions

Without loss of generality, let us consider an arbitrary linear array (as to be shown that the proposed method has no dependence on array geometries, and it can be easily extended to arbitrary plane array for two-dimensional DOA estimate) withM omnidirectional sensors labeled by 1,2,…,M, where the coordinate of the mth sensor is denoted by xm,m=1,2,…,M. Suppose that there are L uncorrelated narrow-band plane waves with unknown DOAs Θ≜θ1,θ2,…,θL impinging on the array from far field, the array received data can be written as [1–28]
(1)xt=AΘst+nt∈ℂM×1,t=1,2,…,N,where N is the number of snapshots, st=s1t,s2t,…,sLtT∈ℂL×1 is the signal waveform, nt=n1t,n2t,…,nMtT∈ℂM×1 is the additive white Gaussian noise (AWGN), and
(2)AΘ≜aθ1,aθ2,…,aθL∈ℂM×Lis the steering vector matrix (also known as array manifold), and each column of AΘ is defined as a so-called steering vector, which can be expressed as
(3)aθ≜ej⋅2π/λ⋅x1⋅sinθ,ej⋅2π/λ⋅x2⋅sinθ,⋯,ej⋅2π/λ⋅xM⋅sinθT∈ℂM×1,where j=−1 and λ are the center wavelength. The goal of the DOA estimation is to find the DOA set Θ, based on N snapshots of array received data xtt=1N.Throughout the paper, it is assumed that the number of sources is known [29] and is smaller than half that of sensors such that L≤M−1/2. It is also assumed that positions and displacements of array elements do not need to be uniform or follow regular patterns, provided that they satisfy the rank M−1 ambiguity restriction [30]. The AWGN nt is assumed to satisfy the following statistical performances [31–33].
(4)EntinTtj=0,EntinTtj=δi−jσn2IM,and the incident signal st is uncorrelated and satisfies [34, 35]
(5)EstsTt=0,EstsHt=Rs,where Rs is the L×L source covariance matrix and is a real diagonal matrix.


### 2.2. Standard MUSIC

WithN snapshots of array received data xtt=1N, one can compute the M×M array output covariance matrix.
(6)R=ExtxHt=EAΘst+nt×AΘst+ntH=AΘRsAHΘ+σn2IM,and further obtained the EVD of R as follows:
(7)R=SΛsSH+GΛnGH,where spanS and spanG are the signal and noise subspaces, respectively. In practical situations, the theoretical R is estimated by
(8)R^=1N∑t=1NxtxHt.Hence, the EVD of the array output covariance matrix is in fact given by(9)R^=S^Λ^sS^H+G^Λ^nG^H.Based on some factsspanS⊥spanG and spanS=spanA, the standard MUSIC suggests to search the L peaks of the following function over −π/2,π/2 to find source DOAs.
(10)fMUSICθ≜10log101aHθG^2dB.The primary advantage of the MUSIC algorithm is its easy implementation with arbitrary array configurations, which is archived at the expense of huge complexity caused by an involved tremendous spectral search step with expensive complex-valued computation.


## 2.1. Signal Model and Basic Assumptions

Without loss of generality, let us consider an arbitrary linear array (as to be shown that the proposed method has no dependence on array geometries, and it can be easily extended to arbitrary plane array for two-dimensional DOA estimate) withM omnidirectional sensors labeled by 1,2,…,M, where the coordinate of the mth sensor is denoted by xm,m=1,2,…,M. Suppose that there are L uncorrelated narrow-band plane waves with unknown DOAs Θ≜θ1,θ2,…,θL impinging on the array from far field, the array received data can be written as [1–28]
(1)xt=AΘst+nt∈ℂM×1,t=1,2,…,N,where N is the number of snapshots, st=s1t,s2t,…,sLtT∈ℂL×1 is the signal waveform, nt=n1t,n2t,…,nMtT∈ℂM×1 is the additive white Gaussian noise (AWGN), and
(2)AΘ≜aθ1,aθ2,…,aθL∈ℂM×Lis the steering vector matrix (also known as array manifold), and each column of AΘ is defined as a so-called steering vector, which can be expressed as
(3)aθ≜ej⋅2π/λ⋅x1⋅sinθ,ej⋅2π/λ⋅x2⋅sinθ,⋯,ej⋅2π/λ⋅xM⋅sinθT∈ℂM×1,where j=−1 and λ are the center wavelength. The goal of the DOA estimation is to find the DOA set Θ, based on N snapshots of array received data xtt=1N.Throughout the paper, it is assumed that the number of sources is known [29] and is smaller than half that of sensors such that L≤M−1/2. It is also assumed that positions and displacements of array elements do not need to be uniform or follow regular patterns, provided that they satisfy the rank M−1 ambiguity restriction [30]. The AWGN nt is assumed to satisfy the following statistical performances [31–33].
(4)EntinTtj=0,EntinTtj=δi−jσn2IM,and the incident signal st is uncorrelated and satisfies [34, 35]
(5)EstsTt=0,EstsHt=Rs,where Rs is the L×L source covariance matrix and is a real diagonal matrix.


