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.

The controllability and observability of networked control systems are studied. Aiming at the networked control system with time-varying delay, the sufficient and necessary conditions for complete controllability and complete observability of the system are presented, respectively. Because of Markov characteristic of the network-induced delay, in terms of stochastic theory, a sufficient and necessary condition for completely mean value controllability of networked control systems is obtained. Further, the conditions that the controllability and observability of networked control systems are equivalent to the initial time-invariant system are given. Controllability and observability realization indexes are also discussed, respectively. The numerical example demonstrates the effectiveness of the proposed theory.

The problem of robustifying linear quadratic regulators (LQRs) for a class of uncertain affine nonlinear systems is considered. First, the exact linearization technique is used to transform an uncertain nonlinear system into a linear one and an optimal LQR is designed for the corresponding nominal system. Then, based on the integral sliding mode, a design approach to robustifying the optimal regulator is studied. As a result, the system exhibits global robustness to uncertainties and the ideal sliding mode dynamics is the same as that of the optimal LQR for the nominal system. A global robust optimal sliding mode control (GROSMC) is realized. Finally, a numerical simulation is demonstrated to show the effectiveness and superiority of the proposed algorithm compared with the conventional optimal LQR.

An indoor location system based on multilayer artificial neural network (ANN) with area division is proposed. The characteristics of recorded signal strength (RSS), or signal to noise ratio (SNR) from each available access points (APs), are utilized to establish the radio map in the off-line phase. And in the on-line phase, the two or three dimensional coordinates of mobile terminals (MTs) are estimated according to the similarity between the new recorded RSS or SNR and fingerprints pre-stored in radio map. Although the feed-forward ANN with three layers is sufficient to describe any nonlinear mapping relationship between inputs and outputs with finite discontinuous points, the efficient inputs for better training performances are difficult to be determined because of complex and dynamic indoor environment. Then, the discussion of distance relativity for different signal characteristics and optimal strategies for multi-mode phenomenon avoidance is presented. And also, the feasibility and effectiveness of this method are verified based on the experimental comparison with normal ANN without area division, K-nearest neighbor (KNN) and probability methods in typical office environment.  

This paper proposes an efficient scheme to reduce the pre-correlation bandwidth effect in the global navigation satellite system (GNSS) receiver filtering process. It is mainly based on the application of a spectral transformation to the satellite-emitted signal that effectively reduces its band. At the receiver's end, this operation causes the spreading of noise over a much wider band than that used by the radio frequency stage. Consequently, the resulting auto-correlation function in the acquisition process acquires properties that enhance considerably the performance of the receiver in the presence of the multipath and noise disturbing phenomena. The simulation results demonstrate that the proposed method is a plausible solution for both multipath and noise problems in the GNSS applications for any limited value of the pre-correlation bandwidth in the receiver filter.

Data envelopment analysis (DEA) is a mathematical programming approach to appraise the relative efficiencies of peer decision-making unit (DMU), which is widely used in ranking DMUs. However, almost all DEA-related ranking approaches are based on the self-evaluation efficiencies. In other words, each DMU chooses the weights it prefers to most, so the resulted efficiencies are not suitable to be used as ranking criteria. Therefore
this paper proposes a new approach to determine a bundle of common weights in DEA efficiency evaluation model by introducing a multi-objective integer programming. The paper also gives the solving process of this multi-objective integer programming, and the solution is proven a Pareto efficient solution. The solving process ensures that the obtained common weight bundle is acceptable by a great number of DMUs. Finally a numeral example is given to demonstrate the approach.

When there is Doppler ambiguity number mutation, keystone formatting is no longer valid because of integration performance deterioration and false velocity estimation. A novel keystone formatting method based on non baseband interpolation is presented. It has a different half blind velocity range comparing with normal keystone formatting. Furthermore, there is non-superposition between half-blind-velocity-range of keystone formatting based on baseband and that of non-baseband interpolation. So a synthesizing keystone formatting is proposed to avoid the half-blind-velocity effect. Simulation results of the proposed method show that integration deterioration and velocity estimation falsity can be eliminated effectively.

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.

This paper deals with the problem of stability for systems with delay varying in an interval. A new Lyapunov functional, which makes use of the information of both the lower and upper bounds of the interval time-varying delay, is proposed to derive some new stability criteria. Furthermore, the relationship of the time-varying delay and its lower bound and upper bound is taken into account. As a result, some less conservative delay-dependent stability criteria are obtained without ignoring any useful information in the derivative of Lyapunov functional, which are established in the forms of linear matrix inequalities. Numerical examples are provided to show that the obtained results are better than existing ones.

