A new type of estimator is developed for the satellite formation to track and inspect on-orbit targets. The follower satellite in the formation works without relative sensors, and its target pointing commands are derived based on relative orbital dynamics. The centralized estimator based on truth measurement is designed, however, this estimator is proved unstable because of the lack of necessary measurement information. After that, an alternative estimator based on pseudo measurement is designed, and its observability and controllability are analyzed to qualitatively evaluate the convergence performance. Finally, an on-orbit target inspection scenario is numerically simulated to verify the performance of the estimator based on pseudo measurement.

Doppler blind zone (DBZ) has a bad influence on the airborne early warning radar, although it has good detection performance for low altitude targets with pulse Doppler (PD) technology. In target tracking, the blind zone can cause target tracking breakage easily. In order to solve this problem, a parallel particle filter (PF) algorithm based on multi-hypothesis motion models (MHMMs) is proposed. The algorithm produces multiple possible target motion models according to the DBZ constraint. Particles are updated with the constraint in each motion model. Once the first measurement from the target which reappears from DBZ falls into the particle cloud formed by any model, the measurementtrack association succeeds and track breakage is avoided. The simulation results show that on the condition of different DBZ ranges, a high association ratio can be got for targets with different maneuverability levels, which accordingly improves the tracking quality.

The distribution probability of the photon interarrival time (PIT) without signal initial phases is derived based on the Poisson model of X-ray pulsar signals, and a pulsar signal detection algorithm employing the PIT sequence is put forward. The joint probability of the PIT sequence is regarded as a function of the distribution probability and used to compare a constant radiation intensity model with the nonhomogeneous Poisson model for the signal detection. The relationship between the number of detected photons and the probabilities of false negative and positive is studied, and the success rate and mean detection time are estimated based on the number of the given photons. For the spacecraft velocity data detection, the changes of time of photon arrival (TOPA) and PIT caused by spacecraft motion are presented first, then the influences on detection are analyzed respectively. By using the analytical pulse profile of PSR B0531+21, the simulation of the Xray pulsar signal detection is implemented. The simulation results verify the effectiveness of the proposed method, and the contrast tests show that the proposed method is suitable for the spacecraft velocity data detection.

The angular glint in the near field plays an important role on radar tracking errors. To predict it more efficiently for electrically large targets, a new method based on graphical electromagnetic computing (GRECO) is proposed. With the benefit of the graphic card, the GRECO prediction method is faster and more accurate than other methods. The proposed method at the first time considers the special case that the targets cannot be completely covered by radar beams, which makes the prediction of radar tracking errors more self-contained in practical circumstances. On the other hand, the process of the scattering center extraction is omitted, resulting in possible angular glint prediction in real time. Comparisons between the simulation results and the theoretical ones validate its correctness and value to academic research and engineering applications.

Intercepted signal blind separation is a research topic with high importance for both military and civilian communication systems. A blind separation method for space-time block code (STBC) systems is proposed by using the ordinary independent component analysis (ICA). This method cannot work when specific complex modulations are employed since the assumption of mutual independence cannot be satisfied. The analysis shows that source signals, which are group-wise independent and use multi-dimensional ICA (MICA) instead of ordinary ICA, can be applied in this case. Utilizing the block-diagonal structure of the cumulant matrices, the JADE algorithm is generalized to the multidimensional case to separate the received data into mutually independent groups. Compared with ordinary ICA algorithms, the proposed method does not introduce additional ambiguities. Simulations show that the proposed method overcomes the drawback and  achieves a better performance without utilizing coding information than channel estimation based algorithms.

Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation (LFM) signals. The smoothed pseudo Winger-Ville distribution (SPWVD) is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio (SNR) circumstance. The performance of this method is confirmed by numerical simulation.

