Multiple maneuvering targets signal processing in high frequency radar is challenging due to the following difficulties: the interference between signals is severe because of significant spread of the target Doppler spectrum, the low signal to clutter ratio (SCR) environment degrades the performance of signal processing algorithms. This paper addresses this challenging problem by using an S²-method and an adaptive clutter rejection scheme. The proposed S²-method improves the S-method by eliminating interference between signals, and thus it enables multi-target signals to be reconstructed individually. The proposed adaptive clutter rejection scheme is based on an adaptive notch filter, which is designed according to the envelop of the clutter spectrum. Experiments with simulated targets added into radar sea clutter echo and real air target data illustrate the effectiveness of the proposed method.

The maximum likelihood (ML) estimator demonstrates remarkable performance in direction of arrival (DOA) estimation for the multiple input multiple output (MIMO) sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization (ACO) is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML (ML DOA estimator based on ACO for MIMO sonar) with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the proposed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.

Multiuser detection can be described as a quadratic optimization problem with binary constraint. Many techniques are available to find approximate solution to this problem. These techniques can be characterized in terms of complexity and detection performance. The “efficient frontier” of known techniques include the decision-feedback, branch-and-bound and probabilistic data association detectors. The presented iterative multiuser detection technique is based on joint deregularized and box-constrained solution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm. The deregularization maximizes the energy of the solution, this is opposite to the Tikhonov regularization where the energy is minimized. However, combined with box-constraints, the deregularization forces the solution to be close to the binary set. We further exploit the boxconstrained dichotomous coordinate descent (DCD) algorithm and adapt it to the nonstationary iterative Tikhonov regularization to present an efficient detector. As a result, the worst-case and average complexity are reduced down to K2.8 and K2.5 floating point operation per second, respectively. The development improves the “efficient frontier” in multiuser detection, which is illustrated by simulation results. Finally, a field programmable gate array (FPGA) design of the detector is presented. The detection performance obtained from the fixed-point FPGA implementation shows a good match to the floating-point implementation.

The track association problem of radar and electronic support measure (ESM) has been considered in the literature for several years. This problem is crucial for radar-to-ESM track fusion and is complicated by the presence of individual systematic errors and measurement errors. In order to improve the track association of radar and ESM sensors, a pseudo-linear filtering algorithm is proposed to estimate the target states and improve the stability of the filter. It is found that, however, the correct probability of radarto-ESM track association decreases as the radar measurement error decreases, when the pseudo-linear filter is used for ESM sensor filtering. In view of the strange phenomenon, this paper analyzes the reason for it by using the statistic theory and further performs Monte Carlo simulation to verify the analysis.

This paper derives the extended ambiguity function for a bistatic multiple-input multiple-output (MIMO) radar system, which includes the whole radar system parameters: geometric sensor configuration, waveforms, range, range rate, target scattering and noise characteristics. Recent research indicates the potential parameter estimate performance of bistatic MIMO radars. And this ambiguity function can be used to analyze the parameter estimate performance for the relationship with the Cramer-Rao bounds of the estimated parameters. Finally, some examples are given to demonstrate the good parameter estimate performance of the bistatic MIMO radar, using the quasi-orthogonal waveforms based on Lorenz chaotic systems.

In the spaceborne/airborne forward-looking bistatic synthetic aperture radar (SA-FBSAR), due to the system platforms’ remarkable velocity difference and the forward-looking mode, the range cell migration (RCM) not only depends on the target’s twodimensional location, but also varies with the range location nonlinearly. And the nonlinearity is not just the slight deviation from the linear part, but exhibits evident nonlinear departure in the RCM trajectory. If the RCM is not properly corrected, nonlinear image distortions would occur. Based on the RCM model, a modified two-step RCM compensation (RCMC) method for SA-FBSAR is proposed. In this method, firstly the azimuth-dependent RCM is compensated by the scaling Fourier transform and the phase multiplication. And then the range-dependent RCM is removed through interpolation. The effectiveness of the proposed RCMC method is verified by the simulation results of both point scatterers and area targets.

A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with multiple time windows is presented. The problems’ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-infirst-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and reoptimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.

Power efficiency and link reliability are of great importance in hierarchical wireless sensor networks (HWSNs), especially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are considered as players. By applying a double-pricing scheme in the definition of OHSs’ utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.

A new fuzzy support vector machine algorithm with dual membership values based on spectral clustering method is proposed to overcome the shortcoming of the normal support vector machine algorithm, which divides the training datasets into two absolutely exclusive classes in the binary classification, ignoring the possibility of “overlapping” region between the two training classes. The proposed method handles sample “overlap” efficiently with spectral clustering, overcoming the disadvantages of over-fitting well, and improving the data mining efficiency greatly. Simulation provides clear evidences to the new method.

