The mode switching between spatial multiplexing (SM) and space-time block code (STBC) diversity is investigated for the multiple-input multiple-output (MIMO) automatic repeat request (ARQ) system. Five important practical factors are considered in the proposed switching scheme: transmit correlation, ARQ technique, packet loss probability (PLP) constraint, discrete rate transmission (DRT) and channel coding. Under the spatially correlated channel, the distributions of the post signal-to-interference-plusnoise
ratio (SINR) for the SM mode and the STBC mode are obtained by using Gamma approximations. Then this paper derives the closed-form expressions of the PLP and the throughput for different modes when the ARQ technique is employed, based on which the mode switching algorithm is proposed to improve the spectral efficency. In the simulation, the correction of the expressions is first verified. Then, the significant gain observed by the proposed algorithm is presented. Since the switching point is the
key parameter to implement the mode switching, this paper also shows how the switching point is affected by the practical factors considered.

For direct sequence spread spectrum (DSSS) receivers, the capability of rejecting narrow-band interference can be significantly improved by a process of frequency-domain interference suppression (FDIS). The key issue of this process is how to determine a threshold to eliminate interference in the frequency domain, which has been extensively studied. However, these previous methods are tedious or very complex. A simple and efficient algorithm based on medians is proposed. The elimination threshold is only related to the median by a scale factor, which can be obtained by the numerical analysis. Simulation results show that the algorithm provides excellent narrow-band interference suppression while only slightly degrading the signal-to-noise ratio (SNR). A one-pass algorithm using logarithmic segmentation is further derived to estimate medians with low computational complexity. Finally, the FDIS is implemented in a field programmable gate array (FPGA) of Xilinx. Experiments are carried out by connecting
the FDIS FPGA to a DSSS receiver, and the results show that the receiver has an effective countermeasure for a 60 dB interference-to-signal ratio (ISR).

Due to low parameter sensitivity for balanced realizations, balanced structure becomes a good candidate for an statespace adaptive infinite impluse response (IIR) filter. Here, using coefficients of the transfer function as the adaptive filtering parameters, a balanced adaptive IIR filtering algorithm is proposed for output-error minimization. The algorithm in the internally balanced realization guarantees that the adaptive IIR filter always minimizes the ratio of maximum-to-minimum eigenvalue of the Grammian
matrices at the each iteration. Simulation results are provided to corroborate the proposed algorithm.

An adaptive approach to select analysis window parameters for linear frequency modulated (LFM) signals is proposed to obtain the optimal 3 dB signal-to-noise ratio (SNR) in the shorttime Fourier transform (STFT) domain. After analyzing the instantaneous frequency and instantaneous bandwidth to deduce the relation between the window length and deviation of the Gaussian window, high-order statistics is used to select the appropriate window length for STFT and get the optimal SNR with the right time-frequency resolution according to the signal characteristic under a fixed sampling rate. Computer simulations have verified the effectiveness of the new method.

An approach is proposed to realize a digital channelized receiver in the fractional Fourier domain (FRFD) for signal intercept applications. The presented architecture can be considered as a generalization of that in the traditional Fourier domain. Since the linear frequency modulation (LFM) signal has a good energy concentration in the FRFD, by choosing an appropriate fractional Fourier transform (FRFT) order, the presented architecture can concentrate the broadband LFM signal into only one sub-channel and that will prevent it from crossing several sub-channels. Thus the performance of the signal detection and parameter estimation after the sub-channel output will be improved significantly. The computational complexity is reduced enormously due to the implementation of the polyphase filter bank decomposition, thus the proposed architecture can be realized as efficiently as in the Fourier domain. The related simulation results are presented to verify the validity of the theories and methods involved in this paper.

A robust digital receiver based on a matched filter (MF) is proposed for the radio frequency identification (RFID) reader system to enhance the reliability of signal processing in the electronic product code (EPC) sensor network (ESN). The performance of the proposed receiver is investigated by examining the anti-collision algorithm in the EPC global Class1 Generation2 protocol. The validity and usefulness are demonstrated by both computer simulations and experiments. Based on the verification  results, comparing with the conventional zero crossing detector (ZCD) based receiver, the proposed receiver is very robust against strong amplitude distortions and considerable frequency deviations happening on the backscattered signal from a passive tag.

