Remote sensing image captioning (RSIC) is an important task in environmental monitoring and disaster assessment. However, existing methods are constrained by redundant feature interference, insufficient multi-scale feature integration, and cross-modal semantic gaps, leading to limited performance in scenarios requiring fine-grained descriptions and semantic integrity, such as disaster assessment and emergency response. In this letter, we propose a Cross-Modal Semantic Alignment Model for Remote Sensing Image Captioning (CSA-RSIC), addressing these challenges with three innovations. First, we designed an Adaptive Feature Selection Module (AFSM) that generates channel weights through dual pooling. The AFSM dynamically weights the most informative features at each scale to improve caption accuracy. Second, we propose a Cross-Scale Feature Aggregation Module (CFAM) that constructs a hierarchical feature pyramid by aligning multi-scale resolutions and performs attention-guided fusion with enhanced weighting via AFSM, ensuring the effective integration of fine-grained and global semantic information. Finally, a novel loss function that combines contrastive learning and consistency loss is proposed to enhance the semantic alignment between visual and textual features. Experiments on three datasets show the advancement of CSA-RSIC over strong baselines, indicating its effectiveness in enhancing both semantic completeness and accuracy.

Physical Layer Authentication (PLA) emerges as a promising security solution, offering efficient identity verification for the Internet of Things (IoT). The advent of 5G/6G technologies has ushered in an era of extensive device connectivity, diverse networks, and complex application scenarios within IoT ecosystems. These advancements necessitate PLA systems that are highly secure, robust, capable of online processing, and adaptable to unknown channel conditions. In this paper, we introduce a novel two-stage PLA framework that synergizes channel prediction with power-delay attributes, ensuring superior performance in mobile and time-varying channel environments. Specifically, our approach employs Sparse Variational Gaussian Processes (SVGP) to accurately model and track real-time channel variations, leveraging historical data for online predictions without incurring significant computational or storage overhead. The second stage of our framework enhances the robustness of the authentication process by incorporating power-delay features, which are inherently resistant to temporal fluctuations, thereby eliminating the need for additional feature extraction in noisy settings. Moreover, our authentication scheme is designed to be distribution-agnostic, utilizing Kernel Density Estimation (KDE) for non-parametric threshold determination in hypothesis testing. Theoretical analysis underpins the generalization capabilities of our proposed method. Simulation results in mobile scenarios reveal that our two-stage PLA framework reduces complexity and significantly improves identity authentication performance, particularly in scenarios with low signal-to-noise ratios.

Remote sensing images encompass a multitude of targets with varying scales and lower resolutions, posing significant challenges for remote sensing image captioning tasks. To fully extract and leverage image features, this paper proposes a multi-scale feature extraction network that enhances the representational capacity of features by integrating different scales, enabling more accurate identification and description of targets. Additionally, we designed a Small Target Attention module to further enhance the network’s sensitivity to densely distributed and small-sized targets. Extensive experiments conducted on three publicly available datasets demonstrate that the proposed method outperforms the compared methods in capturing key information. Moreover, it shows better performance when processing remote sensing images with lower resolutions and small-sized targets.

Cell-free network and intelligent reflecting surface (IRS) are considered as promising technologies for future network capacity and coverage improvement. They offer advantages such as low cost, low energy consumption, and meeting the requirements of green communication. However, the fixed location of IRS limits the flexibility of the entire network. To address this issue, we propose a more comprehensive cell-free network that enhances network capacity and signal coverage by utilizing the reflected signals from an airborne IRS. Our objective is to maximize the weighted transmission rate for users by jointly optimizing the base station (BS) beamforming, passive beamforming of the IRS, and the location of the UAV. Owing to the non-convex and intricate nature of the problem, we decompose it into three subproblems, employing the principles of Lagrangian duality, multi-ratio fractional programming, and the successive convex approximation (SCA) technique for resolution. Simulation results demonstrate that the proposed scheme can significantly improve the weighted transmission rate and effectively enhance the network coverage compared to the benchmarks.

