Jingmin Xin

Computationally efficient subspace-based method for direction-of-arrival estimation without eigendecomposition

A computationally simple direction-of-arrival (DOA) estimation method with good statistical performance is attractive in many practical applications of array processing. In this paper, we propose a new computationally efficient subspace-based method without eigendecomposition (SUMWE) for the coherent narrowband signals impinging on a uniform linear array (ULA) by exploiting the array geometry and its shift invariance property. The coherency of incident signals is decorrelated through subarray averaging, and the null space is obtained through a linear operation of a matrix formed from the cross-correlations between some sensor data, where the effect of additive noise is eliminated. Consequently, the DOAs can be estimated without performing eigendecomposition, and there is no need to evaluate all correlations of the array data. Furthermore, the SUMWE is also suitable for the case of partly coherent or incoherent signals, and it can be extended to the spatially correlated noise by choosing appropriate subarrays. The statistical analysis of the SUMWE is studied, and the asymptotic mean-squared-error (MSE) expression of the estimation error is derived. The performance of the SUMWE is demonstrated, and the theoretical analysis is substantiated through numerical examples. It is shown that the SUMWE is superior in resolving closely spaced coherent signals with a small number of snapshots and at low signal-to-noise ratio (SNR) and offers good estimation performance for both uncorrelated and correlated incident signals.

Computationally Efficient Subspace-Based Method for Two-Dimensional Direction Estimation With L-Shaped Array

In order to mitigate the effect of additive noises and reduce the computational burden, we propose a new computationally efficient cross-correlation based two-dimensional (2-D) direction-of-arrivals (DOAs) estimation (CODE) method for noncoherent narrowband signals impinging on the L-shaped sensor array structured by two uniform linear arrays (ULAs). By estimating the azimuth and elevation angles independently with a one-dimensional (1-D) subspace-based estimation technique without eigendecomposition, where the null spaces are obtained through a linear operation of the matrices formed from the cross-correlation matrix between the received data of two ULAs, then the pair-matching of estimated azimuth and elevation angles is accomplished by searching the minimums of a cost function of the azimuth and elevation angles, where the computationally intensive and time-consuming eigendecomposition process is avoided. Further the asymptotic mean-square-error (MSE) expressions of the azimuth and elevation estimates are derived. The effectiveness of proposed method and the theoretical analysis are verified through numerical examples, and it is shown that the proposed CODE method performs well at low signal-to-noise ratio (SNR) and with a small number of snapshots.

Simple and Efficient Nonparametric Method for Estimating the Number of Signals Without Eigendecomposition

Inspired by the computational simplicity and numerical stability of QR decomposition, a nonparametric method for estimating the number of signals without eigendecomposition (MENSE) is proposed for the coherent narrowband signals impinging on a uniform linear array (ULA). By exploiting the array geometry and its shift invariance property to decorrelate the coherency of signals through subarray averaging, the number of signals is revealed in the rank of the QR upper-trapezoidal factor of the autoproduct of a combined Hankel matrix formed from the cross correlations between some sensor data. Since the infection of additive noise is defused, signal detection capability is improved. A new detection criterion is then formulated in terms of the row elements of the QR upper-triangular factor when finite array data are available, and the number of signals is determined as a value of the running index for which this ratio criterion is maximized, where the QR decomposition with column pivoting is also used to improve detection performance. The statistical analysis clarifies that the MENSE detection criterion is asymptotically consistent. Furthermore, the proposed MENSE algorithm is robust against the array uncertainties including sensor gain and phase errors and mutual coupling and against the deviations from the spatial homogeneity of noise model. The effectiveness of the MENSE is verified through numerical examples, and the simulation results show that the MENSE is superior in detecting closely spaced signals with a small number of snapshots and/or at relatively low signal-to-noise ratio (SNR)

Linear prediction approach to direction estimation of cyclostationary signals in multipath environment

We investigate the estimation of the directions-of-arrival (DOA) of closely spaced narrowband cyclostationary signals in the presence of multipath propagation. By exploiting the spatial and temporal properties of most communication signals, we propose a new cyclic forward-backward linear prediction (FBLP) approach for coherent signals impinging on a uniform linear array (ULA). In the proposed algorithm, the evaluation of the cyclic array covariance matrix is avoided, and the difficulty of choosing the optimal time lag parameter is alleviated. As a result, the proposed approach has two advantages: (1) the computational load is relatively reduced, and (2) the robustness of estimation is significantly improved. The performance of the proposed approach is confirmed through numerical examples, and it is shown that this approach is superior in resolving the closely spaced coherent signals with a small length of array data and at relatively low signal-to-noise ratio (SNR).

Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications

This paper presents a systematic scheme for fusing millimeter wave (MMW) radar and a monocular vision sensor for on-road obstacle detection. As a whole, a three-level fusion strategy based on visual attention mechanism and driver’s visual consciousness is provided for MMW radar and monocular vision fusion so as to obtain better comprehensive performance. Then an experimental method for radar-vision point alignment for easy operation with no reflection intensity of radar and special tool requirements is put forward. Furthermore, a region searching approach for potential target detection is derived in order to decrease the image processing time. An adaptive thresholding algorithm based on a new understanding of shadows in the image is adopted for obstacle detection, and edge detection is used to assist in determining the boundary of obstacles. The proposed fusion approach is verified through real experimental examples of on-road vehicle/pedestrian detection. In the end, the experimental results show that the proposed method is simple and feasible.

