Given that Orthogonal time frequency space (OTFS) modulation provides high robustness to Doppler shifts, its integrated sensing and communications (ISAC) has attracted more attention. In this work, we investigate the fractional delay-Doppler (DD) estimation problem for the OTFS-based ISAC system. We establish a two-dimensional (2D) correlation structure with superimposed multiple frames and propose an efficient Maximum Likelihood algorithm based on multi-frame superposition (ML-MFS) that significantly reduces computational complexity. To further improve the estimation accuracy in multi-target scenarios, we develop an estimation method based on the whale optimization algorithm, named WOA-MFS. Simulation results demonstrate that both algorithms markedly reduce complexity, with the WOA-MFS demonstrating superior estimation accuracy.

Soil moisture detection research, which influences crop growth, land use, and soil erosion, is receiving significant attention. This study proposes a nondestructive, integrated ultrawideband (UWB)-based framework for soil moisture measurement and prediction. The method utilizes a UWB-loaded unmanned aerial vehicle (UAV) to gather radar echo data, circumventing soil damage issues inherent in current research and equipment. We first employ time-frequency analysis methods to convert the echo signals into 2-D spectrograms, constructing datasets labeled with soil moisture. Then, a trained neural network is used to predict the soil moisture at single point. Additionally, a novel interpolation method is proposed to enhance prediction accuracy (ACC) for the ridge-furrow structure of farmland. The experimental results demonstrate that the proposed algorithm achieves a soil moisture measurement ACC of 98% in both vegetated and nonvegetated conditions, indicating strong robustness. In terms of moisture distribution prediction, the mean squared error (mse) of soil moisture spatial distribution prediction is reduced by 42% compared to traditional methods. Therefore, this system provides technical support for efficient, large-scale, and nondestructive soil information collection.

Supervised machine learning technology has greatly improved the accuracy of radar target recognition based on HRRP signals. It relies on complete dataset labels, but the dataset is prone to noise labels due to the instability of data collection and the abstract nature of the HRRP signal itself. This problem will affect the model training robustness and testing accuracy. In this paper, we propose a noisy label learning method, "Contrastive Learning with or without Freeze, CLwF" to solve this problem. CLwF proposes a self-supervised learning algorithm to train models for efficient representation of HRRP signals. We also propose a clean rate estimation module to select the correct fine-tuning strategy for the model training with noisy labels. The experiment verified that CLwF can achieve excellent results under different noise rates.

In recent years, target recognition based on high-resolution range profile (HRRP) has been widely studied in the field of radar automatic target recognition. Compared with traditional methods, deep network model can automatically obtain the deep features of the target and is widely used, but expensive and time-consuming labeling is a difficult task. Inspired by the idea of contrastive learning, a self-supervised framework for learning target features based on double contrastive loss, namely contrastive learning with discriminant loss (CLDL) is proposed. Specifically, we minimize the absolute distance between positive sample pairs and maximize the absolute distance between negative sample pairs through constraints to ensure the proximity of two related samples and the discrimination of unrelated samples under the same HRRP signal. At the same time, the peak clipping strategy is proposed to preserve the useful information of the signal to the greatest extent during data augmentation. The experimental results show that the recognition performance of CLDL is better than the five mainstream unsupervised learning algorithms.

Modeling target scattering accurately from sensors is crucial for imaging and recognizing non-cooperative objects. This work proposes a data-driven back projection (DDBP) method for target scattering modeling. It performs BP on the data of multiaspect high-range resolution profiles (HRRPs) acquired from a radar sensor network within individual snapshots. The advantage of DDBP lies in its independence from Doppler or micro-Doppler features, ensuring robust imaging regardless of the target's motion state and exhibiting satisfactory performance even in time-critical application scenarios. In comparison with ISAR, the DDBP further exploits the spatial scattering energy integration characteristics, leading to enhanced imaging performance. Field experiment results validate the effectiveness of the proposed approach.