Integrated sensing and communication (ISAC), as an emerging technology for 6G, raises a critical security issue that the sensing waveform may expose the private information to suspicious detection targets. In this paper, we design to utilize intelligent reflecting surface (IRS) in ISAC to enhance the secure transmission for non-orthogonal multiple access nodes, and establish an additional line-of-sight link for the detection. An IRS-aided secure transmission scheme is proposed to jointly optimize the jamming, the active transmit precoding at the base station and the passive phase reflecting at the IRS to maximize the sum secrecy rate, subject to the echo signal requirement towards the target. To address the non-convexity of the proposed problem, it is decomposed into two subproblems, enabling the optimization of the transmit jamming and precoding vectors and the phase reflecting matrix, respectively. Then, with the help of successive convex approximation, these subproblems are derived to be convex, and an alternating optimization algorithm is introduced to address the original problem. Simulations verify that the proposed scheme significantly outperforms the benchmarks, and can guarantee the sensing functioning while greatly enhancing the communication security.

Water vapor over oceans is integral to climate research, weather prediction, and various scientific disciplines. Owing to challenges in deploying in situ instruments, water vapor over oceans is predominantly measured using satellite-borne sensors such as the satellite-borne scanning microwave radiometer (SMWR) and satellite-borne optical imager (SOI). Nevertheless, evaluations of satellite-based remote sensing of atmospheric water vapor over oceans using alternative independent techniques remain limited. In this study, we examine the performance of satellite-borne sensors in measuring water vapor over oceans using shipborne global navigation satellite system (GNSS) precipitable water vapor (PWV) from 2014 to 2021. The comprehensive evaluation of satellite-based PWV over oceans reveals that SMWR PWV outperforms SOI PWV by approximately 2 mm in root-mean-square (rms). Among all satellite-borne SMWRs, Fengyun (FY)-3 C Microwave Radiation Imager-1 exhibits superior agreement with a mean value of 0.26 mm and an rms of 2.20 mm. Both SMWRs and SOIs exhibit diminished agreement in wetter areas, especially for SOIs, attributable to heightened sensitivity to cloudy and moist weather conditions. Temporally, most satellite-borne sensors demonstrate stable performance in PWV retrieval over oceans, with no apparent observation drift. In addition, MODIS exhibits slightly better stability performance compared to SMWRs. The inter-technique validations affirm the elevated accuracy and stability of satellite-based PWV over oceans. Nonetheless, long-term calibration and refinement of algorithms remain imperative, particularly in tropical regions.

Integrated sensing and communication (ISAC), as an emerging technology for 6G, raises a critical security issue that the sensing waveform may expose the private information to suspicious detection targets. In this paper, we design to utilize intelligent reflecting surface (IRS) in ISAC to enhance the secure transmission for non-orthogonal multiple access nodes, and establish an additional line-of-sight link for the detection. An IRS-aided secure transmission scheme is proposed to jointly optimize the jamming, the active transmit precoding at the base station and the passive phase reflecting at the IRS to maximize the sum secrecy rate, subject to the echo signal requirement towards the target. To address the non-convexity of the proposed problem, it is decomposed into two subproblems, enabling the optimization of the transmit jamming and precoding vectors and the phase reflecting matrix, respectively. Then, with the help of successive convex approximation, these subproblems are derived to be convex, and an alternating optimization algorithm is introduced to address the original problem, which is guaranteed to converge. Simulations verify that the proposed scheme significantly outperforms the benchmarks, and can guarantee the sensing functioning while greatly enhancing the communication security.

Currently, deep learning methods have been successfully applied to soil property content estimation from soil spectra due to their powerful feature extraction capability. In practical production, it is necessary to estimate the contents of multiple soil properties simultaneously. The accuracy of such estimation heavily depends on the ability of the algorithm to balance multiple estimation tasks. In this letter, a multi-task learning network combining attention mechanism and loss-weight balancing approach based on feature correlation is proposed. First, a parameter-sharing module of a three-layer convolutional neural network (CNN) is constructed. Second, an independent channel importance recalculation module is constructed for each estimation task, which consists of an efficient channel attention (ECA) module. Finally, the features extracted from these two modules are concatenated, and a two-layer CNN is constructed to further extract features for estimating each soil component. Moreover, an improved loss-weight uncertainty technique based on the correlation between soil spectra and property contents is proposed to reconcile the learning effects of multiple estimation tasks. The experimental results on two soil datasets, LUCAS (Land Use/Land Cover Area Frame Survey) 2009 and AfSIS (Africa Soil Information Service), show that this method provides competitive accuracy compared with several state-of-art methods.

