Thinning of antenna arrays has been a popular topic for the last several decades. With increasing computational power, this optimization task acquired a new hue. This paper suggests a genetic algorithm as an instrument for antenna array thinning. The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level (SLL) while maintaining the half-power beamwidth (HPBW) of a full linear antenna array. Based on results from existing papers in the field and known approaches to antenna array thinning, a classification of thinning types is introduced. The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL. The effect of thinning coefficient on main directional pattern characteristics, such as peak SLL and HPBW, is discussed for a number of amplitude distributions.

Most of the existing direction of arrival (DOA) estimation algorithms are applied under the assumption that the array manifold is ideal. In practical engineering applications, the existence of non-ideal conditions such as mutual coupling between array elements, array amplitude and phase errors, and array element position errors leads to defects in the array manifold, which makes the performance of the algorithm decline rapidly or even fail. In order to solve the problem of DOA estimation in the presence of amplitude and phase errors and array element position errors, this paper introduces the first-order Taylor expansion equivalent model of the received signal under the uniform linear array from the Bayesian point of view. In the solution, the amplitude and phase error parameters and the array element position error parameters are regarded as random variables obeying the Gaussian distribution. At the same time, the expectation-maximization algorithm is used to update the probability distribution parameters, and then the two error parameters are solved alternately to obtain more accurate DOA estimation results. Finally, the effectiveness of the proposed algorithm is verified by simulation and experiment.

Lightweight convolutional neural networks (CNNs) have simple structures but struggle to comprehensively and accurately extract important semantic information from images. While attention mechanisms can enhance CNNs by learning distinctive representations, most existing spatial and hybrid attention methods focus on local regions with extensive parameters, making them unsuitable for lightweight CNNs. In this paper, we propose a self-attention mechanism tailored for lightweight networks, namely the brief self-attention module (BSAM). BSAM consists of the brief spatial attention (BSA) and advanced channel attention blocks. Unlike conventional self-attention methods with many parameters, our BSA block improves the performance of lightweight networks by effectively learning global semantic representations. Moreover, BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training, maintaining the network’s lightweight and mobile characteristics. We validate the effectiveness of the proposed method on image classification tasks using the Food-101, Caltech-256, and Mini-ImageNet datasets.

The concept of unmanned weapon system-of-systems (UWSoS) involves a collection of various unmanned systems to achieve or accomplish a specific goal or mission. The mission reliability of UWSoS is represented by its ability to finish a required mission above the baselines of a given mission. However, issues with heterogeneity, cooperation between systems, and the emergence of UWSoS cannot be effectively solved by traditional system reliability methods. This study proposes an effective operation-loop-based mission reliability evaluation method for UWSoS by analyzing dynamic reconfiguration. First, we present a new connotation of an effective operation loop by considering the allocation of operational entities and physical resource constraints. Then, we propose an effective operation-loop-based mission reliability model for a heterogeneous UWSoS according to the mission baseline. Moreover, a mission reliability evaluation algorithm is proposed under random external shocks and topology reconfiguration, revealing the evolution law of the effective operation loop and mission reliability. Finally, a typical 60-unmanned-aerial-vehicle-swarm is taken as an example to demonstrate the proposed models and methods. The mission reliability is achieved by considering external shocks, which can serve as a reference for evaluating and improving the effectiveness of UWSoS.

Complex systems widely exist in nature and human society. There are complex interactions between system elements in a complex system, and systems show complex features at the macro level, such as emergence, self-organization, uncertainty, and dynamics. These complex features make it difficult to understand the internal operation mechanism of complex systems. Networked modeling of complex systems is a favorable means of understanding complex systems. It not only represents complex interactions but also reflects essential attributes of complex systems. This paper summarizes the research progress of complex systems modeling and analysis from the perspective of network science, including networked modeling, vital node analysis, network invulnerability analysis, network disintegration analysis, resilience analysis, complex network link prediction, and the attacker-defender game in complex networks. In addition, this paper presents some points of view on the trend and focus of future research on network analysis of complex systems.

Total variation (TV) is widely applied in image processing. The assumption of TV is that an image consists of piecewise constants, however, it suffers from the so-called staircase effect. In order to reduce the staircase effect and preserve the edges when textures of image are extracted, a new image decomposition model is proposed in this paper. The proposed model is based on the total generalized variation method which involves and balances the higher order of the structure. We also derive a numerical algorithm based on a primal-dual formulation that can be effectively implemented. Numerical experiments show that the proposed method
can achieve a better trade-off between noise removal and texture extraction, while avoiding the staircase effect efficiently.