A new method to construct shift sequence sets is presented. Different shift sequence sets are obtained by changing parameters of the shift sequence. Based on these shift sequence sets, multiple shift distinct array sets with zero-correlation zone (ZCZ) can be obtained by utilizing interleaving technique. It is shown that the resultant ZCZ array sets are optimal or almost optimal with respect to the Tang, Fan, and Matsufuji bound. Compared with previous methods, the proposed method extends the number of shift distinct ZCZ array sets.

Micromotion is an important target feature, although the target micromotion has an unfavorable influence on the synthetic aperture radar (SAR) image interpretation due to defocusing. This paper introduces micromotion parameters into the scattering center model to obtain a hybrid micromotion-scattering center model, and then proposes an optimization algorithm based on the maximal likelihood estimation to solve the model for jointly obtaining target motion and scattering parameters. Initial value estimation methods using targets’ ghost images are then presented to guarantee the global and fast convergence. Simulation results show the effectiveness of the proposed algorithm especially in high precision estimation and multiple targets processing.

A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section (RCS). Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.

Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems (SoS) for surface warship formation (CASoSSWF) to the biological immune system (BIS) according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS, and then designs the models of components and the architecture for a monitoring agent, a regulating agent, a killer agent, a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system, the multi-agent system (MAS), the vaccine and the danger theory (DT). Moreover a new immune multi-agent model using vaccine based on DT (IMMUVBDT) for the cooperative air-defense SoS is advanced. The immune response and immune mechanism of the CASoSSWF are analyzed. The model has a capability of memory, evolution, commendable dynamic environment adaptability and self-learning, and embodies adequately the cooperative air-defense mechanism for the CASoSSWF. Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.

With applying the information technology to the military field, the advantages and importance of the networked combat are more and more obvious. In order to make full use of limited battlefield resources and maximally destroy enemy targets from arbitrary angle in a limited time, the research on firepower nodes dynamic deployment becomes a key problem of command and control. Considering a variety of tactical indexes and actual constraints in air defense, a mathematical model is formulated to minimize the enemy target penetration probability. Based on characteristics of the mathematical model and demands of the deployment problems, an assistance-based algorithm is put forward which combines the artificial potential field (APF) method with a memetic algorithm. The APF method is employed to solve the constraint handling problem and generate feasible solutions. The constrained optimization problem transforms into an optimization problem of APF parameters adjustment, and the dimension of the problem is reduced greatly. The dynamic deployment is accomplished by generation and refinement of feasible solutions. The simulation results show that the proposed algorithm is effective and feasible in dynamic situation.

Methods of the comprehensive evaluation have been studied for many years. However, the change speed of evaluated objects was rarely considered by the existing evaluation methods. An evaluation matrix is proposed to remedy this deficiency. Firstly, the change speed state (CSS) of the evaluated objects is analyzed based on double inspiriting control lines (DICLs), and a matrix of the CSS is constructed. Then, 72 elements in the matrix are analyzed, and formulas describing each CSS are given. The efficiency of the proposed evaluation matrix is proved when the CSS merges with the change speed trend (CST) in the dynamic comprehensive evaluation. Finally, a computing example shows that the proposed evaluation matrix is feasible in the dynamic comprehensive evaluation with the speed feature.

An overview on nonlinear reconfigurable flight control approaches that have been demonstrated in flight-test or highfidelity simulation is presented. Various approaches for reconfigurable flight control systems are considered, including nonlinear dynamic inversion, parameter identification and neural network technologies, backstepping and model predictive control approaches. The recent research work, flight tests, and potential strength and weakness of each approach are discussed objectively in order to give readers and researchers some reference. Finally, possible future directions and open problems in this area are addressed.

The problem of the robust D-stability analysis for linear systems with parametric uncertainties is addressed. For matrix polytopes, new conditions via the affine parameter-dependent Lyapunov function of uncertain systems are developed with the benefit of the scalar multi-convex function. To be convenient for applications, such conditions are simplified into new linear matrix inequality (LMI) conditions, which can be solved by the powerful LMI toolbox. Numerical examples are provided to indicate that this new approach is less conservative than previous results for Hurwitz stability, Schur stability and D-stability of uncertain systems under certain circumstances.

