Fei Wang

Bio: Fei Wang is an academic researcher from Nanjing University of Aeronautics and Astronautics. The author has contributed to research in topics: Low probability of intercept radar & Radar. The author has an hindex of 16, co-authored 110 publications receiving 1146 citations. Previous affiliations of Fei Wang include Nanjing University.

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

Abstract: The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

Power Minimization-Based Robust OFDM Radar Waveform Design for Radar and Communication Systems in Coexistence

Abstract: This paper considers the problem of power minimization-based robust orthogonal frequency division multiplexing (OFDM) radar waveform design, in which the radar coexists with a communication system in the same frequency band. Recognizing that the precise characteristics of target spectra are impossible to capture in practice, it is assumed that the target spectra lie in uncertainty sets bounded by known upper and lower bounds. Based on this uncertainty model, three different power minimization-based robust radar waveform design criteria are proposed to minimize the worst-case radar transmitted power by optimizing the OFDM radar waveform, which are constrained by a specified mutual information requirement for target characterization and a minimum capacity threshold for communication system. These criteria differ in the way the communication signals scattered off the target are considered: 1) as useful energy, 2) as interference, or 3) ignored altogether at the radar receiver. Numerical simulations demonstrate that the radar transmitted power can be efficiently reduced by exploiting the communication signals scattered off the target at the radar receiver. It is also shown that the robust waveforms bound the worst-case power-saving performance of radar system for any target spectra in the uncertainty sets.

Joint Target Assignment and Resource Optimization Framework for Multitarget Tracking in Phased Array Radar Network

Abstract: In this article, a joint target assignment and resource optimization (JTARO) strategy is proposed for the application of multitarget tracking in phased array radar network system. The key mechanism of our proposed JTARO strategy is to employ the optimization technique to jointly optimize the target-to-radar assignment, revisit time control, bandwidth, and dwell time allocation subject to several resource constraints, while achieving better tracking accuracies of multiple targets and low probability of intercept (LPI) performance of phased array radar network. The analytical expression for Bayesian Cramer–Rao lower bound with the aforementioned adaptable parameters is calculated and subsequently adopted as the performance metric for multitarget tracking. After problem partition and reformulation, an efficient three-stage solution methodology is developed to resolve the underlying mixed-integer, nonlinear, and nonconvex optimization problem. To be specific, in Step 1, the revisit time for each target is determined. In Step 2, we implement the joint signal bandwidth and dwell time allocation for fixed target-to-radar assignments, which combine the cyclic minimization algorithm and interior point method. In Step 3, the optimal target-to-radar assignments are obtained, which results in the minimization of both the tracking accuracy for multiple targets and the total dwell time consumption of the network system. Simulation results are provided to demonstrate the advantages of the presented JTARO strategy, in terms of the achievable multitarget tracking accuracy and LPI performance of phased array radar network.

Joint Subcarrier Assignment and Power Allocation Strategy for Integrated Radar and Communications System Based on Power Minimization

Abstract: This paper investigates the problem of power minimization-based joint subcarrier assignment and power allocation (PM-JSAPA) for an integrated radar and communications system (IRCS). In the considered system, the integrated transmitter is responsible to fulfill both radar and communications purposes simultaneously. The proposed PM-JSAPA strategy aims to minimize the total radiated power of the IRCS by jointly optimizing the available subcarriers and power resource allocation in order to achieve improved power-saving performance, subject to a predetermined mutual information (MI) for target parameter estimation and a specified data information rate (DIR) for wireless communications. The formulated problem falls into a mixed integer nonlinear programming (MINLP) problem, which is generally NP-hard. Then, an efficient three-step resource allocation framework is designed to solve the resulting optimization problem. Specifically, in Step 1, the subcarrier assignment for both radar and communications purposes is determined. In Step 2 and Step 3, we perform power allocation for different applications with the obtained subcarrier assignment results in Step 1, respectively. Finally, numerical simulation results are presented to demonstrate the superior power-saving performance of the proposed PM-JSAPA strategy and the effectiveness of the proposed solutions.