Radar sensor networks (RSNs) have more degrees of freedom than single radar system, and significantly improve the target angular resolution and parameter identifiability. This work proposes an intelligent waveform optimization strategy based on fuzzy Q learning (FQL) for multi-target tracking (MTT) of RSN in a cluttered environment. Specifically, this method adopts a distributed fusion architecture, each radar node independently detects and tracks multiple targets, and uses the covariance intersection (CI) fusion algorithm to solve the unknown correlation of each radar node. Combined with the target error covariance predicted by the Riccati equation, an FQL waveform optimization method is designed to select the best transmission from the waveform library. It integrates the radar and targets into a closed loop and updates the transmit waveform in real time according to the status of the targets to maximize global MTT performance. Simulation verifies that the proposed method’s tracking performance and CPU time are superior to the Q-learning.

The utilization of machine learning techniques has greatly improved the accuracy of micro-Doppler(m-D) signatures-based radar signal recognition. However, the "catastrophic forgetting" problem commonly exists in data-driven algorithms severely limits the adaptability of recognition algorithms in real-world applications, as models cannot incrementally train and learn new categories. In this paper, we propose an incremental learning method, "Boundary Transfer and Uncertainty Augmentation (BTUA)" for continuous learning of m-D signatures. BTUA utilizes boundary transfer(BT) to generate pseudo-decision boundaries for old categories and avoid the forgetting problem. It also employs an uncertainty augmentation(UA) algorithm to enhance the model’s generalization and improve the correctness of feature extraction. Finally, the validation demonstrates the advantages of our algorithm in terms of both accuracy and practicality.

The impulse-radio ultra-wideband (IR-UWB) radar has excellent range resolution and penetration, and excels in vital sign monitoring (i.e., respiratory and heart rate). At present, the majority of approaches merely split the echoes into several components and then manually localize the vital signs signal. In this paper, the maximum a posteriori (MAP) estimation-based joint expectation maximization (EM) and particle swarm optimization (PSO) (MEBJEP) method is proposed. Based on the MAP estimation, we combine the EM algorithm and PSO to estimate the frequency and amplitude of vital signs. The experimental results demonstrated that the proposed method is capable of capturing more reliable data in a non-contact way from a probabilistic perspective, compared to the commonly used chirp Z-transform and moving target indicator (CZT-MTI) method and the variational mode decomposition (VMD) method. This work illustrates the value of non-contact vital signs monitoring via IR-UWB radars in the body remote sensing field.

Cognitive radar automatically adjusts its waveform via ceaseless interaction with the environment and learning from the experience. The waveform development of cognitive radar has been attracting much attention in improving tracking performance. In this paper, we propose an intelligent radar target tracking strategy based on variable nonlinear frequency modulated waveforms (NLFM). The strategy considers the combination of constant velocity (CV), constant acceleration (CA), and constant turning (CT) motion for high maneuvering targets. A library of NLFM is constructed and the entropy reward Q-Learning (ERQL) method is designed to perform joint waveform parameters selection. It merges the radar and target into a closed loop to provide the optimum target tracking performance, updating the waveform in real-time as the target state changes. Numerical results show that the tracking performance of our proposed method is much better than that of the linear frequency modulated waveform (LFM) pure parameter selection method.

The future Internet comprising multimodal mobile wireless sensors (MMWSs) dramatically increases the data dimension and complexity compared to the traditional homogeneous networks. It consists of various types of nodes that can be deployed in different spatial domains to form the space–air–ground–ocean-integrated network (SAGOI-Net). A key challenge to Internet of Things (IoT) with SAGOI-Net is harnessing the real-time data from these sensors to obtain target characteristics and realize target tracking, especially in rough environment with obstacles and enemy threats. In this article, we propose an approach—fuzzy logic and flocking control under an extended Kalman filter (EKFFL) to provide sensor nodes trajectory formation. With the tools from control systems and signal processing, this approach improves the commonly adopted flocking control algorithm and fully employs multi modality of data. Compared with the modified Kalman consistency filter (KCF) approach, the proposed EKFFL achieves higher target tracking accuracy with a shorter time period of trajectory formation. The tradeoff between the data delay and tracking accuracy is also analyzed. Based on its advantages, the EKFFL approach can be applied in broad scenarios, such as unmanned border awareness systems, environmental monitoring, and intelligent transportation.

