The aim of this paper is to solve the problems of multitarget tracking in clutter. Firstly, the data association of measurement-to-target is formulated as an integer programming problem. Through using the linear programming (LP) based branchand-bound method and adjusting the constraint conditions, an optimal set integer programming (OSIP) algorithm is then proposed for tracking multiple non-maneuvering targets in clutter. For the case of maneuvering targets, this paper introduces the OSIP algorithm into the filtering step of the interacting multiple model (IMM) algorithm resulting in the IMM based on OSIP algorithm. Extensive Monte Carlo simulations show that the presented algorithms can obtain superior estimations even in the case of high density noises.

In order to avoid the system performance deterioration caused by the wireless fading channel and imperfect channel estimation in cognitive radio networks, the spectrum sharing problem with the consideration of feedback control information from the primary user is analyzed. An improved spectrum sharing algorithm based on the combination of the feedback control information and the optimization algorithm is proposed. The relaxation method is used to achieve the approximate spectrum sharing model, and the spectrum sharing strategy that satisfies the individual outage probability constraints can be obtained iteratively with the observed outage probability. Simulation results show that the proposed spectrum sharing algorithm can achieve the spectrum sharing strategy that satisfies the outage probability constraints and reduce the average outage probability without causing maximum transmission rate reduction of the secondary user.

A joint power control and relay selection scheme is considered for a cooperative and cognitive radio system where a secondary network shares spectrum with the primary network. In the secondary network, two secondary users (SUs) communicate with each other via an assist relay. The main point is to provide
the best system performance to SUs while maintaining the interference power at primary user (PU) under a certain level. Using convex optimization, a closed-form solution is obtained when optimizing the power allocation among the two nodes and relay. Based on this result, a joint power control and relay selection scheme with fewer variable dimensions is proposed to maximize the achievable rate of the secondary system. Simulation results demonstrate that
the sum rate of the cognitive two-way relay network increases compared with a random relay selection and fixed power allocation.

A new iterative algorithm is proposed to reconstruct an unknown sparse signal from a set of projected measurements. Unlike existing greedy pursuit methods which only consider the atoms having the highest correlation with the residual signal, the proposed algorithm not only considers the higher correlation atoms but also reserves the lower correlation atoms with the residual signal. In the lower correlation atoms, only a few are correct which usually impact the reconstructive performance and decide the reconstruction dynamic range of greedy pursuit methods. The others are redundant. In order to avoid redundant atoms impacting the reconstructive accuracy, the Bayesian pursuit algorithm is used to eliminate them. Simulation results show that the proposed algorithm can improve the reconstructive dynamic range and the reconstructive accuracy. Furthermore, better noise immunity compared with the existing greedy pursuit methods can be obtained.

Since the satellite communication goes in the trend of high-frequency and fast speed, the coefficients updating and the precision of the traditional pre-distortion feedback methods need to be further improved. On this basis, this paper proposes dual loop feedback pre-distortion, which uses two first-order Volterra filter models to reduce the computing complexity and a dynamic error adjustment model to construct a revised feedback to ensure a better pre-distortion performance. The computation complexity, iterative convergence speed and precision of the proposed method are theoretically analyzed. Simulation results show that this dual loop feedback pre-distortion can speed the updating of coefficients and ensure the linearity of the amplifier output.

A novel multi-observer passive localization algorithm based on the weighted restricted total least square (WRTLS) is proposed to solve the bearings-only localization problem in the presence of observer position errors. Firstly, the unknown matrix perturbation information is utilized to form the WRTLS problem. Then, the corresponding constrained optimization problem is transformed into an unconstrained one, which is a generalized Rayleigh quotient minimization problem. Thus, the solution can be got through the generalized eigenvalue decomposition and requires no initial state guess process. Simulation results indicate that the proposed algorithm can approach the Cramer-Rao lower bound (CRLB), and the localization solution is asymptotically unbiased.

A two-dimensional direction-of-arrival (DOA) and polarization estimation algorithm for coherent sources using a linear vector-sensor array is presented. Two matrices are first constructed by the receiving data. The ranks of the two matrices are only related to the DOAs of the sources and independent of their coherency. Then the source’s elevation is resolved via the matrix pencil (MP) method, and the singular value decomposition (SVD) is used to reduce the noise effect. Finally, the source’s steering vector is estimated, and the analytics solutions of the source’s azimuth and polarization parameter can be directly computed by using a vector cross-product estimator. Moreover, the proposed algorithm can achieve the unambiguous direction estimates, even if the space between adjacent sensors is larger than a half-wavelength. Theoretical and numerical simulations show the effectiveness of the proposed algorithm.

Multi-view laser radar (ladar) data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.

Orthogonal waveform design is quite an important issue for waveform diversity systems. A chaos based method for the orthogonal discrete frequency coding waveform (DFCW) design is proposed to increase the insufficient orthogonal waveform number and their finite coding length. Premises for chaos choosing and the frequency quantification method are discussed to obtain the best correlation properties. Simulation results show the validity of the theoretic analysis.

