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wy520521's starred repositories

MMEE_WLSM1

we improve the weighted least squares method (WLSM) with minimum model error principle. With the knowledge of measurements and measurement error covariance matrix, the method of solving two-point boundary value problems, invariant imbedding method, is adopted to derive the estimate algorithm, which recursively estimates the uncertainty errors and linear errors of discrete system or nonlinear system.

Language:MATLAB200

Indoor-Positioning

基于WIFI/Wi-Fi的室内定位系统，主要功能包括：数据采集+WiFi定位+PDR辅助定位+路径规划

11600

ekf_loam

Language:C++7700

trajectory-tracking-ilc

Use iterative learning control (ILC) for trajectory tracking task with the existence of model mismatch. MPC is also used for comparison.

Language:MATLABMIT700

VesselTrajectoryPrediction

lstm-rnn, seq2seq model and attention-seq2seq model for vessel trajectory prediction.

Language:Python7600

trajectory-prediction-transformers

Human trajectory estimation using attention-based transformer networks

Language:Jupyter Notebook1300

Pedestrians-trajectories-prediction-in-indoor-environment-with-sCREEN-Dataset-

Prediction of pedestrian trajectories in indoor environment

Language:Jupyter Notebook100

Trajectory-Prediction-Using-Dead-Reckoning

The 'Trajectory Prediction Using Dead Reckoning' project specializes in forecasting the future paths of dynamic objects, including vehicles and pedestrians. Leveraging Dead Reckoning algorithms, the project aims to enhance trajectory prediction accuracy, providing valuable insights for applications in autonomous vehicles and pedestrian safety

Language:Jupyter Notebook400

C-AVOID

A lightweight LSTM recurrent neural network for vehicle trajectory prediction

Language:Python1300

Vehicle-trajectory-prediction

Vehicle trajectory prediction involves using deep sort and YOLO for object detection to track a car's coordinates in video. These coordinates, along with speed data, are stored in a database. This dataset is then used to train a model, often using LSTM, to predict future vehicle coordinates based on historical data.

Language:Python700

imu_uwb_fusion

Language:C++600

Gauss_CMAC

Matlab

Language:Matlab1500

ASMC

This is a MATLAB Code related to the article "Adaptive Robust Control with Slipping Parameters Estimation Based on Intelligent Learning for Wheeled Mobile Robot". This code denotes ASMC method for the WMR for trajectory tracking in presence of wheel slip, uncertainties and disturbances.

Language:MATLAB600

DeepFit

Code for the paper "DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares"

Language:PythonNOASSERTION13900

LOS-NLOS-Classification-CNN

UWB LOS-NLOS Signal Classification using CNN

Language:Python3700

SEL-CNN

Code for A_Simple_Efficient_Light-weighted_CNN_for_5G_LOS_NLOS_Identification

Language:PythonMIT700

bmlat

Bayesian Multilateration

Language:Jupyter Notebook100

Trilateration-Matlab

Matlab Code for Trilateration Algorithm based on the paper "An algebraic solution to the multilateration problem"

Language:MATLAB300

Localization-Algorithm

Sensor fusion (UWB+IMU+Ultrasonic), using Kalman filter and 3 different multilateration algorithms (Least square and Recursive Least square and gradient descent)

Language:MATLABMIT4700

Inertial-Navigation-System

Development and application of an INS from IMU data.

Language:MATLAB200

INS_LOOSE

This solution was designed for inertial navigation system of in-pipe inspection. The 15-states extended Kalman filter was used to estimate angle error , speed error,position error,gyroscope drift and accelerometer bias.

Language:MATLAB700

AE6505-KalmanFiltering-Vehicle-Lane-Estimation

In this work, a vehicle lane precision method was proposed with the sensor fusion of Global Navigation Satellite System(GNSS) and Inertial Measurement Unit(IMU) sensors that are built-in common smartphones inside.

Language:MATLAB1300