Direction of Arrival Estimation Accuracy Enhancement via Using Displacement Invariance Technique

A new algorithm for improving Direction of Arrival Estimation (DOAE) accuracy has been carried out. Two contributions are introduced. First, Doppler frequency shift that resulted from the target movement is estimated using the displacement invariance technique (DIT). Second, the effect of Doppler frequency is modeled and incorporated into ESPRIT algorithm in order to increase the estimation accuracy. It is worth mentioning that the subspace approach has been employed into ESPRIT and DIT methods to reduce the computational complexity and the model’s nonlinearity effect. The DOAE accuracy has been verified by closedform Cramér-Rao bound (CRB).The simulation results of the proposed algorithm are better than those of the previous estimation techniques leading to the estimator performance enhancement.


Introduction
Direction of arrival Estimation (DOAE) for an antenna array is an important process because DOA is the creator of the tracking gate dimensions (the azimuth and the elevation).Accurate DOAE for the moving target leads to the reduction of the angle glint error which affects the accuracy of the tracking radars.
Doppler frequency shift is a significant phenomenon induced by the target movement.Lack of attention to the influence of Doppler shift on the ESPRIT method leads to DOAE process with poor accuracy.
The ESPRIT and its extracts have been widely studied in one-dimensional (1D) DOAE for uniform linear array (ULA) [1][2][3][4], nonuniform linear array (NULA) for spatial multiresolution [5][6][7][8], or temporal multiresolution [9][10][11] and also extended to two-dimensional (2D) DOAE [12][13][14][15][16][17][18].All of these ESPRIT methods have been developed to upgrade the accuracy of DOAE with low calculation costs.However, these works did not pay attention to the effect of the Doppler frequency of the moving target on the DOAE accuracy.On the other hand, some other great efforts have been done to estimate Doppler frequency via using the fast Fourier transform (FFT), but it has a high order computation time for a large number of samples [19].Another method applying ESPRIT technique to compute Doppler shift via employing the rotational factor resulted from time delay sampling [7,8,20].However, these methods require intensive matrix computations or iterative optimization techniques.
This paper presents firstly a novel method to detect the target movement via estimating its Doppler frequency by comparing the displacement invariances of transmitter and receiver arrays, which enables the measurement of the sensor array displacement invariance fluctuation that resulted from wavelength change induced by the target movement.Secondly, for target moves with uniform acceleration, Doppler frequency correction has been embedded into the ESPRIT algorithm to refine the DOA estimated value.Subspace concept has been applied to reduce the model's nonlinearity effect and to realize parallel processing which leads to the enhancement of the estimation accuracy with low computational load [5,6].
The paper is organized as follows.In Section 2, the twodimensions time multiresolution ESPRIT (2D T-ESPRIT) DOAE technique has been introduced, the time multiresolution displacement invariance technique (T-DIT), and the Doppler effect of the moving target have been incorporated into the estimation algorithm.In Section 3, the simulation results are presented, and Section 4 is the conclusion.

Proposed Algorithm
2.1.The Measurement Model.In this model, the transmission medium is assumed to be isotropic and nondispersive, so that the radiation propagates in straight lines, and the sources are assumed to be as a far field away the array.Consequently, the radiation impinging on the array is a summation of the plane waves.The signals are assumed to be narrow-band processes, and they can be considered to be sample functions of a stationary stochastic process or deterministic functions of time [3].Considering that there are  narrow-band signals and the center frequency  is assumed to have the same  0 = 2, the th signal can be written as where   () is the signal of the th emitting source at time instant , Ψ  is the carrier phase angles that are assumed to be random variables, each uniformly distributed on [0, 2] and all statistically independent of each other, and   is the incident electric field that can be written as components form.
As a general expression, we omit the subscript, and then where   and   are the horizontal and the vertical components of the field, respectively.
Define  ∈ [0, /2] as the auxiliary polarization angle and  ∈ [−, ] as the polarization phase difference, and then, The incident field can be also expressed in Cartesian coordinate system Figure 1 shows that a planar antenna array has elements indexed as ,  along  and  directions, respectively.For any pair (, ), its coordinate is (, ) = (( − 1)Δ  , ( − 1)Δ  ), where  = 1, . . ., ,  = 1, . . ., , and Δ  and Δ  are reference displacements between neighbor elements along and axes.The array elements are oriented in  plane, and the space phase factors along  and  directions are expressed as where (  ,   ) denote the th source elevation angle and azimuth angle, respectively, and  is the wavelength of the th signal.The measurement vector can be expressed as where stands for the additive white Gaussian noise (AWGN).From ( 3) and (4), we got For receiving the array, the whole receiving factors in subspaces matrix are included in [(  ,   )]; that is, where ⊗ denotes the Kronecker product.So International Journal of Antennas and Propagation 3 The receiving model can be rewritten as, where () The subspace approach not only decreases the computational load as a result of shrinking the matrix dimensions, but also reduces the influence of nonlinearity when dealing with the signal inside each small time step as a linear part.Using the T-ESPRIT method, the whole data is divided into  snapshots at each time   second with sampling frequency   .Then, it picks up enough data  enclosed by each snapshot  with time period  =   / as short as possible.So, from (11), each receiving signal measurement value through th subspace is given as The index  runs as  = 1, 2, . . .,  snapshots.Therefore, the whole space-time steering data matrix can be expressed as where For the th subspace, data matrix can be expressed as For T-ESPRIT scheme, the ESPRIT algorithm is used in an appropriate picked data represented in (13) for each () subspace, shown in (14), in parallel for the same sampling accuracy; thus, reducing the calculations load and consequently saving time is achieved.

