Zhongfu Ye

Brightness preserving histogram equalization with maximum entropy: a variational perspective

Histogram equalization (HE) is a simple and effective image enhancing technique, however, it tends to change the mean brightness of the image to the middle level of the permitted range, and hence is not very suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. This paper proposes a novel extension of histogram equalization, actually histogram specification, to overcome such drawback as HE. To maximize the entropy is the essential idea of HE to make the histogram as flat as possible. Following that, the essence of the proposed algorithm, named brightness preserving histogram equalization with maximum entropy (BPHEME), tries to find, by the variational approach, the target histogram that maximizes the entropy, under the constraints that the mean brightness is fixed, then transforms the original histogram to that target one using histogram specification. Comparing to the existing methods including HE, brightness preserving bi-histogram equalization (BBHE), equal area dualistic sub-image histogram equalization (DSIHE), and minimum mean brightness error bi-histogram equalization (MMBEBHE), experimental results show that BPHEME can not only enhance the image effectively, but also preserve the original brightness quite well, so that it is possible to be utilized in consumer electronic products.

On the Resiliency of MUSIC Direction Finding Against Antenna Sensor Coupling

Many classical direction of arrival (DOA) estimation algorithms suffer from sensitivity to sensor coupling. By applying a group of auxiliary sensors in a uniform linear array (ULA), we prove the resiliency of the MUSIC direction finding algorithm against array sensor coupling. We show that the performance of MUSIC algorithm under antenna array with unknown coupling can be very close to the case with known coupling. We can also estimate the mutual coupling coefficients before refining the DOA estimates by utilizing an extended sensor array. Moreover, our analysis on the effect of mutual coupling in direction finding illustrates the existence of some blind angles which should be avoided when the array is designed. Our simulation results corroborate our analysis.

DOA Estimation for Uniform Linear Array with Mutual Coupling

An algorithm is presented for direction-of-arrival (DOA) estimation in the presence of unknown mutual coupling based on the generalized eigenvalues utilizing signal subspace eigenvectors (GEESE) algorithm for uniform linear array (ULA). It is not an iterative algorithm, and a spectral peak search is not required. The DOA can be accurately estimated without any calibration sources since the effects of mutual coupling can be eliminated by the inherent mechanism of the proposed algorithm. An algorithm for estimating the mutual coupling coefficients is also proposed. Simulation results demonstrate the effectiveness of the proposed algorithms.

2-D DOA Estimation in the Presence of Mutual Coupling

We present a 2-D direction of arrival (DOA) estimation algorithm in the presence of unknown mutual coupling for the uniform rectangular array (URA) based on the multiple signal classification (MUSIC) algorithm. By setting the sensors on the boundary of the URA as auxiliary sensors, it can accurately estimate the DOAs without any calibration sources or iterative operations. We prove that the effect of mutual coupling can be eliminated by the inherent mechanism of the proposed method. Twice search technique is used to reduce the computation of the 2-D spectrum search. Moreover, we provide a method to estimate the mutual coupling coefficients after getting the DOA estimates. Simulation results confirm the effectiveness of the proposed algorithm.

DOA Estimation Based on Sparse Signal Recovery Utilizing Weighted

In this letter, a new DOA estimation method based on sparse signal recovery is proposed. We utilize the Capon spectrum to design a weighted l1-norm penalty in order to further enforce the sparsity and approximate the original l0-norm. A theoretical guidance for choosing a proper regularization parameter is also presented according to the dual form of the original problem. Simulation results demonstrate the effectiveness and efficiency of the proposed method.

l_{1}

A new direction of arrival (DOA) estimation method is proposed for uniform linear array in this paper when both uncorrelated and coherent sources are present. The uncorrelated and coherent sources are estimated at two different stages. The uncorrelated sources are first estimated using conventional subspace methods, and then they are eliminated by exploiting the symmetric configuration of the array. Finally the remaining coherent sources are estimated by reconstructing a non-Toeplitz matrix. The number of sources resolved by our method can exceed the number of array elements. Simulation results demonstrate the effectiveness and efficiency of our proposed method.

-Norm Penalty

The problem of direction-of-arrival (DOA) estimation for sparse array is addressed. The perspective that DOA estimation in virtual array response model can be cast as the problem of sparse recovery is introduced. Two methods are proposed, based on different optimization problems, which are solvable using second-order cone (SOC) programming. Without the knowledge of the number of sources, the proposed methods yield superior performances, which are verified by numerical simulations.

DOA Estimation by Exploiting the Symmetric Configuration of Uniform Linear Array

A novel deflation approach to direction of arrival (DOA) estimation for symmetric uniform linear array is proposed in this letter to cope with the scenario where both uncorrelated sources and coherent sources are presented. The uncorrelated sources are first estimated using conventional subspace methods, and then their effect is eliminated by two deflation methods: one exploits the symmetric configuration of the array, and the other utilizes oblique projection. After deflation, a Toeplitz matrix is constructed for DOA estimation of the remaining coherent sources. The number of sources resolved by our approach can exceed the number of array elements. Simulation results demonstrate the effectiveness and efficiency of our proposed methods.

DOA Estimation for Sparse Array via Sparse Signal Reconstruction

Recently, a new robust adaptive beamforming (RAB) technique was proposed to remove the signal of interest (SOI) component from the sample covariance matrix based on interference-plus-noise covariance matrix reconstruction, which utilizes the Capon spectrum estimator integrated over a region separated from the direction of the SOI. However, the extreme condition of the reconstruction-based technique, that the precise information about the array structure is known in advance, is almost impossible in practice. In this paper, a novel method to reconstruct the interference-plus-noise covariance matrix is proposed. Considering the imprecise prior information about the array structure, which means that the array may be uncalibrated, we use an annulus uncertainty set to constrain the steering vectors of the interferences. Then we integrate the Capon spectrum over the surface of the annulus, by which we can obtain the reconstructed interference matrix without containing the SOI. Since the integral interval is a high-dimensional domain, which is very difficult to solve, we use a discrete sum method to calculate the integral approximately. With the reconstructed interference-plus-noise matrix, the nominal steering vector can be corrected via maximizing the beamformer output power by solving a quadratically constrained quadratic programming (QCQP) problem. The previous reconstruction method can be seen as a special case of the proposed one. The main advantage is that the proposed algorithm is robust against unknown arbitrary-type mismatches. Theoretical analysis and simulation results demonstrate the effectiveness and robustness of the proposed algorithm.

A Deflation Approach to Direction of Arrival Estimation for Symmetric Uniform Linear Array

In this letter, an efficient direction-of-arrival (DOA) estimation method is proposed with a uniform linear array when uncorrelated and coherent signals coexist. The method can effectively eliminate the possible false DOA estimates of uncorrelated signals, and only the true DOA estimates can be retained. After estimating the DOAs and power of uncorrelated signals, the information in them can be eliminated from the signal subspace. Then, we can obtain a matrix that only contains the information of coherent signals, which we call the C-matrix. Finally, the coherent signals are resolved with a new constructed matrix by the C-matrix. The theoretical analysis and simulation results confirm the effectiveness of our proposed method.