In order to solve the problem of coherent signal subspace method (CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival (DOA) estimation method of wideband source, which is based on iterative adaptive spectral reconstruction, is proposed. Firstly, the wideband signals are divided into several narrowband signals of different frequency bins by discrete Fourier transformation (DFT). Then, the signal matched power spectrum in referenced frequency bins is computed, which can form the initial covariance matrix. Finally, the linear restrained minimum variance spectral (Capon spectral) of signals in other frequency bins are reconstructed using sequential iterative means, so the DOA can be estimated by the locations of spectral peaks. Theoretical analysis and simulation results show the proposed method based on the iterative spectral reconstruction for the covariance matrices of all sub-bands can avoid the problem of determining the signal subspace accurately with the coherent signal subspace method under the conditions of small samples and low signal to noise ratio (SNR), and it can also realize full dimensional focusing of different sub-band data, which can be applied to coherent sources and can significantly improve the accuracy of DOA estimation. 

The earth observation satellites (EOSs) scheduling problem for emergency tasks often presents many challenges. For example, the scheduling calculation should be completed in seconds, the scheduled task rate is supposed to be as high as possible, the disturbance measure of the scheme should be as low as possible, which may lead to the loss of important observation opportunities and data transmission delays. Existing scheduling algorithms are not designed for these requirements. Consequently, we propose a rolling horizon strategy (RHS) based on event triggering as well as a heuristic algorithm based on direct insertion, shifting, backtracking, deletion, and reinsertion (ISBDR). In the RHS, the driven scheduling mode based on the emergency task arrival and control station time window events are designed to transform the long-term, large-scale problem into a short-term, small-scale problem, which can improve the schedulability of the original scheduling scheme and emergency response sensiti-vity. In the ISBDR algorithm, the shifting rule with breadth search capability and backtracking rule with depth search capability are established to realize the rapid adjustment of the original plan and improve the overall benefit of the plan and early completion of emergency tasks. Simultaneously, two heuristic factors, namely the emergency task urgency degree and task conflict degree, are constructed to improve the emergency task scheduling guidance and algorithm efficiency. Finally, we conduct extensive experiments by means of simulations to compare the algorithms based on ISBDR and direct insertion, shifting, deletion, and reinsertion (ISDR). The results demonstrate that the proposed algorithm can improve the timeliness of emergency tasks and scheduling performance, and decrease the disturbance measure of the scheme, therefore, it is more suitable for emergency task scheduling.

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.

The Ocean 4A scatterometer, expected to be launched in 2024, is poised to be the world’s first spaceborne microwave scatterometer utilizing a digital beamforming system. To ensure high-precision measurements and performance stability across diverse environments, stringent requirements are placed on the dynamic range of its receiving system. This paper provides a detailed exposition of a field-programmable gate array (FPGA)-based automatic gain control (AGC) design for the spaceborne scatterometer. Implemented on an FPGA, the algorithm harnesses its parallel processing capabilities and high-speed performance to monitor the received echo signals in real time. Employing an adaptive AGC algorithm, the system generates gain control codes applicable to the intermediate frequency variable attenuator, enabling rapid and stable adjustment of signal amplitudes from the intermediate frequency amplifier to an optimal range. By adopting a purely digital processing approach, experimental results demonstrate that the AGC algorithm exhibits several advantages, including fast convergence, strong flexibility, high precision, and outstanding stability. This innovative design lays a solid foundation for the high-precision measurements of the Ocean 4A scatterometer, with potential implications for the future of spaceborne microwave scatterometers.

According to the measurement principle of the traditional interferometer, a narrowband signal model is established and used, however, for wideband signals or multiple signals, this model is invalid. For the problems of direction finding with interferometer for wideband signals and multiple signals scene, a frequency domain phase interferometer is proposed and the concrete implementation scheme is given. The proposed method computes the phase difference in frequency domain, and finds multi-target results with judging the spectrum amplitude changing, and uses the frequency phase difference to compute the arrival angle. Theoretical analysis and simulation results show that the proposed method effectively solves the problem of the angle estimation with phase interferometer for wideband signals, and has good performance in multiple signals scene with non-overlapping spectrum or partially overlapping. In addition, the wider the signal bandwidth, the better direction finding performance of this algorithm.

The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus, we consider system safety as a control problem based on the system-theoretic accident model, the processes (STAMP) model and the system theoretic process analysis (STPA) technique to compensate the deficiency of traditional techniques. Meanwhile, system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly, we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.

