A novel low-cost adaptive square-root cubature Kalman filter (LCASCKF) is proposed to enhance the robustness of process models while only increasing the computational load slightly. It is well-known that the Kalman filter cannot handle uncertainties in a process model, such as initial state estimation errors, parameter mismatch and abrupt state changes. These uncertainties severely affect filter performance and may even provoke divergence. A strong tracking filter (STF), which utilizes a suboptimal fading factor, is an adaptive approach that is commonly adopted to solve this problem. However, if the strong tracking SCKF (STSCKF) uses the same method as the extended Kalman filter (EKF) to introduce the suboptimal fading factor, it greatly increases the computational load. To avoid this problem, a low-cost introductory method is proposed and a hypothesis testing theory is applied to detect uncertainties. The computational load analysis is performed by counting the total number of floating-point operations and it is found that the computational load of LCASCKF is close to that of
SCKF. Experimental results prove that the LCASCKF performs as well as STSCKF, while the increase in computational load is much lower than STSCKF.

A new two-stage reduced-dimension space-time adaptive processing (STAP) approach, which combines the subcoherent processing interval (sub-CPI) STAP and the principal component analysis (PCA), is proposed to achieve a more enhanced convergence measure of effectiveness (MOE). Furthermore, in the case of the subspace leakage phenomenon, the proposed STAP method is modified to hold the fast convergence MOE by using the covariance matrix taper (CMT) technique. Both simulation and real airborne radar data processing are provided to analyze the convergence MOE performance of the proposed STAP methods. The results show the proposed method is more suitable for the practical radar applications when compared with the conventional sub-CPI STAP method.

Based on the works of Greve and Rahmat-Samii, the electromagnetic (EM) performance of the reflector antenna with non-uniform surface errors along radius is further addressed. A mathematical model is developed to describe the weighting function for the non-uniform surface errors along radius. Then, some discussions on the peak gain loss (PGL) and the first sidelobe level increase (SLLI) caused by the non-uniform surface errors are presented and several significant radiation characteristics of the reflector with non-uniform errors are pointed out. Last, based on the proposed model, the weighted root mean square (RMS) value of the surface errors is produced to evaluate the EM performance and several representative cases with different non-uniform errors are presented with good results. Results show that the weighted RMS value should be taken into account for a better quality evaluation of the reflector surface.

This study proposes a novel multi-fractal spectrumbased approach to distinguish linear block codes from its selfsynchronous scrambled codes. Given that the linear block code and self-synchronous scrambled linear block code share the property of linear correlation, the existing linear correlation-based identification method is invalid for this case. This drawback can be circumvented by introducing a novel multi-fractal spectrum-based method. Simulation results show that the new method has high robustness and under the same conditions of bit error, the lower the code rate, the higher the recognition rate. Thus, the method has significant potential for future application in engineering.

A new long term integration algorithm is proposed for high-dynamic targets, which can resolve the problems of spectrum spread, frequency walk and pseudorandom noise (PRN) code phase curvature caused by the motion of targets. This algorithm first applies a keystone transform based improved discrete polynomial-phase transform (KT-IDPT) to estimate the Doppler chirp rate. Then, based on the estimated Doppler chirp rate, dechirping and envelope translation are performed on the partial correlation results to correct the spectrum spread and the code phase curvature. The simulation results demonstrate that the proposed method has low integration loss and computational burden.

In order to estimate the systematic error in the process of maneuvering target adaptive tracking, a new method is proposed. The proposed method is a linear tracking scheme based on a modified input estimation approach. A special augmentation in the state space model is considered, in which both the systematic error and the unknown input vector are attached to the state vector. Then, an augmented state model and a measurement model are established in the case of systematic error, and the corresponding filter formulas are also given. In the proposed scheme, the original state, the acceleration and the systematic error vector can be estimated simultaneously. This method can not only solve the maneuvering target adaptive tracking problem in the case of systematic error, but also give the system error value in real time. Simulation results show that the proposed tracking algorithm operates in both the non-maneuvering and the maneuvering modes, and the original state, the acceleration and the systematic error vector can be estimated simultaneously.

For spaceborne synthetic aperture radar (SAR) imaging, the dispersive ionosphere has significant effects on the propagation of the low frequency (especially P-band) radar signal. The ionospheric effects can be a significant source of the phase error in the radar signal, which causes a degeneration of the image quality in spaceborne SAR imaging system. The background ionospheric effects on spaceborne SAR through modeling and simulation are analyzed, and the qualitative and quantitative analysis based on the spatio-temporal variability of the ionosphere is given. A novel ionosphere correction algorithm (ICA) is proposed to deal with the ionospheric effects on the low frequency spaceborne SAR radar signal. With the proposed algorithm, the degradation of the image quality caused by the ionosphere is corrected. The simulation results show the effectiveness of the proposed algorithm.