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.

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 spaceborne inverse synthetic aperture radar (ISAR) has attracted significant attention due to its extensive observation range and imaging performance. However, the complex motion between spaceborne platform and the air target leads to complex signal modulation. The scattering anisotropy and occlusion lead to the scattering points missing problem. These problems pose a serious challenge to the traditional ISAR imaging algorithms. Aiming at the above problems, this paper proposes a joint intra-frame and inter-frame imaging algorithm based on spaceborne ISAR. In this algorithm, we divide the long coherent processing interval into several sub-apertures using the narrow-band tracking data, and each-order terms of the signal within sub-aperture is derived in detail. Then, the intra-frame algorithm based on the parametric minimized image entropy search is proposed to correct the spatial-variant phase errors caused by the complex relative motion. As to the well-focused images from different views and the rotation parameters obtained in sub-apertures, the inter-frame algorithm based on wavelet transform can perform image registration and image fusion to obtain more detailed target feature information and more complete target structure. In simulated and real-measured data experiments, the effectiveness and superiority of the proposed algorithm are validated.

Spatial precession is a special micro-motion of the spinning-directional target, and the micro-Doppler signature of the cone-shaped target with precession is studied. The micro-motion model of precession is built first, and then the micro-Doppler model is developed based on the proposed concept of micro-motion matrix, by which the theoretical formula of micro-Doppler signature of precession is derived. In order to further approach to the actual case, the occlusion effect is firstly considered in micro-Doppler, and the simulated result with occlusion effect is well in accordance with the measured result in microwave anechoic chamber, which suggests that the micro motion model and micro-Doppler model of precession are both valid.

A novel H_∞ tracking-based decentralized indirect adaptive output feedback fuzzy controller for a class of uncertain large-scale nonlinear systems is developed. By virtue of the proper filtering of the observation error dynamics, the observer-based decentralized indirect adaptive fuzzy control scheme is presented for a class of large-scale nonlinear systems using the combination of _H∞ tracking technique, a fuzzy adaptive observer and fuzzy inference systems. The output feedback and adaptation mechanisms are both robust and implementable indeed owing to their freedom from the unavailable observation error vector. All the signals of the closed-loop largescale system are guaranteed to stay uniformly bounded and the output errors take on _H∞ tracking performance. Simulation results substantiate the effectiveness of the proposed scheme.

A pre-processing procedure is designed for a spacesurface bistatic synthetic aperture radar (SS-BSAR) system when a time domain image formation algorithm is employed. Three crucial technical issues relating to the procedure are fully discussed. Firstly, unlike image formation algorithms operating in the frequency domain, a time domain algorithm requires the accurate global navigation satellite system (GNSS) time and position. This paper proposes acquisition of this information using a time-and-spatial transfer with precise ephemeris and interpolation. Secondly, synchronization errors and compensation methods in SS-BSAR are analyzed. Finally, taking the non-ideal factors in the echo and the compatibility of image formation algorithms into account, a matched filter based on the minimum delay is constructed. Experimental result using real data suggest the pre-processing is functioning properly.

A novel adaptive detection scheme both for point-like and distributed targets in the presence of Gaussian disturbance in the partially homogeneous environment (PHE) is proposed. The novel detection scheme is based on the orthogonal projection technique. Both the case of known covariance matrix structure and the case of unknown covariance matrix structure are considered. For the former case, the closed-form statistical property of the novel detectors is derived. When the covariance matrix is unknown, the corresponding detectors have higher probabilities of detection (PDs) than their natural competitors. Moreover, they ensure constant false alarm rate (CFAR) property.

The design of robust H∞ filtering problem of polytopic uncertain linear time-delay systems is addressed. The uncertain parameters are supposed to reside in a polytope. A parameterdependent Lyapunov function approach is proposed for the design of filters that ensure a prescribed H∞ performance level for all admissible
uncertain parameters, which is different from the quadratic framework that entails fixed matrices for the entire uncertainty domain. This idea is realized by carefully selecting the structure of the matrices involved in the products with system matrices. An extended H∞ sufficient condition for the existence of robust estimators is formulated in terms of linear matrix inequalities, which can be solved via efficient interior-point algorithms.