A system of systems (SoS) composes a set of independent constituent systems (CSs), where the degree of authority to control the independence of CSs varies, depending on different SoS types. Key researchers describe four SoS types with descending levels of central authority: directed, acknowledged, collaborative and virtual. Although the definitions have been recognized in SoS engineering, what is challenging is the difficulty of translating these definitions into models and simulation environments. Thus, we provide a goal-based method including a mathematical baseline to translate these definitions into more effective agent-based modeling and simulations. First, we construct the theoretical models of CS and SoS. Based on the theoretical models, we analyze the degree of authority influenced by SoS characteristics. Next, we propose a definition of SoS types by quantitatively explaining the degree of authority. Finally, we recognize the differences between acknowledged SoS and collaborative SoS using a migrating waterfowl flock by an agent-based model (ABM) simulation. This paper contributes to the SoS body of knowledge by increasing our understanding of the degree of authority in an SoS, so we may identify suitable SoS types to achieve SoS goals by modeling and simulation.

Jamming suppression is traditionally achieved through the use of spatial filters based on array signal processing theory. In order to achieve better jamming suppression performance, many studies have applied blind source separation (BSS) to jamming suppression. BSS can achieve the separation and extraction of the individual source signals from the mixed signal received by the array. This paper proposes a perspective to recognize BSS as spatial band-pass filters (SBPFs) for jamming suppression applications. The theoretical derivation indicates that the processing of mixed signals by BSS can be perceived as the application of a set of SBPFs that gate the source signals at various angles. Simulations are performed using radar jamming suppression as an example. The simulation results suggest that BSS and SBPFs produce approximately the same effects. Simulation results are consistent with theoretical derivation results.

The consensus problem of the distributed attitude synchronization in the spacecraft formation flying is considered. Firstly, the attitude dynamics of a rigid body spacecraft is described by modified Rodriguez parameters (MRPs). Then global stable distributed cooperative attitude control laws are proposed for different cases. In the first case, the control law guarantees the state consensus during the attitude synchronization. In the second case, the control law ensures both the attitudes synchronizing to a desired constant attitude and the angular velocities converging at zero. In the third case, an attitude consensus control law with bounded control input is proposed. Finally, the effectiveness and validity of the control laws are demonstrated by simulations of six rigid bodies formation flying.

Traditional multi-band frequency selective surface (FSS) approaches are hard to achieve a perfect resonance response in a wide band due to the limit of the onset grating lobe frequency determined by the array. To solve this problem, an approach of combining elements in different period to build a hybrid array is presented. The results of series of numerical simulation show that multi-periodicity combined element FSS, which are designed using this approach, usually have much weaker grating lobes than the traditional FSS. Furthermore, their frequency response can be well predicted through the properties of their member element FSS. A prediction method for estimating the degree of expected grating lobe energy loss in designing multi-band FSS using this approach is provided.

Cloud manufacturing is a specific implementation form of the "Internet + manufacturing" strategy. Why and how to develop cloud manufacturing platform (CMP), however, remains the key concern of both platform operators and users. A microscopic model is proposed to investigate advantages and diffusion forces of CMP through exploration of its diffusion process and mechanism. Specifically, a three-stage basic evolution process of CMP is innovatively proposed. Then, based on this basic process, a more complex CMP evolution model has been established in virtue of complex network theory, with five diffusion forces identified. Thereafter, simulations on CMP diffusion have been conducted. The results indicate that, CMP possesses better resource utilization, user satisfaction, and enterprise utility. Results of simulation on impacts of different diffusion forces show that both the time required for CMP to reach an equilibrium state and the final network size are affected simultaneously by the five diffusion forces. All these analyses indicate that CMP could create an open online cooperation environment and turns out to be an effective implementation of the "Internet + manufacturing" strategy.

In this paper, we propose a beam space coversion (BSC)-based approach to achieve a single near-field signal localization under uniform circular array (UCA). By employing the centro-symmetric geometry of UCA, we apply BSC to extract the two-dimensional (2-D) angles of near-field signal in the Vandermonde form, which allows for azimuth and elevation angle estimation by utilizing the improved estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. By substituting the calculated 2-D angles into the direction vector of near-field signal, the range parameter can be consequently obtained by the 1-D multiple signal classification (MUSIC) method. Simulations demonstrate that the proposed algorithm can achieve a single near-field signal localization, which can provide satisfactory performance and reduce computational complexity.

