An accurate, complete and realistic channel model is required to accurately analyze the system performance of a multiple input multiple output (MIMO) broadband satellite mobile communication system with dual-orthogonal polarized antennas (DPAs). In most current studies, the channel characteristic matrix (CCM) is always formed by an independent identical distribution (i.i.d) model of Rayleigh or Rice distribution and nevertheless incomplete and inaccurate to describe a broadband dual-orthogonal polarized MIMO land mobile satellite (BDM-LMS) channel. This paper focuses on establishing the BDM-LMS channel statistical model, which combines the 4-state broadband LMS channel model, the time selective fading features, the channel covariance information (CCI) channel model and polarization correlations between antennas. The modeling steps of the channel model are introduced. The main emphasis is placed on the effects of the factors, such as antenna numbers, temporal correlations, terminal environments, elevation angles and polarization correlations between the DPAs, on the channel capacity in the BDM-LMS system. Many simulation results are provided to illustrate the effects of these factors through comparisons of the transmit rate, ergodic capacity and outage capacity with different factor values. Besides, the MIMO outage capacity advantages, which indicate the benefits of MIMO compared with a single input single output (SISO) system under the same channel condition, are also studied under i.i.d or BDM-LMS channel.

The satellite transponder is a widely used module in satellite missions, and the most concerned issue is to reduce the noise of the transferred signal. Otherwise, the telemetry signal will be polluted by the noise contained in the transferred signal, and the additional power will be consumed. Therefore, a method based on wavelet packet de-noising (WPD) is introduced. Compared with other techniques, there are two features making WPD more suitable to be applied to satellite transponders: one is the capability to deal with time-varying signals without any priori information of the input signals; the other is the capability to reduce the noise in band, even if the noise overlaps with signals in the frequency domain, which provides a great de-noising performance especially for wideband signals. Besides, an oscillation detector and an averaging filter are added to decrease the partial oscillation caused by the thresholding process of WPD. Simulation results show that the proposed algorithm can reduce more noises and make less distortions of the signals than other techniques. In addition, up to 12 dB additional power consumption can be reduced at –10 dB signal-to-noise ratio (SNR).

Time-differences-of-arrival (TDOA) and gain-ratios-ofarrival (GROA) measurements are used to determine the passive source location. Based on the measurement models, the constrained weighted least squares (CWLS) estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem (SDP), which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good performance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.

A new direction-of-arrival (DOA) estimation algorithm for wideband sources is introduced. The new method obtains the output of the virtual arrays in the signal bandwidth using cubic spline function interpolation techniques. The narrowband highresolution algorithm is then used to get the DOA estimation. This
technique does not require any preliminary knowledge of DOA angles. Simulation results demonstrate the effectiveness of the method.

Recent research shows that it is possible to achieve the full-duplex system by cancelling strong self-interference signals, which can be divided into three classes respectively, i.e., passive cancellation, active cancellation and digital cancellation. This paper tries to achieve the full-duplex system without using active cancellation, thus a full-duplex system using a joint mechanism based on a novel passive cancellation method and a novel digital cancellation method is proposed. Therein, a good antenna placement guided by the theory of the antenna electromagnetic field for the passive cancellation is presented. For the proposed digital cancellation method, unlike previous separate mechanisms, it is designed by using the recursive least square (RLS) algorithm jointly with passive cancellation. The self-interference channel state information (CSI) is transferred as the input of digital cancellation to balance the performance and the complexity. Experimental results show that the proposed self-interference cancellation mechanism can achieve about 85 dB which is better than the previous research. Meanwhile, this design provides a better performance compared with half-duplex with both line-of-sight channel and nonline-of-sight channel.

A novel adaptive detector for airborne radar space-time adaptive detection (STAD) in partially homogeneous environments is proposed. The novel detector combines the numerically stable Krylov subspace technique and diagonal loading technique, and it uses the framework of the adaptive coherence estimator (ACE). It can effectively detect a target with low sample support. Compared with its natural competitors, the novel detector has higher probability of detection (PD), especially when the number of the training data is low. Moreover, it is shown to be practically constant false alarm rate (CFAR).

It is potentially useful to perform deception or cover jamming using the rotating angular reflectors since they can form deception echoes along range and azimuth. Inspired by the coherent jamming and micro-motion modulation, a novel active method is proposed for inverse synthetic aperture radar (ISAR). Radar pulses are sampled and frequency-modulated along azimuth by sinusoidal signal, and then the jamming signals are retransmitted to the radar and the jamming images are induced after ISAR imaging. Therein, the jamming principle, key parameters and the jamming effect are discussed. The simulated data verify the effectiveness of the jamming method.