A class of latent ancestral graph for modelling the
dependence structure of structural vector autoregressive(VAR)
model affected by latent variables is proposed.The graphs are
mixed graphs with possibly two kind of edges,namely directed and
bidirected edges.The vertex set denotes random variables at dif-
ferent times.In Gaussian case,the latent ancestral graph leads to
a simple parameterization model.A modified iterative conditional
fitting algorithm is presented to obtain maximum likelihood esti-
mation of the parameters.Furthermore,a log-likelihood criterion
is used to select the most appropriate models.Simulations are
performed using illustrative examples and results are provided to
demonstrate the validity of the methods.

This paper deals with system engineering and design methodology for super low altitude satellites in the view of the computational mission analysis. Due to the slight advance of imaging instruments, such as the focus of camera and the image element of charge coupled device (CCD), it is an innovative and economical way to improve the camera’s resolution to enforce the satellite to fly on the lower altitude orbit. DFH-3, the mature satellite bus developed by Chinese Academy of Space Technology, is employed to define the mass and power budgets for the computational mission analysis and the detailed engineering design for super low altitude satellites. An effective iterative algorithm is proposed to solve the ergodic representation of feasible mass and power budgets at the flight altitude under constraints. Besides, boundaries of mass or power exist for every altitude, where the upper boundary is derived from the maximum power, while the minimum thrust force holds the lower boundary before the power reaching the initial value. What’s more, an analytical algorithm is employed to numerically investigate the coverage percentage over the altitude, so that the nominal altitude could be selected from all the feasible altitudes based on both the mass and power budgets and the repetitive ground traces. The local time at the descending node is chosen for the nominal sun-synchronous orbit based on the average evaluation function. After determining the key orbital elements based on the computational mission analysis, the detailed engineering design on the configuration and other subsystems, like power, telemetry telecontrol and communication (TT&C), and attitude determination and control system (ADCS), is performed based on the benchmark bus, besides, some improvements to the bus are also implemented to accommodate the flight at a super low altitude. Two operation strategies, drag-free closed-loop mode and on/off open-loop mode, are presented to maintain the satellite’s altitude. Finally, a flight planning schedule for the satellite is demonstrated from its launch into the initial altitude at the very beginning to its decay to death in the end.

In order to solve the problem that the ripple-effect analysis for the operational architecture of air defense systems (OAADS) is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put forward by extending granular computing. Based on that perational
units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “fournetwork within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations between two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.

The frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) is a light-weight, cost-effective, high-resolution imaging radar, which is suitable for a small flight platform. The signal model is derived for FMCW SAR used in unmanned aerial vehicles (UAV) reconnaissance and remote sensing. An appropriate algorithm is proposed. The algorithm performs the range cell migration correction (RCMC) for continuous non-chirped raw data using the energy invariance of the scaling of a signal in the scale domain. The azimuth processing is based on step transform without geometric resampling operation. The complete derivation of the algorithm is presented. The algorithm performance is shown by simulation results.

To validate the potential space-time adaptive processing (STAP) algorithms for airborne bistatic radar clutter suppression under nonstationary and non-Gaussian clutter environments, a statistically non-Gaussian, spacetime clutter model in varying bistatic geometrical scenarios is presented. The inclusive effects of the model contain the range dependency of bistatic clutter spectrum and clutter power variation in range-angle cells. To capture them, a new approach to coordinate system conversion is initiated into formulating bistatic geometrical model, and the bistatic non-Gaussian amplitude clutter representation method based on a compound model is introduced. The veracity of the geometrical model is validated by using the bistatic configuration parameters of multi-channel airborne radar measurement (MCARM) experiment. And simulation results manifest that the proposed model can accurately shape the space-time clutter spectrum tied up with specific airborne bistatic radar scenario and can characterize the heterogeneity of clutter amplitude distribution in practical clutter environments.

A method for maneuvering target tracking based on in-
ductive factor of posture information is proposed.A distinguished
characteristic of video frequency tracking is that it can capture
the target posture changes from its picture easily,and the posture
change means the motive model of the target will change.This
information is very important to predict the trace of maneuvering
target.Based on this idea,the quantified values of the target pos-
ture change are obtained using Hough algorithm,this key values
are defined as inductive factor of posture information,and then,the
multiple grey trace predict models are established and the degrees
of fuzzy subordinate values for every model are calculated with the
inductive factor,the maneuvering extent values are determined
by a new analysis method of stochastic differential equations for
each model used to modify the degree of fuzzy subordinate values,
these constitute the weighted values for every grey predict collec-
tion.Finally,the synthesis predicting weighted result is obtained.
The experimental results show that the new method is superior to
the conventional algorithm.

An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportionalintegral- derivative (PID) parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor (PMSM) under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.

