Bin Liao

Bio: Bin Liao is an academic researcher from Shenzhen University. The author has contributed to research in topics: Covariance matrix & Beamforming. The author has an hindex of 25, co-authored 166 publications receiving 2022 citations. Previous affiliations of Bin Liao include Xidian University & University of Hong Kong.

DOA Estimation and Tracking of ULAs with Mutual Coupling

Abstract: A class of subspace-based methods for direction-of-arrival (DOA) estimation and tracking in the case of uniform linear arrays (ULAs) with mutual coupling is proposed. By treating the angularly-independent mutual coupling as angularly-dependent complex array gains, the middle subarray is found to have the same complex array gains. Using this property, a new way for parameterizing the steering vector is proposed and the corresponding method for joint estimation of DOAs and mutual coupling matrix (MCM) using the whole array data is derived based on subspace principle. Simulation results show that the proposed algorithm has a better performance than the conventional subarray-based method especially for weak signals. Furthermore, to achieve low computational complexity for online and time-varying DOA estimation, three subspace tracking algorithms with different arithmetic complexities and tracking abilities are developed. More precisely, by introducing a better estimate of the subspace to the conventional tracking algorithms, two modified methods, namely modified projection approximate subspace tracking (PAST) (MPAST) and modified orthonormal PAST (MOPAST), are developed for slowly changing subspace, whereas a Kalman filter with a variable number of measurements (KFVM) method for rapidly changing subspace is introduced. Simulation results demonstrate that these algorithms offer high flexibility and effectiveness for tracking DOAs in the presence of mutual coupling.

MIMO Radar Waveform Design With PAPR and Similarity Constraints

Abstract: The paper investigates the joint design of transmit waveform and receive filter for multiple-input multiple-output radar in the presence of signal-dependent interference, subject to a peak-to-average-power ratio constraint as well as a waveform similarity constraint. Owing to this kind of signal dependence and constraints, the formulated optimization problem of the output signal-to-interference-plus-noise ratio (SINR) maximization is NP-hard. To this end, an auxiliary variable is first introduced to modify the original problem, and then a primal-dual algorithm based on the block successive upper-bound minimization method of multipliers (BSUM-M) is developed to deal with the resulting problem. Moreover, an active set method is exploited to solve the quadratic programming problem involved in each update procedure of the proposed BSUM-M algorithm. Finally, numerical simulations are performed to demonstrate the superiority of the proposed algorithm over state-of-the-art methods in terms of the output SINR, beampattern, computational complexity, pulse compression, and ambiguity properties.

Direction Finding With Partly Calibrated Uniform Linear Arrays

Abstract: A new method for direction finding with partly calibrated uniform linear arrays (ULAs) is presented. It is based on the conventional estimation of signal parameters via rotational invariance techniques (ESPRIT) by modeling the imperfections of the ULAs as gain and phase uncertainties. For a fully calibrated array, it reduces to the conventional ESPRIT algorithm. Moreover, the direction-of-arrivals (DOAs), unknown gains, and phases of the uncalibrated sensors can be estimated in closed form without performing a spectral search. Hence, it is computationally very attractive. The Cramer-Rao bounds (CRBs) of the partly calibrated ULAs are also given. Simulation results show that the root mean squared error (RMSE) performance of the proposed algorithm is better than the conventional methods when the number of uncalibrated sensors is large. It also achieves satisfactory performance even at low signal-to-noise ratios (SNRs).

Communication-Aware Waveform Design for MIMO Radar With Good Transmit Beampattern

Abstract: Designing the radar waveform, which ensures spectral compatibility with the communication systems, is known to be challenging. In addition to having the desirable transmitter efficiency, it is also anticipated to share the good pulse compression property of the reference waveform. In this paper, we investigate the problem of waveform design for multiple-input multiple-output radar with good transmit beampattern under certain practical constraints (e.g., peak to average power ratio and similarity constraints) in coexistence with communication systems. The waveform is designed by minimizing a weighted summation of the beampattern integrated sidelobe-to-mainlobe ratio and waveform energy over the space-frequency bands. Since the resulting problem is NP-hard, two approximated primal-dual algorithms based on the alternating direction method of multipliers algorithm are proposed. Concretely, the proposed algorithms update the primal variable by minimizing the linearized and quadratic approximations of the augmented Lagrangian function of the original problem, respectively. The closed-form solution of each primal variable is provided. Moreover, both the convergence and computational complexities of these two algorithms are discussed. Numerical simulations are provided to demonstrate the effectiveness of the proposed approaches.

Transceive Beamforming With Accurate Nulling in FDA-MIMO Radar for Imaging

Abstract: Beamforming plays a crucial role in synthetic aperture radar (SAR) for interference mitigation and ambiguity unaliasing. In this article, a series of novel beamforming methods for SAR systems is proposed based on nulling the transceive beampattern accurately in frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) scheme. In general, these methods are implemented by assigning artificial interferences with prescribed powers within the given rectangular regions in the joint transmit–receive spatial frequency domain. In specific, according to the predefined null depths, closed-form expressions of artificial interference powers are first formulated. Then, iteration algorithms are developed to update the interference-plus-noise covariance matrix and the designed weight vector. In such a way, a trough-like transceive beampattern with arbitrarily distributed broadened nulls is formed in the joint transmit–receive spatial frequency domain. As a result, interferences mixed in signals received by SAR can be suppressed effectively. Numerical simulations and experimental results are provided to corroborate the effectiveness of the proposed methods.

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Abstract: Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS).

A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization

Abstract: Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.