The problem of multisensors fusion target trajectory tracking under the Bayesian variational inference (VI) is to find the jointly accurate posterior distribution. In this article, a joint optimization method, called VINFNet, combining inference modeling and data-driven is proposed. The proposed VINFNet incorporates the respective advantages of state-space latent inference models and deep generative network that can explicitly model the physical process of target motion and construct complex posterior distributions of target trajectory through a series of invertible mappings. Specifically, the joint probabilistic representation of the multisensors latent variable is generated by VI, with the optimization on the evidence lower bound (ELBO) to guarantee convergence. However, finding the approximate posterior distribution of targets in VI is a crucially intractable problem. Therefore, a normalized flow generation model under a multilayers perceptron strategy is proposed to recover the approximate posterior distribution of targets, overcoming the challenge of choosing the posterior distribution during VI. The proposed VINFNet neither requires the computation of complex Jacobi matrices as in model-based algorithms such as Kalman filter (KF) nor lacks interpretability as in data-driven neural networks. Simulations and ablation experiments evaluate that the VINFNet algorithm outperforms prevalent methods regarding convergence, accuracy, robustness, and effectiveness via fusing different data sources.

Cognitive radar (CR) automatically improves itself via ceaseless interaction with the environment and learning from the experience. It continuously adjusts its waveform and parameters and illuminates strategies based on obtained knowledge to achieve robust target tracking despite complex and changing scenarios. Waveform development for CR has attracted sustaining attention in promoting tracking performance. In this article, we propose a novel framework of CR waveform selection for the tracking of high maneuvering targets in a cluttered environment with an interactive multimodel (IMM) probabilistic data association (PDA) algorithm. Based on this framework, criterion-based optimization (CBO) and entropy-rewarded Q-learning (ERQL) methods are designed to perform waveform selection, which is divided into pure parameters selection and joint selection of waveforms and parameters. This method integrates the radar target into a closed loop and realizes the real-time update of the transmitted waveform with the change of the target state, to achieve the best tracking performance of the target. The simulations performed on radar target tracking have demonstrated that the proposed ERQL method outperforms the existing method in both time complexity and tracking accuracy. Furthermore, field experiments have confirmed that the ERQL method is more effective in target tracking than the existing method.

Compressive sensing(CS) can greatly reduce the number of sampling points of signals, and therefore it is widely adopted in ultra-wideband(UWB) sensor systems. However, how to reconstruct the sensing signal from the compressed signal accurately is still an open problem because original signals do not always satisfy the sparse hypothesis that is required in CS. Typically, an appropriate CS reconstruction algorithm should be designed for a particular scenario, such as signal encoding, optical imaging and soil dynamic monitoring, etc. Unfortunately, soil data is susceptible to climatic factors, which leads to unsatisfactory performance of traditional reconstruction algorithms. To improve the accuracy of CS reconstruction for volatile signals as UWB soil echoes, we propose a novel deep learning based CS algorithm, named SFDLCS (select-first-decide-later compressive sensing) for UWB sensor signal reconstruction. In this algorithm, a search network is designed to perform the non-linear mapping from compressed residuals to non-zero elements in sensor signal, and a decision network is designed to characterize the distribution of UWB signals. These two networks form a ”select first, decide later” structure, which greatly improves the accuracy of signal reconstruction by utilizing the correlation of non-zero elements of the sensor signal. The effectiveness of this SFDLCS is demonstrated based on measured UWB soil data acquired by a P440 UWB sensor. Compared with traditional reconstruction algorithms, the proposed algorithm achieves both lower reconstruction error and stronger robustness in the noisy environment.

Multisensors multitargets tracking is an important task, and the difficulty is how to form the real trajectory of each target accurately. In this article, we construct a complete system for tracking highly maneuvering multitargets in the case of multisensors asynchronous sampling. Our system divides the entire tracking task into three modules: space–time calibration, point-trajectory data association, and trajectory data fusion. An improved least-squares (LS) virtual fusion method is proposed to correct the asynchronous sampling time, and the fast vector calibrates the multisensors’ spatial state. In the point-trajectory data association module, an adaptive K-nearest neighbors (AK-NN) algorithm is proposed, which employs the adaptive threshold forming multiple trajectories. In trajectory data fusion, a CV+CT+S multimotion model is proposed with the multisensors’ probabilistic data association filter (PDAF) algorithm to track highly maneuvering multitargets actively. The results show that our system performs accurately for moving maneuvering multitargets tracking in complex situations.

