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In this study, a collaborative radar selection and transmit resource allocation strategy is proposed for multitarget tracking applications in multiple distributed phased array radar networks with imperfect detection performance. The closed-form expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with imperfect detection performance is obtained and adopted as the criterion function to characterize the precision of target state estimates. The key concept of the developed strategy is to collaboratively adjust the radar node selection, transmitted power, and effective bandwidth allocation of multiple distributed phased array radar networks to minimize the total transmit power consumption in an imperfect detection environment. This will be achieved under the constraints of the predetermined tracking accuracy requirements of multiple targets and several illumination resource budgets to improve its radio frequency stealth performance. The results revealed that the formulated problem is a mixed-integer programming, nonlinear, and nonconvex optimization model. By incorporating the barrier function approach and cyclic minimization technique, an efficient four-step-based solution methodology is proposed to solve the resulting optimization problem. The numerical simulation examples demonstrate that the proposed strategy can effectively reduce the total power consumption of multiple distributed phased array radar networks by at least 32.3% and improve its radio frequency stealth performance while meeting the given multitarget tracking accuracy requirements compared with other existing algorithms.

A star tracker is a widely used celestial sensor in astronomical navigation systems, which calculates the spacecraft’s high-precision attitude by observing stars in space to obtain several star vectors. Existing star identification algorithms typically require the selection of a specific anchor star (e.g., the nearest neighbor star), using the line connecting the target star and the anchor star as the rotational reference axis to achieve rotation invariance in the star identification algorithm. However, this approach makes the entire identification algorithm overly dependent on the anchor star, resulting in insufficient identification accuracy in cases of excessive positional noise or a high number of false stars. In this paper, we adopt the angles between any neighboring stars and the distances ratio between neighboring stars and the observed star as the initial matching criteria. We then calculate the matching with each navigation star using the accumulated angle in the counterclockwise direction based on this criterion. The navigation star with the highest matching is identified. Unlike other identification algorithms that require selecting the nearest neighbor star which can be easily affected by interference as the rotational reference axis, our method effectively achieves rotation invariance in star identification by leveraging angle information. Therefore, it exhibits better tolerance to positional noise, magnitude noise, and false stars, particularly demonstrating higher robustness against focal length variations.

In this article, a mission planning and trajectory optimization scheme in unmanned aerial vehicle (UAV) swarm for track deception against radar networks is proposed. The core of this scheme is to formulate the track deception problem as a model with the objective of simultaneously maximizing the number of phantom tracks while minimizing the total flight distance of the UAV swarm, subject to the constraints of UAV kinematic performance, phantom track rotation angles, and a homology test. It is shown that the formulated track deception problem is a mixed-integer programming, multivariable, and non-linear optimization model. By incorporating mission planning based on platform reuse and a particle swarm optimization (PSO) algorithm, a three-stage solution methodology is proposed to tackle the above problem. Through joint optimization for mission planning and flight trajectories of the UAV swarm, a low-speed UAV swarm is capable of generating a number of high-speed phantom tracks. Numerical results demonstrate that the proposed scheme enables a low-speed UAV swarm to generate as many high-speed phantom tracks as possible, effectively achieving track deception against radar network.

For the resource allocation problem of multitarget tracking in a spectral coexistence environment, this study proposes a joint transmit power and dwell time allocation algorithm for radar networks. First, the predicted Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of radar node selection, transmit power and dwell time is derived as the performance metric for multi-target tracking accuracy. On this basis, a joint optimization model of transmit power and dwell time allocation for multitarget tracking in radar networks under spectral coexistence is built to collaboratively optimize the radar node selection, transmit power and dwell time of radar networks, This joint optimization model aims to minimize the multitarget tracking BCRLB while satisfying the given transmit resources of radar networks and the predetermined maximum allowable interference energy threshold of the communication base station. Subsequently, for the aforementioned optimization problem, a two-step decomposition method is used to decompose it into multiple subconvex problems, which are solved by combining the Semi-Definite Programming (SDP) and cyclic minimization algorithms. The simulation results showed that, compared with the existing algorithms, the proposed algorithm can effectively improve the multitarget tracking accuracy of radar networks while ensuring that the communication base station works properly.