Coarse DOAE Based on T-ESPRIT.
The ESPRIT algorithm is based on a covariance formulation [2]; that is, where R is the correlation matrix of the array output signal matrix and R is the autocorrelation matrix of the signal.The subscript  denotes the complex conjugate transpose.
Then,  2 =  1   , where   is the diagonal matrix with diagonal elements Δ  .Consequently, Ê forms the 2×(−1) two submatrices, Ê1 and Ê2 .Then, the diagonal elements Δ  of   are the eigenvalues of the unique matrix Note that, for multiple signals processing, the ordering of the eigenvectors of Ψ  is generally permuted relative to the ordering of the eigenvectors of Ψ  .So, it is essential to pair the eigenvalues of Ψ  and Ψ  .Thus, for all possible permutations, the eigenvalue of Ψ  corresponding to Δ   , the eigenvalue International Journal of Antennas and Propagation of Ψ  , is the element from the Δ  V set which realizes the following minimization: for   ,  V ,   = 1, 2, . . ., .Therefore, And the coarse estimation of the arrival angles (  ,   ) can be calculated as

T-DIT for Doppler Frequency Estimation.
In T-DIT, we apply T-ESPRIT method also on a signal (  ) generated from a transmitted signal sample and has its phase; then, we compare its displacement invariance with the displacement invariance of the received signal, which enables the Doppler frequency shift estimation via measuring the wavelength change.
Due to target movement, the arrival arguments of the displacement invariances for the colocated planar array at the receiver should be calculated as arg where λ , λ are the fluctuated wavelength components of the received wave into antenna plane and they differ from the transmitted wavelength because of the Doppler frequency   caused by the target velocity ⃗ V  [21][22][23].From Figure 2, it is obvious that the wavelengths λ and λ have expressions caused by the velocity components V  and V  :  Substituting into (21), arg where We define  as a wave velocity in free space, and for a target moves with uniform acceleration  is the angle between the direction of wave propagation and the target velocity vector ⃗ V  .Its value changes according to target maneuver.  sign depends on  that indicates the target direction toward or away from the antenna position-target maneuver, as declared in Table 1.
For the transmitted signal, where there is no Doppler effect, the departure arguments are calculated as arg arg  where departure and arrival angles are almost equal for the colocated case.From ( 26), (27), and ( 23)- (25), and due to time multiresolution as T-ESPRIT used in DOAE, the Doppler shift inside each time subspace for each target (   ) can be estimated as follows: And (  ) is the coarse estimated value of the elevation angle.

Target Direction Estimation.
Obviously, it has been found from Figure 3 that target direction estimation depends on V  direction and   value as indicated in Table 2.
It is clear that our main problem is to estimate V  sign to determine the target direction.By dividing (27) on ( 24), we have the following: and λ = λ =  for stationary target.

