For maneuvering target tracking, the interactive multiple model (IMM) algorithm and its variants have shown good performance, among which the variable structure IMM (VSIMM) algorithm is the most widely studied one. The approximation degree of the algorithm and the matching degree of the model subset will affect the performance of the VSIMM. In addition, considering that smoothing can provide better estimation of the target state, a new adaptive VSIMM filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. First, an accurate model of the VSIMM algorithm is established, in which the IMM is run in parallel with and independently from the different model subsets, and the state estimation of the model subset with the highest probability is selected as the final estimation result. Then, an adaptive VSIMM (AVSIMM) algorithm was designed based on the VSIMM algorithm. The adaptation is reflected in the construction of a new model subset from the original model subsets, which improves the matching degree between the model subset and the actual maneuvering model of the target. Finally, by smoothing the filtering data of the AVSIMM algorithm, the AVSIMMFS algorithm is obtained. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Simulation results show that the tracking performance of the AVSIMMFS algorithm is better than that of other methods.

An MC (minor component) is usually referred to as the noise part of a time-varying signal. Extracting multiple MCs from an input signal is very useful in many practical applications. Compared with single MC estimating algorithms and subspace tracking algorithms, multiple MC extraction algorithms have a wider range of applications and greater research significance. To address the existing issues with multiple MC extraction algorithms, such as the need to estimate parameters in advance and with numerous constraints, this paper proposes a novel multiple MC extraction algorithm by adding a diagonal weighted matrix to the Möller algorithm. The proposed algorithm’s fixed points are analyzed using ODE (ordinary differential equation) methods, and it is demonstrated that the algorithm achieves stable convergence only when the weight matrix converges to the desired MCs of the signal. The simulation results illustrate the effectiveness of the proposed algorithm.

For a defender protecting a target from an incoming high-maneuvering homing missile, an Optimal Predictive Guidance Law (OPGL) is proposed based on a guidance law identification algorithm. First, a guidance law identification algorithm based on the Interactive Multiple Guidance law Model (IMGM) is designed. IMGM uses multiple sub-filters for parallel processing, and there is interaction between the inputs and outputs of the sub-filters. Second, the VSIMGM algorithm is further designed based on the IMGM algorithm. In this algorithm, a new guidance law model subset is constructed based on the original guidance law model set. By reducing the number of models, the model error is reduced, and the identification probability of the guidance law model is improved. Then, based on the identification result of the identification algorithm, the OPGL is given by using the variational method through the steps of linearizing the kinematic model and reducing the order of state equation. The OPGL reduces the overload requirement of the defender and improves interception accuracy without assuming the maneuver mode of a homing missile. Finally, the effectiveness of the proposed OPGL and guidance law identification algorithm based on VSIMGM is demonstrated using numerical simulation. Compared with the guidance law identification algorithm based on MMAE and IMGM, the guidance law identification algorithm based on VSIMGM can quickly, stably, and accurately identify the guidance law. In addition, based on the guidance law identification results of the homing missile, compared with proportional navigation, augmented proportional navigation, differential game guidance (DGG), and sliding mode guidance (SMG), OPGL can reduce the required overload of the missile and achieve accurate interception.

The deterministic discrete-time method is a dominant approach for analyzing neural network algorithms. To address the issue where conventional convergence conditions impose stringent restrictions on the range of learning factors, this paper proposes a principal component estimation algorithm with an adaptive learning factor, which guarantees global convergence. The convergence of the algorithm is analyzed using the deterministic discrete-time method, and conditions ensuring convergence are established. Unlike convergence conditions for other algorithms, the proposed algorithm’s convergence conditions eliminate restrictions on the learning factor, thereby extending its feasible range. Simulation results demonstrate that the proposed algorithm effectively resolves ill-conditioned matrix problems. When compared with existing algorithms, the proposed algorithm exhibits significantly faster convergence speed than several current methods.

This paper establishes a hierarchical structural model for the brittleness link of computer network security, building upon definitions of brittleness, research methods and structure models. In addition, using the fuzzy analytic hierarchy process, the brittleness link of the system is quantitatively analyzed, and the relative importance of each subsystem of the computer network security system was obtained, to establish an important basis for improving the security of the system and the ability to resist accidents.

The large amount of quality information generated during the technical preparation of the test object and the use and maintenance of the test support equipment in equipment testing is an important content of equipment testing information management and an important component of the equipment testing quality management system. This article starts from the practice of quality information management in the technical preparation process of the test object, analyzes the main problems in the current information management mode, explores how to more effectively carry out information management of equipment testing quality, and proposes the use of networked information management mode and data mining methods in information management, which can greatly improve the level of equipment testing quality information management.