A trajectory shaping guidance law based on virtual angle (TSGLBVA) is proposed for a re-entry vehicle with the constraints of terminal impact angles and their time derivatives. In the view of differential properties of the maneuvering trajectory, a virtual angle and a virtual radius are defined. Also, the shaping trajectory of the vehicle is established by the polynomials of the virtual angle. Then, four optimized parameters are selected according to the theorem of parameters transformation presented in this paper. Finally, a convergent variant of the Nelder-Mead algorithm is adopted to obtain the reference trajectory, and a trajectory feedback tracking guidance law is designed. The simulation results demonstrate that the TSGLBVA ensures the re-entry vehicle to impact a target precisely from a specified direction with small terminal load factor command, as well as to obtain a maximum or constrained terminal velocity according to various requirements.

An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network (FBFN). The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.

This paper studies delay dependent robust stability and the stabilization problem of nonlinear perturbed systems with time varying delay. A new set of sufficient conditions for the stability of open as well as close loop systems are obtained in the sense of Lyapunov-Krasovskii. To reduce the conservatism, the work exploits the idea of splitting the delay interval into multiple equal regions so that less information on the time delay can be imposed to derive the results. The derived criterion not only improves the upper bounds of the time delay but also does not require the derivative of the delay to be known at prior. Easily testable sufficient criteria are presented in terms of linear matrix inequalities. It is shown that the derived conditions are very less conservative while comparing the maximum allowable upper bound of delay with the existing results in literature.

This paper proposes an application of compressive imaging systems to the problem of wide-area video surveillance systems. A parallel coded aperture compressive imaging system and a corresponding motion target detection algorithm in video using compressive image data are developed. Coded masks with random Gaussian, Toeplitz and random binary are utilized to simulate the compressive image respectively. For compressive images, a mixture of the Gaussian distribution is applied to the compressed image field to model the background. A simple threshold test in compressive sampling image is used to declare motion objects. Foreground image retrieval from underdetermined measurement using the total variance optimization algorithm is explored. The signal-to-noise ratio (SNR) is employed to evaluate the image quality recovered from the compressive sampling signals, and receiver operation characteristic (ROC) curves are used to quantify the performance of the motion detection algorithm. Experimental results demonstrate that the low dimensional compressed imaging representation is sufficient to determine spatial motion targets. Compared with the random Gaussian and Toeplitz mask, motion detection algorithms using the random binary phase mask can yield better detection results. However using the random Gaussian and Toeplitz phase mask can achieve high resolution reconstructed images.

To verify the effectiveness of the integrated importance measure (IIM) for multi-state coherent systems of k level, the definition and physical meaning of IIM are demonstrated. Then, the improvement potential and Δ-importance measures are generalized to multi-state coherent systems based on the system performance level, and the relationships between IIM and traditional importance measures are discussed. The characteristics of IIM are demonstrated in both series and parallel systems. Also, an application to an oil transportation system is given. The comparison results show that: (i) IIM has some useful properties that are not possessed by traditional importance measures; (ii) IIM is effective in evaluating the component role in multi-state systems when the component reliability and the failure rate are simultaneously considered.

Redundant technology plays an important role in improving the reliability and fault-tolerance of the airborne avionics systems. A Markov state transition model is introduced to the reliability analysis of the redundant inertial navigation system (RINS) in airborne navigation systems. An information processing mechanism based on difference filtering is put forward to strengthen the consistency between the outputs of the equal-precision inertial navigation system (INS). On this basis, the homologous fault monitoring algorithm is designed to realize the homologous fault monitoring of RINS. The simulation is carried out based on the above algorithms, and the results verify the effectiveness of the proposed fault monitoring algorithm based on difference filtering. Research results have good reference value for the configuration and design of RINS in airborne integrated avionics systems.