In this paper, a sequential fusion filter estimator based on event-triggered (ET) multi-sensor nonlinear systems and Cubature Kalman filter, that is ETCKF-SF, is studied, in which the observation noise of each sensor is synchronously correlated, and the observation noise of each sensor is correlated with the process noise at the previous moment. Because the energy of each sensor in the network system is limited, in order to reduce unnecessary sensor energy loss, an ET mechanism is used to reduce the communication rate from the sensor to the fusion center. Sequential fusion filter estimator based on ET mechanism multi-sensor nonlinear systems and innovation analysis is studied, firstly. At the same time, in the design process of the sequential fusion filter, the estimator of the observation noise based on the ET mechanism will also be designed. Secondly, the numerical implementation of the proposed algorithm will be derived based on the third-degree spherical-radial rule Cubature Kalman filter (CKF). Finally, the proposed algorithm is compared with the extended Kalman filter (EKF) based on Taylor series expansion. The effectiveness of the proposed algorithm is verified by the comparison of simulation examples.

In many military and civilian applications, synthetic aperture radar (SAR) image target detection plays a vital role. However, current methods for SAR target detection generally fail to balance speed and accuracy, thus making it impossible to deploy them to real-world engineering applications. In addition, strong scattering, multi scales, high density, complex background interference and speckle noise make it remarkably challenging to extract effective target information and disentangle background noise from the target information, ultimately resulting in high missing and false alarm rates. To address these issues, a unified dynamic SAR target detection architecture (DAFDet) with asymptotic fusion enhancement and feature encoding decoupling is proposed in this paper. Firstly, a dynamic architecture is constructed by cascading two identical detectors and integrating a designed decision maker. This decision maker can automatically decide the inference route by calculating the difficulty score of a SAR image, which ensures efficient inference speed while achieving high accuracy. Secondly, an asymptotic fusion enhancement feature pyramid network (AFEFPN) is developed, which can avoid the loss and degradation of target information in multi-stage transmissions through direct interactions of nonadjacent levels, thereby enhancing the extraction of valid target information. Suppression of background noise is achieved by modeling the importance of different feature channels of the fused features. Finally, a task-oriented decoupled head (TODH) is proposed to boost the localization and classification abilities of the model in complex scenarios. It decouples feature encoding at the source, thus providing task-oriented feature context. Numerous experiments on four widely adopted datasets reveal that DAFDet obtains efficient inference speed and optimal detection accuracy, achieving new state-of-the-art target detection performance. Source code will be provided at https://github.com/yangyahu1994/DAFDet.

This paper discusses the problem of state estimation for non‐linear stochastic uncertain systems with time‐correlated additive noise and packet dropout compensations. Both process noise and measurement noise are additive forms of noise that can be described as Gaussian first‐order Markov processes. The stochastic uncertainty of the system is described by synchronous correlated white multiplicative noise. When the measurement information from the current epoch is lost, a compensation strategy based on measurement information prediction is used. First, the state information, process noise, and measurement noise are augmented to enable the construction of a new vector to be estimated, and the filter, predictor, and smoother of the system are designed using a Gaussian iterative estimation algorithm based on innovation analysis. The multiplicative noise in the process equation is estimated in real time. Second, a numerical implementation of the proposed algorithm is derived based on the third‐degree spherical‐radial Cubature Kalman filter(CKF). Finally, the proposed algorithm is compared with an extended Kalman filter (EKF) based on Taylor series expansion. The effectiveness of the proposed algorithm is verified by comparing the simulation results.

This paper addresses the issue of event-triggered state estimation in non-Gaussian wireless sensor networks. With the aim of saving energy and enhancing the estimation performance, the dual-triggered distributed maximum correntropy Kalman filter with measurement noise variance compensation (ET-DMCKF-R) is proposed. To alleviate the communication burden, a dual-trigger mechanism is introduced, which consists of the transmission of its own measurement information and estimates from neighboring nodes, while thresholds are used to balance the estimation performance and communication costs. Subsequently, a cost function is formulated by combining an upper bound on the prediction covariance and measurement information. The gain matrices and state estimates are then derived via maximum correntropy criterion. It avoids cross-covariances, which reduces computational burden and improves real-time performance. Further, considering the impact of inaccurate variance on the correntropy caused by outlier measurements, a fading matrix is designed to compensate for the variance, thereby improving the accuracy and robustness. In addition, it is proved that the estimation error of the presented algorithm is exponentially bounded in mean square based on the Lyapunov function. Finally, the simulation results confirm the effectiveness of the algorithm.

This paper begins by exploring the challenge of event-triggered state estimations in nonlinear systems, grappling with packet dropout and correlated noise. A communication mechanism is introduced that determines whether to transmit measurement values based on whether event-triggered conditions are violated, thereby minimizing redundant communication data. In designing the filter, noise decorrelation is initially conducted, followed by the integration of the event-triggered mechanism and the unreliable network transmission system for state estimator development. Subsequently, by combining the three-degree spherical–radial cubature rule, the numerical implementation steps of the proposed state estimation framework are outlined. The performance estimation analysis highlights that by adjusting the event-triggered threshold appropriately, the estimation performance and transmission rate can be effectively balanced. It is established that when there is a lower bound on the packet dropout rate, the covariance matrix of the state estimation error remains bounded, and the stochastic stability of the state estimation error is also confirmed. Ultimately, the algorithm and conclusions that are proposed in this paper are validated through a simulation example of a target tracking system.