Two-Dimensional Direction Estimation for a Mixture of Noncoherent and Coherent Signals

This paper deals with the two-dimensional (2-D) direction-of-arrival (DOA) estimation of a mixture of noncoherent (including uncorrelated and partially correlated) and coherent (i.e., fully correlated) narrowband signals impinging on a planar sensor array composed of two parallel uniform linear arrays (ULAs). An oblique projection based approach for 2-D direction estimation (OPADE) is proposed by using some cross-correlations between the received array data. In the proposed OPADE, the oblique projection is utilized to isolate the coherent signals from the noncoherent ones and the effect of additive noise is alleviated, while the computationally intensive eigendecomposition is avoided, and the estimated elevation and azimuth angles are paired automatically. Further, an iterative alternating scheme is presented to improve the estimation accuracy of the oblique projector and hence that of the DOAs of coherent signals. The Cramér-Rao lower bound (CRB) for the mixture of noncoherent and coherent signals is also derived explicitly, where the prior knowledge of the signal correlation is incorporated into the 2-D DOA estimation of noncoherent signals. Finally the effectiveness of the OPADE and the theoretical analysis are substantiated through numerical examples.

HEB: deploying and managing hybrid energy buffers for improving datacenter efficiency and economy

Today, an increasing number of applications and services are being hosted by large-scale data centers. The massive and irregular load surges challenge data center power infrastructures. As a result, power mismatching between supply and demand has emerged as a crucial issue in modern data centers which are either under-provisioned or powered by intermittent power sources. Recent proposals have employed energy storage devices such as the uninterruptible power supply (UPS) systems to address this issue. However, current approaches lack the capacity of efficiently handling the irregular and unpredictable power mismatches. In this paper, we propose Hybrid Energy Buffering (HEB), the first heterogeneous and adaptive strategy that incorporates super-capacitors (SCs) into existing data centers to dynamically deal with power mismatches. Our techniques exploit diverse energy absorbing characteristics and intelligent load assignment policies to provide efficiency-and scenario- aware power mismatch management. More attractively, our management schemes make the costly energy storage devices more affordable and economical for datacenter-scale usage. We evaluate the HEB design with a real system prototype. Compared with a homogenous battery energy buffering system, HEB could improve energy efficiency by 39.7%, extend UPS lifetime by 4.7×, reduce system downtime by 41% and improve renewable energy utilization by 81.2%. Our TCO analysis shows that HEB manifests high ROI and is able to gain more than 1.9× peak shaving benefit during an 8-years period. It allows datacenters to adapt to various power supply anomalies, thereby improving operational efficiency, resiliency and economy.

MSE-based regularization approach to direction estimation of coherent narrowband signals using linear prediction

This paper addresses the problem of directions of arrival (DOAs) estimation of coherent narrowband signals impinging on a uniform linear array (ULA) when the number of signals is unknown. By using an overdetermined linear prediction (LP) model with a subarray scheme, the DOAs of coherent signals can be estimated from the zeros of the corresponding prediction polynomial. Although the corrected least squares (CLS) technique can be used to improve the accuracy of the LP parameters estimated from the noisy array data, the inversion of the resulting matrix in the CLS estimation is ill-conditioned, and then, the CLS estimation becomes unstable. To combat this numerical instability, we introduce multiple regularization parameters into the CLS estimation and show that determining the number of coherent signals is closely related to the truncation of the eigenvalues. An analytical expression of the mean square error (MSE) of the estimated LP parameters is derived, and it is clarified that the number of signals can be determined by comparing the optimal regularization parameters with the corresponding eigenvalues. An iterative regularization algorithm is developed for estimating directions without any a priori knowledge, where the number of coherent signals and the noise variance are estimated from the noise-corrupted received data simultaneously.

Natural scene text detection with multi-layer segmentation and higher order conditional random field based analysis

The contrasts in RGB channels are integrated to segment image into multi layers.The multi-layer segmentation is implemented with a graph cuts based model.A higher order CRF based connected component analysis is used. Text detection in natural scene images is a hot and challenging problem in pattern recognition and computer vision. Considering the complex situations in natural scene images, we propose a robust two-steps method in this paper based on multi-layer segmentation and higher order conditional random field (CRF). Given an input image, the method separates text from its background by using multi-layer segmentation, which decomposes the input image into nine layers. Then, the connected components (CCs) in these different layers are obtained as candidate text. These candidate text CCs are verified by higher order CRF based analysis. Inspired from the multistage information integration mechanism of visual brains, features from three different levels, including separate CCs, CC pairs and CC strings, are integrated by a higher order CRF model to distinguish text from non-text. The remaining CCs are then grouped into words for easy evaluation. Experiments on the ICDAR datasets and street view dataset show that the proposed method achieves the state-of-art in natural scene text detection.

Leveraging Heterogeneous Power for Improving Datacenter Efficiency and Resiliency

Power mismatching between supply and demand has emerged as a top issue in modern datacenters that are under-provisioned or powered by intermittent power supplies. Recent proposals are primarily limited to leveraging uninterruptible power supplies (UPS) to handle power mismatching, and therefore lack the capability of efficiently handling the irregular peak power mismatches. In this paper we propose hPower, the first heterogeneous energy buffering strategy that incorporates supercapacitors into existing datacenters to handle power mismatch. Our technique exploits power supply diversity and smart load assignment to provide efficiency-aware and emergency-aware power mismatch management. We show that hPower could improve energy efficiency by 30 percent, extend UPS lifetime by 4.3×, and reduce system downtime by 36 percent. It allows datacenters to adapt themselves to various power supply anomalies, thereby improving operational efficiency and resiliency.