Simulation Results and Comparisons
Considering the 2D-DOAE process with the additive white Gaussian noise (AWGN), the parameters are given;   = 25 MHz.Assume total 25 temporal snapshots, pickup enclosed data  = 20 times, V  = 250 m/sec, and  = 3 GHz.
A received signal model is simulated using 200 independent Monte Carlo simulations.In order to validate the T-ESPRIT method, it has been used in the planar case with number of elements, such as (, ) = (8,8), with displacement values Δ  = Δ  = /2, and with initial values of  = 45 ∘ and  = 60 ∘ .It is found that the computational load was reduced as a result of reducing the measurement matrix dimension to (2 × ) instead of (2 × ) and employing the subspaces parallel processing concept.Table 3 represents the computational time and complexity of the proposed T-ESPRIT method in terms of number of flip-flops.It is obvious that the computational load has been reduced as a result of a simultaneous processing for  subspaces with each having  snapshots instead of processing for one space that has a large number of snapshots , ( = ) snapshots.
Figure 5 plotted the RMSEs for stationary target with different numbers of elements.The results in Figure 5 indicate that the errors are getting closer to the CRB due to the increase in the number of elements.
Figure 6 showed the errors of the T-ESPRIT method for different maneuver cases according to .For simplicity, we pointed to the three cases by  1 ,  2 , and  3 due to the different three values of , where  1 is the maneuver at  = 0 ∘ ,  2 is the maneuver at  = 95 ∘ , and  3 is the maneuver at  = 89 ∘ (the highest maneuver because  is close to 90 ∘ ).The displayed results showed that the lowest maneuver ( 1 ) RMSEs curve is the closest to CRB [24,25] on the contrary to the highest maneuver ( 3 ) RMSEs curve which clarify the effect of the target movement on the estimation accuracy.Results in Figures 5 and 6 indicate that the RMSEs of the stationary target DOAE are more close to the CRB even in low SNR, but, in case of moving targets as shown in Figure 6, the RMSEs of the maneuvering target tend to be closer to the CRB with higher SNR.
The T-DIT method has been used to estimate Doppler frequency via measuring the fluctuation of the sensor array displacement invariance while the method used in [20] employed the rotational factor that resulted from time delay.Figure 7 showed that the T-DIT algorithm results are better than those of [20] algorithm.This improvement was due to the decrease of the errors caused by the model nonlinearity effect.Additionally, in order to introduce the improvement in the computation time compared to estimate Doppler frequency via using the FFT method, Table 4 represents a comparison between the proposed method computation time and the different FFT methods mentioned in [26].
The results in Table 4 show that the computation time is reduced to 11.09% compared to the best computation time which has been realized via using PWZFFT method [26].This improvement has been realized because of using the temporal subspaces concept which enables parallel processing.
Note that the Doppler correction for 2D-DOAE may increase the total computational time which is consumed to obtain the 2D-DOAE.The computational time of the 2D-DOAE with Doppler correction implementation is (10.2 msec) as indicated in Table 3.However, the computational time is still 57.63% of the conventional ESPRIT computational time.
Figure 8 showed the T-ESPRIT errors with   embedding at  = 0 ∘ (  = 5000 Hz).The accuracy improvement of the 2D-DOAE using proposed algorithm (T-ESPRIT with   embedding) has been verified by comparing the resulted RMSEs with the RMSEs of the 2D Beam ESPRIT, the quaternion ESPRIT algorithms, and the ESPRIT-Like used in [14], [15], and [16], respectively; Figure 8 shows this comparison results.
Comparison results displayed in Figure 8 show that the proposed algorithm has a better performance, especially at a low SNR.This upgrade has been realized firstly due to the increase of DOAE accuracy when using the T-ESPRIT algorithm applying the subspace approach which decreases the errors caused by the model nonlinearity effect and secondly due to Doppler correction, which reduces the DOAE uncertainty associated with the effect of the target movement.
The integrated analysis for Figures 6 and 9 showed us that the proposed algorithm not only enhanced the accuracy of the estimation process, but also illuminated the effect of  target maneuver on the estimation accuracy, which revealed the Doppler frequency effect on the estimation process.Thus, the 2D-DOAE accuracy has been improved by embedding the Doppler effect that resulted from target movement into T-ESPRIT algorithm.For more validation, this algorithm has been applied for two targets moving with different courses to highlight the good performance of this method; the first target is moving away from the antenna then turns toward it with initial value of  = 85 ∘ .On the contrary, the second target moving course started with moving toward the antenna then moving away from it with initial value of  = 265 ∘ and α = 3 ∘ /sec for both of them.Comparison between true and estimated angles (, ) of the moving two targets is shown at Figures 9 and 10, respectively.Figure 11 showed comparison between true and estimated flight course plots of the two moving targets.Clearly, we can figure out that the process of integration between DIT algorithm to estimate Doppler frequency and the modified ESPRIT algorithm makes the estimated flight course more close to the real one, which means less angle glint error (almost up to 0.15 ∘ -0.3 ∘ ) as shown in Figures 9 and 10.Simply, we can say that the developed algorithm achieved success in increasing the DOAE accuracy with low computational load leading to the increase of the estimator efficacy.
From Table 3, it has been found that the proposed algorithm requires (2[ + 4() 2 + 8() 3 ]) flops, while the conventional ESPRIT algorithm needs (2[4() 2 + 8() 3 ]) flops.The developed ESPRIT algorithm requires about 57.6% of the computational time compared with that of the classical ESPRIT algorithm.Simply, it has been found that the developed ESPRIT method achieved success in increasing the DOAE accuracy with low computational load leading to the increase of the estimator efficacy.

Figure 2 :
Figure 2: Target linear velocity components into antenna plane.

Figure 3 :
Figure 3: Target direction dependence on V  and   .

Figure 4 :
Figure 4: T-DIT and Doppler correction for T-ESPRIT algorithm (a) general scheme and (b) detailed scheme.
) and 4(b) show the use of T-DIT to refine DOAE process.

Figure 5 :Figure 6 :
Figure 5: RMSEs versus SNR of T-ESPRIT with different number of elements for stationary target.

Figure 11 :
Figure 11: The moving target flight course.

Table 1 :
Relation between target direction and Doppler sign.

Table 2 :
Target direction estimation cases.
Modified T-ESPRIT.The moving target echo signal is shifted by the Doppler effect.The more accurate T-ESPRIT algorithm should consider the effect of the Doppler frequency shift due to the target movement as mentioned in the previous section.Substitute by the Doppler frequency estimated value to refine angles estimated values as follows: sin   cos   ) Δ  sin   cos   , sin   sin   ) Δ  sin   sin   , (Δ  ) + arg  (Δ  ) ⋅ (    2 ) sin   sin   = ( 2Δ   ) ⋅ sin   sin   . (Δ  ) = sin   sin   ⋅ [

Table 3 :
Comparison of the required computation time and complexity.

Table 4 :
Comparison of the required computation time.