The joint optimization problem of transmit power and dwell time of radar for asynchronous multi-target tracking in heterogeneous multiple radar networks with imperfect detection is investigated. Firstly, all the asynchronous measurements from different radar node in each fusion sampling interval are fused into composite measurement, thus the Bayesian Cramér-Rao Lower Bound (BCRLB) analytical expression of the asynchronous target tracking error with parameters such as radar node selection, transmit power and dwell time with imperfect detection is derived and used as the asynchronous target tracking accuracy measure. Based on this, a joint optimization model of transmit power and dwell time for asynchronous multi-target tracking in heterogeneous multiple radar networks with imperfect detection is established, with the optimization objective of minimizing the asynchronous multi-target tracking error and the constraints of given system transmit resource limitations, the parameters such as radar node selection, transmit power and dwell time in different radar networks are designed adaptively and optimally so as to improve the asynchronous multi-target tracking accuracy of the heterogeneous multiple radar networks system. Finally, a four-step decomposition algorithm combined with the Sequential Quadratic Programming (SQP) algorithm and cyclic minimization method is used to solve the optimization problem. Simulation results demonstrate that the asynchronous multi-target tracking accuracy of the heterogeneous multiple radar networks outperforms existing algorithms.

Abstract The Li metal is an ideal anode material owing to its high theoretical specific capacity and low electrode potential. However, its high reactivity and dendritic growth in carbonate-based electrolytes limit its application. To address these issues, we propose a novel surface modification technique using heptafluorobutyric acid. In-situ spontaneous reaction between Li and the organic acid generates a lithiophilic interface of lithium heptafluorobutyrate for dendrite-free uniform Li deposition, which significantly improves the cycle stability (Li/Li symmetric cells >1200 h at 1.0 mA cm−2) and Coulombic efficiency (>99.3%) in conventional carbonate-based electrolytes. This lithiophilic interface also enables full batteries to achieve 83.2% capacity retention over 300 cycles under realistic testing condition. Lithium heptafluorobutyrate interface acts as an electrical bridge for uniform lithium-ion flux between Li anode and plating Li, which minimizes the occurrence of tortuous lithium dendrites and lowers interface impedance.

Simultaneous infection and exudate management remain a great challenge in wound treatment. As one of the most promising approaches, current asymmetric dressings are basically prepared through electrospinning with different kinds of materials, and the interfacial interaction remains unexplored. Herein, the first single‐substrate‐based asymmetric dressing with a well‐entangled interface, large absorptive capacity, effective fluid reflux prevention, excellent antibacterial, and antipenetration properties is developed. The asymmetric dressing is composed of layered hydrophilic and hydrophobic bottlebrushes (sodium polyacrylate and polystyrene‐functionalized bacterial cellulose), which are assembled using stepwise vacuum filtration, accompanied by loading triclosan into the solution of hydrophilic bottlebrushes. The biocompatibility and feasibility of the asymmetric dressing pave the way in real wound management.

This paper investigates the joint optimization problem of transmit resources and trajectory planning for target tracking in airborne radar networks. First, the analytical expression for the Bayesian Cramér-Rao Lower Bound (BCRLB) with the variables of the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes is derived and adopted as the metric function to evaluate the target tracking accuracy. In addition, the analytical expression of intercept probability with the variables of the radar transmit power, dwell time, and speed and heading angle of airborne nodes is also derived and utilized as the metric function to gauge the radio frequency stealth performance of the overall system. On this basis, a joint optimization model of transmit resources and trajectory planning for target tracking in airborne radar networks is established to jointly optimize the radar transmit power, dwell time, transmit signal Gaussian pulse length and signal bandwidth, and speed and heading angle of airborne nodes. This is done to minimize the target estimation error BCRLB under the constraints of given system resources, aircraft maneuvering and intercept probability threshold, thereby improving the target tracking accuracy of airborne radar network. Subsequently, a five-step decomposition iterative algorithm incorporating the particle swarm algorithm is used to solve the underlying optimization problem. The simulation results demonstrate that the target tracking accuracy of the proposed algorithm outperforms other existing approaches.