Learning is widely used in intelligent planning to shorten the planning process or improve the plan quality. This paper aims at introducing learning and fatigue into the classical hierarchical task network (HTN) planning process so as to create better highquality plans quickly. The process of HTN planning is mapped during a depth-first search process in a problem-solving agent, and the models of learning in HTN planning is conducted similar to the learning depth-first search (LDFS). Based on the models, a learning method integrating HTN planning and LDFS is presented, and a fatigue mechanism is introduced to balance exploration and exploitation in learning. Finally, experiments in two classical domains are carried out in order to validate the effectiveness of the proposed learning and fatigue inspired method.

The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems. However, most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov jump system, few literature is related to the estimation problem of nonlinear stochastic hybrid systems with state dependent transitions. According to this problem, a new methodology which relaxes quite a restrictive assumption that the mode transition process must satisfy Markov properties is proposed. In this method, a general approach is presented to model the state dependent transitions, the state and output spaces are discreted into cell space which handles the nonlinearities and computationally intensive problem offline. Then maximum a posterior estimation is obtained by using the Bayesian theory. The efficacy of the estimator is illustrated by a simulated example.

An adaptive fuzzy sliding mode control (AFSMC) approach is proposed for a robotic airship. First, the mathematical model of an airship is derived in the form of a nonlinear control system. Second, an AFSMC approach is proposed to design the attitude control system of airship, and the global stability of the closed-loop system is proved by using the Lyapunov stability theorem. Finally, simulation results verify the effectiveness and robustness of the proposed control approach in the presence of model uncertainties and external disturbances.

To improve the reliability and accuracy of the global positioning system (GPS)/micro electromechanical system (MEMS)-inertial navigation system (INS) integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive (FAR) modeling, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the traditional adaptive neuro-fuzzy inference system (ANFIS) method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS (SCANFIS) combined with the extended Kalman filter (EKF) is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the efficiency of the proposed methods.

The recently invented artificial bee colony (ABC) algorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of finding a neighboring food source. This paper presents an improved ABC algorithm with mutual learning (MutualABC) that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The performance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algorithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.

The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded system, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine (RT-EFSM) model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class (timeCTEC) are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics realtime embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.

The learning Bayesian network (BN) structure from data is an NP-hard problem and still one of the most exciting challenges in the machine learning. In this work, a novel algorithm is presented which combines ideas from local learning, constraintbased, and search-and-score techniques in a principled and effective way. It first reconstructs the junction tree of a BN and then performs a K2-scoring greedy search to orientate the local edges in the cliques of junction tree. Theoretical and experimental results show the proposed algorithm is capable of handling networks with a large number of variables. Its comparison with the well-known K2 algorithm is also presented.

As a promising technique to enhance the spatial resolution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially dependent both within pixels and between them. The spatial attraction is used as a tool to describe the dependence. First, the spatial attractions between pixels, subpixel/pixel spatial attraction model (SPSAM), are described by the modified SPSAM (MSPSAM) that estimates the attractions according to the distribution of sub-pixels within neighboring pixels. Then a mixed spatial attraction model (MSAM) for sub-pixel mapping is proposed that integrates the spatial attractions both within pixels and between them. According to the expression of the MSAM maximumising the spatial attraction, the genetic algorithm is employed to search the optimum solution and generate the sub-pixel mapping results. Experiments show that compared with SPSAM, MSPSAM and pixel swapping algorithm modified by initialization from SPSAM (MPS), MSAM can provide higher accuracy and more rational sub-pixel mapping results.

Classical network reliability problems assume both networks and components have only binary states, fully working or fully failed states. But many actual networks are multi-state, such as communication networks and transportation networks. The nodes and arcs in the networks may be in intermediate states which are not fully working either fully failed. A simulation approach for computing the two-terminal reliability of a multi-state network is described. Two-terminal reliability is defined as the probability that d units of demand can be supplied from the source to sink nodes under the time threshold T. The capacities of arcs  may be in a stochastic state following any discrete or continuous distribution. The transmission time of each arc is also not a fixed number but stochastic according to its current capacity and demand. To solve this problem, a capacitated stochastic coloured Petri net is proposed for modelling the system behaviour. Places and transitions respectively stand for the nodes and arcs of a network. Capacitated transition and self-modified token colour with route information are defined to describe the multi-state network. By the simulation, the two-terminal reliability and node importance can be estimated and the optimal route whose reliability is highest can also be given. Finally, two examples of different kinds of multistate networks are given.

Systems with a hidden degradation process are pervasive in the real world. Degrading critical components will undermine system performance and pose potential failures in the future. Prognostic aims at predicting potential failures before it evolves into faults. A prognostic procedure based on expectation maximization and unscented Kalman filter is proposed. System state, sensor measurement and hidden degradation process are viewed as data (incomplete or missing) in the expectation maximization method. System state and hidden degradation process are estimated by a unscented Kalman filter upon sensor measurements. Component-specific parameters in a degradation process are identified on the estimation of the degradation process. Residual life is measured by the median of estimated residual life distribution. The proposed procedure is verified by simulations on a first-order capacitor-resistance circuit with degrading resistance. Residual life estimation consists conservatively with the trend and is evaluated in terms of relative errors. Simulation results are reasonable. The proposed prognostic method expects applications in practice.