Ocean surface winds play an essential role in regulating the earth’s weather and climate, and the Cyclone GNSS (CYGNSS) mission launched in 2016 is designed specially to monitor the ocean wind speed. In this study, an innovative model is developed based on a deep learning method to retrieve the ocean wind speed by making full use of the spatiotemporal information of CYGNSS observations. The proposed model named CNN-LSTM is established based on two modules, i.e., the Convolution Neural Network (CNN) module that extracts the spatial features around the Specular Point (SP) from a Two-Dimensional matrix of delay-Doppler Map (DDM) and the Long Short-Term Memory (LSTM) module which extracts the temporal features over a time series. The performance of the ocean wind speed derived from CNN-LSTM is assessed with the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) products. The results show that the wind speed derived from CNN-LSTM reveals an accuracy of 1.34 m/s in terms of root mean square error (RMSE) values, showing an improvement of about 36.8%, 14.6%, 6.3%, when compared to the official retrieval algorithm called Minimum Variance Estimator (MVE), Multilayer Perceptron (MLP) net, and the CNN, respectively, confirming the feasibility and effectiveness of the designed method. Among all the experiments in this study which apply machine learning-based algorithms, the wind speed achieved by CNN-LSTM presents the smallest RMSE value. Furthermore, the error analyses of the wind speed retrieval in spatial and temporal scale are also discussed, which indicate the robust performance of CNN-LSTM model. The results show that the CNN-LSTM model proposed in this study contributes to offering efficient processing of Global Navigation Satellite Systems Reflectometry (GNSS-R) observations and fully exploits the capabilities of high-accurate ocean wind speed retrieval on a global scale.

The fifth-generation Internet of Things (5G-IoT) requires more capable channel coding methods to obtain low-latency and high-reliability communication systems. Hence, an improved ordered tree-based single-parity-check (OT-SPC) list decoder for polar code is designed in this article to reduce the complexity and keep the reliability. The proposed method introduces the ordered error pattern set of the single-parity-check (SPC) list decoder, which collects all the necessary error patterns. We first define a relationship between these patterns and present the concepts of the necessary error pattern and the ordered error pattern set. Then, we adapt the ordered tree data structure and the asynchronous tournament sorters to relieve the complexity increase of the ordered error pattern set’s introduction. The reliability relationships of different patterns are stored in an ordered tree, and the tournament sorting algorithm is modified separately for the log-likelihood ratio values of the received sequence and the path metrics of the decoding paths. In this scheme, the candidate codewords of the SPC list decoder are constructed in the descending order of their probability, which significantly reduces the calculation and comparison consumption. Eventually, the results show that the OT-SPC list decoder obtains a superior performance in decoding throughput and latency while preserving the error–correction performance.

Rate-splitting multiple access (RSMA) is an emerging multiple access strategy, with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) as its two special cases. RSMA divides the messages required by users into the private and common parts, and the private streams are naturally suitable for the secure transmission. In this paper, we establish a unified rate-splitting framework to ensure the secure transmission for the three multiple access systems mentioned above. The precoders at the multi-antenna transmitter are conjointly optimized to improve the transmission rate of common message. Successive interference cancellation (SIC) is utilized, and the private message for the downlink broadcasting RSMA network can be effectively hidden in the high-power common message. Simulation results demonstrate that the proposed rate-splitting framework can effectively guarantee the secure transmission of the secrecy information.

At present, deep learning method relies on its strong feature extraction ability has been successfully applied to the estimation of soil organic carbon (SOC) content with hyperspectral data. However, due to the high dimensionality of hyperspectral data and equal treatment of all bands, the performance of these methods is hampered by learning features from useless bands. To address this issue, in this letter, attention mechanism is combined with convolutional neural network (CNN) to assign different weights to different bands of the hyperspectral data. This method constructs a three-layer CNN with a squeeze-and-excitation module at the front of it. Then, five attention-based CNNs are combined to establish an ensemble regression system with diversity. The inputs of each branch in this system are the original hyperspectral data and its transformed data. Moreover, an improved label distribution smoothing technique is proposed to address the problem of imbalanced samples. The experimental results on three soil datasets, LUCAS (Land Use/Land Cover Area Frame Survey) 2009, LUCAS2015 and AfSIS (Africa Soil Information Service), show that this method obtains good estimation performance compared with several state-of-art methods, especially in the areas with high SOC content which has small sample sizes.