In the complex and changeable marine environment, it is difficult to achieve high-performance detection based on statistical theory for small sea surface detectors in practical applications. In this work, a method for detecting small targets on the sea surface based on sequence-features is proposed from the perspectives of feature extraction and feature classification on a basis of real data. The sequence-feature method firstly extracts three sequence-features from the radar echo sequence, namely, the instantaneous phase, Doppler spectrum entropy, and short-time Fourier transform (STFT) marginal spectrum. The features are used to train the bidirectional long short-term memory networks (Bi-LSTM) model so that it has the ability to distinguish the target and the sea clutter. The measured dataset verifies that the proposed detector can achieve the detection of sea surface targets with controllable false alarm rate, and the performance is better than several existing detectors.

Sixth-generation (6G) wireless communications and networks will continue to move to higher frequency and wider bandwidth, with much higher data rate and spectral efficiency. Given the heterogeneity and densification of Internet of Things (IoT), 6G wireless network may need to be extended to modern random access (RA) for IoT applications, which can be achieved via smart protocol design and advanced signal processing and communications technologies. The modern RA techniques, such as massive multiple-input–multiple-output (MIMO), OFDMA, nonorthogonal multiple access (NOMA), sparse signal processing, or new orthogonal design techniques provide good candidacy for 6G-enabled IoT. In 6G, grant-free transmission should be designed for distributed IoT applications. IoT applications are often involved in self-organizing decision making. 6G-enabled IoT will take advantage of the recent development of artificial intelligence (AI) techniques. It will generate new knowledge and understanding and accelerate discovery and innovation in IoT. Each of these efforts is designed to amplify the intrinsically multidisciplinary nature of the emerging field of IoT. The 6G-enabled IoT will establish theoretical, technical, and ethical frameworks that will be applied to tackle many challenges in IoT, advancing technology for humanity.

The data in the Internet of Things (IoT) and the sixth-generation (6G) wireless networks increase dramatically with higher dimensions compared to the traditional wireless networks. Compressed sensing (CS) has been adopted to effectively reduce the amount of transmitting signal with sparsity and recover accurately at the receiver. It has been proved that better recovery performance can be achieved via deep learning-based CS approaches. However, these methods require a mass of relevant data to train neural networks (NNs), not adapted for the case of small sample data. In this article, a convolution-based transfer learning CS (CTCS) model is proposed to reconstruct the compressed signal based on transfer learning. Ultrawide band (UWB) radar echo signal and Mnist hand-written data set are selected to evaluate the performance of CTCS. It is verified that the proposed model outperforms other traditional reconstruction algorithms in 6G-IoT under different noise levels, measurement numbers, and signal sparsities.

The accurate estimation of the clutter covariance matrix (CCM) is crucial for space-time adaptive processing (STAP). In this letter, a new intrinsic cyclic characteristic of CCM is found. Then, a novel STAP is proposed based on the cyclic characteristic. In the proposed method, the cyclic CCMs, i.e., the temporal cyclic CCM, the spatial cyclic CCM, and the spatial-temporal cyclic CCM, are first constructed based on the cyclic characteristic. Then, the cyclic CCMs are employed as the secondary data, and the more accurate CCM estimation is obtained by averaging the cyclic CCMs and the estimated CCM of the existing STAP methods. Compared with the existing methods, the proposed method has the following advantages: (1) the proposed method can be directly combined with the various existing STAP methods to improve their performance, (2) the output signal-to-clutter-plus-noise ratio (SCNR) of the proposed method is 2.055 dB higher than that of the traditional STAP methods reported in [7]-[9], and (3) the output SCNR of the proposed method is 1.704 dB higher than that of the knowledge-aided STAP (KA-STAP) reported in [16].

The sensing application of space surveillance has put forward challenges to the Internet of Things (IoT). However, current moving algorithms in IoT rarely aim for target surveillance. In view of energy efficiency for multimodal signals in IoT, this paper mainly investigate three typical target trajectories: 1) line; 2) square; and 3) circle. On a basis of target learning, two types of collaborative sensor movement algorithms are proposed and compared. One approach is based on genetic fuzzy tree (GFT) and the other is based on the neural network (NN). Both algorithms can balance the energy consumption and the tracking performance. Simulation results show that the GFT-based algorithm outperforms NN-based algorithm in tracking error, but it demands more computational cost than that of NN-based scheme. This important result can provide intellectual sensing support in IoT applications, such as target surveillance, anti-terrorism, and unmanned border awareness.

