Longting Huang

Parameter estimation and identifiability in bistatic multiple-input multiple-output radar

An iterative ESPRIT-like algorithm is devised for direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in multiple-input multiple-output radar. Our proposal can handle identical DODs and DOAs, and provides autopairing of the angle parameters. Furthermore, it is proved that the multiple signal classification methodology cannot identify (MN - 1) targets, where M and N are the element numbers in the transmit and receive antennas, respectively. Simulation results are included to evaluate the performance of the proposed algorithm.

Multidimensional Sinusoidal Frequency Estimation Using Subspace and Projection Separation Approaches

In this correspondence, a computationally efficient method that combines the subspace and projection separation approaches is developed for R -dimensional (R-D) frequency estimation of multiple sinusoids, where R ≥ 3, in the presence of white Gaussian noise. Through extracting a 2-D slice matrix set from the multidimensional data, we devise a covariance matrix associated with one dimension, from which the corresponding frequencies are estimated using the root-MUSIC method. With the use of the frequency estimates in this dimension, a set of projection separation matrices is then constructed to separate all frequencies in the remaining dimensions. Root-MUSIC method is again applied to estimate these single-tone frequencies while multidimensional frequency pairing is automatically attained. Moreover, the mean square error of the frequency estimator is derived and confirmed by computer simulations. It is shown that the proposed approach is superior to two state-of-the-art frequency estimators in terms of accuracy and computational complexity.

Underdetermined direction-of-departure and direction-of-arrival estimation in bistatic multiple-input multiple-output radar

In this paper, target localization using bistatic multiple-input multiple-output radar where the source number exceeds the sizes of the transmit and receive arrays, denoted by M and N, respectively, is addressed. We consider the Swerling II target in which the radar cross section varies in different pulses. Two algorithms for joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation of the targets are devised. The first one is a subspace-based estimator which is computationally simpler and can identify up to 2(M-1)(2N-1) sources, assuming that N>=M. The second is a maximum likelihood method with a higher estimation accuracy, where the DODs and DOAs are solved via alternating optimization. Simulation results are included to compare their mean square error performance with the Cramer-Rao lower bound.

Target estimation in bistatic MIMO radar via tensor completion

In this paper, the problem of target estimation in bistatic multiple-input multiple-output (MIMO) radar is tackled via low-rank tensor completion. Our solution consists in jointly computing the direction-of-departure and direction-of-arrival parameters of a sparse target scene, where only partial data are collected at the front-end during multiple pulse periods. By recasting the data model as a low-rank third-order tensor with missing entries, an accelerated proximal gradient line-search algorithm coupled with rank detection is devised to obtain an accurate rank estimate in a noisy environment with unknown number of targets. Computer simulation results demonstrate the effectiveness of the proposed method, which outperforms several state-of-the-art algorithms dealing with the same problem.

Tensor Decomposition and PCA Jointed Algorithm for Hyperspectral Image Denoising

Denoising is a critical preprocessing step for hyperspectral image (HSI) classification and detection. Traditional methods usually convert high-dimensional HSI data to 2-D data and process them separately. Consequently, the inherent structured high-dimensional information in the original observations may be discarded. To overcome this disadvantage, this letter tackles an HSI denoising by jointly exploiting Tucker decomposition and principal component analysis (PCA). A truncated Tucker decomposition method based on noise power ratio (NPR) analysis and jointed with PCA is presented. We call this jointed method as NPR-Tucker+PCA. Experimental results show that the proposed method outperforms existing methods in the sense of peak signal-to-noise ratio performance.

Truncated nuclear norm minimization for tensor completion

In this paper, a tensor n-mode matrix unfolding truncated nuclear norm is proposed, which is extended from the matrix truncated nuclear norm, to tensor completion problem. The alternating direction method of multipliers is utilized to solve this optimization problem. Meanwhile, the original two-step solution of the matrix truncated nuclear norm is reduced to one step. Employing the intermediate results returned by singular value shrinkage operator, rank information of each tensor unfolding matrix is not required and thus the computational complexity of the devised approach is not demanding. Computer simulation results demonstrate the effectiveness of the proposed method.

Sparse and Truncated Nuclear Norm Based Tensor Completion

One of the main difficulties in tensor completion is the calculation of the tensor rank. Recently a tensor nuclear norm, which is equal to the weighted sum of matrix nuclear norms of all unfoldings of the tensor, was proposed to address this issue. However, in this approach, all the singular values are minimized simultaneously. Hence the tensor rank may not be well approximated. In addition, many existing algorithms ignore the structural information of the tensor. This paper presents a tensor completion algorithm based on the proposed tensor truncated nuclear norm, which is superior to the traditional tensor nuclear norm. Furthermore, to maintain the structural information, a sparse regularization term, defined in the transform domain, is added into the objective function. Experimental results showed that our proposed algorithm outperforms several state-of-the-art tensor completion schemes.

Low peak-to-average ratio OFDM chirp waveform diversity design

Large time-bandwidth product waveform diversity design is a challenging topic in multiple-input multiple-output radar high-resolution imaging because existing methods usually can generate only two large time-bandwidth product waveforms. This paper proposes a new low peak-to-average ratio (PAR) orthogonal frequency division multiplexing chirp waveform diversity design through randomly subchirp modulation. This method can easily yield over two orthogonal large time-bandwidth product waveforms. More waveforms means that more degrees-of-freedom can be obtained for the system. The waveform performance is evaluated by the ambiguity function. It is shown that the designed waveform has the superiorities of a large time-bandwidth product which means high range resolution and low transmit power are allowed for the system, almost constant time-domain and frequency-domain modulus, low PAR and no range-Doppler coupling response in tracking moving targets.

Tensor Completion Based on Structural Information

In tensor completion, one of the challenges is the calculation of the tensor rank. Recently, a tensor nuclear norm, which is a weighted sum of matrix nuclear norms of all unfoldings, has been proposed to solve this difficulty. However, in the matrix nuclear norm based approach, all the singular values are minimized simultaneously. Hence the rank may not be well approximated. This paper presents a tensor completion algorithm based on the concept of matrix truncated nuclear norm, which is superior to the traditional matrix nuclear norm. Since most existing tensor completion algorithms do not consider of the tensor, we add an additional term in the objective function so that we can utilize the spatial regular feature in the tensor data. Simulation results show that our proposed algorithm outperforms some the state-of-the-art tensor/matrix completion algorithms.

ℓ 1 -norm based nonparametric and semiparametric approaches for robust spectral analysis

The problem of frequency estimation can be solved by parametric, non-parametric or semi-parametric methods. The representative nonparametric and semiparametric methods, namely, iterative adaptive approach (IAA) and sparse learning via iterative minimization (SLIM) have been recently proposed. Since both of them are not robust to impulsive noise, their extensions, ℓ1-IAA and ℓ1-SLIM are derived to provide accurate spectral estimation in the presence of the heavy-tailed noise in this paper. In our study, the nonlinear frequency estimation problem is mapped to a linear model whose parameters are updated according to the ℓ1-norm and iteratively reweighted least squares. Simulation results are included to demonstrate the outlier resistance performance of the proposed algorithms.