Integrated sensing and communication (ISAC) is an up-and-coming technique for future 6G networks. However, the communication message carried by the detection waveform will face the risk of being eavesdropped, which leads to the challenge of wireless security for ISAC networks. In this paper, we leverage non-orthogonal multiple access (NOMA) to support more users for the ISAC network, with the precoding well designed to guarantee the security. Specifically, we formulate a joint precoding optimization problem to maximize the sum secrecy rate for multiple users via artificial jamming, where the superimposed signal for NOMA users can be concurrently employed for the target detection. Since the optimization problem is non-convex, it is transformed into a convex one based on successive convex approximation (SCA), where the Taylor’s approximation and second-order cone (SOC) constraint are further applied. Then, we propose an iterative algorithm, through which the original optimization problem can be solved effectively. Simulation results show that the proposed secure NOMA-ISAC scheme can guarantee the secure transmission while ensuring the sensing performance.

Water vapor over oceans is significant for numerical weather prediction (NWP) and climate research. Ocean platform-based global navigation satellite system (GNSS) which can sense the atmospheric water vapor is becoming an important supplement for water vapor measurements over oceans. However, the application of ocean platform-based GNSS meteorology is normally based on geodetic GNSS receivers, which implies the high cost of hardware. In this contribution, we investigate the potential of retrieving real-time water vapor over oceans with a low-cost receiver (u-blox F9P), and a geodetic GNSS receiver (Trimble NetR9) is also equipped in the experiment vessel. The post-processed Trimble NetR9 zenith total delay (ZTD) estimates and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 precipitable water vapor (PWV) products are used for the validation of real-time ZTDs and PWV values. The results show that the real-time ZTDs derived from the low-cost multi-GNSS (GPS + Galileo) observations obtain a difference of over 2.13 cm in root-mean-square (RMS) compared to the post-processed ZTDs with an averaged initialization time of approximately 40 mins. In addition, compared to ERA5 PWV, the real-time PWV derived from u-blox F9P multi-GNSS observations shows a difference in RMS of approximately 4 mm. Although u-blox F9P multi-GNSS performs relatively worse than Trimble NetR9 multi-GNSS in real-time ZTD/PWV estimates, the accuracy of low-cost GNSS receivers derived water vapor over oceans can still meet the requirements for NWP and nowcasting, which demonstrates promising prospects in supplementing the measurements of water vapor over oceans.

The wet tropospheric correction (WTC) retrieved from the onboard calibration microwave radiometer (CMR) of Haiyang-2A (HY-2A) is critical in monitoring the global sea level. However, the CMR WTC became significantly biased from June 2017 due to the failure of the 18.7-GHz band, which caused massive errors in the sea surface height (SSH) measurements. We investigate the accuracy of the CMR WTC derived from the two remaining bands to address this problem. A comprehensive evaluation using multisource data demonstrates that the dual-band + backscattering coefficient (BC) algorithm achieves comparable accuracy to the three-band algorithm, and it does not suffer from any large errors when the equipment works well. Hence, we calibrated the HY-2A CMR data with the dual-band + BC algorithm when the 18.7-GHz band failed, and the accuracy of the CMR WTC is improved from 2.34 to 1.39 cm compared with European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 data. In addition, the SSH measurements are improved significantly by a maximum of 2 cm in mean value using the dual-band + BC WTC during the failure period of HY-2A CMR. Compared with Jason-3 SSH measurements, the HY-2A with dual-band + BC shows a slightly larger difference than HY-2A with three-band by 0.1 cm in rms. This method prolongs the operational lifetime of the HY-2A CMR and could be used in the reprocessing of HY-2A observations.

Atmospheric water vapor plays an essential role in climate change and weather forecasting. However, monitoring water vapor with high spatial and temporal resolutions remains a challenge, especially over ocean regions where observations are insufficient. Shipborne global navigation satellite systems (GNSSs) contribute to enriching water vapor measurements over oceans and also can help validate satellite observations. Due to the lack of long-time serial observations, the performance of shipborne GNSS-derived precipitable water vapor (PWV) is inadequately evaluated on the global ocean scale. In this study, an overall assessment of shipborne GNSS PWV over global oceans is performed based on six voyages from 2014 to 2018. In coastal areas, the PWV differences of shipborne GNSS with respect to (w.r.t.) ground-based GNSS and ground-launched radiosonde data are 2.64 and 2.85 mm in the root mean square (rms), respectively. In open oceans, compared to ship-launched radiosonde profiles and satellite measurements, shipborne GNSS PWV shows the rms of differences of 2.54 and 2.53 mm, respectively. In addition, the rms of PWV differences between the whole track of shipborne GNSS PWV and National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) products is 2.96 mm. The intertechnique validations demonstrate that the accuracy of shipborne GNSS PWV is superior to 3 mm, which meets the requirements of climate research and numerical weather prediction (NWP).