## 2.2. Standard MUSIC

WithN snapshots of array received data xtt=1N, one can compute the M×M array output covariance matrix.
(6)R=ExtxHt=EAΘst+nt×AΘst+ntH=AΘRsAHΘ+σn2IM,and further obtained the EVD of R as follows:
(7)R=SΛsSH+GΛnGH,where spanS and spanG are the signal and noise subspaces, respectively. In practical situations, the theoretical R is estimated by
(8)R^=1N∑t=1NxtxHt.Hence, the EVD of the array output covariance matrix is in fact given by(9)R^=S^Λ^sS^H+G^Λ^nG^H.Based on some factsspanS⊥spanG and spanS=spanA, the standard MUSIC suggests to search the L peaks of the following function over −π/2,π/2 to find source DOAs.
(10)fMUSICθ≜10log101aHθG^2dB.The primary advantage of the MUSIC algorithm is its easy implementation with arbitrary array configurations, which is archived at the expense of huge complexity caused by an involved tremendous spectral search step with expensive complex-valued computation.


## 3. The Proposed Algorithm

We begin our algorithm by considering theM×M real part of the array output covariance. Using (6), we have
(11)R≜ReR=R+R∗2=AΘRsAHΘ+A∗ΘRsATΘ2+σn2I=ReBΘ+σn2I,where BΘ is defined as
(12)BΘ≜AΘRsAHΘ.UsingAΘ=ReAΘ+jImAΘ, BΘ can be expressed as
(13)BΘ=ReAΘ+jImAΘ⋅Rs⋅ReTAΘ−jImTAΘ=ReAΘRsReTAΘ+ImAΘRsImTAΘ+jImAΘRsReTAΘ−ReAΘRsImTAΘ.Therefore, we have(14)ReBΘ=ReAΘRsReTAΘ+ImAΘRsImTAΘ=ReAΘImAΘ×Rs00Rs×ReTAΘImTAΘ≜AΘRs~ATΘ,where AΘ c and Rs~ are given by
(15)AΘ≜ReAΘImAΘ∈RM×2L,Rs~≜Rs00Rs∈R2L×2L.Using (5) and noting that Rs=Rs∗, we have
(16)Rs~=EstsHtEstsTtEs∗tsHtEs∗tsTt≜Est~st~H,where st~ is given by
(17)st~≜sts∗t∈ℂ2L×1.Inserting (14) as well as (16) into (11), we obtain
(18)R=AΘRs~ATΘ+σn2IM=AΘEst~st~HATΘ+σn2IM=EAΘst~+nt×AΘst~+ntH≜Ext~xt~H,where xt~ is given by
(19)xt~≜AΘst~+nt∈ℂM×1.Comparing (18) and (19) with (1) and (6), the real part of the array covariance matrix can be regarded as an entire array covariance matrix of a virtual array with a manifold AΘ. The output sensor vector of this virtual array is xt~ and the incident signal on this virtual array is st~ with its covariance matrix given by ℝs~. Therefore, (19) provides a virtual signal model for DOA estimation, which is similar to the conventional one given by (1).Because DOA information is also contained inAΘ, one can exploit the idea of eigenvalue analysis to perform the EVD of the virtual array covariance matrix R and construct a MUSIC-like spectral for DOA estimate. Noting that RT=R, which implies that R is a symmetrical real matrix. Therefore, the EVD computation
(20)R=UℚUTrequires only real-valued flops [36]. Using (6), it is shown in the appendix that the eigenvalues of R are given by