Abstract A low probability of intercept (LPI)‐based cooperative node selection and transmit resource allocation (CNS‐TRA) strategy is proposed for multi‐target tracking in multiple radars architecture (MRA). The key idea of the proposed CNS‐TRA strategy is to coordinate the radar node selection and the transmit resource that is, dwell time, transmit power, and effective bandwidth allocation to improve the LPI performance, under the constraints of predefined target tracking accuracy requirement and several resource budgets. By incorporating the above controllable parameters, the Bayesian Cramér‐Rao lower bound is calculated and used as the accuracy metric for target tracking. Subsequently, this paper develops a fast and effective two‐stage‐based solution methodology to solve the resulting non‐convex and non‐linear optimisation problem. Specifically, the optimisation problem can be decomposed into two sub‐problems, that is, the radar node selection sub‐problem and the transmit resource allocation sub‐problem. The active‐set method and the interior point approach are utilised to solve those two sub‐problems, respectively. Simulation results demonstrate that the CNS‐TRA strategy has superiority over other existing algorithms and can achieve better LPI performance for MRA.

Although NAND (Not And) flash-based Solid-State Drive (SSD) has recently demonstrated a significant performance advantage against hard disk, it still suffers from non-negligible performance under-utilization issues as the access conflict often occurs during servicing IO requests due to the share mechanism (e.g., several chips share one channel bus, several planes share one data register inside the die). Many research works have been devoted to minimizing access conflict by redesigning IO scheduling, cache replacement, and so on. These works have achieved reasonable results; however, the potential data similarity characterization is not utilized fully in prior works to alleviate access conflict. The basic idea is that, as data duplication is common in many workloads where data with the same content from different requests could be distributed to the address with minimized access conflict (i.e., the address does not share the same channel or chip), the logic address is mapped to more than one physical address. Therefore, the data can be read out from candidate pages when the channel or chip of its original address is busy. Motivated by this idea, we propose Data Similarity aware Flash Translation Layer (DS-FTL), which mainly includes a content-aware page allocation scheme and a multi-path read scheme. The DS-FTL enables maximization of the channel-level and chip-level parallelism and avoids the read stall induced by bus-shared mechanisms. We also conducted a series of experiments on SSDsim, with the subsequent results depicting the effectiveness of our scheme. Compared with the state-of-art, our scheme reduces read latency by 35.3% on average in our workloads.

This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches.

Abstract To protect the satellite communication uplink signal (SCUS) from being intercepted by an eavesdropper, this study proposes several tactics to ensure SCUS being in the low probability of intercept (LPI) state. First, based on the satellite link equation, an optimisation model for LPI SCUS is established with coding and synchronisation of symbol and carrier frequency. However, when the satellite is on the synchronisation orbit, the LPI model of SCUS is still very likely to be intercepted by an eavesdropper that might be close to the SCUS. Aiming at this problem, the second tactic is to mask LPI SCUS with directed and power limited jamming. The purpose of using directed and power limited jamming is to reduce direction‐finding accuracy of the eavesdropper, and to achieve the optimal LPI performance with the minimum radiation energy. The high‐order cumulate algorithm is used to predict the signal‐to‐noise ratio of mixed signal in the second tactic. The simulation results show that the model of LPI SCUS can effectively reduce the detection probability compared with the traditional satellite uplink signals, and the second tactic can effectively reduce the direction‐finding accuracy of the SCUS by the eavesdropper.