The research and application of wireless local area networks (WLAN) technology are in a stage of rapid development. It has been one of research focuses of the wireless communications field. Through the use of enhanced single-user (SU)/multi-user (MU) multiple input multiple output (MIMO)-orthogonal frequency division multiplexing (OFDM) technology, the next generation WLAN IEEE 802.11ac dramatically increases the throughput. An improved MIMO-OFDM scheme based on modulation diversity is
proposed for the next generation WLAN. It uses two-dimensional modulation diversity to the current IEEE 802.11ac transmission scheme. Through the space-time-frequency component interleaver and the rotational modulation, the proposed scheme exhibits high spectral efficiency and low error rate in fading channels. The simulation results show that the proposed scheme significantly outperforms the SU/MU MIMO-OFDM scheme in the current IEEE 802.11ac standard, which is up to 5 dB.

Opportunistic array radar (OAR) is a new generation radar system based on the stealth of the platform, which can improve the modern radar performance effectively. Designing the orthogonal code sets with low autocorrelation and cross-correlation is a key issue for OAR. This paper proposes a novel hybrid genetic algorithm (HGA) and designs the polyphase orthogonal code sets with low autocorrelation and cross-correlation properties, which can be used in the OAR system. The novel algorithm combines with simulated annealing (SA) and genetic algorithm (GA), adds in keeping best individuals and competition in small scope, and introduces grey correlation evaluation to evaluate fitness function. These avoid the premature convergence problem existed in GA and enhance the global searching capability. At last, the genetic results are optimized to obtain the best solution by using greedy algorithm. The simulation results show that the proposed algorithm is effective for the design of orthogonal phase signals used in OAR
systems.

Research on the range anomaly suppression algorithm in laser radar (ladar) range images is significant in the application and development of ladar. But most of existing algorithms cannot protect the edge and linear target well while suppressing the range anomaly. Aiming at this problem, the differences among the edge, linear target, and range anomaly are analyzed and a novel algorithm based on neighborhood pixels detection is proposed. Firstly, the range differences between current pixel and its neighborhood pixels are calculated. Then, the number of neighborhood pixels is detected by the range difference threshold. Finally, whether the current pixel is a range anomaly is distinguished by the neighborhood pixel number threshold. Experimental results show that the new algorithm not only has a better range anomaly suppression
performance and higher efficiency, but also protects the edge and linear target preferably compared with other algorithms.

The coherence is a measure for the accuracy of the interferometric phase, and the synthetic aperture radar (SAR) interferometric coherence is affected by several sources of the decorrelation noise. For the circular SAR (CSAR) imaging geometry, the system response function is in the form of the Bessel function which brings a high sidelobe, and the high sidelobe of CSAR will be an important factor influencing the interferometric coherence. The effect of the high sidelobe on the coherence is analyzed and deduced. Based on the interferometric characteristics of the slight difference in the viewing angles and the potential pixel offset in the interferometric SAR (InSAR) images, a relation between the radar impulse response and the coherence loss function is derived. From the relational model, the coherence loss function due to the high sidelobe of CSAR is then deduced, and compared with that of the conventional SAR. It is shown that the high sidelobe of CSAR focusing signal will severely affect the baseline decorrelation
and coregistration decorrelation. Simulation results confirm the theoretical analysis and quantitatively show the baseline and coregistration decorrelation degradation due to the high sidelobes of CSAR.

This paper discusses the problem of direction of departure (DOD) and direction of arrival (DOA) estimation for a bistatic multiple input multiple output (MIMO) radar, and proposes an improved reduced-dimension Capon algorithm therein. Compared with the reduced-dimension Capon algorithm which requires pair matching between the two-dimensional angle estimation, the proposed algorithm can obtain automatically paired DOD and DOA estimation without debasing the performance of angle estimation in bistatic MIMO radar. Furthermore, the proposed algorithm has a lower complexity than the reduced-dimension Capon algorithm, and it is suitable for non-uniform linear arrays. The complexity of the proposed algorithm is analyzed and the Cramer-Rao bound (CRB) is also derived. Simulation results verify the usefulness of the proposed algorithm.