(21)ξi=βi+σn2,i=1,2,…,2L−K,σn2,i=2L−K+1,2,…,M,where KK≤L/2 is the number of pairs of symmetrical sources in Θ, β1,β2,…,β2L−K are the 2L−K eigenvalues of matrix
(22)DΘ≜ℍΘRs~ℍHΘ∈ℂM×M,and ℍΘ is defined as
(23)ℍΘ≜12AΘA∗Θ∈ℂM×2L.According to (21), R has 2L−K significant and M−2L+K smallest eigenvalues. Therefore, (20) can be further written as
(24)ℝ=∑i=12L−KξiuiuiH+∑j=2L−K+1MξjujujH=SℚsST+GℚnGT,where ℚs contains the 2L−K significant eigenvalues ξi,i=1,2,…,2L−K, ℚn contains the M−2L+K smallest eigenvalues ξj,j=1,2,…,M−2L+K, and S and G are the signal and noise matrices such that
(25)ℚs=diagξ1,ξ2,…,ξ2L−K∈ℝ2L−K×2L−K,ℚn=diagξ2L−K+1,ξ2L−K+2,…,ξM∈ℝM−2L+K×M−2L+K,S=u1,u2,…,u2L−K∈ℝM×2L−K,G=u2L−K+1,u2L−K+2,…,uM∈ℝM×M−2L+K.SinceAΘ is the virtual manifold matrix associated with R, by using the orthogonality between the signal and the noise subspaces, we obtain
(26)spanS⊥spanG,spanS=spanAΘ.BecauseAΘ=ReAΘImAΘ is in fact a combined manifold matrix covering both ReAΘ and ImAΘ, we have
(27)spanAΘ=spanareθ1,areθ2,…,areθL,aimθ1,aimθ2,…,aimθL,where areθ≜Reaθ∈ℝM×1 and aimθ≜Imaθ∈ℝM×1 are two real vectors, given by
(28)areθ≜Reaθ=cos2πλ⋅x1⋅sinθcos2πλ⋅x2⋅sinθ⋮cos2πλ⋅xM⋅sinθ∈ℝM×1,aimθ≜Imaθ=sin2πλ⋅x1⋅sinθsin2πλ⋅x2⋅sinθ⋮sin2πλ⋅xM⋅sinθ∈ℝM×1.Based on (26) and (27), we can define a MUSIC-like spectral for DOA estimation as follows
(29)fProposedθ=10log101areTθG^2+aimTθG^2.Sinceareθ, aimθ, and G are all real, we must have
(30)fProposedθ=fProposed−θ=0,which means that the minima of fProposedθ over half of the angular field of view, that is, −π/2,0 or 0,π/2, is either true DOAs or their images. Thus, we can search over half of the angular field of view 0,π/2 to obtain T candidate angles
(31)Θ+≜θ^1,θ^2,…,θ^Tand select the L true DOAs from Θ+ and its mirror
(32)Θ−≜−Θ+=−θ^1,−θ^2,…,−θ^T,by a further step to solve the ambiguity problem. Because for one true DOA θk, fProposedθ shows two symmetrical peaks at angles θk and −θk simultaneously. Therefore, for L true DOAs, fProposedθ gives at most L peaks over half of the angular field of view. This happens if and only if any two of the L true DOAs are not symmetrical. Oppositely, fProposedθ generates at least L/2 peaks over half of the angular field of view, and this happens if and only if all the L true DOAs are paired symmetrical themselves. In other words, the number of candidate angels T satisfies
(33)L2≤T≤L.The conventional beamformer (CBF) [37] is exploited to select the L true DOAs among Θ+ and Θ−. As the CBF takes its maxima only at the true DOAs, the CBF spectral amplitudes responding to the true DOAs must be much larger than those associated with their symmetrical mirrors. On the other hand, as the number of the true DOAs is known in advance, the L true DOAs can be easily selected among the candidate angles by