Current research on target detection and recognition from synthetic aperture radar (SAR) images is usually carried out separately. It is difficult to verify the ability of a target recognition algorithm for adapting to changes in the environment. To realize the whole process of SAR automatic target recognition (ATR), especially for the detection and recognition of vehicles, an algorithm based on kernel fisher discriminant analysis (KFDA) is proposed. First, in order to make a better description of the difference between the background and the target, KFDA is extended to the detection part. Image samples are obtained with a dual-window approach and features of the inner and outer window samples are extracted by using KFDA. The difference between the features of inner and outer window samples is compared with a threshold to determine whether a vehicle exists. Second, for the target area, we propose an improved KFDA-IMED (image Euclidean distance) combined with a support vector machine (SVM) to recognize the vehicles. Experimental results validate the performance of our method. On the detection task, our proposed method obtains not only a high detection rate but also a low false alarm rate without using any prior information. For the recognition task, our method overcomes the SAR image aspect angle sensitivity, reduces the requirements for image preprocessing and improves the recognition rate.

To handle the electromagnetic problems of the bi-static radar cross section (RCS) calculation of scatterer in a wide frequency band, a finite-difference time-domain (FDTD) extrapolation method combining with discrete Fourier transform (DFT) is proposed. By comparing the formulas between the steady state field extrapolation method and the transient field extrapolation method, a novel extrapolation method combining with DFT used in FDTD is proposed when a transient field incident wave is introduced. With the proposed method, the full-angle RCS distribution in a wide frequency band can be achieved through one-time FDTD calculation. Afterwards, the back-scattering RCS distributions of a double olive body and a sphere-cone body are calculated. Numerical results
verify the validity of the proposed method.

The optimal imaging time selection of ship targets for shore-based inverse synthetic aperture radar (ISAR) in high sea conditions is investigated. The optimal imaging time includes optimal imaging instants and optimal imaging duration. A novel method for optimal imaging instants selection based on the estimation of the Doppler centroid frequencies (DCFs) of a series of images obtained over continuous short durations is proposed. Combined with the optimal imaging duration selection scheme using the image contrast maximization criteria, this method can provide the ship images with the highest focus. Simulated and real data processing results verify the effectiveness of the proposed imaging method.

There are many proposed optimal or suboptimal algorithms to update out-of-sequence measurement(s) (OoSM(s)) for linear-Gaussian systems, but few algorithms are dedicated to track a maneuvering target in clutter by using OoSMs. In order to address the nonlinear OoSMs obtained by the airborne radar located on a moving platform from a maneuvering target in clutter, an interacting multiple model probabilistic data association (IMMPDA) algorithm with the OoSM is developed. To be practical, the algorithm is based on the Earth-centered Earth-fixed (ECEF) coordinate system where it considers the effect of the platform’s attitude and the curvature of the Earth. The proposed method is validated through the Monte Carlo test compared with the performance of the standard IMMPDA algorithm ignoring the OoSM, and the conclusions show that using the OoSM can improve the tracking performance, and the shorter the lag step is, the greater degree the performance is improved, but when the lag step is large, the performance is not improved any more by using the OoSM, which can provide some references for engineering application.

System-of-systems (SoS) engineering involves a complex process of refining high-level SoS requirements into more detailed systems requirements and assessing the extent to which the performances of to-be systems may possibly satisfy SoS capability objectives. The key issue is how to model such requirements to automate the process of analysis and assessment. This paper suggests a meta-model that defines both functional and nonfunctional features of SoS requirements for command and control, communication, computer, intelligence, surveillance reconnaissance (C4ISR) systems. A domain-specific modeling language is defined by extending unified modeling language (UML) constructed of class and association with fuzzy theory in order to model the fuzzy concepts of performance requirements. An efficiency evaluation function is introduced, based on B´ezier curves, to predict the effectiveness of systems. An algorithm is presented to transform domain models in fuzzy UML into a requirements ontology in description logic (DL) so that requirements verification can be automated with a popular DL reasoner such as Pellet.

The VIKOR method is a multi-criteria decision making aid, which employs linear normalization to offer compromise solutions and has been successfully applied to various group decision making problems. However, the conventional VIKOR techniques used to integrate group judgments and the information loss arising from defuzzification are problematic and distort final outcomes. An improved integration method, which is optimization-based, is proposed. And it can handle fuzzy criteria values and weights. The precondition for accurately defuzzifying triangular fuzzy numbers is identified. Several effective defuzzification procedures are proposed to improve the extant VIKOR, and a comprehensive evaluation framework is offered to aid multi-criteria group decision making. Finally, a numerical example is provided to illustrate the practicability of the proposed method.