Joint Optimization Scheme for Subcarrier Selection and Power Allocation in Multicarrier Dual-Function Radar-Communication System

Abstract: Dual-function radar-communications (DFRC) system has been recognized as a promising solution to alleviate the radio frequency spectrum congestion and the shortage of spectrum resources. In this article, we address the problem of designing the joint subcarrier selection and power allocation scheme to minimize the power consumption of a DFRC system, under a general scenario in which the DFRC system is capable of performing a primary radar purpose and a secondary communications purpose in the meantime. In particular, the key mechanism is to minimize the total radiated power of the multicarrier DFRC system by jointly selecting the best possible subcarriers for radar and communications purposes in sequence and allocating the optimal power resource on the corresponding subcarriers, under the constraints of a predefined mutual information for target characterization and a desired communications data rate for information transmission. The resulting problem is formulated as a two-variable nonconvex optimization problem, one for subcarrier selection and the other for power allocation. Then, after convex relaxation reformulation and problem partition, an efficient three-step solution technique is developed for the joint optimization scheme, which combines the cyclic minimization algorithm and Karush–Kuhn–Tuckers optimality conditions. Finally, numerical results are provided to validate the theoretical findings and to verify the effectiveness of the proposed joint optimization scheme.

A Meteorological Distribution System for High Resolution Terrestrial Modeling (MicroMet)

Abstract: An intermediate-complexity, quasi–physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are distributed: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, surface pressure, and precipitation. To produce these distributions, MicroMet assumes that at least one value of each of the following meteorological variables are available for each time step, somewhere within, or near, the simulation domain: air temperature, relative humidity, wind speed, wind direction, and precipitation. These variables are collected at most meteorological stations. For the incoming solar and longwave radiation, and surface pressure, either MicroMet can use its submodels to generate these fields, or it can create the distributions from observations as part of a data assimilation procedure. MicroMet includes a preprocessor component that analyzes meteorological data, then identifies and corrects potential deficiencies. Since providing temporally and spatially continuous atmospheric forcing data for terrestrial models is a core objective of MicroMet, the preprocessor also fills in any missing data segments with realistic values. Data filling is achieved by employing a variety of procedures, including an autoregressive integrated moving average calculation for diurnally varying variables (e.g., air temperature). To create the distributed atmospheric fields, spatial interpolations are performed using the Barnes objective analysis scheme, and subsequent corrections are made to the interpolated fields using known temperature–elevation, wind–topography, humidity–cloudiness, and radiation–cloud–topography relationships.

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

Abstract: The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

Direction-of-Arrival Estimation for Coprime Array via Virtual Array Interpolation

Abstract: Coprime arrays can achieve an increased number of degrees of freedom by deriving the equivalent signals of a virtual array. However, most existing methods fail to utilize all information received by the coprime array due to the non-uniformity of the derived virtual array, resulting in an inevitable estimation performance loss. To address this issue, we propose a novel virtual array interpolation-based algorithm for coprime array direction-of-arrival (DOA) estimation in this paper. The idea of array interpolation is employed to construct a virtual uniform linear array such that all virtual sensors in the non-uniform virtual array can be utilized, based on which the atomic norm of the second-order virtual array signals is defined. By investigating the properties of virtual domain atomic norm, it is proved that the covariance matrix of the interpolated virtual array is related to the virtual measurements under the Hermitian positive semi-definite Toeplitz condition. Accordingly, an atomic norm minimization problem with respect to the equivalent virtual measurement vector is formulated to reconstruct the interpolated virtual array covariance matrix in a gridless manner, where the reconstructed covariance matrix enables off-grid DOA estimation. Simulation results demonstrate the performance advantages of the proposed DOA estimation algorithm for coprime arrays.

Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data

Abstract: Land surface temperature is an important factor in many areas, such as global climate change, hydrological, geo-/biophysical, and urban land use/land cover. As the latest launched satellite from the LANDSAT family, LANDSAT 8 has opened new possibilities for understanding the events on the Earth with remote sensing. This study presents an algorithm for the automatic mapping of land surface temperature from LANDSAT 8 data. The tool was developed using the LANDSAT 8 thermal infrared sensor Band 10 data. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature were compared. The results showed that, for the first case, the standard deviation was 2.4°C, and for the second case, it was 2.7°C. For future studies, the tool should be refined with in situ measurements of land surface temperature.