(34)Θ^=θ1^,θ2^,…,θL^=argminθ∈Θ+,Θ−fCBFθ,where
(35)fCBFθ≜10log10aHθR^aθdB.Although using CBF to exclude the symmetrical mirror DOAs means that there is an additional step involved in the proposed estimator, the complexity of this further step is substantially lower since we only need to compute the productaHθR^aθ for at most 2L spectral points. Detailed steps for implementing the proposed algorithm are summarized in Algorithm 1.Algorithm 1: The Proposed Algorithm.
Require:xtt=1N→N snapshots of the array output vectors.
Return:Θ^=θl^l=1L→a set of L estimated source DOAs.
1. Estimate R^=1/N∑t=1NxtxHt and obtain its real part ReR^;
2. Compute ReR^=S^ℚ^sS^T+G^ℚ^nG^T to get the noise matrix G;
3. Construct fProposedθ=10log101/areTθG^2+aimTθG^2;
4. Search fProposedθ over only 0,π/2 to get T peak angles Θ+=θ^1,θ^2,…,θ^T;
5. Obtain 2T candidate angles Θ+ and Θ−=−Θ+;
6. Select the L true DOAs by Θ^=argminθ∈Θ+,Θ−aHθR^aθ.We end up this section by a comparison of primary real-valued computational flops required by MUSIC [3], U-MUSIC [19], and the proposed method, which is shown in Table 2. In the table, J stands for the total number of search points over −π/2,π/2. Because EVD and spectral search dominate the total complexity, the common flops required by the three algorithms for the estimation of R and the slight flops required by the proposed method for solving the ambiguity problem are omitted.Table 2
Comparison of real-valued computational flops.
Algorithms
Real-valued computational flops
MUSIC
4×M2L+2+4×JM+1M−L
U-MUSIC
M2L+2+JL+1M−L
Proposed
M22L−K+2+JM+1M−2L+KUsing the fast subspace decomposition (FSD) technique [38], the EVD computation on a M×M real matrix for a signal subspace of dimension L costs M2L+2 real-valued flops. As the proposed method has 2L−K equivalent signals, this term for the proposed method is given by M22L−K+2. Since the standard MUSIC needs to compute the EVD of the complex matrix R, this term for MUSIC is given by 4×M2L+2. Because aθ∈CM×1 and G∈ℂM×M−L, computing the MUSIC spectral for J sample points over −π/2,π/2 costs 4×JM+1M−L real-valued flops [39]. Since the proposed method involves a compressed search over 0,π/2or−π/2,0, it only needs to compute J/2 sample points. Taking into account that areθ∈ℝM×1, aimθ∈ℝM×1 and G∈ℝM×M−2L+K, the complexity of spectral search for the proposed method is significantly reduced to JM+1M−2L+K.It can be concluded from Table2 that our method has the lowest complexity among the three algorithms. Moreover, because we generally have J≫M [8, 15], about 3/4 = 75% complexities are reduced by the new method as compared to the standard MUSIC.


