To mitigate the deleterious effects of clutter and jammer, modern radars have adopted adaptive processing techniques such as constant false alarm rate (CFAR) detectors which are widely used to prevent clutter and noise interference from saturating the radar's display and preventing targets from being obscured. This paper concerns with the detection analysis of the novel version of CFAR schemes (cell-averaging generalized trimmed-mean, CA_GTM) in the presence of additional outlying targets other than the target under research. The spurious targets as well as the tested one are assumed to be fluctuating in accordance with the χ²-model with two-degrees of freedom. In this situation, the processor performance is enclosed by the swerling models (SWI and SWII). Between these bounds, there is an important class of target fluctuation which is known as moderately fluctuating targets. The detection of this class has many practical applications. Structure of the CA_GTM detector is described briefly. Detection performances for optimal, CA_TM, CA, trimmed-mean (TM) and ordered-statistic (OS) CFAR strategies have been analyzed and compared for desired probability of false alarm and determined size of the reference window. False alarm rate performance of these processors has been evaluated for different strengths of interfering signal and the effect of correlation among the target returns on the detection and false alarm performances has also been studied. Our numerical results show that, with a proper choice of trimming parameters, the novel model CA TM presents an ideal detection performance outweighing that of the Neyman-Pearson detector on condition that the tested target obeys the SWII model in its fluctuation. Although the new models CA_OS and CA_TM can be treated as special cases of the CA_GTM algorithm, their multi-target performance is modest even it has an enhancement relative to that of the classical CA scheme. Additionally, they fail to maintain the false alarm rate constant when the operating environment is of type target multiplicity. Moreover, the non-coherent integration of M pulses ameliorates the processor performance either it operates in homogeneous or multi-target environment.

For acquiring high energy efficiency and the maximal throughput, a new time slot structure is designed for energy harvesting (EH) cognitive radio (CR). Considering the CR system with EH and cooperative relay, a best cooperative mechanism (BCM) is proposed for CR with EH. To get the optimal estimation performance, a quantum fireworks algorithm (QFA) is designed to resolve the difficulties of maximal throughput and EH, and the proposed cooperative mechanism is called as QFA-BCM. The proposed QFA combines the advantages of quantum computation theory with the fireworks algorithm (FA). Thus the QFA is able to obtain the optimal solution and its convergence performance is proved. By using the new cooperation mechanism and computing algorithm, the proposed QFA-BCM method can achieve comparable maximal throughput in the new timeslot structure. Simulation results have proved that the QFA-BCM method is superior to previous non-cooperative and cooperative mechanisms.

This paper investigates the jamming sensing performance of the simultaneous transmit and receive based cognitive anti-jamming (SCAJ) receiver impaired by phase noise in local oscillators (LO) over fading channels. Firstly, energy detection (ED) based on the jamming to noise ratio (JNR) of the high frequency bands SCAJ receiver with phase noise under different channels is analyzed. Then, the probabilities of jamming detection and false alarm in closed-form for the SCAJ receiver are derived. Finally, the modified Bayesian Cramer-Rao bound (BCRB) of jamming sensing for the SCAJ receiver is presented. Simulation results show that the performance degradation of the SCAJ system due to phase noise is more severe than that due to the channel fading in the circumstances where the signal bandwidth (BW) is kept a constant. Moreover, the signal BW has an effect on the phase noise in LO, and the jamming detection probability of the wideband SCAJ receiver with lower phase noise outperforms that of the narrowband receiver using the same center frequency. Furthermore, an accurate phase noise estimation and compensation scheme can improve the jamming detection capability of the SCAJ receiver in high frequency bands and approach to the upper bound.

Traditional global navigation satellite system (GNSS) terminals for satellite navigation adopt independent channels to track the signals from different satellites, which results in a lack of information interaction between the channels. Inspired by the vector tracking idea, and drawing lessons from the principle that in the position domain the Taylor expanded pseudorange observations can be used for positioning via the least squares method, this paper proposes a novel least squares-based multi-channel parameter joint estimation (MPJE) method in the signal domain, which not only retains the advantages of channel fusion, but also maintains the flexibility and diversity of the localization algorithm. With achieving optimal carrier to noise ratio as the goal, the proposed method obtains the required code loop and carrier loop parameters for signal tracking in the domain of whole channels. Experimental results indicate that this method fully achieves the assistant fusion advantages of frequency lock loop (FLL), phase lock loop (PLL) and delay lock loop (DLL), making good use of the robustness and dynamic properties of the FLL and the measurement accuracy of the DLL, and is helpful for achieving stable and accurate signal tracking under weak signals and high dynamic stress environments.

Transmit waveform optimization is critical to radar system performance. There have been a fruit of achievements about waveform design in recent years. However, most of the existing methods are based on the assumption that radar is smart and the target is dumb, which is not always reasonable in the modern electronic warfare. This paper focuses on the waveform design for radar and the extended target in the environment of electronic warfare. Three different countermeasure models between smart radar and dumb target, smart target and dumb radar, smart radar and smart target are proposed. Taking the signal-to-interferenceplus-noise ratio (SINR) as the metric, optimized waveforms for the first two scenarios are achieved by the general water-filling method in the presence of clutter. For the last case, the equilibrium between smart radar and smart target in the presence of clutter is given mathematically and the optimized solution is achieved through a novel two-step water-filling method on the basis of minmax theory. Simulation results under different power constraints show the power allocation strategies of radar and target and the output SINRs are analyzed.

