I Introduction
The problem of multidimensional (MD) frequency estimation arises in many applications. For the D frequency estimation in wireless communication systems, the two dimensions correspond to the delay and angle of each path. Physical arguments and a growing body of experimental evidence suggest that utilizing the structures benefits and improves the channel reconstruction performance [1]. Another application is for the D frequency estimation in radar systems, one can interpret one dimension as time delays, one dimension as Doppler shifts, and the other as spatial frequencies. Since each pair of time delay, Doppler shift and spatial frequency specifies the localization, speed and direction of arrival (DOA) of a scatter, respectively, estimating these parameters is of great importance for target localization and tracking [2]. Traditional methods including D Discrete Fourier Transform (DFT), subspace based approaches such as D MUSIC [3], D ESPRIT [4], Matrix Enhancement Matrix Pencil (MEMP) [5] and the multidimensional folding (MDF) techniques [6, 7].
Recently, compressed sensing based approaches have been proposed for D frequency estimation, especially for D frequency estimation. Provided that the frequencies lie on the DFT grid, the signal can be recovered via efficient minimization [8]. However, due to spectral leakage of the offgrid frequencies, the signal is not exactly sparse and conventional compressed sensing (CS) algorithms may result in significant performance degradation [9]. Consequently, grid refinement and continuous CS approaches have been developed. In [10, 11], the Newtonalized orthogonal matching pursuit (NOMP) is proposed for D frequency estimation, which first detects the grid and then iteratively refines the frequencies. Besides, the model order is estimated by the constant false alarm (CFAR) criterion with the knowledge of the noise variance. The computation complexity of NOMP is low, and the estimation performance is good. For the latter, [12] proposes a nuclear norm minimization over multifold Hankel matrices approach, [13] develops a reweighted trace minimization (RWTM) plus matrix pencil and (auto)pairing (MaPP) method, where RWTM fully exploits the multilevel Toeplitz (MLT) structure and utilizes the sparsity to obtain the covariance estimates, and MaPP calculates the Vandermonde decomposition. As [12, 13] involve solving a semidefinite programming (SDP), their computation complexities are very high.
A more recent approach is the variational line spectral estimation (VALSE) algorithm, which uses a complete Bayesian treatment by imposing the frequencies as random variables
[14]. Such method naturally allows for representing and operating with the uncertainty of the frequency estimates, in addition to only that of the weights. Numerically, accounting for the frequency uncertainty benefits the line spectral estimation performance. VALSE automatically estimates the model order, noise variance, and provides the uncertain degrees of the frequency estimates. Besides, the computation is low, compared to the nuclear norm minimization based approaches. It was also extended to the multisnapshot case in array signal processing [15] and quantized setting in highspeed sampling [16]. In contrast, this work develops MDVALSE to deal with the multidimensional (MD) frequency estimation problem.The main contribution of this work is as follows: Firstly, MDVALSE is rigorously developed, which is nontrival from the following two aspects: one is that the multifrequencies are treated and updated as a whole, another aspect is that even the probability density function (PDF) of the multifrequencies can be approximated as a multivariate von Mises distribution, computing expectations of is still hard. We novelly project the PDF of the multifrequencies as independent vonMises distribution, which allows computation of . Secondly, the D fast Fourier transform (FFT) approach is adopted to reduce the computation complexity significantly. Finally, numerical experiments are conducted to demonstrate the excellent performance of MDVALSE.
Ii Problem Setup
Consider a measurement model for dimensional line spectra estimation problem expressed as
(1) 
where is the noiseless line spectral composed of complex sinusoids
(2) 
denotes the complex amplitude of the th frequency, and the th element is , is the th element of and , is the additive white Gaussian noise (AWGN) and , where
(3) 
For simplicity, we define
(4) 
Since the number of spectral is unknown, an overcomplete model is adopted, where the number of spectral is supposed to be and [14]. Consequently, the binary hidden variables are introduced to characterize whether the th component is active or not. The probability mass function (PMF) is , where and
(5) 
Besides, , where
follows a BernoulliGaussian distribution
(6) 
where is the Dirac delta function. From (5) and (6), it can be seen that controls the probability of the th component being active. For measurement model (1), the likelihood is
(7) 
Let and be the model and estimated parameters. In general, one should first find the maximum likelihood (ML) estimates of by marginating the posterior PDF of , then find the maximum a posterior (MAP) or minimum mean squared error (MMSE) estimates of . However, the above approach is computationally intractable and thus an iterative algorithm is developed in the ensuing section.
Iii MDVALSE Algorithm
The variational approach tries to find a surrogate PDF via minimizing the KullbackLeibler (KL) divergence between and , which is equivalent to maximize
(8) 
Similar to [14], is imposed to have the following structures
(9) 
Maximizing with respect to all the factors is also intractable. Similar to the GaussSeidel method, is optimized over each factor , and separately with the others being fixed. Let be the set of all latent variables. Maximizing (8) with respect to the posterior approximation of each latent variable yields
(10) 
where the expectation is with respect to all the variables except and the constant ensures normalization of the PDF.