Abstract A large number of initialised false tracks in clutter environments will cause a heavy burden on the detection and track‐processing system. The quantity of initialised false tracks in clutter used to be estimated by experience or simulation, which lacks a fundamental explanation in theory. In order to estimate the amount of linear initialised false tracks with analytical expressions, statistical estimators are proposed based on prior knowledge of the clutter in a two‐dimensional scenario. Meanwhile, the differential Hough transform is presented to verify the estimators. To be specific, firstly, a one‐dimensional scenario is illustrated to present the statistical estimators of the number of initialised linear and equal‐length false tracks, which is based on statistical estimation theory. Secondly, regarding the clutter map as a uniform distributed binary image, the estimators described at first are extended to a two‐dimensional situation. Finally, to verify the validity of the proposed estimators, a differential Hough transform algorithm is utilised to obtain the number of linear and equal‐length tracks in the simulation. The results show that the estimators proposed herein are reasonable in a certain clutter environment.

Abstract Background Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making. Methods Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms. Results 430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model. Conclusions Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT. Graphic abstract

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks.

This paper studies a coalition game theoretic power allocation algorithm for multi-target detection in radar networks based on low probability of intercept (LPI). The main goal of the algorithm is to reduce the total radiated power of the radar networks while satisfying the predetermined target detection performance of each radar. Firstly, a utility function that comprehensively considers both target detection performance and the radiated power of the radar networks is designed with LPI performance as the guiding principle. Secondly, it causes a coalition to form between cooperating radars, and radars within the same coalition share information. On this basis, a mathematical model for power allocation in radar networks based on coalition game theory is established. The model takes the given target detection performance as a constraint and maximizing system energy efficiency and optimal power allocation as the optimization objective. Furthermore, this paper proposes a game algorithm for joint coalition formation and power allocation in a multi-target detection scenario. Finally, the existence and uniqueness of the Nash equilibrium (NE) solution are proven through strict mathematical deduction. Simulation results validate the effectiveness and feasibility of the proposed algorithm.

Abstract This paper proposes a collaborative transmit resource scheduling and waveform selection (CTRSWS) strategy for target tracking in multistatic radar system which consists of one transmitter and an arbitrary number of receivers. The main mechanism of the proposed CTRSWS strategy is to exploit the optimisation technique to jointly optimise the illumination power, dwell time, waveform bandwidth, and pulse length under the constraints of several resource budgets and the predefined waveform library, aiming at improving the low probability of intercept (LPI) performance and target tracking accuracy of multistatic radar system simultaneously. The analytical expressions for the probability of intercept and the trace of the predicted error covariance matrix corresponding to the target state estimation are derived and adopted to evaluate the LPI performance and target tracking accuracy, respectively. Subsequently, the resulting non‐convex and non‐linear optimisation problem is resolved by an efficient and fast four‐stage solution methodology. Several numerical results are provided to verify the effectiveness and superiority of the proposed CTRSWS scheme in terms of the achievable LPI performance and target tracking accuracy of multistatic radar system.

Abstract Carbon‐based nanomaterials have been regarded as promising non‐noble metal catalysts for renewable energy conversion system (e.g., fuel cells and metal–air batteries). In general, graphitic skeleton and porous structure are both critical for the performances of carbon‐based catalysts. However, the pursuit of high surface area while maintaining high graphitization degree remains an arduous challenge because of the trade‐off relationship between these two key characteristics. Herein, a simple yet efficient approach is demonstrated to fabricate a class of 2D N‐doped graphitized porous carbon nanosheets (GPCNSs) featuring both high crystallinity and high specific surface area by utilizing amine aromatic organoalkoxysilane as an all‐in‐one precursor and FeCl3·6H2O as an active salt template. The highly porous structure of the as‐obtained GPCNSs is mainly attributed to the alkoxysilane‐derived SiOx nanodomains that function as micro/mesopore templates; meanwhile, the highly crystalline graphitic skeleton is synergistically contributed by the aromatic nucleus of the precursor and FeCl3·6H2O. The unusual integration of graphitic skeleton with porous structure endows GPCNSs with superior catalytic activity and long‐term stability when used as electrocatalysts for oxygen reduction reaction and Zn–air batteries. These findings will shed new light on the facile fabrication of highly porous carbon materials with desired graphitic structure for numerous applications.

