The key challenge of the extended target probability hypothesis density (ET-PHD) filter is to reduce the computational complexity by using a subset to approximate the full set of partitions. In this paper, the influence for the tracking results of different partitions is analyzed, and the form of the most informative partition is obtained. Then, a fast density peak-based clustering (FDPC) partitioning algorithm is applied to the measurement set partitioning. Since only one partition of the measurement set is used, the ET-PHD filter based on FDPC partitioning has lower computational complexity than the other ET-PHD filters. As FDPC partitioning is able to remove the spatially close clutter-generated measurements, the ET-PHD filter based on FDPC partitioning has good tracking performance in the scenario with more clutter-generated measurements. The simulation results show that the proposed algorithm can get the most informative partition and obviously reduce computational burden without losing tracking performance. As the number of clutter-generated measurements increased, the ET-PHD filter based on FDPC partitioning has better tracking performance than other ET-PHD filters. The FDPC algorithm will play an important role in the engineering realization of the multiple extended target tracking filter.

This paper highlights the memristor bridge-based lowpass filter (LPF) and improved image processing algorithms along with a novel adaptive Gaussian filter for denoising image and a new Gaussian pyramid for scale invariant feature transform (SIFT). First, a novel kind of LPF based on the memristor bridge is designed, whose cut-off frequency and other traits are demonstrated to change with different time and memristance. In light of the changeable parameter of the memristor bridge-based LPF, a new adaptive Gaussian filter and an improved SIFT algorithm are presented. Finally, experiment results show that the peak signalto-noise ratio (PSNR) of our denoising is bettered more than 2.77 dB compared to the corresponding of the traditional Gaussian filter, and our improved SIFT performances including the number of matched feature points and the percent of correct matches are higher than the traditional SIFT, which verifies feasibility and effectiveness of our algorithm.

Aiming at a novel missile-borne detector in the optional burst height proximity fuze, a self-adaptive high-resolution forward-looking imaging algorithm (SAHRFL-IA) is presented. The echo data are captured by the missile-borne detector in the target regions; thereby the azimuth angulation accuracy at the same distance dimension is improved dynamically. Thus, azimuth information of the targets in the detection area may be obtained accurately. The proposed imaging algorithm breaks through the conventional misconception of merely using azimuth discrimination curves under ideal conditions during monopulse angulation. The real-time echo data from the target region are used to perform error correction for this discrimination curve, and finally the accuracy of the azimuth angulation may reach the optimum at the same distance dimension. A series of experiments demonstrate the validity, reliability and high performance of the proposed imaging algorithm. Azimuth angulation accuracy may reach ten times that of the detection beam width. Meanwhile, the running time of this algorithm satisfies the requirements of missile-borne platforms.

Constant envelope with a fractional Fourier transformorthogonal frequency division multiplexing (CE-FrFT-OFDM) is a special case of a constant envelope OFDM (CE-OFDM), both being energy efficient wireless communication techniques with a 0 dB peak to average power ratio (PAPR). However, with the proper selection of fractional order, the first technique has a high bit error rate (BER) performance in the frequency-time selective channels. This paper performs further analysis of CE-FrFT-OFDM by examining its spectral efficiency (SE) and energy efficiency (EE) and compare to the famous OFDM and FrFT-OFDM techniques. Analytical and comprehensive simulations conducted show that, the CE-FrFT-OFDM has five times the EE of OFDM and FrFT-OFDM systems with a slightly less SE. Increasing CE-FrFT-OFDM's transmission power by increasing its amplitude to 1.7 increases its SE to match that of the OFDM and FrFT-OFDM systems while slightly reducing its EE by 20% to be four times that of OFDM and FrFTOFDM systems. OFDM and FrFT-OFDM's amplitude fluctuations cause rapid changing output back-off (OBO) power requirements and further reduce power amplifier (PA) efficiency while CE-FrFTOFDM stable operational linear range makes it a better candidate and outperforms the other techniques when their OBO exceeds 1.7. Higher EE and low BER in time-frequency selective channel are attracting features for CE-FrFT-OFDM deployment in mobile devices.

Many safety-critical applications that utilize the global navigation satellite system (GNSS) demand highly accurate positioning information, as well as highly integrity and reliability. Due to GNSS signals are easily distorted by the interferences or disturbances, the signal quality monitoring (SQM) is necessary to detect the presence of dangerous signal distortions. In this paper, we developed an SQM software for binary offset carrier (BOC) modulated navigation signals. Firstly, the models of BOC signal with ideal and distortion are presented respectively. Then the architecture of SQM software is proposed. Moreover, the effect of the white gaussian noise (WGN) and the front-end filter on the correlation peak of the receiver is analyzed. Finally, the biases induced by the signal distortion are evaluated. The experiments simulate the relationships between the code phase shift and the normalized correlation value in the case of the signal digital distortion and the analog distortion. The simulation results demonstrate that the proposed SQM method can effectively monitor the signal distortion and accurately estimate the correlation peak deviation caused by the distortion.