Due to the factorization property of (9), the frequencies can be estimated from the marginal distribution as
(11a)  
(11b) 
where returns the angle.
Given that , the posterior PDF of is
(12) 
For the given posterior PDF , the mean and covariance of the weights is estimated as
(13a)  
(13b) 
Let be the set of indices of the nonzero components of , i.e., . In the following, we detail the procedures.
Iiia Inferring the frequencies
For each , we maximize with respect to the factor . For , remains unchanged. For , substituting (11) and (13) in (10), is obtained as
(14) 
where is
(15) 
where denotes the conjugate operation. While it is hard to obtain the analytical results of (11) for the PDF (14), is projected as the independent von Mises distribution. A Newton step is implemented to refine the previous estimates, i.e.,
(16a)  
(16b) 
where is the exponential part of and the detail of the inverse of is given in [17],
returns a vector with elements being the diagonal of the matrix. As a result,
and can be approximated as(17a)  
(17b) 
In Section IV, the accuracy of the approximation (17a) is demonstrated numerically.
IiiB Inferring the weights and support
Next are fixed and is maximized w.r.t. . Define the matrices and as
(18a)  
(18b) 
where denotes the th element of . It can be shown that updating , and are the same as [14] and details are omitted here.
IiiC Estimating the model parameters
IiiD Summary of MDVALSE algorithm
The initialization of MDVALSE algorithm is presented. For the first step of initialization, we randomly extract one dimensional data from and use the method in [14] to initialize . For the later, we choose to initialize in a sequential manner. For the th step, the noncoherent PDF
(20) 
is projected to independent von Mises distribution, where . Here ND FFT is implemented to obtain the approximated independent von Mises distribution. Then the MDVALSE can be obtained and the procedures are similar to [14, Algorithm 3].
Now we analyze the computation complexity of MDVALSE. The complexity of initialization is dominated by dimensional FFT. For the th dimension, the number of grid is where can be viewed as the oversampling factor. Thus the initialization has complexity . For each iteration, the complexity is dominated by the estimate of and the projection of as independent VM. According to [14], the calculation of has complexity and in general . For the projection operation, the complexity is which are mainly dominated by the calculation and inverse of Hessian matrix in (16b). Therefore, the computational complexity of MDVALSE is where denotes the number of whole iterations. For , the computation complexity can be simplified as .
Iv Numerical Simulation
In this section, substantial numerical experiments are conducted to evaluate the performance of MDVALSE algorithm. The normalized mean squared error (NMSE) of , MSE of and the correct model order estimated probability are adopted as the performance metrics. The frequencies estimation error is averaged over the trials in which
for a given simulation point. We define nominal signaltonoise ratio (SNR) as
). The Algorithm stops when or , where is the number of iterations.At first, the approximations of noncohent PDF (20) and coherent PDF (14) as independent von Mises distribution are validated numerically and results are shown in Fig. 1. Note that for , the approximation deviates a little away from the true PDF due to the inaccuracy of the FFT results. As increases to , the approximation is high and the two PDFs are indistinguishable around the peak.
Iva Estimation from 2D case
In this simulation, we consider a 2D case with signals and true frequencies are
The complex weight coefficients are generated i.i.d. from complex normal distribution
. The number of measurements is . As for performance comparison, reweighted trace minimization (RWTM) algorithm proposed in [13] is chosen and the performances of RWTM and MDVALSE are investigated in Fig. 2. It can be seen that for noiseless case in Fig. 2, MDVALSE and RWTM algorithms both estimate well. Numerically, the MSE of frequencies of RWTM is lower than that of MDVALSE. For noisy case in Fig. 2, MDVALSE still performs well, while RWTM overestimates the model order. This demonstrates that MDVALSE is more robust against noise than RWTM.IvB Estimation from 4D case
The performance of MDVALSE algorithm for 4D case versus SNR is investigated. The magnitudes and phases of the complex weight coefficients are generated i.i.d. from normal distribution
, respectively. The number of sinusoids is and the wraparound distance between any two frequencies is at least radians for each dimension. MC trails are performed and results are shown in Fig. 3. MDVALSE works well and performs better as SNR or number of measurements increases.V Conclusion
This paper develops the MDVALSE for multidimensional line spectral estimation, where the multidimensional frequencies are treated as a whole and their PDF are projected as independent von Mises distribution for tractable inference. MDVALSE inherits the advantages of automatically estimating the model order, number of nuisance parameters such as noise variance, and providing the uncertain degree of frequency estimates. Numerical results demonstrate the effectiveness of MDVALSE.
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