Thermal safety during the synthesis of a Grignard reagent under different dosing rates was evaluated in this work. A reaction calorimeter (SIMULAR) was used to investigate the heat release under isothermal experiment in the range of 0.5–2.0 g⋅min−1 dosing rates. The thermal decomposition of the Grignard reagent was analyzed using accelerating rate calorimetry (ARC). Furthermore, the risk assessment of thermal runaway was analyzed using a risk matrix and a Stoessel criticality diagram. The results indicate that decreasing the dosing rate can decrease the risk level of synthesizing the Grignard reagent from class 3 to class 1. However, the risk level was class 3 at different dosing rates when the induction period was considered. In the case of cooling failure, the most hazardous moment was the induction period, followed by the dosing period. These findings provide further evidence that the dose rate of 0.5 g⋅min−1 was safer to produce Grignard reagents.

This study aims at investigating drivers' risk perception ability. To achieve this objective, the risk sensitivity and risk judgment thresholds of drivers of different ages were calculated. Five scenarios of intersections with risks were established for the driving simulator experiment. The driving behavior data of fourteen younger drivers and fourteen elderly drivers during the risky event and the subjective evaluation of risk by an expert driver were collected. The expert driver's conflict degree and subjective feeling were combined to classify the risk level of driving scene; then, fuzzy signal detection was used to calculate the driver's risk sensitivity (d') and judgment threshold (β). The β with the greatest difference was selected for cluster analysis and drivers were divided into four types according to the threshold. Finally, a driver classification discriminant model was constructed based on Fisher discriminant analysis. The results show that d' and β of younger drivers are both better than those of elderly drivers, younger drivers can detect and respond risks in time, while elderly drivers need be closer to risks and have intuitive feelings to make judgments. The results of the cluster analysis showed that younger drivers account for a large proportion of the sensitive type, indicating that younger drivers can find risks more sensitively in risk scenarios than elderly drivers, while elderly drivers easily ignore risks due to physical and psychological weakness. The correlation analysis showed that age, saccade amplitude and heart rate are the main factors that affect the risk judgment threshold.

The authors use polyacrylamide grafted graphene oxide as a molecular brush to coat the commercial polypropylene separator. The bilayer design combines lithiophilicity chemistry and high mechanical strength, rendering high performance Li metal anodes and offering a strategy for the design of separators.

The magnetometer is a vital measurement component for attitude measurement of near-Earth satellites and autonomous magnetic navigation, and monitoring health is significant. However, due to the compact structure of the microsatellites, the stray magnetic changes caused by the complex working conditions of each system will inevitably interfere with the magnetometer measurement. In addition, due to the limited capacity of the satellite–ground measurement channels and the telemetry errors caused by the harsh space environment, the magnetic data collected by the ground station are partially missing. Therefore, reconstructing the telemetry data on the ground has become one of the key technologies for establishing a high-precision magnetometer twin model. In this paper, firstly, the stray magnetic interference is eliminated by correcting the installation matrix for different working conditions. Then, the autocorrelation characteristics of the residuals are analyzed, and the TCN-SE (temporal convolutional network-squeeze and excitation) network with long-term memory is designed to model and extrapolate the historical residual data. In addition, MAE (mean absolute error) is used to analyze the data without missing at the corresponding time in the forecast period and decreases to 74.63 nT. The above steps realize the accurate mapping from the simulation values to the actual values, thereby achieving the reconstruction of missing data and establishing a solid foundation for the judgment of the health state of the magnetometer.