## 4. Simulation Results

Numerical simulations with 500 Monte Carlo trials are conducted to demonstrate the performance of the proposed method on a linear array composed ofM=10 sensors, where L=2 sources are considered throughout the simulations. For the root mean square error (RMSE) comparison, the Cramer-Rao lower bound (CRLB) given in [40] is also applied for reference and the RMSE is defined as
(36)RMSE≜10log1011000∑i=1500∑l=12θ^il−θil2dB,where θ^il is the ith estimated value of the lth signal incident angle θl. For L sources, the signal-to-noise ratio (SNR) is defined as
(37)SNR≜10log10Pavgσn2dB,where Pavg=1/L∑l=1LPl denotes the average power of all sources, and Pl=Esl2t is the power of thelth,l=1,2,…,L source.In the first simulation, we use a ULA to estimate the DOAs of two sources located atθ1=30° and θ2=40°. Five algorithms including the standard MUSIC [3], ESPRIT [9], U-MUSIC [19], RV-MUSIC [39], and the proposed method are applied in the simulation.Figure1 compares the RMSEs of different algorithms against the SNR, where the number of snapshots is set as N=200. It is seen from Figure 1 that U-MUSIC is the most accurate one among the four methods as expected, although it can be only used with CSAs. It is also seen from the figure that with a significantly reduced complexity, our method can still provide a close accuracy to the standard MUSIC and the RV-MUSIC algorithms, especially with SNR > 0 dB. In addition, the proposed has a much better accuracy than ESPRIT.Figure 1
RMSE versus the SNR, ULA,N=200, θ1=30°, θ2=40°, and M=10.To see more clearly the performance of the new method with a ULA, Figure2 plots the RMSEs of different algorithms as functions of the number of snapshots, where the SNR is fixed as SNR = 5 dB. It can be concluded again from Figure 2 that the proposed method can provide acceptable estimation accuracy which is very close to MUSIC and RV-MUSIC and is much better than ESPRIT.Figure 2
RMSE versus the number of snapshots, ULA, SNR = 5 dB,θ1=30°, θ2=40°, and M=10.In the second simulation, we examine fast DOA estimation with nonuniform linear arrays (NULAs), where two closely spaced sources located atθ1=25° and θ2=28° are considered. We use a minimum redundancy linear array (MRLA) [41] to assess the performance of the proposed method and compare it to that of the standard MUSIC. Note that neither the shift invariant geometry or the centrosymmetrical structure exists in a MRLA, and hence ESPRIT [9] and most state-of-the-art real-valued methods [19–23] including U-MUSIC cannot be used in such an array directly without array mapping techniques [28].In Figure3, we set again the number of snapshots as N=200 and compare the RMSEs of different algorithms against the SNR. In Figure 4, we fix SNR = 5 dB and plot RMSEs as functions of the number of snapshots, where the amounts of snapshots vary from a wide range over N=20 to N=5120.Figure 3
RMSE versus SNR, MRLA,N=200, θ1=25°, θ2=28°, and M=10.Figure 4
RMSE versus the number of snapshots, SNR = 5dB, MRLA,θ1=25°, θ2=28°, and M=10.It can be seen clearly from Figure3 and Figure 4 that the three methods perform closely to each other. More specifically, the standard MUSIC slightly outperforms RV-MUSIC and the proposed technique, especially with low SNRs and small numbers of snapshots. However, as the proposed method is a real-valued DOA estimator which reduces about 75% complexities, it makes an efficient trade-off between complexity and accuracy as compared to the standard MUSIC.In the third simulation, we examine the performance of the proposed method with different numbers of signals in Figure5 and Figure 6, where the simulation parameters are given in the captions. As can be seen clearly from the two figures that our method provides very close RMSEs to the standard MUSIC with different numbers of signals. It is also seen from the two figures that the performances of both the two method decreases slightly when L increases. This is because when L increases, the dimensions of noise subspaces decreases oppositely.Figure 5
RMSE versus SNR respect to different numbers of signals, ULA,N=100, and M=14.Figure 6
RMSE versus the number of snapshots respect to different numbers of signals, ULA, SNR = 10 dB andM=14.In the last simulation, we use a ULA to plot the simulation times of DOA estimation by MUSIC [3], U-MUSIC [19], RV-MUSIC [39] and the proposed method as functions of the number of sensors in Figure 7, where we set SNR = 10 dB, N=200, and two sources located at θ1=10° and θ2=20° are considered. The simulated results are given by a PC with Intel(R) Core(TM) Duo T5870 2.0 GHz CPU and 1 GB RAM by running the Matlab codes in the same environment, where a common fine search gird ∆θ=0.0053° is used the three methods. It can be concluded from Figure 7 that although the proposed method is slightly efficient than the RV-MUSIC algorithm, it is the most efficient one among the four techniques, and it costs a simulation time being about 4 times lower than that of MUSIC.Figure 7
Simulation time versus the number of sensors, ULA, SNR = 10 dB,N=200, θ1=10°, and θ2=20°.


## 5. Conclusions

We have proposed an efficient DOA estimation algorithm, in which both tasks of eigenvalue decomposition and spectral search are implemented with low-complexity real-valued computations. In addition, the proposed method can be used with arbitrary array configurations, which shows implementation advantage over most existing unitary methods. Complexity analysis and simulation demonstrate that with about 75% reduced complexity, the new method is able to provide a good DOA estimation accuracy which is very close to the standard MUSIC.



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*Source: 3284619-2018-04-29.xml*