The problem of robust H_∞ control for uncertain neutral stochastic systems with time-varying delay is discussed. The parameter uncertaintie is assumed to be time varying norm-bounded. First, the stochastic robust stabilization of the stochastic system without disturbance input is investigated by nonlinear matrix inequality method. Then, a full-order stochastic dynamic output feedback controller is designed by solving a bilinear matrix inequality (BMI), which ensures a prescribed stochastic robust H_∞ performance level for the resulting closed loop system with nonzero disturbance input and for all admissible uncertainties. An illustrative example is provided to show the feasibility of the controller and the potential of the proposed technique.

An adaptive channel estimation algorithm for the channel length is proposed to construct a channel estimation model suitable for orthogonal frequency division multiplexing (OFDM) underwater acoustic communication signals for the dependence of traditional channel estimation algorithms on channel length information. This algorithm can be adopted to evaluate channel estimation quality in real time and to adaptively adjust the channel length of the channel estimation algorithm according to the evaluation result, which satisfies the need of accurate estimation of unknown underwater acoustic channels and communication application; based on the study on the relationship between the OFDM communication bit error rate and the subcarrier signal to noise ratio, a self-adjusting optimization scheme for OFDM subcarrier transmitting power is proposed, which realizes underwater communication with the low bit error rate through higher energy efficiency. The validity of the research content is verified through simulation and field experiments.

Space-time adaptive processing (STAP) has been proven to be one of the best techniques capable of detecting weak moving targets in strong clutter environment and has been widely applied in airborne ground moving target indication (GMTI) radar. This paper applies an amplitude and phase estimation (APES) approach to two aspects of the STAP algorithm. Firstly, APES is applied to accurately describe the clutter characteristic in angle-Doppler domain. Then, APES is incorporated into the standard STAP algorithm to improve its performance without increasing transmitting/receiving channel and pulse number. The experimental examples show that the detection performance can be improved by using the APES technique, as well as the high computational complexity can be avoided.

 Adopting“simultaneous transmitting, simultaneous receiving”operational scheme, instantaneous polarization radar (IPR) can measure target polarization scattering matrix (PSM) using only once target echoes in two orthogonal polarization channels. Firstly, signal model and signal process are advanced under narrowband condition. Secondly, measurement performances of two typical IPR waveforms are analyzed in detail. At last, field experiments are carried out using X-band IPR system designed by National University of Defense Technology (NUDT), China. Compared with results obtained by alternative polarization measurement scheme, following results can be obtained: the difference of relative amplitude measurement results is smaller than 2 dB and that of relative phase measurement results is smaller than 10?, verifying the validity of instantaneous polarization measurement scheme.

To investigate a class of nonlinear network control system, a robust fault diagnosis method is presented based on the robust state observer. To access the objective that the designed robust filter is maximally tolerant to disturbances and sensitive to fault, the robustness and stability properties of the fault diagnosis scheme are established rigorously. Using the residual vector, a fault tolerant controller is established in order to guarantee the stability of the closed-loop system, and the controller law can be obtained by solving a set of linear matrix inequalities. Then, some relevant sufficient conditions for the existence of a solution are given by applying Lyapunov stability theory. Finally, a simulation example is performed to show the effectiveness of the proposed approach.

A new method to design a quantum controller which directly controls the probability density function (PDF) of quantum systems is proposed. Based on the quantum model from the PDF perspective, two specific control algorithms are proposed with uniform and non-uniform fields, respectively. Then a detailed control algorithm with convergence analysis is given for the small error case. By appropriately estimating the selected Lyapunov function, more accurate control effect is achieved. The proposed scheme provides a constructive method to find appropriate parameters for controller design.

For a large-scale adaptive array, the heavy computational load and the high-rate data transmission are two challenges in the implementation of an adaptive digital beamforming system. An efficient parallel digital beamforming (DBF) algorithm based on the least mean square algorithm (PLMS) is proposed. An appropriate method is found to partition the least mean square (LMS) algorithm into a number of operational modules, which can be easily executed in a distributed-parallel-processing fashion. As a result, the proposed PLMS algorithm provides an effective solution that can alleviate the bottleneck of high-rate data transmission and reduce the computational cost. PLMS requires less computational load than that of the conventional parallel algorithms based on the recursive least square (RLS) algorithm, as well as it is easier to be implemented to do real time adaptive array processing. Moreover, low sidelobe of the beam pattern is obtained by constraining the static steering vector with Tschebyscheff coefficients. Finally, a scheme of the PLMS algorithm using distributed-parallel-processing system is also proposed. The simulation results demonstrate that the PLMS algorithm has the same interference cancellation performance as that of the conventional LMS algorithm. Moreover, the PLMS algorithm can obtain the same good beamforming performance, regardless how the algorithm is partitioned. It is expected that the proposed algorithm will be used in a large-scale adaptive array system to deal with real time adaptive digital beamforming processing.