Validating the wet path delay (WPD) is essential to determining satellite microwave radiometer errors that may deteriorate sea level measurement accuracy. The global navigation satellite system (GNSS) is a favored method for assessing WPD for altimetry. Over Chinese coastal waters, however, validations have not been performed, primarily because of the large gaps between International GNSS Service sites. Herein, we report a long-term assessment of the Jason-2/3 radiometers and the initial performance of the Chinese HY-2B radiometer via comparisons of WPD values derived from three types of GNSS networks along the Chinese coast. A new method based on the second-order derivative of WPD along satellite tracks was developed to detect where land contamination first appears. The results indicate that this method is more sensitive to land influences; the distance at which land contamination first appears was estimated to be approximately 40–50 km over the China coast sea. To compare the WPD at different heights, such as GNSS sites and sea level surface, an exponential function containing the corresponding decay coefficients was adopted. The WPD decay coefficients for each GNSS site were recalculated using the 3-D ERA5 pressure level fields instead of the empirical value of 2000. The results show that the new coefficient may reduce the differences between WPD values at different heights. This WPD comparison over Chinese coastal waters indicates average WPD differences of 3 (Jason-2) and −1 mm (Jason-3), showing a high level of consistency between the GNSS sites and the satellite radiometers. The uncertainties in the WPD differences were estimated to be 14–21 and 7–24 mm for the Jason-2/3 and HY-2 radiometers, respectively, which is consistent with previous studies using global GNSS. After removing the GNSS uncertainty and the spatial decorrelation, the WPD uncertainties of the Jason-2, Jason-3, and HY-2 radiometers over the regional area were estimated to be 13, 14, and 11 mm, respectively. The long-term evaluation of the WPD differences with the GNSS sites indicates that the drift rates of the Jason-2/3 radiometers over China coast waters are 0.3 and −0.9 mm/y, respectively.

Since the content of soil organic carbon (SOC) is significantly correlated with the soil reflection spectrum, hyperspectral remote sensing technology provides an effective new choice for the estimation of soil properties. At present, deep learning method relies on its strong feature extraction ability and has been successfully applied to the field of data analysis. This paper attempts to apply the deep learning method to the estimation of SOC content and proposes a method combining the convolutional neural networks (CNN) and quantile regression (QR). This method constructs a three-layer CNN and adjusts the network structure with the idea of QR. The experimental results are presented for two soil datasets, LUCAS (Land Use/Land Cover Area Frame Survey) and AfSIS (Africa Soil Information Service), and compared with several advanced deep learning and traditional machine learning regression models. The experimental results show that this method performs well in estimation.

The security of wireless information transmission in large‐scale multi‐input and multioutput (MIMO) is the focus of research in wireless communication. Recently, a new artificial noise—SCO‐AN which shows no orthogonality to the channel, is proposed to overcome the shortcomings of traditional artificial noise. In the previous research, the optimization function of SCO‐AN is not convex, and its extremum cannot be obtained. Usually, nonconvex optimization algorithms or iterative relaxation algorithms are used to get the maximum value of the optimization objective function. Nonconvex optimization algorithms or iterative relaxation algorithms are greatly affected by the initial value, and the extremum cannot be obtained by a nonconvex optimization algorithm or iterative relaxation algorithm. In this paper, we creatively apply the strong law of large numbers to obtain the optimal value of the optimization function of SCO‐AN under the condition of large‐scale MIMO: the strong law of large numbers is applied to obtain the ergodic lower bound (ELB) expression of SC for SCO‐AN. The power allocation (PA) problem of the SCO‐AN system is discussed. We use a statistical method to get the formula for calculating the optimal power distribution coefficient of the SCO‐AN system. The transmitter can use the optimal power ratio of PA to distribute the transmitted power without using the PA algorithm. The effect of imperfect channel state information is discussed. Through simulation, we found that more power should be generated for SCO‐AN if the channel estimation is imperfect and the proposed method can achieve better security performance in the large‐scale MIMO system.