Time-delay and Doppler shift estimation is a basic task for pulse-Doppler radar processing. For low-rate sampling of echo signals, several kinds of compressive sampling (CS) pulse-Doppler (CSPD) radar are developed with different analog-to-information conversion (AIC) systems. However, a unified metric is absent to evaluate their parameter estimation performance. Towards this end, this paper derives the deterministic Cramer-Rao bound (CRB) for the joint delay-Doppler estimation of CSPD radar to quantitatively analyze the estimate performance. Theoretical results reveal that the CRBs of both time-delays and Doppler shifts are inversely proportional to the received target signal-to-noise ratio (SNR), the number of transmitted pulses and the sampling rate of AIC systems. The main difference is that the CRB of Doppler shifts also lies on the coherent processing interval. Numerical experiments validate these theoretical results. They also show that the structure of the AIC systems has weak influence on the CRBs, which implies that the AIC structures can be flexibly selected for the implementation of CSPD radar.

An online pulse interleaving scheduling algorithm is proposed for a solution to the task scheduling problem in the digital array radar (DAR). The full DAR task structure is explicitly considered in a way that the waiting duration is able to be utilized to transmit or receive subtasks, namely the pulse interleaving, as well as the receiving durations of different tasks are able to be overlapped. The algorithm decomposes the pulse interleaving scheduling analysis into the time constraint check and the energy constraint check, and schedules online all kinds of tasks that are able to be interleaved. Thereby the waiting duration and the receiving duration in the DAR task are both fully utilized. The simulation results verify the performance improvement and the high efficiency of the proposed algorithm compared with the existing ones.

This paper researches the adaptive scheduling problem of multiple electronic support measures (multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming (ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter (UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.

To reach a higher level of autonomy for unmanned combat aerial vehicle (UCAV) in air combat games, this paper builds an autonomous maneuver decision system. In this system, the air combat game is regarded as a Markov process, so that the air combat situation can be effectively calculated via Bayesian inference theory. According to the situation assessment result, adaptively adjusts the weights of maneuver decision factors, which makes the objective function more reasonable and ensures the superiority situation for UCAV. As the air combat game is characterized by highly dynamic and a significant amount of uncertainty, to enhance the robustness and effectiveness of maneuver decision results, fuzzy logic is used to build the functions of four maneuver decision factors. Accuracy prediction of opponent aircraft is also essential to ensure making a good decision; therefore, a prediction model of opponent aircraft is designed based on the elementary maneuver method. Finally, the moving horizon optimization strategy is used to effectively model the whole air combat maneuver decision process. Various simulations are performed on typical scenario test and close-in dogfight, the results sufficiently demonstrate the superiority of the designed maneuver decision method.

In the design problem of low earth orbit (LEO) reconnaissance satellite constellation, optimization of coverage performance is the design goal in most current methods. However, in the using process, the user only concerns with the detection capabilities rather than coverage performance. To establish the relationship between these two aspects, the reconnaissance processes of normal stochastic targets are considered and the mathematic models of detection processes are built. The indicators of coverage performance are used to evaluate the detection probability and expectation of detection time delay, which are important factors in reconnaissance constellation estimation viewed from military intelligence discipline. The conclusions confirmed by the final simulation will be useful in LEO reconnaissance constellation design, optimization and evaluation.

Given a non-equidistant sequence or an equidistant series with one or more outliers, a grey interpolation approach considering the time lags is established for producing the missing data or correcting the abnormal values. To accomplish this, a new grey incidence model, called the grey dynamic incidence model GDIM(t), is constructed for determining whether the factors are effective to the known factor and what the time lag is between a useful factor and the specified sequence. Based on the results of the GDIM(t) model, two programming problems are designed to obtain the upper and lower bounds of the unknown or abnormal values which are regarded as grey numbers. The solutions based on the particle swarm optimization (PSO) for the nonlinear programming problems are given. To explain how it can be used in practice, this new grey interpolation approach is applied to correct an abnormal value in the sequence of an agriculture environment problem.

A robust controller for bank to turn (BTT) missiles with aerodynamic fins and reaction jet control system (RCS) is developed based on nonlinear control dynamic models comprising couplings and aerodynamic uncertainties. The fixed time convergence theory is incorporated with the sliding mode control technique to ensure that the system tracks the desired command within uniform bounded time under different initial conditions. Unlike previous terminal sliding mode approaches, the bound of settling time is independent of the initial state, which means performance metrics like convergence rate can be predicted beforehand. To reduce the burden of control design in terms of robustness, extended state observer (ESO) is introduced for uncertainty estimation with the output substituted into the controller as feedforward compensation. Cascade control structure is employed with the proposed control law and therein the compound control signal is obtained. Afterwards, control inputs for two kinds of actuators are allocated on the basis of their inherent characteristics. Finally, a number of simulations are carried out and demonstrate the effectiveness of the designed controller.