Internet of Things is coming to critical missions such as battlefield, border patrol, search and rescue, critical structure monitoring and surveillance, etc. Internet of Things (IoT) in Critical Missions or Internet of Mission-Critical Things (IoMCT) is propelled by the convergence of sensing, communication, computing, and control. To support IoMCT, the mission-critical networks will need to be flexible and interactive, and still work despite limited bandwidth, intermittent connectivity and with a large number of devices on the network. The focus of IoMCT is to improve surveillance utilizing a network, not fusion of disparate sensor products. Such adaptation, management, and re-organization of information sources, devices, and networks must be accomplished almost entirely autonomously, in order to avoid imposing additional burdens on the humans, and without much reliance on support and maintenance services.

Soil moisture (SM) retrieval using wireless signals has become a research focus with the development of sensor devices in Internet of Things. Studies applying groundpenetrating radar have improved the accuracy of SM retrieval; however, the field-scaled data are hardly satisfactory mainly due to the frequency response of the antenna, and it is not costeffective for farmers to monitor the soil conditions. In this paper, we compare two fuzzy logic systems (FLSs): 1) type-1 FLS and 2) adaptive network-based fuzzy inference system (ANFIS) to extract fuzzy parameters of soil. Moreover, two machine learning algorithms: 1) random forest (RF) and 2) artificial neural network with principal component analysis are applied in the SM classifications. Nine types of UWB soil echoes of different texture and volume water content (VWC) are collected and investigated using our approaches. Final analysis shows that ANFIS with RF provides the best VWC correct recognition rate compared to other algorithms.