The theory of compressed sensing (CS) provides a new chance to reduce the data acquisition time and improve the data usage factor of the stepped frequency radar system. In light of the sparsity of radar target reflectivity, two imaging methods based on CS, termed the CS-based 2D joint imaging algorithm and the CS-based 2D decoupled imaging algorithm, are proposed. These methods incorporate the coherent mixing operation into the sparse dictionary, and take random measurements in both range and azimuth directions to get high resolution radar images, thus can remarkably reduce the data rate and simplify the hardware design of the radar system while maintaining imaging quality. Experimentsfrom both simulated data and measured data in the anechoic chamber show that the proposed imaging methods can get more focused images than the traditional fast Fourier transform method. Wherein the joint algorithm has stronger robustness and can provide clearer inverse synthetic aperture radar images, while the decoupled algorithm is computationally more efficient but has slightly degraded imaging quality, which can be improved by increasing measurements or using a robuster recovery algorithm nevertheless

To utilizing the characteristic of radar cross section (RCS) of the low detectable aircraft, a special path planning algorithm to eluding radars by the variable RCS is presented. The algorithm first gives the RCS changing model of low detectable aircraft, then establishes a threat model of a ground-based air defense system according to the relations between RCS and the radar range coverage. By the new cost functions of the flight path, which consider both factors of the survival probability and the distance of total route, this path planning method is simulated based on the Dijkstra algorithm, and the planned route meets the flight capacity constraints. Simulation results show that using the effective path planning algorithm, the low detectable aircraft can give full play to its own advantage of stealth to achieve the purpose of silent penetration.

A mathematical model to determine the optimal production lot size for a deteriorating production system under an extended product inspection policy is developed. The last-K product inspection policy is considered so that the nonconforming items can be reduced, under which the last K products in a production lot are inspected and the nonconforming items from those inspected are reworked. Consider that the products produced towards the end of a production lot are more likely to be nonconforming, is proposed an extended product inspection policy for a deteriorating production system. That is, in a production lot, product inspections are performed among the middle K1 items and after inspections, all of the last K2 products are directly reworked without inspections. Our objective here is the joint optimization of the production lot size and the corresponding extended inspection policy such that the expected total cost per unit time is minimized. Since there is no closed form expression for our optimal policy, the existence for the optimal production inspection policy and an upper bound for the optimal lot size are obtained. Furthermore, an efficient solution procedure is provided to search for the optimal policy. Finally, numerical examples are given to illustrate the proposed model and indicate that the expected total cost per unit time of our product inspection model is less than that of the last-K inspection policy.

In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named adaptive resource allocation scheme (ARAS) for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod’s lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in centrol processing unit (CPU) and memory resource usage rate.

A specialized Hungarian algorithm was developed here for the maximum likelihood data association problem with two implementation versions due to presence of false alarms and missed detections. The maximum likelihood data association problem is formulated as a bipartite weighted matching problem. Its duality and the optimality conditions are given. The Hungarian algorithm with its computational steps, data structure and computational complexity is presented. The two implementation versions, Hungarian forest (HF) algorithm and Hungarian tree (HT) algorithm, and their combination with the naïve auction initialization are discussed. The computational results show that HT algorithm is slightly faster than HF algorithm and they are both superior to the classic Munkres algorithm.

In the applications of joint control and robot movement, the joint torque estimation has been treated as an effective technique and widely used. Researches are made to analyze the kinematic and compliance model of the robot joint with harmonic drive to acquire high precision torque output. Through analyzing the structures of the harmonic drive and experiment apparatus, a scheme of the proposed joint torque estimation method based on both the dynamic characteristics and unscented Kalman filter (UKF) is designed and built. Based on research and scheme, torque estimation methods in view of only harmonic drive compliance model and compliance model with the Kalman filter are simulated as guidance and reference to promote the research on the torque estimation technique. Finally, a promoted torque estimation method depending on both harmonic drive compliance model and UKF is designed, and simulation results compared with the measurements of a commercial torque sensor, have verified the effectiveness of the proposed method.

This paper presents a path planning approach for rotary unmanned aerial vehicles (R-UAVs) in a known static rough terrain environment. This approach aims to find collision-free and feasible paths with minimum altitude, length and angle variable rate. First, a three-dimensional (3D) modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs. Considering the length, height and tuning angle of a path, the path planning of R-UAVs is described as a tri-objective optimization problem. Then, an improved multi-objective particle swarm optimization algorithm is developed. To render the algorithm more effective in dealing with this problem, a vibration function is introduced into the collided solutions to improve the algorithm efficiency. Meanwhile, the selection of the global best position is taken into account by the reference point method. Finally, the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine. Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.