The star tracker is a prerequisite device to realize high-precision attitude determination for a spacecraft. However, due to the errors in optical lens machining, optical path assembly, and temperature alternation, optical instruments suffer from some amount of optical geometric distortion, resulting in declining star tracker accuracy. The on-orbit distortion correction of star images is indispensable for precise performance. In this paper, a novel single-layer 2D Legendre neural network (2DLNN) to automatically correct the geometric distortion of the star tracker is proposed. An offline training method grounded on batch star images and an online training algorithm based on sequential star images are designed, respectively. The 2DLNN realizes the ground-based and on-orbit online correction of optical geometric distortion for the star tracker. The 2DLNN features self-learning, lifelong learning, and good adaptability. The single-layer neural network is simple, quick convergence, which is suitable for on-orbit implementation. The simulations demonstrate that the average distortion error can be reduced to less than 0.04 px after ground-based training. In the earth-orientation mode of the LEO satellite, the on-orbit sequential training algorithm can converge in 2500 star images under 1 frame/s. The proposed 2DLNN can achieve high-precision correction at the sub-pixel level, effectively improving the star tracker’s attitude determination accuracy.

This paper investigates the problem of non-cooperative game theoretic power allocation (NGTPA) for distributed multiple-radar architectures in a spectrum sharing environment, where multiple radars coexist with a communication system in the same frequency band. The primary objective of the multiple-radar system is to minimize the power consumption of each radar by optimizing the transmission power allocation, which is constrained by a predefined signal-to-interference-plus-noise ratio requirement for target detection and a maximum interference tolerant limit for communication system. Since each radar is rational and selfish to maximize its own utility, we utilize the non-cooperative game theoretic technique to tackle the distributed power allocation problem. Taking into consideration the target detection performance and received interference power at the communication receiver, a novel utility function is defined and employed as the optimization criterion for the NGTPA strategy. Furthermore, the existence and uniqueness of the proposed game's Nash equilibrium point are analytically proved. An iterative power allocation algorithm with low computational complexity and fast convergence is developed, where the optimal value of each radar's transmission power is simultaneously updated at the same time step. Numerical simulations are provided to verify the analysis and evaluate the performance of the proposed strategy as a function of the system parameters. It is shown that the distributed algorithm is effective for power allocation and could protect the communication system with limited implementation overhead.

The integration of radar and communications systems can provide great advantages, such as enhanced efficiency, structure simplification, less occupied hardware resources, and interference mitigation, compared with traditional individual radar and communications applications. Extensive studies have presented achieving improved system performance, whereas the problem of low probability of intercept (LPI)-based waveform design for the integrated system is seldom discussed in the literature. In this paper, an LPI-based optimal orthogonal frequency multiplexing modulation (OFDM) waveform design strategy is developed for an integrated radar and communications system (IRCS). The dedicated transmitter in this system transmits integrated OFDM waveform for simultaneously target parameter estimation and downlink communications. The basis of the underlying strategy is to employ the optimization technique to design the integrated OFDM waveform of IRCS in order to minimize the total radiated power, while satisfying the specified requirements of target parameter estimation and data information rate. We analytically show that the resulting optimization problem is convex and can be solved by formulating the Karush-Kuhn-Tuckers optimality conditions. Numerical simulation results demonstrate that our proposed strategy can solve the waveform design problem in the IRCS with low complexity, and the LPI performance of the IRCS can efficiently be enhanced by utilizing the proposed integrated OFDM waveform design strategy.

The star tracker is widely used for high-accuracy missions due to its high accuracy position high autonomy and low power consumption. On the other hand, the ability of interference suppression of the star tracker has always been a hot issue of concern. A SLIC-DBSCAN-based algorithm for extracting effective information from a single image with strong interference has been developed in this paper to remove interferences. Firstly, the restricted LC (luminance-based contrast) transformation is utilized to enhance the contrast between background noise and the large-area interference. Then, SLIC (the simple linear iterative clustering) algorithm is adopted to segment the saliency map and in this process, optimized parameters are harnessed. Finally, from these segments, features are extracted and superpixels with similar features are combined by using DBSCAN (density-based spatial clustering of applications with noise). The proposed algorithm is proved effective by successfully removing large-area interference and extracting star spots from the sky region of the real star image.