The phrase ‘Big Data’ refers to large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future, as defined by U.S. National Science Foundation in its recent solicitation. The research of Big Data will accelerate the progress of scientific discovery and innovation; lead to new fields of inquiry that would not otherwise be possible, encourage the development of new data analytic tools and algorithms; facilitate scalable, accessible, and sustainable data infrastructure; increase understanding of human and social processes and interactions; and promote economic growth and improved health and quality of life. The new knowledge, tools, practices, and infrastructures produced will enable breakthrough discoveries and innovation in science, engineering, medicine, commerce, education, and national security. Big Data presents critical requirements for security in data collection and transmission of selected data through a communication network. This special issue contains 11 papers selected from submissions to the open call for papers on Security in Big Data. These papers highlight some of the current research interests and achievements in the area of security in Big Data. The wide use of high-performance image acquisition devices and powerful image-processing software has made it easy to tamper images for malicious purposes. The paper by Zhang et al. proposes an effective framework for revealing image-splicing forgery. The experiment results show that the proposed method can perform better than some state-of-the-art methods in terms of the detection performance over the Columbia image-splicing detection evaluation data set. Network coding has emerged some exciting future because of its smart technology in wireless sensor networks. At the same time, it is facing security attacks, especially conspiracy attack. The paper by Du et al. proposes a weakly secure scheme from the perspective of topology. Considering the performance of this scheme, an advanced scheme is put forward later. Simulations show that the two strategies can prevent cooperative eavesdroppers from acquiring any useful information transmitted from source node to sink node, and the performance of advanced scheme is better. Traditionally, jamming to the wireless system is a fatal threat to the security of home area networks (HANs), which impedes the two-way data transmission between electric devices and the smart meter and thus deteriorates the reliability of the in-home communication of Smart Grid. The paper by Li et al. incorporates the power line system into the HAN and proposes a hybrid architecture of orthogonal frequency-division multiplexing-based wireless communication and power line communication for the Smart Grid security application. With this new solution, the channel diversity of the HAN is realized, and the communication reliability is still guaranteed even when the wireless channel suffers from jamming. Information of multi-cells is big data because of the enormous quantities of various cells as well as their parameters and status. To securely and efficiently integrate all the cells' information and trace multi-cells are challenging because of varying number of the multi-cells, as well as the complicacy of the multi-cells' movement. The paper by Yin and Sun proposes an automatic big data integration algorithm based on the optical transfer function. The experimental results show that the algorithm can securely and efficiently integrate all the cell information and simultaneously track a large quantity of cells. Real-time digital video presents great challenges on processing and storage and is a typical example in Big Data. How to secure and efficiently transmit digital video is critical. The paper by Zhang et al. uses the distributed compressed sensing to deal with video coding. To reduce the orthogonal matching pursuit algorithm computational complexity, quantum-behaved particle swarm optimization algorithm is used to reconstruct video signal. Simulation results demonstrate that it can obtain the better reconstructed video with low sample value and it can guarantee safety performance. Wireless image sensor network generates a large number of images from the distributed camera sensors. The image data need to be delivered securely and efficiently to the sink in many circumstances. The current node-disjoint multipath and dispersive routings cannot provide enough security and efficiency for the image data collection and transportation. The paper by Su and Hu proposes an ellipse batch dispersive routing algorithm to address the secure and efficient data collection issue in wireless image sensor network. The smart grid system is composed of the power infrastructure and communication infrastructure and thus is characterized by the flow of electric power and information, respectively. The 24/7 information collection and transmission in smart grid is a good example of Big Data. The transmission of Big Data in smart grid needs wireless network, which introduces additional vulnerabilities, given the scale of potential threats. Therefore, the physical layer security issue is of first priority in the study of smart grid and has already attracted substantial attention in the industry and academia. The paper by Wang et al. aims to present a general overview of the physical layer security in wireless smart grid and covers the effective countermeasures proposed in the literature of smart grid to date. Security is a very broad topic; particular attention has been paid in communications, networking on security issues. However, in practical applications, providing security services increases the computation and the occupation of system resources. This problem is particularly important when energy is a limited resource for mobile communication devices operating on battery. Thus, energy-efficient security devices are very necessary for the communication. The paper by Yuan and Liang designed a new low voltage, low power consumption comparator for successive approximation register analog to digital converter to improve the energy efficiency in the problem of secure communication. Big data presents critical requirements for security in data collection and transmission of selected data through a communication network. The paper by Chen et al. presents a new secure transmission for big data based on nested sparse sampling and coprime sampling. With nested sampling and coprime sampling, besides the advantage of higher spectrum efficiency, big data could also achieve higher power spectral density for binary frequency shift keying signal. It proves that both nested sampling and coprime sampling could be used in big data transmission to resist interference, while guaranteeing the transmission performance. With the rapid adoption of cloud storage services, a great deal of data is being stored at remote servers, so a new technology, client-side deduplication, which stores only a single copy of repeating data, is proposed to identify the client's deduplication and save the bandwidth of uploading copies of existing files to the server. It was recently found, however, that this promising technology is vulnerable to a new kind of attack in which by learning just a small piece of information about the file, namely, its hash value, an attacker is able to obtain the entire file from the server. The paper by Yang et al. proposes a cryptographically secure and efficient scheme for a client to prove to the server his ownership on the basis of actual possession of the entire original file instead of only partial information about it. The paper by Wang et al. presents the definitions of big data and anomaly detection. The theory of ultra-wideband radar and the through-wall detection of a human model based on ultra-wideband radar are briefly introduced. The target criterion with wavelet packet transform is deduced, and the procedure for the through-wall human detection with statistical process control is constructed. The radar echo signals are collected at stationary and moving statuses of a human being for three types of walls. The experimental results demonstrate the effective of through-wall target detection based on the proposed algorithm. We would like to thank all authors for contributing papers to the special issue. We appreciate the staff of Security and Communication Networks for their support in editing this special issue. Qilian Liang is a University Distinguished Scholar Professor in the Department of Electrical Engineering, University of Texas at Arlington. He received the BS degree from Wuhan University in 1993, MS degree from Beijing Uni- versity of Posts and Telecommunica- tions in 1996, and PhD degree from University of Southern California (USC) in May 2000, all in Electrical Engineering. Prior to joining UTA in August 2002, he was a Member of Technical Staff in Hughes Network Systems Inc. at San Diego, California. His research interests include wireless sensor networks, wireless communications, signal processing, information theory, radar systems, and wireless networks. Dr. Liang has published more than 270 journal and conference papers. He received 2002 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, 2003 U.S. Office of Naval Research (ONR) Young Investigator Award, 2005 UTA College of Engineering Outstanding Young Faculty Award, 2007, 2009, 2010 U.S. Air Force Summer Faculty Fellowship Program Award, 2012 UTA College of Engineering Excellence in Research Award, 2013 UTA Outstanding Research Achievement or Creative Activity Award, and was inducted into UTA Academy of Distinguished Scholars in 2015. Jian Ren received the BS and MS degrees both in mathematics from Shaanxi Normal University and received the PhD degree in EE from Xidian University, China. He is an Associate Professor in the Department of ECE at Michigan State University. His current research interests include cryptography, network security, energy efficient sensor network security protocol design, privacy-preserving communications, secure and efficient cloud computing, and cognitive networks. He is a recipient of the US National Science Foundation Faculty Early Career Development (CAREER) award in 2009. Dr. Ren is a senior member of the IEEE. Jing Liang received the BS and MS degrees from Beijing University of Posts and Telecommunications, China in 2003 and 2006, respectively, and PhD degree from University of Texas at Arlington in August 2009, all in Electrical Engineering. She is currently a Professor in the Department of Electrical Engineering at University of Electronic Science and Technology of China. Her current research interests include radar sensor networks, collaborative and distributed signal processing, wireless communications, wireless networks, and fuzzy logic systems. Baoju Zhang is a Professor at the College of Physical and Electrical Information, Tianjin Normal Uni- versity. She received the BS degree from Tianjin Normal University in 1990, MS degree from Tianjin Nor- mal University in 1993, and PhD degree from Tianjin University in 2002. She was a Postdoctoral Fellow at Tianjin University from 2002 to 2004. Her research interests include radar sensor networks, digital audio and video technology, image compressing and coding, and compressive sensing. Yiming Pi was born in 1968 in China. He obtained PhD degree in Electronic Engineering from University of Electronic Science and Technology of China in 1993. Since 2002, he has been a Professor of Department of EE, University of Electronic Science and Technology of China. He is a councilor of Signal Processing Society in the Chinese Institute of Electronics and has served in organizing several international conferences in the field of Signal Processing and Radar Systems. He became IEEE Senior Member in 2011. He had been the leaders of some Natural Science Funding of China. He has more than 100 publications in the conferences and journals of IEEE/IET. His research interests are radar imaging, signal processing and terahertz technology, and so on. Chenglin Zhao received his BS degree in Tianjin University in 1986, MS degree and PhD degree in Beijing University of Posts and Telecommu- nications in 1993 and 1997, respec- tively. He is a Professor of the Key Lab of the ubiquitous wireless of Education Ministry, Information and Telecommunication engineering college, Beijing University of Post and Telecommunication. His main research areas include radar sensor networks, wireless broadband interconnection, wireless sensor network, and digital signal processing and its applications.

