SCA-YOLO: a new small object detection model for UAV images

State Estimation Algorithms for Localization: A Survey

For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise with unknown and bounded characteristics is modeled by using bounded ellipsoidal regions. At the same time, local estimators are designed at the sensor link nodes to filter out the noise interference in the localization system. The local estimator is designed using the dual set-membership filtering algorithm. It uses the dual principle to find the minimizing ellipsoid that can contain the nonlinear function by solving the optimization problem with semi-infinite constraints, and a first-order conditional gradient algorithm is used to solve the optimization problem with a low computational complexity. Meanwhile, the communication confusion among multiple sensors causes the problem of unknown correlation. The obtained estimates of local filters are fused at the fusion center by designing a distributed sequential ellipsoid intersection fusion estimation algorithm to obtain more accurate fusion localization results with lower computational cost. Finally, the stability and reliability of the proposed distributed fusion algorithm are verified by designing a simulation example of a multi-source nonlinear system.

https://www.mdpi.com/1424-8220/23/2/698/pdf?version=1673597418

Distributed State Fusion Estimation of Multi-Source Localization Nonlinear Systems

In the emergency rescue mission, the primary task is to achieve the satellite’s orientation to the sun and ensure the energy supply. For this purpose, it is necessary to determine the rotation angle of solar array relative to satellite body. However, the rotation angle cannot be measured directly by the angle sensor in case of satellite emergency. To handle this issue, an indirect measurement method of solar array rotation angle is proposed in this paper. First, the indirect point measurement value of solar array rotation angle is given via the relationship between satellite body coordinate frame and solar array coordinate frame and the Rodrigues’ rotation formula. Then, the interval analysis technique is used to determine the upper and lower bounds of rotation angle and evaluate the influence of measurement noises. Based on the determined point measurement value and the interval analysis results, a zonotopic Kalman filter-based strategy is further proposed to improve the measurement accuracy. Finally, the effectiveness and validity of the proposed approach are demonstrated by numerical simulations, which illustrate its engineering application value.

Indirect measurement of solar array rotation angle for satellite emergency rescue

This paper presents an invariant sets approach for chaos synchronization in a class of master-slave chaotic systems affected by bounded perturbations. The method provides the optimal state-feedback gain in terms of the minimal ellipsoid that guarantees minimum synchronization error bound. The problem of finding the optimal invariant ellipsoid is formulated in terms of a semi-definite programming problem that can be easily solved using various simulation and calculus tools. The effectiveness of the proposed criterion is illustrated by numerical simulations on the synchronization of Chua's systems.

/pdf/invariant-ellipsoids-method-for-chaos-synchronization-in-a-2agbxgh9.pdf

Invariant Ellipsoids Method for Chaos Synchronization in a Class of Chaotic Systems

This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and (Formula presented.) filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.

Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises:

A context- and level-aware feature pyramid network for object detection with attention mechanism

This article investigates the localization problem of the automatic guided vehicle (AGV) system. In order to improve the reliability and flexibility of the localization process, a distributed sensor network structure is introduced to realize the localization of the AGV. Moreover, considering the influence of unknown-but-bounded noise and the accuracy requirements of the localization, a novel global set-membership filtering approach is proposed to obtain accurate localization results including a distributed set-membership filtering (DSMF) strategy and a circumscribed rectangle method. First, a DSMF strategy is designed to obtain local state estimation ellipsoids. Sufficient conditions for the existence of the state estimation ellipsoids are derived and a convex optimization process is developed to obtain the optimal local estimation ellipsoids. Then, a circumscribed rectangle method is proposed to fuse all local state estimation ellipsoids and obtain global set-membership filtering results. The proposed fusion method does not have a complicated optimization process, and can obtain more accurate estimation ellipsoids than local estimation results. Performance analysis verifies the effectiveness of the proposed global set-membership filtering approach.

A Novel Global Set-Membership Filtering Approach for Localization of Automatic Guided Vehicles

Remote Localization of Network-based Automatic Guided Vehicles with a Novel Quantized Set-membership Approach

This paper investigates the quantized set-membership filtering (SMF) problem for network-based automatic guided vehicle (AGV) systems in the presence of unknown-but-bounded (UBB) process and measurement noises. Considering the bandwidth constraints of the communication networks, the measurement outputs of the AGV are quantized before transmitting to the remote filter. Two independent quantizers are designed to quantize each variable in the two-dimensional output vector separately. Then, an SMF scheme is presented to solve the problem of the remote state estimation for the AGV with UBB noises and the quantization error. Next, a recursive convex optimization algorithm is provided to obtain the state estimation ellipsoid sets for the considered AGV. Simulation results demonstrate the effectiveness of the proposed method.

Quantized Set-membership Filtering for Automatic Guided Vehicles with Unknown-but-bounded Noises

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