Cover ratio of cloud is a very important factor which affects the quality of a satellite image, therefore cloud detection from satellite images is a necessary step in assessing the image quality. The study on cloud detection from the visual band of a satellite image is developed. Firstly, we consider the differences between the cloud and ground including high grey level, good continuity of grey level, area of cloud region, and the variance of local fractal dimension (VLFD) of the cloud region. A single cloud region detection method is proposed. Secondly, by introducing a reference satellite image and by comparing the variance in the dimensions corresponding to the reference and the tested images, a method that detects multiple cloud regions and determines whether or not the cloud exists in an image is described. By using several Ikonos images, the performance of the proposed method is demonstrated.

Inverse synthetic aperture radar (ISAR) image can be represented and reconstructed by sparse recovery (SR) approaches. However, the existing SR algorithms, which are used for ISAR imaging, have suffered from high computational cost and poor imaging quality under a low signal to noise ratio (SNR) condition. This paper proposes a fast decoupled ISAR imaging method by exploiting the inherent structural sparse information of the targets. Firstly, the ISAR imaging problem is decoupled into two sub-problems. One is range direction imaging and the other is azimuth direction focusing. Secondly, an efficient two-stage SR method is proposed to obtain higher resolution range profiles by using jointly sparse information. Finally, the residual linear Bregman iteration via fast Fourier transforms (RLBI-FFT) is proposed to perform the azimuth focusing on low SNR efficiently. Theoretical analysis and simulation results show that the proposed method has better performence to efficiently implement higher-resolution ISAR imaging under the low SNR condition.

This paper focuses on the jamming problem of bistatic synthetic aperture radar (BiSAR), and a jamming method against BiSAR based on modulation theory is proposed. The proposed jamming method modulates the BiSAR signal with the cosinusoidal phase to generate multi-false targets in range, and further rotates the jammer to generate multi-false targets in azimuth. The range multi-false targets and azimuth multi-false targets form the two-dimensional cover jamming or deception jamming, which can protect the important targets efficiently. The number of false targets, the interval of false targets, and the jamming square can be adjusted flexibly by setting different range jamming parameters and azimuth jamming parameters. The jamming performance and the choosing criteria of jamming parameters are also discussed. Finally, the simulated data verify the effectiveness of the jamming method.

Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge. An essential problem about discrete BNs is learning conditional probability table (CPT) parameters. If training data are sparse, purely data-driven methods often fail to learn accurate parameters. Then, expert judgments can be introduced to overcome this challenge. Parameter constraints deduced from expert judgments can cause parameter estimates to be consistent with domain knowledge. In addition, Dirichlet priors contain information that helps improve learning accuracy. This paper proposes a constrained Bayesian estimation approach to learn CPTs by incorporating constraints and Dirichlet priors. First, a posterior distribution of BN parameters is developed over a restricted parameter space based on training data and Dirichlet priors. Then, the expectation of the posterior distribution is taken as a parameter estimation. As it is difficult to directly compute the expectation for a continuous distribution with an irregular feasible domain, we apply the Monte Carlo method to approximate it. In the experiments on learning standard BNs, the proposed method outperforms competing methods. It suggests that the proposed method can facilitate solving real-world problems. Additionally, a case study of Wine data demonstrates that the proposed method achieves the highest classification accuracy.

As the air combat environment becomes more complicated and changeable, accurate threat assessment of air target has a significant impact on air defense operations. This paper proposes an improved generalized intuitionistic fuzzy soft set (GIFSS) method for dynamic assessment of air target threat. Firstly, the threat assessment index is reasonably determined by analyzing the typical characteristics of air targets. Secondly, after the GIFSS at different time is obtained, the index weight is determined by the intuitionistic fuzzy set entropy and the relative entropy theory. Then, the inverse Poisson distribution method is used to determine the weight of time series, and then the time-weighted GIFSS is obtained. Finally, threat assessment of five air targets is carried out by using the improved GIFSS (I-GIFSS) and comparison methods. The validity and superiority of the proposed method are verified by calculation and comparison.

A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform.

In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the POMDP framework. The elements of the POMDP model are analyzed and described. The state transfer law in the model can be described by the method of interactive multiple model (IMM) due to the diversity of the target motion law, which is used to switch the motion model to accommodate target maneuvers, and hence improving the tracking accuracy. The simulation results show that the model can achieve efficient planning for the UAV route, and effective tracking for the target. Furthermore, the path planned by this model is more reasonable and efficient than that by using the single state transition law.

Cross iteration often exists in the computational process of the simulation models, especially for control models. There is a credibility defect tracing problem in the validation of models with cross iteration. In order to resolve this problem, after the problem formulation, a validation theorem on the cross iteration is proposed, and the proof of the theorem is given under the cross iteration circumstance. Meanwhile, applying the proposed theorem, the credibility calculation algorithm is provided, and the solvent of the defect tracing is explained. Further, based on the validation theorem on the cross iteration, a validation method for simulation models with the cross iteration is proposed, which is illustrated by a flowchart step by step. Finally, a validation example of a sixdegree of freedom (DOF) flight vehicle model is provided, and the validation process is performed by using the validation method. The result analysis shows that the method is effective to obtain the credibility of the model and accomplish the defect tracing of the validation.