Abstract Security is a very broad topic; particular attention has been paid in communications, networking on security issues. However, in practical applications, providing security services increases the computation and the occupation of system resources. This problem is particularly important when the energy is a limited resource for mobile communication devices operating on battery. Thus, energy efficient security devices are very necessary for the communication. This paper designed a new low voltage, low power consumption comparator for successive approximation register analog to digital converter to improve the energy efficiency in the problem of secure communication. Then, analyze the energy consumption of the new comparator security system compared with traditional design. We mainly focus on three communication protocols, which use the block cipher. The results show that the system's energy consumption is greatly reduced when the new comparator is used, Copyright © 2013 John Wiley & Sons, Ltd.

In this paper we propose a new type of radar sensor network (RSN) - high-resolution passive radar sensor network (HRPRSN) that consists of 1 transmitter and several passive radar sensors (RSs). The use of high-resolution RS will increase the probability of separating targets in range. However, we prove in this work that one RS can not detect all targets from time to time, thus spatial diversity is necessary to improve the detection performance. Passive RSN is proposed due to the fact that it highly reduces interferences and improves safety. The HRPRSN model for multitarget detection, to the best of our knowledge, is proposed for the first time. The relations between each RS and target are revealed via analysis on time-delay, Doppler shift, target range resolution, etc. Simulation results demonstrate the performance advantage in HRPRSN for multitarget detection in terms of probability of detection (Pd) and probability of false alarm (Pf). Unlike single RS that Pdand Pfmust increase or decrease together, HRPRSN can achieve high Pdand low Pfsimultaneously.