Considering the problem of multiple ballistic missiles tracking of boost-phase ballistic missile defense, a boost-phase tracking algorithm based on multiple hypotheses tracking (MHT) concept is proposed. This paper focuses on the tracking algorithm for hypothesis generation, hypothesis probability calculation, hypotheses reduction and pruning and other sectors. From an engineering point of view, a technique called the linear assignment problem (LAP) used in the implementation of M-best feasible hypotheses generation, the number of the hypotheses is relatively small compared with the total number that may exist in each scan, also the N-scan back pruning is used, the algorithm’s efficiency and practicality have been improved. Monte Carlo simulation results show that the proposed algorithm can track the boost phase of multiple ballistic missiles and it has a good tracking performance compared with joint probability data association (JPDA).

This article introduces a fleet composition algorithm for a fleet of intermediate carriers, which should deliver a swarm of miniature unmanned aerial vehicles (mini-UAVs) to a mission area. The algorithm is based on the sequential solution of several knapsack problems with various constraints. The algorithm allows both to form an initial set of required types of intermediate carriers, and to generate a fleet of intermediate carriers. The formation of a fleet of intermediate carriers to solve a suppression of enemy air
defense (SEAD) problem is presented to illustrate the proposed algorithm.

In the future lunar exploration programs of China, soft landing, sampling and returning will be realized. For lunar explorers such as Rovers, Landers and Ascenders, the inertial navigation system (INS) will be used to obtain high-precision navigation information. INS propagates position, velocity and attitude by integration of sensed accelerations, so initial alignment is needed before INS can work properly. However, traditional ground-based initial alignment methods cannot work well on the lunar surface because of its low rotation rate (0.55?/h). For solving this problem, a new autonomous INS initial alignment method assisted by celestial observations is proposed, which uses star observations to help INS estimate its attitude, gyroscopes drifts and accelerometer biases. Simulations show that this new method can not only speed up alignment, but also improve the alignment accuracy. Furthermore, the impact factors such as initial conditions, accuracy of INS sensors, and accuracy of star sensor on alignment accuracy are analyzed in details, which provide guidance for the engineering applications of this method. This method could be a promising and attractive solution for lunar explorer’s initial alignment.

A novel gain-scheduled switching control method for the longitudinal motion of a flexible air-breathing hypersonic vehicle (FAHV) is proposed. Firstly, velocity and altitude are selected as scheduling variables, a polytopic linear parameter varying (LPV) model is developed to represent the complex nonlinear longitudinal dynamics of the FAHV. Secondly, based on the obtained polytopic LPV model, the flight envelope is divided into four smaller subregions, and four gain-scheduled controllers are designed for these
parameter subregions. Then, by the defined switching characteristic function, these gain-scheduled controllers are switched in order to guarantee the closed-loop FAHV system to be asymptotically stable and satisfy a given tracking error performance criterion. The condition of gain-scheduled switching controller synthesis is given in terms of linear matrix inequalities (LMIs) which can be easily solved by using standard software packages. Finally, simulation results show the effectiveness of the presented method.

For the robustness problem of open-loop P-type iterative learning control under the influence of measurement noise which is inevitable in actual systems, an adaptive adjustment algorithm of iterative learning nonlinear gain matrix based on error amplitude is proposed and two nonlinear gain functions are given. Then with the help of Bellman-Gronwall lemma, the robustness proof is derived. At last, an example is simulated and analyzed. The results show that when there exists measurement noise, the
proposed learning law adjusts the learning gain matrix on line based on error amplitude, thus can make a compromise between learning convergence rate and convergence accuracy to some extent: the fast convergence rate is achieved with high gain in initial learning stage, the strong robustness and high convergence accuracy are achieved at the same time with small gain in the end learning stage, thus better learning results are obtained.