The various uncertainties of Mars environment have great impact on the process of vehicles entering the atmosphere. To improve the robustness of control system against the model errors and to reduce the computational burden, an optimal feedback based tracking control law is developed. The control scheme presented in this paper determines the amplitude and the reversals of bank angle respectively in the longitudinal and lateral flight plane. At each control cycle, the amplitude of the bank angle is obtained by an optimal feedback controller to minimize tracking errors. The control gains are tuned according to the closed-loop error dynamics by using optimization methods. The bank reversals are executed if the crossrange exceeds a predetermined corridor which is designed by setting a boundary function. The accuracy and robustness of the proposed closed-loop optimal feedback based control law in tracking the reference trajectory is verified via 500 deviation simulations, in which modeling errors and external disturbances are considered.

In this paper, a new adaptive optimal guidance law with impact angle and seeker's field-of-view (FOV) angle constraints is proposed. To this end, the generalized optimal guidance law is derived first. A changeable impact angle weighting (IAW) coefficient is introduced and used to modify the guidance law to make it adaptive for all guidance constraints. After integrating the closed-form solution of the guidance command with linearized engagement kinematics, the analytic predictive models of impact angle and FOV angle are built, and the available range of IAW corresponding to constraints is certain. Next, a calculation scheme is presented to acquire the real-time value of IAW during the entire guidance process. When applying the proposed guidance law, the IAW will keep small to avoid a trajectory climbing up to limit FOV angle at an initial time but will increase with the closing target to improve impact position and angle accuracy, thereby ensuring that the guidance law can juggle orders of guidance accuracy and constraints control.

Gravity-aided inertial navigation is a hot issue in the applications of underwater autonomous vehicle (UAV). Since the matching process is conducted with a gravity anomaly database tabulated in the form of a digital model and the resolution is 2'×2', a filter model based on vehicle position is derived and the particularity of inertial navigation system (INS) output is employed to estimate a parameter in the system model. Meanwhile, the matching algorithm based on point mass filter (PMF) is applied and several optimal selection strategies are discussed. It is obtained that the point mass filter algorithm based on the deterministic resampling method has better practicability. The reliability and the accuracy of the algorithm are verified via simulation tests.

This paper is concerned with the global boundedness problem for a class of stochastic nonlinear systems with matched conditions. The uncertainties in the systems are due to parameter variations and external stochastic disturbance. Only the matched conditions and the possible bound of the uncertainties are demanded. Based on the stochastic Lyapunov stability theory, an explicit controller is constructed in the gradient direction, which renders responses of the closed-loop systems be globally bounded in probability. When the systems degrade to linear systems, the controller becomes linear. Illustrative examples are given to show the effectiveness of the proposed method.

The quantum bacterial foraging optimization (QBFO) algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant, which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO (IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity. The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation (PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.

A co-design scheme of event-triggered sampling mechanism and active fault tolerant control (FTC) is developed. Firstly, a fault diagnosis observer is designed to estimate both the fault and the state simultaneously by using the event-triggered sampled output. Some H_∞ constraints between the estimation errors and the event-triggered sampling mechanism are established to ensure the estimation accuracy. Then, based on the constraints and the obtained fault information, an event-triggered detector and a static fault tolerant controller are co-designed to guarantee the stability of the faulty system and to reduce the sensor communication cost. Furthermore, the problem of the event detector and dynamic FTC co-design is also investigated. Simulation results of an unstable batch reactor are finally provided to illustrate the effectiveness of the proposed method.

Dempster-Shafer evidence theory (DS theory) is widely used in brain magnetic resonance imaging (MRI) segmentation, due to its efficient combination of the evidence from different sources. In this paper, an improved MRI segmentation method, which is based on fuzzy c-means (FCM) and DS theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict information in the pictures. Then, the neighborhood information and the different influences of spatial location of neighborhood pixels are taken into consideration to handle the spatial information. Finally, the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images and the MRI images illustrate that our proposed method is more effective in image segmentation.

Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition (FPPR) model and the dualdictionary sparse representation classification (DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory (ORL) and face recognition technology (FERET) databases, and also reflect comparisons with SRC, linear regression classification (LRC), and two-phase test sample sparse representation (TPTSSR).

Most of the maintenance optimization models in condition-based maintenance (CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model (HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo (MCMC) simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model (HMM), the age-based replacement policy, Hotelling's T², multivariate exponentially weihted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.

Compared with accurate diagnosis, the system's selfdiagnosing capability can be greatly increased through the t/k-diagnosis strategy at most k vertexes to be mistakenly identified as faulty under the comparison model, where k is typically a small number. Based on the Preparata, Metze, and Chien (PMC) model, the n-dimensional hypercube network is proved to be t/k-diagnosable. In this paper, based on the Maeng and Malek (MM)^* model, a novel t/k-fault diagnosis (1≤k≤4) algorithm of ndimensional hypercube, called t/k-MM^*-DIAG, is proposed to isolate all faulty processors within the set of nodes, among which the number of fault-free nodes identified wrongly as faulty is at most k. The time complexity in our algorithm is only O(2ⁿn²).