Ultra-wideband (UWB) technology is a potential candidate solution for Wireless Sensor Network (WSN) and Internet of Things (IoT) applications due to the advantages of high-speed communication and accurate positioning. Channel estimation plays a key role in UWB systems, especially in low signal-to-noise ratio (SNR) environments. In this paper, a noise-resistance channel estimation method based on compressed sensing is proposed for accurate estimation of UWB channels contaminated by noise. The proposed algorithm based on compressed sensing relies on the fact that the multipath signals of UWB have a sparse representation in the time domain or other domains. However, the reconstruction of multipath signals is difficult owing to the additive noise and noise-folding effect. In order to improve the noise robustness, UWB multipath signals are sparse representation by using the orthogonal basis of an eigen-based dictionary. Three approaches are applied to the proposed noise-resistance algorithm, including average pilot noise reduction, denoising whitening measurement, and sparsity adaptive matching pursuit (SAMP) algorithm. The improved relative residue is proposed to calculate the iteration termination condition of SAMP. Simulation results demonstrate that the algorithm provides good noise-resistance performances in terms of normalized mean square error (NMSE) of channel estimation and the bit error rate of Rake receivers, especially in low SNR environments.

In cognitive radio network, the licensed spectrum for the primary user can be accessed in an opportunistic manner by secondary user, or unlicensed user. As a key technology of cognitive radio, spectrum sensing has an irreplaceable position. In this paper, we proposed a parallel CNN-LSTM network based deep learning algorithms for spectrum sensing. As much modulated signals and noise data as possible are generated to train the model to accommodate detection of multiple types signal. Various experiments are performed to prove the effectiveness of proposed method, and requiring no prior knowledge about the information of licensed user or channel state. The simulation results show that the model can detect multiple modulation types under a large scale of SNRs, especially in low SNR.

The research for co-channel multi-signal modulation classification has become urgent with the increasing shortage of spectral bandwidth. Single-signal modulation classification methods which have been widely studied are not applicable for co-channel multi-signal modulation classification problem. In this paper, we developed a method for co-channel multi-signal modulation classification based on Convolution Neural Network(CNN). The proposed method can identify 31 mixed signals from 5 modulation types. The proposed method are also found to be robust to the changes of SNR from 0dB to 15dB. The experiments are performed to prove the effectiveness of the proposed method.

Modern intelligent transport systems focus on the integration of multiple sensors to obtain hybrid navigation schemes. A key issue of a hybrid scheme is distribution of the information sharing coefficients (ISCs) of subsystems and the fusion of parallel multiple observations of navigation sensors. Recently, deep learning methods, particularly convolutional neural networks (CNNs), have achieved great success in image processing tasks. However, there has been limited work in using deep learning for multisensor-based integrated navigation solutions. In this letter, we propose an ensemble learner-based classification and information fusion method, in which estimation error covariance matrices provided by local adaptive filters are used as input for the classifier, and the triple numbers of ISCs are determined by the proposed scheme. The results validate the effectiveness of the proposed scheme, in which the adequately trained ensemble learner can detect the degradation of a subsystem that may suffer atypical observations or faults and consequently can adjust the corresponding ISC in real time.

CFAR is a widely used algorithm for target detection in SAR images. The simplicity of computation and stable performance make it a key role in practice. However, for man-made target detection, conventional CFAR has a limited performance because of the non-adaptive processing window and the varieties of categories, sizes and structures of targets. In order to detect man-made targets with different size and complex structures in high resolution SAR images, an improved CFAR algorithm with an adaptive processing window named Adaptive-Window CFAR is proposed. A global guard window obtained by pre-detection adaptively is used to take place of the guard window in conventional CFAR makes AW -CFAR an algorithm with both adaptive threshold and adaptive processing window. In this case, the size of detectable targets is not fixed anymore and targets of different sizes and complex structures are detectable in AW-CF AR. Images with different resolutions and environments, which contain different categories of man-made targets, are used in the experiments. The experimental results show that the AW -CFAR inherits the simplicity of computation and stable performance of the conventional CFAR and has a better performance of man-made target detection in high resolution SAR images.

Automatic modulation classification (AMC) plays an important role in cognitive radio and is widely studied recent years. However, most existing AMC schemes must be deployed under their training SNRs, which makes them highly dependent on the accuracy of channel estimation. The classifiers may need to be re-trained to fit the varying channel condition. To address this problem, a feature combination method aiming to find noise-robust features under fading channels is proposed in this paper. Stacked auto encoder is deployed to explore robust features from an extracted feature set, and these new features is then used to train a support vector machine (SVM). Numerical results shows that the generalization ability of SVMs trained with new features can be significantly improved; therefore the method is robust to SNR variation.