Structural redundancy elimination in case resource pools (CRP) is critical for avoiding performance bottlenecks and maintaining robust decision capabilities in cloud computing services. For these purposes, this paper proposes a novel approach to ensure redundancy elimination of a reasoning system in CRP. By using α entropy and mutual information, functional measures to eliminate redundancy of a system are developed with respect to a set of outputs. These measures help to distinguish both the optimal feature and the relations among the nodes in reasoning networks from the redundant ones with the elimination criterion. Based on the optimal feature and its harmonic weight, a model for knowledge reasoning in CRP (CRPKR) is built to complete the task of query matching, and the missing values are estimated with Bayesian networks. Moreover, the robustness of decisions is verified through parameter analyses. This approach is validated by the simulation with benchmark data sets using cloud SQL. Compared with several state-of-the-art techniques, the results show that the proposed approach has a good performance and boosts the robustness of decisions.

A novel group decision-making (GDM) method based on intuitionistic fuzzy sets (IFSs) is developed to evaluate the ergonomics of aircraft cockpit display and control system (ACDCS). The GDM process with four steps is discussed. Firstly, approaches are proposed to transform four types of common judgement representations into a unified expression by the form of the IFS, and the features of unifications are analyzed. Then, the aggregation operator called the IFSs weighted averaging (IFSWA) operator is taken to synthesize decision-makers’ (DMs’) preferences by the form of the IFS. In this operator, the DM’s reliability weights factors are determined based on the distance measure between their preferences. Finally, an improved score function is used to rank alternatives and to get the best one. An illustrative example proves the proposed method is effective to valuate the ergonomics of the ACDCS.

The goal of this research is to develop an emergency disaster relief mobilization tool that determines the mobilization levels of commodities, medical service and helicopters (which will be utilized as the primary means of transport in a mountain region struck by a devastating earthquake) at pointed temporary facilities, including helicopter-based delivery plans for commodities and evacuation plans for critical population, in which relief demands are considered as uncertain. The proposed mobilization model is a two-stage stochastic mixed integer program with two objectives: maximizing the expected fill rate and minimizing the total expenditure of the mobilization campaign. Scenario decomposition based heuristic algorithms are also developed according to the structure of the proposed model. The computational results of a numerical example, which is constructed from the scenarios of the Great Wenchuan Earthquake, indicate that the model can provide valuable decision support for the mobilization of post-earthquake relief, and the proposed algorithms also have high efficiency in computation.

An adaptive unscented Kalman filter (AUKF) and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter (UKF) when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

It is difficult to determine the stability of linear systems with interval delays (LID systems) because the roots of the characteristic polynomials of the systems are continuous and vary in a complex plane with the delay. To solve the problem, this paper develops a stability test of LID systems by resorting to 2-D hybrid polynomials and 2-D Hurwitz-Schur stability. Comparing with the existing test approaches for LID systems, the proposed 2-D Hurwitz-Schur stability test is easy to apply, and can obtain closed form constraint conditions for system parameters. This paper proposes some theorems as sufficient conditions for the stability of LID systems, and also reveals that recent results about the stability test of linear systems with any delays (LAD systems) are not suitable for LID systems because they are very conservative for the stability of LID systems.

This paper aims at solving the state filtering problem for linear systems with state constraints. Three classes of typical state constraints, i.e., linear equality, quadratic equality and inequality, are discussed. By using the linear relationships among different state variables, a reduced-order Kalman filter is derived for the system with linear equality constraints. Afterwards, such a solution is applied to the cases of the quadratic equality constraint and inequality constraints and the two constrained state filtering problems are transformed into two relative constrained optimization problems. Then they are solved by the Lagrangian multiplier and linear matrix inequality techniques, respectively. Finally, two simple tracking examples are provided to illustrate the effectiveness of the reduced-order filters.

For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key research problems. State list extracting means to calculate the optimal shared state path from state trajectories with cycles. A state list extracting algorithm checks cyclic state lists of a current state in the state trajectory, condensing the optimal action set of the current state. By reinforcing the optimal action selected, the action policy of cyclic states is optimized gradually. The state list extracting is repeatedly learned and used as the experience knowledge which is shared by teams. Agents speed up the rate of convergence by experience sharing. Competition games of preys and predators are used for the experiments. The results of experiments prove that the proposed algorithms overcome the lack of experience in the initial stage, speed up learning and improve the performance.

This paper presents an improved gravitational search algorithm (IGSA) as a hybridization of a relatively recent evolutionary algorithm called gravitational search algorithm (GSA), with the free search differential evolution (FSDE). This combination incorporates FSDE into the optimization process of GSA with an attempt to avoid the premature convergence in GSA. This strategy makes full use of the exploration ability of GSA and the exploitation ability of FSDE. IGSA is tested on a suite of benchmark functions. The experimental results demonstrate the good performance of IGSA.

To deal with the adverse influence of model failures on Kalman filtering (KF) estimation, it is necessary to investigate the generalized reliability theory, including the model failure detection and identification method as well as the separability and reliability theories. Although the generalized reliability theory for the least square has been discussed for many decades, the generalized reliability theory of KF is not widely discussed. Compared with the least square, KF includes not only the measurement model, but also the dynamic model. In KF, the predicted value of the state parameters from the dynamic model is considered as pseudomeasurements and combined with the observed measurements to compose the form of the least square. According to the reliability of the least square, the generalized reliability of KF is derived. Then, the dynamic model failure of precise point positioning is simulated to demonstrate the usage of the generalized reliability theory. The results show that the adverse influence of the dynamic model failure is more severe than that of the measurement model. Moreover, it is recommended that the model failure identification should always be used even if the overall model test passes. It is shown that the derived generalized reliability measures are suitable for the generalized KF estimation.

The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration (RCA), which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.