The state prediction based on the unscented Kalman filter (UKF) for nonlinear stochastic discrete-time systems with linear measurement equation is investigated. Predicting future states by using the information of available measurements is an effective method to solve time delay problems. It not only helps the system operator to perform security analysis, but also allows more time for operator to take better decision in case of emergency. In addition, predictive state can make the system implement real-time monitoring and achieve good robustness. UKF has been popular in state prediction because of its advantages in handling nonlinear systems. However, the accuracy of prediction degrades notably once a filter uses a much longer future prediction. A confidence interval (CI) is proposed to overcome the problem. The advantages of CI are that it provides the information about states coverage, which is useful for treatment-plan evaluation, and it can be directly used to specify the margin to accommodate prediction errors. Meanwhile, the CI of prediction errors can be used to correct the predictive state, and thereby it improves the prediction accuracy. Simulations are provided to demonstrate the effectiveness of the theoretical results.

Many Bayesian learning approaches to the multi-layer perceptron (MLP) parameter optimization have been proposed such as the extended Kalman filter (EKF). This paper uses the unscented Kalman particle filter (UPF) to train the MLP in a selforganizing state space (SOSS) model. This involves forming augmented state vectors consisting of all parameters (the weights of the MLP) and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS model for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.

A novel tunable-quality-factor (tunable-Q) contourlet transform for geometric image representation is proposed. The Laplacian pyramid in original contourlet decomposes a signal into channels that have the same bandwidth on a logarithmic scale, and is not suitable for images with different behavior in frequency domain. We employ a new tunable-Q decomposition defined in the frequency domain by which one can flexibly tune the bandwidth of decomposition channels. With an acceptable redundancy, this
tunable-Q contourlet is also anti-aliasing and its basis is sharply localized in the desired area of frequency and spatial domain. Our experiments in nonlinear approximation and denoising show that the contourlet using a better-suitable quality factor can achieve a more promising performance and often outperform wavelets and the previous contourlets both in visual quality and in terms of peak signal-to-noise ratio.

Artificial neural network has been used successfully to develope the automatic spike extraction. In order to address some of the problems before the wireless transmission of the implantable chip, the automatic spike sorting method with low complexity and high efficiency is proposed based on the hybrid neural network with the principal component analysis network (PCAN) and normal boundary response (NBR) self-organizing mapping (SOM) network classifier. An automatic PCAN technique is used to reduce the dimension and eliminate the correlation of the spike signal. The NBR-SOM network performs the spike sorting challenge and improves the classification performance. The experimental results show that based on the hybrid neural network, the spike sorting method achieves the accuracy above 97.91% with signals containing five classes. The proposed NBR-SOM network classifier is to further improve the stability and effectiveness of the classification system.

A new troubleshooting algorithm for solving assignment problem based on existing algorithms is proposed, and an analysis on the related theory is given. By applying the new troubleshooting algorithm to the Lagrange relaxation algorithm of the multi-dimensional assignment problem of data association for multi-passive-sensor multi-target location systems, and comparing the simulation results with that of the Hungarian algorithm which is the classical optimal solving algorithm, and the multi-layer ordersearching
algorithm which is a sub-optimal solving algorithm, the performance and applying conditions of the new algorithm are summarized. Theory analysis and simulation results prove the effectiveness and superiority of the new algorithm.

Remaining useful life (RUL) estimation is termed as one of the key issues in prognostics and health management (PHM). To achieve RUL estimation for individual equipment, we present a degradation data-driven RUL estimation approach under the collaboration between Bayesian updating and expectation maximization (EM) algorithm. Firstly, we utilize an exponential-like degradation model to describe equipment degradation process and update stochastic parameters in the model via Bayesian approach. Based on the Bayesian updating results, both probability distribution of the RUL and its point estimation can be derived. Secondly, based on the monitored degradation data to date, we give a parameter estimation approach for non-stochastic parameters in the degradation model and prove that the obtained estimation is unique and optimal in each iteration. Finally, a numerical example and a practical case study for global positioning system (GPS) receiver are provided to show that the presented approach can model degradation process and achieve RUL estimation effectively and generate better results than a previously reported approach in literature.