Various vehicle applications in the future will require reliable and accurate vehicle positioning techniques. Nowadays, hybrid schemes combining multiple sensors have been promising solutions for high precision positioning. However, positioning error can be remarkably affected by the temporal alignment and fusion algorithms in practice. In this paper, we propose a decentralized fusion structure containing an inertial navigation system (INS), a GPS receiver, a RFID reader, and an odometer. The update rates of the sensors are different, and the INS/GPS integration presents severe performance degradation in urban area. To achieve an effective alignment and fusion of the sensors, we propose a concept of confidence field to indicate the confidence levels of subsystems for changing driving environments. A confidence field-based alignment and fusion algorithm and its simplification are proposed when we use the weighted least squares curve method. Time biases of the sensors are also considered in local adaptive filters. Simulation results demonstrates the performance of the proposed scheme with the proposed algorithms, especially in GPS- denied environments.

Digital self-interference cancellation is a potential technique for full-duplex (FD) communication system. However, for digital self- interference (SI) cancellation, limited dynamic range of analog-to-digital converters (ADCs) is a severe challenge. Against this background, a dynamic range (DR) expanding scheme for ADC based on detail information compensation is proposed in this paper, in order to improve the DR of ADCs. Moreover, channel estimation is performed on the basis of half-duplex (HD) training for digital SI cancellation. Analytical and simulation results demonstrate the effectiveness of the proposed scheme as a viable solution for full-duplex communication system.

With the capacity of overcoming radio spectrum shortages for wireless communications in smart grids, cognitive radio enabled Advanced Metering Infrastructure (CR-AMI) networks are expected to enhance the efficiency and practicability of future smart grids. As an integral component of the smart grid ecosystem, CR-AMI networks are practically deployed as a static multi-hop wireless mesh network. This paper focuses on the investigation of an novel RPL-based routing protocol for enhancing the energy efficiency in CR-AMI networks. In accordance with practical requirements of green communications in smart grids, the proposed routing protocol adopts the energy efficiency over virtual distance as the core of routing mechanism such that the energy-efficient route can be achieved. In addition, the protocol has the mechanism for primary (licensed) users protection whilst meeting the utility requirements of cognitive radio users. System-level evaluation shows that the proposed routing protocol has better performances compared with existing routing protocols for cognitive radio- enabled AMI networks.

In this paper, a Differential Chaos Shift Keying system based on Generalized Frequency Division Multiplexing (GFDM-DCSK) is proposed to achieve a system that has higher energy efficiency than DCSK system, solves the delay problem of DCSK system and decreases the decoding complexity of the GFDM system. Neither channel state information nor channel estimation circuits are needed at the receiver side. Numerous simulation results show that the proposed GFDM-DCSK system has better BER performance than DCSK system.

The performance of Global Satellite Positioning System / Inertial Navigation System (GPS/INS) integrated navigation system based on Kalman Filter (KF) is greatly influenced by measurement information related to GPS. However, it can be unreliable: it can be lost and the statistical characteristics of the measurement noise can change. Thus, the performance of navigation will get worse. Therefore, a filter algorithm for the integrated navigation system based on the Interacting Multiple Model-Adaptive Filter (IMM-AF) is proposed in this paper. Two measurement noise models for small Gaussian noise and non-small Gaussian noise are designed respectively to be applied to the algorithm; one step prediction algorithm for the case of GPS signal loss is also combined. The results of the experiment of the integrated navigation system of mobile robot show that, compared with KF or IMM, IMM-AF algorithm presents higher accuracy and better robustness, with almost the same update time.

HEVC employs a quad-tree based Coding Unit (CU) structure to achieve a significant improvement in coding efficiency compared with previous standards. However, the computational complexity is greatly increased. We proposed a fast mode decision algorithm to reduce intra coding complexity. Firstly, an initial candidate list of intra modes is constructed for each Prediction Unit (PU). The prediction mode correlation between adjacent quad-tree coding levels and between temporal neighbouring frames is used to predict the most likely coding mode. The number of prediction mode that need to be evaluated in residual quad-tree (RQT) process is further reduced by taking the Hadamard cost of prediction mode into consideration. Simulation results show that the proposed algorithm saves encoding time by up to 51% compared with the HM 13.0 implementation, while having a negligible impact on rate distortion.

High Efficiency Video Coding (HEVC) incorporates a flexible quad-tree block partitioning scheme and up to 35 prediction modes for intra coding. This enables a significant improvement in coding efficiency compared with previous standards. The superior coding efficiency of HEVC is achieved at the expense of greatly increased complexity. A fast Coding Unit (CU) depth decision algorithm is proposed to reduce the computational requirement for intra coding. An adaptive double thresholds scheme is employed to classify the homogeneity of video content. The classification is used to reduce the number of Rate Distortion (RD) evaluations in the CU depth decision process. The partition information of the temporally co-located CU and the spatially neighbouring CUs is jointly utilised to further narrow the depth range that needs to be evaluated. The computational complexity of HEVC intra coding is therefore reduced. Simulation results show that the proposed algorithm reduces encoding time by up to 57% compared with the HM 13.0 implementation, while having a negligible impact on rate distortion, with PSNR losses of 0.01dB and increases in bit-rate of 0.31%.