In order to obtain better inverse synthetic aperture radar (ISAR) image, a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband. The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices. To analyse the superiority of the modified algorithm, the mathematical expression of equivalent signal to noise ratio (SNR) is derived, which can validate our proposed algorithm theoretically. In addition, compared with the conventional matrix pencil (MP) algorithm and the conventional root-multiple signal classification (Root-MUSIC) algorithm, the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations. Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.

To track the nonlinear, non-Gaussian bearings-only maneuvering target accurately online, the constrained auxiliary particle filtering (CAPF) algorithm is presented. To restrict the samples into the feasible area, the soft measurement constraints are implemented into the update routine via the $\ell$₁ regularization. Meanwhile, to enhance the sampling diversity and efficiency, the target kinetic features and the latest observations are involved into the evolution. To take advantage of the past and the current measurement information simultaneously, the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors. As a result, the corresponding weights are more evenly distributed, and the posterior distribution of interest is approximated well with a heavier tailor. Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.

This paper considers the non-line-of-sight (NLOS) vehicle localization problem by using millimeter-wave (MMW) automotive radar. Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results. Firstly, an electromagnetic (EM) wave NLOS multipath propagation model for vehicle scene is established. Subsequently, with the help of available multipath echoes, a complete NLOS vehicle localization algorithm is proposed. Finally, simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.

Moth-flame optimization (MFO) is a novel metaheuristic algorithm inspired by the characteristics of a moth's navigation method in nature called transverse orientation. Like other metaheuristic algorithms, it is easy to fall into local optimum and leads to slow convergence speed. The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms. In the present study, we propose a chaos-enhanced MFO (CMFO) by incorporating chaos maps into the MFO algorithm to enhance its performance. The chaotic map is utilized to initialize the moths' population, handle the boundary overstepping, and tune the distance parameter. The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one. The performance of the CMFO is also verified by using two real engineering problems. The statistical results clearly demonstrate that the appropriate chaotic map (singer map) embedded in the appropriate component of MFO can significantly improve the performance of MFO.

Numerous works prove that existing neighbor-averaging graph neural networks (GNNs) cannot efficiently catch structure features, and many works show that injecting structure, distance, position, or spatial features can significantly improve the performance of GNNs, however, injecting high-level structure and distance into GNNs is an intuitive but untouched idea. This work sheds light on this issue and proposes a scheme to enhance graph attention networks (GATs) by encoding distance and hop-wise structure statistics. Firstly, the hop-wise structure and distributional distance information are extracted based on several hop-wise ego-nets of every target node. Secondly, the derived structure information, distance information, and intrinsic features are encoded into the same vector space and then added together to get initial embedding vectors. Thirdly, the derived embedding vectors are fed into GATs, such as GAT and adaptive graph diffusion network (AGDN) to get the soft labels. Fourthly, the soft labels are fed into correct and smooth (C&S) to conduct label propagation and get final predictions. Experiments show that the distance and hop-wise structures encoding enhanced graph attention networks (DHSEGATs) achieve a competitive result.

In this paper, the newly-derived maximum correntropy Kalman filter (MCKF) is re-derived from the M-estimation perspective, where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel function is a special case of many robust cost functions. Based on the derivation process, a unified form for the robust Gaussian filters (RGF) based on M-estimation is proposed to suppress the outliers and non-Gaussian noise in the measurement. The RGF provides a unified form for one Gaussian filter with different cost functions and a unified form for one robust filter with different approximating methods for the involved Gaussian integrals. Simulation results show that RGF with different weighting functions and different Gaussian integral approximation methods has robust anti-jamming performance.

Redundant technology plays an important role in improving the reliability and fault-tolerance of the airborne avionics systems. A Markov state transition model is introduced to the reliability analysis of the redundant inertial navigation system (RINS) in airborne navigation systems. An information processing mechanism based on difference filtering is put forward to strengthen the consistency between the outputs of the equal-precision inertial navigation system (INS). On this basis, the homologous fault monitoring algorithm is designed to realize the homologous fault monitoring of RINS. The simulation is carried out based on the above algorithms, and the results verify the effectiveness of the proposed fault monitoring algorithm based on difference filtering. Research results have good reference value for the configuration and design of RINS in airborne integrated avionics systems.