For a netted radar system to counteract the deception electronic countermeasure (ECM) signals, an effective electronic counter countermeasure (ECCM) approach is proposed. The proposed approach is realized based on the new signaling strategy for the temporal pulse diversity, which makes use of transmitting pulses at each pulse repetition interval (PRI) with specific transmission pulse block, and then following proper processing and information fusion. The existence of the deceptive ECM signal is confirmed by one station, while the other stations in the netted radar with same parameters applied the pulse diversity skillfully. Meanwhile, this method ensured that, pulse diversity can be applied in netted radar. The performance assessment shows that the proposed solutions are effective in presence of ECM signals. This algorithm has been demonstrated by simulations. The presented simulation results are in excellent consensus with theoretical predictions.

For the multi-mode radar working in the modern electronic battlefield, different working states of one single radar are prone to being classified as multiple emitters when adopting traditional classification methods to process intercepted signals, which has a negative effect on signal classification. A classification
method based on spatial data mining is presented to address the above challenge. Inspired by the idea of spatial data mining, the classification method applies nuclear field to depicting the distribution information of pulse samples in feature space, and digs out the hidden cluster information by analyzing distribution characteristics. In addition, a membership-degree criterion to quantify the correlation among all classes is established, which ensures classification accuracy of signal samples. Numerical experiments show that the presented method can effectively prevent different working states of multi-mode emitter from being classified as several emitters, and achieve higher classification accuracy.

A self-organized integrated air-ground detection swarm is tentatively applied to achieve reentry vehicle landing detection, such as searching and rescuing a manned spaceship. The detection swarm consists of multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The UAVs can access a detected object quickly for high mobility, while the UGVs can comprehensively investigate the object due to the variety of carried equipment. In addition, the integrated air-ground detection swarm is capable of detecting from the ground and the air simultaneously. To accomplish the coordination of the UGVs and UAVs, they are all regarded as individuals of the artificial swarm. Those individuals make control decisions independently of others based on the self-organizing strategy. The overall requirements for the detection swarm are analyzed, and the theoretical model of the self-organizing strategy based on a combined individual and environmental virtual function is established. The numerical investigation proves that the self-organizing strategy is suitable and scalable to control the detection swarm. To further inspect the engineering reliability, an experiment set is established in laboratory, and the experimental demonstration shows that the self-organizing strategy drives the detection swarm forming a close range and multiangular surveillance configuration of a landing spot.

With the development of operationally responsive space (ORS) and on-board processing techniques, the end users can receive the observation data from the ORS satellite directly. To satisfy the demand for reducing the requirements-tasking-effects cycle from one day to hours, the various resources of the whole
data acquisition chain (including satellites, ground stations, data processing centers, users, etc.) should be taken into an overall consideration, and the traditional batch task planning mode should be transformed into the user-oriented task planning mode. Considering there are many approaches for data acquisition due to the new techniques of ORS satellite, the data acquisition chain task planning problem for ORS satellite can be seen as the multimodal route planning problem. Thereby, a framework is presented using label-constrained shortest path technique with the conflict resolution. To apply this framework to solve the ORS satellite task planning problem, the preprocessing and the conflict resolution strategies are discussed in detail. Based on the above work, the user-oriented data acquisition chain task planning algorithm for ORS satellite is proposed. The exact solution can be obtained in polynomial time using the proposed algorithm. The simulation experiments validate the feasibility and the adaptability of the proposed approach.

To fully display the modeling mechanism of the novel fractional order grey model (FGM (q,1)), this paper decomposes the data matrix of the model into the mean generation matrix, the accumulative generation matrix and the raw data matrix, which are consistent with the fractional order accumulative grey model (FAGM (1,1)). Following this, this paper decomposes the accumulative data difference matrix into the accumulative generation matrix, the q-order reductive accumulative matrix and the raw data matrix, and then combines the least square method, finding that the differential order affects the model parameters only by affecting the formation of differential sequences. This paper then summarizes matrix decomposition of some special sequences, such as the sequence generated by the strengthening and weakening operators, the jumping sequence, and the non-equidistance sequence. Finally, this paper expresses the influences of the raw data transformation, the accumulation sequence transformation, and the differential matrix transformation on the model parameters as matrices, and takes the non-equidistance sequence as an example to show the modeling mechanism.

Fuzzy sets theory cannot describe the neutrality degree of data, which has largely limited the objectivity of fuzzy time series in uncertain data forecasting. With this regard, a multi-factor highorder intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to get unequal intervals, and a more objective technique for ascertaining membership and non-membership functions of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on multidimensional intuitionistic fuzzy modus ponens inference are established. Finally, contrast experiments on the daily mean temperature of Beijing are carried out, which show that the novel model has a clear advantage of improving the forecast accuracy.

