dahaha114's starred repositories
ccd_extendedKalmanFilter
The implementation of the constrained continuous-discrete extended Kalman Filter for the state estimation in the nonlinear systems and demonstration of its functionality on the model of waste removal by bacteria
Optimal_State_Estimation_Kalman_H_Infinity_and_Nonlinear_Approaches_Learning
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
els-cas-templates
Elsevier latex template 'els-cas-templates'（爱思唯尔期刊-latex双栏版本）
Maneuvering-Target-State-Est
3D, Use IMM strategy,combine KF UKF PF...
NEO-GGIW-PMBM
This repository contains the Matlab implementations of the NEO-GGIW-PMBM tracker
fusion-ekf
An extended Kalman Filter implementation in C++ for fusing lidar and radar sensor measurements.
Navigation-Learning
我的导航学习笔记，内容涵盖导航定位开源程序的源码解读 ( 包括：RTKLIB、GAMP、GINav、Ginan、PSINS、SoftGNSS、KF-GINS、GICI-Lib 等)、开源项目梳理、书籍讲义、博客翻译、教程讲座推荐；本仓库会长期更新，分享出来，既是跟大家做个交流，也激励着自己坚持学下去；所有内容都可以随意转载，原始文件都放在这了，欢迎在我的基础上整理出自己的一套笔记。
HGMMEllFit
Robust ellipse fitting, hierarchical Gaussian mixture models (HGMM), outliers, noise.
FrankWolfe-and-GradientProjection-Method
Frank-Wolfe算法和梯度投影法MATLAB实现
SLAM-ON-VICTORIA-PARK-DATASET-AND-A-SIMULATOR
EKF SLAM USING KNOWN AND UNKNOWN CORRESPONDENCES
LiDAR-Point-Cloud-Preprocessing-matlab
Pre-processing Technique of LIDAR PCD Data Using KITTI-Dataset
Generalized-minimum-error-entropy-for-robust-learning
Matlab code for Generalized minimum error entropy for robust learning
Modified-Localized-Cholesky
An implementation of the modified Cholesky algorithm with localization
nlgreyfast
Toolbox for system identification of nonlinear state space grey-box models using CasADi
pmtksupport
Various packages used by PMTK.
Fusion-of-Infrared-and-Visible-Images-for-Surveillance-Applications
A novel image fusion technique is presented for integrating infrared and visual images. Integration of images from the identical or various sensing modalities can deliver the specified information that can't be delivered by viewing the sensor outputs individually and consecutively. This work proposes an Artificial Neural Network (ANN) based image fusion technique using Gaussian smoothness and Joint Bilateral Filter. The Gaussian smoothness and the Joint Bilateral Filter decompose the source images. The implementation involves the elimination of fine-scale information with Gaussian filtering, extraction of edge and structure with joint bilateral filtering iteration. The decomposition has edge-preserving and scale-aware properties to enhance the acquisition of detail layer. Two layers of rules are used to combine the decomposed layers and the combined layers are used to reconstruct the final fused image. An enhanced fused image is obtained through ANN for a better visual understanding and target detection. The proposed framework is evaluated using quantitative metrics such as Standard Deviation, Edge Dependent Fusion Quality Index, Entropy, Mean Square Error, Peak Signal to Noise ratio, Naturalness Image Quality Evaluator and Structural Similarity Index. This work outperforms visually as well as quantitatively and achieves better performance with the reduced complexity.
NonlinearEstimationToolbox
Nonlinear Estimation Toolbox
tsrt78-signal-processing
MATLAB labs for course TSRT78 Signal processing at Linköping university
ship-simulator
Simulator of wave and ship dynamics in MATLAB. Developed as a prototype during a summer internship at UMS Skeldar.
MATLAB-tools
A collection of different MATLAB scripts and tools
Vanilla_PF_ICP
Iterative closest point via particle filtering
Perez-Vieites2021_NestedGaussianFilter
Code for the nested Gaussian filters (NGFs), in particular, an implementation of an unscented Kalman filter (UKF) combined with a bank of extended Kalman filters (EKFs). Other algorithms are implemented to compare performance.