Based on the asymptotic spectral distribution of sample covariance matrix, a new low probability of interception (LPI) signal detection method for multi-antenna intercept receiver is proposed via reforming the noise sequence into a Wishart matrix. The authors deduce the asymptotic cumulative distribution function (CDF) of eigenvalues of sample covariance matrix. Also a numerical Kullback–Leibler divergence of the empirical spectral CDF based on test samples from the deduced asymptotic CDF is established, which is treated as the test statistic. By comparing with other signal detection methods, simulation results show the new LPI signal detection method is superior and robust.

Radar network systems have been demonstrated to offer numerous advantages for target tracking. In this paper, a low probability of intercept (LPI)-based joint dwell time and bandwidth optimization strategy is proposed for multi-target tracking in a radar network. Since the Bayesian Cramer–Rao lower bound (BCRLB) provides a lower bound on parameter estimation, it can be utilized as the accuracy metric for target tracking. In this strategy, in order to improve the LPI performance of the radar network, the total dwell time consumption of the underlying system is minimized, while guaranteeing a predetermined tracking accuracy. There are two adaptable parameters in the optimization problem: one for dwell time, and the other for bandwidth allocation. Since the nonlinear programming-based genetic algorithm (NPGA) can solve the nonlinear problem well, we develop a method based upon NPGA to solve the resulting problem. The simulation results demonstrate that the proposed strategy has superiority over traditional algorithms, and can achieve a better LPI performance of this radar network.

This paper presents a novel Nash bargaining solution (NBS)-based cooperative game-theoretic framework for power control in a distributed multiple-radar architecture underlying a wireless communication system. Our primary objective is to minimize the total power consumption of the distributed multiple-radar system (DMRS) with the protection of wireless communication user’s transmission, while guaranteeing each radar’s target detection requirement. A unified cooperative game-theoretic framework is proposed for the optimization problem, where interference power constraints (IPCs) are imposed to protect the communication user’s transmission, and a minimum signal-to-interference-plus-noise ratio (SINR) requirement is employed to provide reliable target detection for each radar. The existence, uniqueness and fairness of the NBS to this cooperative game are proven. An iterative Nash bargaining power control algorithm with low computational complexity and fast convergence is developed and is shown to converge to a Pareto-optimal equilibrium for the cooperative game model. Numerical simulations and analyses are further presented to highlight the advantages and testify to the efficiency of our proposed cooperative game algorithm. It is demonstrated that the distributed algorithm is effective for power control and could protect the communication system with limited implementation overhead.

In this paper, the problem of low probability of intercept (LPI)-based radar waveform design for distributed multiple-radar system (DMRS) is studied, which consists of multiple radars coexisting with a wireless communication system in the same frequency band. The primary objective of the multiple-radar system is to minimize the total transmitted energy by optimizing the transmission waveform of each radar with the communication signals acting as interference to the radar system, while meeting a desired target detection/characterization performance. Firstly, signal-to-clutter-plus-noise ratio (SCNR) and mutual information (MI) are used as the practical metrics to evaluate target detection and characterization performance, respectively. Then, the SCNR- and MI-based optimal radar waveform optimization methods are formulated. The resulting waveform optimization problems are solved through the well-known bisection search technique. Simulation results demonstrate utilizing various examples and scenarios that the proposed radar waveform design schemes can evidently improve the LPI performance of DMRS without interfering with friendly communications.