In order to handle the semi-supervised problem quickly and efficiently in the twin support vector machine (TWSVM) field, a semi-supervised twin support vector machine (S²TSVM) is proposed by adding the original unlabeled samples. In S²TSVM, the addition of unlabeled samples can easily cause the classification hyper plane to deviate from the sample points. Then a centerdistance principle is proposed to pre-classify unlabeled samples, and a pre-classified S²TSVM (PS²TSVM) is proposed. Compared with S²TSVM, PS²TSVM not only improves the problem of the samples deviating from the classification hyper plane, but also improves the training speed. Then PS²TSVM is smoothed. After smoothing the model, the pre-classified smooth S²TSVM (PS³TSVM) is obtained, and its convergence is deduced. Finally, nine datasets are selected in the UCI machine learning database for comparison with other types of semi-supervised models. The experimental results show that the proposed PS³TSVM model has better classification results.

A prescribed performance neural controller to guarantee tracking quality is addressed for the near space kinetic kill vehicle (NSKKV) to meet the state constraints caused by side window detection. Different from the traditional prescribed performance control in which the shape of the performance function is constant, this paper exploits new performance functions which can change the shape of their function according to different symbols of initial errors and can ensure the error convergence with a small overshoot. The neural backstepping control and the minimal learning parameters (MLP) technology are employed for exploring a prescribed performance controller (PPC) that provides robust tracking attitude reference trajectories. The highlight is that the transient performance of tracking errors is satisfactory and the computational load of neural approximation is low. The pseudo rate (PSR) modulator is used to shape the continuous control command to pulse or on-off signals to meet the requirements of the thruster. Numerical simulations show that the proposed method can achieve state constraints, pseudo-linear operation and high accuracy.

This paper studies time-varying fault-tolerant formation tracking problems for the multiple cruise missile system under directed topologies subjected to actuator failures. Firstly, the timevarying fault-tolerant formation tracking process for the multiple cruise missile system is divided into the guidance loop and the control loop. Then protocols are constructed to accomplish distributed fault-tolerant formation tracking in the guidance loop with the adaptive updating mechanism, in the condition where neither the knowledge about actuator malfunctions nor any global information of the communication topology remains available. Moreover, sufficient conditions to accomplish formation tracking are presented, and it is shown that the multiple cruise missile system can carry on the predefined time-varying fault-tolerant control (FTC) formation tracking through the active disturbances rejection controller (ADRC) and the proportion integration (PI) controller by the way of the fault-tolerant protocol utilizing the designed strategies, in the event of actuator failures. At last, numerical analysis and simulation are designed to verify the theoretical results.

The control problem of a class of parabolic distributed parameter systems (DPSs) is investigated by using mobile agents with capabilities of sensing and actuating. The guidance strategies of mobile agents based on coverage optimization methods are proposed to improve the control performance of the system and make the state norm of the system converge to zero faster. The coverage optimization problems are constructed based on the measurement of each agent. By solving the coverage optimization problems, the local optimal moving direction of each agent can be obtained. Then the gradient-based agent motion control laws are established. With the indicator function and the surface delta function, this method is generalized to n-dimensional space, and suitable for any sensing region with piecewise smooth boundaries. The stability and control performance of the system are analyzed. Numerical simulations show the effectiveness of the proposed methods.

A simple repairable system with one repairman is considered. As the system working age is up to a specified time T, the repairman will repair the component preventively, and it will go back to work as soon as the repair finished. When the system failure, the repairman repair it immediately. The time interval of the preventive repair and the failure correction is described with the extended geometric process. Different from the available replacement policy which is usually based on the failure number or the working age of the system, the bivariate policy (T, N) is considered. The explicit expression of the long-run average cost rate function C(T, N) of the system is derived. Through alternatively minimize the cost rate function C(T, N), the optimal replacement policy (T^*, N^*) is obtained, and it proves that the optimal policy is unique. Numerical cases illustrate the conclusion, and the sensitivity analysis of the parameters is carried out.

Basing on discrete event simulation, a reliability simulation algorithm of the phased-mission system with multiple states is put forth. Firstly, the concepts and main characters of phasedmission system are discussed, and an active and standby redundancy (AS) tree structure method to describe the system structure of each mission phase is brought forward. Secondly, the behavior of the phased-mission system with multiple states is discussed with the theory of state chart. Thirdly, basing on the discrete event simulation concept, a simulation algorithm to estimate reliability parameters of the phased-mission system with multiple states is explored. Finally, an example is introduced and analyzed, and the analysis result verifies the algorithms. The simulation algorithm is practical and versatile, for it can model complex behavior of phased-mission system flexibly, and more reliability parameters to understand system operation can be attained.