Motivated by recent advances on Compressive Sensing (CS), we study the sparsity of sense-through-foliage radar signals. Based on CLEAN method, we obtain the impulse response for sense-through-foliage communication channels for three different radars, 200MHz, 400MHz, and UWB radars. Channel impulse responses for the above three different kinds of channels demonstrate that the sense-through-foliage signals are very sparse, which means CS is possible to be applied to sense-through-foliage radar signals to tremendously reduce the sampling rate. We apply CS and linear programming to sparse signal compression and recovery, and it turns out that we could achieve compression ratio of 32:1 with perfect recovery for the UWB radar signals.

In this paper, we design a network of small unmanned aerial vehicles (SUAVs) for passive location of RF emitters. Each small UAV is equipped with multiple electronic surveillance (ES) sensors to provide local mean distance estimation based on received signal strength indicator (RSSI). Fusion center will determine the location of the target through UAV triangulation. Different with previous existing studies, our method is on a basis of an empirical path loss and log-normal shadowing model, from a wireless communication and signal processing vision to offer an effective solution. The performance degradation between UAVs and fusion center is taken into consideration other than assuming lossless communication. We analyze the geolocation error and the error probability of distance based on the proposed system. The result shows that this approach provides robust performance for high frequency RF emitters.

In this paper, we design a network of distributed radar sensors that work in an ad hoc fashion, but are grouped together by an intelligent clusterhead. This system is named Radar Sensor Network (RSN). A RSN not only provides spatial resilience for target detection and tracking compared to traditional radars, but also alleviates inherent radar defects such as the blind speed problem. This interdisciplinary area offers a new paradigm for parallel and distributed sensor research. We propose both coherent and noncoherent RSN detection systems applying selection combination algorithm (SCA) performed by clusterhead to take the advantage of spatial diversity. Monte Carlo simulations show that proposed RSN can provide much better detection performance than that of single radar sensor for fluctuating targets, in terms of probability of false alarm and miss detection. We also analyze the impact of Doppler shift on both coherent and noncoherent RSN detection systems at the presence of clutter. The result is that the coherent system is more robust to the noncoherent RSN.

In this paper, we study target detection in foliage environment. When radar echoes are in good quality, the detection of target can be achieved by applying short time Fourier transform (STFT) to the received UWB radar waveform. We compare our approach in case of no target as well as with target against the scheme in which 2-D image was created via adding voltages with the appropriate time offset. Results show that our approach can detect target more easily. When radar echoes are in poor condition and single radar is unable to carry out the detection, we employ both Radar Sensor Networks (RSN) and RAKE structure to combine the echoes from different radar members and finally detect the target.

In this paper, the foliage penetration measurement data is provided by Air Force Office of Scientific Research (AFOSR). When radar echoes are in good quality, the detection of target can be achieved by applying our differential based technology on received single UWB radar waveform. We compared our approach in case of no target as well as with target against the scheme in which 2-D image was created via adding voltages with the appropriate time offset. Results show that our approach can work much better. When radar echoes are in poor condition and single radar is unable to carry out the detection, we employ both radar sensor networks (RSN) and RAKE structure to combine the echoes from different radar members and successfully detect the target.

In this paper, we propose that the foliage clutter follows log-logistic model using maximum likelihood (ML) parameter estimation as well as the root mean square error (RMSE) on PDF curves between original clutter and statistical model data. The measured clutter data is provided by Air Force Office of Scientific Research (AFOSR). In addition to investigating the log-logistic model, we also compare it with other popular clutter models, namely log-normal, Weibull and Nakagami. We show that the log-logistic model not only achieves the smallest standard deviation (STD) error on estimated model parameters, but also has the best goodness-of-fit and smallest RMSE. Further, the performance of detection at presence of foliage clutter is theoretically analyzed.

In this paper we propose orthogonal waveforms and equal gain combination algorithm in radar sensor network (RSN) to alleviate blind speed problem. We also design a fuzzy logic system (FLS) to optimize the number of radars in RSN. Simulation results show that our FLS-based RSN can not only balance the number of radars and QoS in terms of probability of miss detection (PMD), but to some extent achieve constant PMD with different system configuration.