In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of fulfilling the requirements of both radar and communications simultaneously. First, assuming that the signal-to-noise ratio (SNR) corresponding to the target surveillance path is much weaker than that corresponding to the line of sight path at radar receiver, the analytically closed-form expression for the probability of false alarm is calculated, whereas the closed-form expression for the probability of detection is not analytically tractable and is approximated due to the fact that the received signals are not zero-mean Gaussian under target presence hypothesis. Then, an LPI-based optimal power allocation strategy is presented to minimize the total transmission power for information signal and radar waveform, which is constrained by a specified information rate for the communication receiver and the desired probabilities of detection and false alarm for the radar receiver. The well-known bisection search method is employed to solve the resulting constrained optimization problem. Finally, numerical simulations are provided to reveal the effects of several system parameters on the power allocation results. It is also demonstrated that the LPI performance of the joint bistatic radar and communication system can be markedly improved by utilizing the proposed scheme.

In this study, the modified Cramér-Rao lower bounds (MCRLBs) on the joint estimation of target position and velocity is investigated for a universal mobile telecommunication system (UMTS)-based passive multistatic radar system with antenna arrays. First, we analyze the log-likelihood redfunction of the received signal for a complex Gaussian extended target. Then, due to the non-deterministic transmitted data symbols, the analytically closed-form expressions of the MCRLBs on the Cartesian coordinates of target position and velocity are derived for a multistatic radar system with N t UMTS-based transmit station of L t antenna elements and N r receive stations of L r antenna elements. With the aid of numerical simulations, it is shown that increasing the number of receiving elements in each receive station can reduce the estimation errors. In addition, it is demonstrated that the MCRLB is not only a function of signal-to-noise ratio (SNR), the number of receiving antenna elements and the properties of the transmitted UMTS signals, but also a function of the relative geometric configuration between the target and the multistatic radar system.The analytical expressions for MCRLB will open up a new dimension for passive multistatic radar system by aiding the optimal placement of receive stations to improve the target parameter estimation performance.

Widely distributed radar network architectures can provide significant performance improvement for target detection and localization. For a fixed radar network, the achievable target detection performance may go beyond a predetermined threshold with full transmitted power allocation, which is extremely vulnerable in modern electronic warfare. In this paper, we study the problem of low probability of intercept (LPI) design for radar network and propose two novel LPI optimization schemes based on information-theoretic criteria. For a predefined threshold of target detection, Schleher intercept factor is minimized by optimizing transmission power allocation among netted radars in the network. Due to the lack of analytical closed-form expression for receiver operation characteristics (ROC), we employ two information-theoretic criteria, namely, Bhattacharyya distance and J-divergence as the metrics for target detection performance. The resulting nonconvex and nonlinear LPI optimization problems associated with different information-theoretic criteria are cast under a unified framework, and the nonlinear programming based genetic algorithm (NPGA) is used to tackle the optimization problems in the framework. Numerical simulations demonstrate that our proposed LPI strategies are effective in enhancing the LPI performance for radar network.

This paper investigates the joint target parameter (delay and Doppler) estimation performance of linear frequency modulation (LFM)-based radar networks in a Rice fading environment. The active radar networks are composed of multiple radar transmitters and multichannel receivers placed on moving platforms. First, the log-likelihood function of the received signal for a Rician target is derived, where the received signal scattered off the target comprises of dominant scatterer (DS) component and weak isotropic scatterers (WIS) components. Then, the analytically closed-form expressions of the Cramer-Rao lower bounds (CRLBs) on the Cartesian coordinates of target position and velocity are calculated, which can be adopted as a performance metric to access the target parameter estimation accuracy for LFM-based radar network systems in a Rice fading environment. It is found that the cumulative Fisher information matrix (FIM) is a linear combination of both DS component and WIS components, and it also demonstrates that the joint CRLB is a function of signal-to-noise ratio (SNR), target’s radar cross section (RCS) and transmitted waveform parameters, as well as the relative geometry between the target and the radar network architectures. Finally, numerical results are provided to indicate that the joint target parameter estimation performance of active radar networks can be significantly improved with the exploitation of DS component.