Summary form only given. Grid-based model is used to describe 3D objects in many circumstances. It can represent fine structure of objects by using small grid. However, small grid causes problem that data of grid-based model occupies much space, which leads to difficulties of transmission and storage. This paper presents an effective compression method for 3D data. In this method, 8-byte float coordinates of each grid are transferred to 1-bit binary codes. Then the binary data is coded by octree further. The experiment result shows that the 3D data can be efficiently compressed. The method is lossless. The complete raw data can be obtained by decoding.

Traffic sign classification is an important function for driver assistance systems. In this paper, we propose a hierarchical method for traffic sign classification. There are two hierarchies in the method: the first one classifies traffic signs into several super classes, while the second one further classifies the signs within their super classes and provides the final results. Two perspective adjustment methods are proposed and performed before the second hierarchy, which significantly improves the classification accuracy. Experimental results show that the proposed method gets an accuracy of 99.52% on the German Traffic Sign Recognition Benchmark (GTSRB), which outperforms the state-of-the-art method. In addition, it takes about 40 ms to process one image, making it suitable for realtime applications.

We present a traffic sign detection method which has won the first place for the prohibitory and mandatory signs and the third place for the danger signs in the GTSDB competition. The method uses the histogram of oriented gradient (HOG) and a coarse-to-fine sliding window scheme. Candidate ROIs are first roughly detected within a small-sized window, and then further verified within a large-sized window for higher accuracy. Experimental results show that the proposed method achieves high recall and precision ratios, and is robust to various adverse situations including bad lighting condition, partial occlusion, low quality and small projective deformation.

In this paper, we propose a signal superposition layer multiplexing for the scalable video codec (SVC) coded video wireless broadcasting system. The SVC coded video layers are grouped into various groups and each group is encoded independently into a signal layer. The various signal layers are multiplexed using signal superposition and then transmitted. We introduce successive group decoder, originally used for interference channels, to decode the multiple signal layers from the received superimposed signals. Based on the optimal decoding order of the successive group decoder, we propose the signal layer allocation which guarantees that each receiver first decodes the signal layer encoded from the SVC base layer and then decodes those encoded from the SVC enhancement layers. From simulation results, the signal superposition SVC layer multiplexing shows significant peak signal-noise-ratio (PSNR) improvement for the reconstructed video, compared with the orthogonal channel SVC layer multiplexing, e.g., the time division multiple access (TDMA).

Polarimetric SAR (POLSAR) can offer more information than single-polarized SAR that has the capability to image in all weather and day-night conditions. The multi-polarization mode and wide swath requirements result in a huge amount of data, which may exceed the on-board storage and downlink bandwidth. Effective compression of POLSAR raw data is clearly one of the best solutions. This paper presents a compression method for POLSAR raw data with the relative phase between co-polarized and cross-polarized channel being reserved. In this method, the amplitude is quantized for Rayleigh distribution with the phase of HH and VV channel quantized for uniform distribution. In order to reserve relative phase information, the phase difference between co-polarized (HH and VV) and cross-polarized (HV and VH) channel is optimally quantized for triangular distribution. Results show that by quantizing the phase difference, the relative phase information between HH (VV) and HV (VH) channel can be well preserved.

3D city models typically consist of thousands of buildings in different types. We usually reconstruct these buildings automatically from high-resolution satellite or airborne imagery. However, for detailed roof reconstruction, 2D information offered by imagery data is not enough while DSM data is necessary. In this paper, we propose a novel level set framework for 3D building models in LOD2 with geometry structure of typical roofs. Local information is introduced towards multiphase and multichannel level set method. Its energy function is minimized when each part of roof data corresponds to the same normal vector as feature values for level set segmentation. The advantage of this method is that for complex building models, roof primitives as well as roof topology graph can be extracted from high-resolution DSM data with high accuracy, evaluated by completeness of segmentation and RMSE of 3D reconstruction. Thus, LOD2 building models can be reconstructed automatically with good performance. The very promising experimental results demonstrate the potentials of our method for large-scale building reconstruction in LOD2.