The semi-Lagrangian relaxation (SLR), a new exact method for combinatorial optimization problems with equality constraints, is applied to the quadratic assignment problem (QAP). A dual ascent algorithm with finite convergence is developed for solving the semi-Lagrangian dual problem associated to the QAP. We perform computational experiments on 30 moderately difficult QAP instances by using the mixed integer programming solvers, Cplex, and SLR+Cplex, respectively. The numerical results not only further illustrate that the SLR and the developed dual ascent algorithm can be used to solve the QAP reasonably, but also disclose an interesting fact: comparing with solving the unreduced problem, the reduced oracle problem cannot be always effectively solved by using Cplex in terms of the CPU time.

This paper proposes a novel composite dual-control by combing the integral sliding mode control (ISMC) method based on the finite time convergence theory with extended state observer (ESO) for a tracking problem of a missile with tail fins and reactionjet control system (RCS). First, the ISMC method based on finite time convergence is utilized to design the control law of tail fins and the pulse control of RCS for the dual-control system, ensuring the system with rapid response and high accuracy of tracking. Then, ESO is employed for the estimation of aerodynamic disturbances influenced by the airflow of thruster jets. With the characteristic of high accuracy estimation of ESO, the chattering free tracking performance of the attack angle command and the robustness of the control law are achieved. Meanwhile, the stability of the dual-control system is analyzed based on finite time convergence stability theorem and Lyapunov’s theorem. Finally, numerical simulations demonstrate the effectiveness of the proposed design.

Based on the idea of backstepping design, distributed coordinated tracking problems under directed topology are discussed for multiple Euler-Lagrange (EL) systems. The dynamic leader case is considered. First, with the parameter-linearity property, a distributed coordinated adaptive control scheme is proposed for EL systems in the presence of parametric uncertainties. Then, subject to nonlinear uncertainties and external disturbances, an improved adaptive control algorithm is developed by using neural-network (NN) approximation of nonlinear functions. Both proposed algorithms can make tracking errors for each follower ultimately bounded. The closed-loop systems are investigated by using the combination of graph theory, Lyapunov theory, and Barbalat Lemma. Numerical examples and comparisons with other methods are provided to show the effectiveness of the proposed control strategies.

The chattering characteristic of sliding mode control is analyzed when it is applied in distributed control systems (DCSs). For a DCS with random time delay and packet dropout, a discrete switching system model with time varying sampling period is constructed based on the time delay system method. The  reaching law based sliding mode controller is applied in the proposed system. The exponential stability condition in the form of linear matrix inequality is figured out based on the multi-Lyaponov function method. Then, the chattering characteristic is analyzed for the switching system, and a chattering region related with time varying sampling period and external disturbance is proposed. Finally, numerical examples are given to illustrate the validity of the analysis result.

Both D-stability and finite L2-gain properties are studied for a class of uncertain discrete-time systems with timevarying network-induced delays. By using coordinate transform and delay partition, the D-stability and H∞ performance problems for such networked control systems (NCSs) are equivalently transferred into the corresponding problems for switching systems with arbitrary switching. Then, a sufficient condition for the existence of the robust D-stabilizing controllers is derived in terms of linear matrix inequality (LMI), and the design method is also presented for the state feedback controllers which guarantee that all the closed-loop poles remain inside the specified disk D(α,r) and the desired disturbance attenuation level. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed results.

Due to the frequency of occlusion, cluttering and low contrast edges, gray intensity based active contour models often fail to segment meaningful objects. Prior shape information is usually utilized to segment desirable objects. A parametric shape prior model is proposed. Firstly, principal component analysis is employed to train object shape and transformation is added to shape representation. Then the energy function is constructed through a combination of shape prior energy, gray intensity energy and shape constraint energy of the kernel density function. The object boundary extraction process is converted into the parameters solving process of object shape. Besides, two new shape prior energy functions are defined when desirable objects are occluded by other objects or some parts of them are missing. Finally, an alternating decent iteration solving scheme is proposed for numerical implementation. Experiments on synthetic and real images demonstrate the robustness and accuracy of the proposed method.

A novel content based image retrieval (CBIR) algorithm using relevant feedback is presented. The proposed framework has three major contributions: a novel feature descriptor called color spectral histogram (CSH) to measure the similarity between images; two-dimensional matrix based indexing approach proposed for short-term learning (STL); and long-term learning (LTL). In general, image similarities are measured from feature representation which includes color quantization, texture, color, shape and edges. However, CSH can describe the image feature only with the histogram. Typically the image retrieval process starts by finding the similarity between the query image and the images in the database; the major computation involved here is that the selection of top ranking images requires a sorting algorithm to be employed at least with the lower bound of O(n log n). A 2D matrix based indexing of images can enormously reduce the search time in STL. The same structure is used for LTL with an aim to reduce the amount of log to be maintained. The performance of the proposed framework is analyzed and compared with the existing approaches, the quantified results indicates that the proposed feature descriptor is more effectual than the existing feature descriptors that were originally developed for CBIR. In terms of STL, the proposed 2D matrix based indexing minimizes the computation effort for retrieving similar images and for LTL, the proposed algorithm takes minimum log information than the existing approaches.