This paper proposes a simple constant-stress accelerated life test (ALT) model from Burr type XII distribution when the data are Type-I progressively hybrid censored. The maximum likelihood estimation (MLE) of the parameters is obtained through the numerical method for solving the likelihood equations. Approximate confidence interval (CI), based on normal approximation to the asymptotic distribution of MLE and percentile bootstrap CI is derived. Finally, a numerical example is introduced and then a Monte Carlo simulation study is carried out to illustrate the proposed method.

The evaluation of air combat decision-making has garnered significant attention due to its potential to effectively mitigate losses resulting from erroneous decisions. However, existing research primarily focuses on static evaluation methods. Therefore, this paper proposes a dynamic multi-round decision evaluation method based on the characteristics of multi-round unmanned aerial vehicle air combat under opponent’s optimal strategy. In order to determine objective weights, an improved multi-attribute decision making method is proposed, which incorporates the proximity as a correction coefficient for evaluation indicators, utilizing the cosine similarity instead of Euclidean distance, and incorporating both actual and theoretical objective weights to prevent data mutations. Subsequently, the game theory is employed to reasonably adjust subjective and objective weights to obtain comprehensive weights. To address the issues related to the ambiguity and randomness during the evaluation process, a reverse cloud generator is utilized to determine the center of gravity of the cloud model using comprehensive weights while employing the weighted deviation degree for evaluating air combat decision-making effectiveness. By activating the cloud generator through the cloud model, the optimal strategies for each round of air combat are determined, thereby completing the dynamic evaluations for multi-round sequential decision-making processes. Finally, the feasibility and effectiveness of the proposed method are verified through simulations.

Failure prediction plays an important role for many tasks
such as optimal resource management in large-scale system.
However, accurately failure number prediction of repairable largescale
long-running computing (RLLC) is a challenge because of
the reparability and large-scale. To address the challenge, a general
Bayesian serial revision prediction method based on Bootstrap
approach and moving average approach is put forward, which can
make an accurately prediction for the failure number. To demonstrate
the performance gains of our method, extensive experiments
on the data of Los Alamos National Laboratory (LANL) cluster is
implemented, which is a typical RLLC system. And experimental
results show that the prediction accuracy of our method is 80.2 %,
and it is a greatly improvement with 4 % compared with some
typical methods. Finally, the managerial implications of the models
are discussed.

An integration-centric approach is proposed to handle inadequate information in the system readiness level (SRL) assessment using the evidential reasoning (ER) algorithm. Current SRL assessment approaches cannot be applied to handle inadequate information as the input. The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity. Two case studies are performed to validate the efficiency of the proposed approach. And these studies are also performed to study how the inadequate information will affect the assessment result. And the differences caused by the system’s structure. The importance of the system’s structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.

This paper presents a joint high order statistics (HOS) and signal-to-noise ratio (SNR) algorithm for the recognition of multiple-input multiple-output (MIMO) radar signal without a priori knowledge of the signal parameters. This method is capable of recognizing the MIMO radar signal as well as discriminating it from single-carrier signal adopted by conventional radar. Meanwhile, the sub-carrier number of the none-coding MIMO radar signal is estimated. Extensive simulations are carried out in different operating conditions. Simulation results prove the feasibility and indicate that the recognition probability could reach over 90% when the value of SNR is above 0 dB.

To investigate the low-complex and high-precise tracking method, a novel single link tracking scheme based on UWB localization is proposed. Two antenna arrays are settled at the fixed station (FS) to receive the UWB signal from mobile terminal (MT), one FS is enough for the proposed scheme to track the MT. The UWB multipath detection algorithm for time difference of arrival (TDOA) estimation is presented and TDOA is thus adopted to estimate angle of arrival (AOA), avoiding the synchronization and complicated beamforming for AOA. The impacts of localization errors, concluding multipath and non-line-of-sight (NLOS) errors are simulated for the proposed track scheme. It is demonstrated that the simulation curve can match the real target moving, and the feasibility of the proposed scheme is proved.

A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed. The pre-registration is achieved by transforming the multi-pose models to the standard frontal model’s reference frame using the principal axis analysis algorithm. Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics. These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration. Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.