Huijun Ma - IECE

Free Access | Research Article | 08 June 2024

GPS Tracking Based on Stacked-Serial LSTM Network

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Chinese Journal of Information Fusion | Volume 1, Issue 1: 50-62, 2024 | DOI:10.62762/CJIF.2024.361889

Abstract

Maneuvering target tracking is widely used in unmanned vehicles, missile navigation, underwater ships, etc. Due to the uncertainty of the moving characteristics of maneuvering targets and the low sensor measurement accuracy, trajectory tracking has always been an open research problem and challenging work. This paper proposes a trajectory estimation method based on LSTM neural network for uncertain motion characteristics. The network consists of two LSTM networks with stacked serial relationships, one of which is used to predict the movement dynamics, and the other is used to update the track's state. Compared with the classical Kalman filter based on the maneuver model, the method proposed... More >

Graphical Abstract

GPS Tracking Based on Stacked-Serial LSTM Network

Free Access | Research Article | 29 May 2024 | Cited: 6

Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 40-48, 2024 | DOI:10.62762/TIS.2024.137329

Abstract

Nowadays, state estimation is widely used in fields such as autonomous driving and drone navigation. However, in practical applications, it is difficult to obtain accurate target motion models and noise covariance.This leads to a decrease in the estimation accuracy of traditional Kalman filters. To address this issue, this paper proposes an adaptive model free state estimation method based on attention parameter learning module. This method combines Transformer's encoder with Long Short Term Memory Network (LSTM), and obtains the system's operational characteristics through offline learning of measurement data without modeling the system dynamics and measurement characteristics. In addition,... More >

Graphical Abstract

Parameter Adaptive Non-Model-Based State Estimation Combining Attention Mechanism and LSTM

Free Access | Research Article | 27 May 2024 | Cited: 5

YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 30-39, 2024 | DOI:10.62762/TIS.2024.137321

Abstract

In recent years, deep learning techniques have been increasingly applied to the detection of remote sensing images. However, the substantial size variation and dense distribution of objects in these images present significant challenges to detection algorithms. Current methods often suffer from low efficiency, missed detections, and inaccurate bounding boxes. To address these issues, this paper presents an improved YOLO algorithm, YOLOv7-bw, designed for efficient remote sensing image detection, thereby advancing object detection applications in the remote sensing industry. YOLOv7-bw enhances the original SPPCSPC pooling pyramid network by incorporating a Bi-level Routing Attention module, w... More >

Graphical Abstract

YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image

Free Access | Research Article | 25 May 2024 | Cited: 3

Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data

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Hanchi Ren

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Yeqing Wang

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Huijun Ma

IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 10-18, 2024 | DOI:10.62762/TIS.2024.136898

Abstract

To predict future trends based on the data from sensors is an important technology for many applications, such as the Internet of Things, smart cities, etc. Based on the predicted results, further decisions and system controls can be made. Raw sensor data sets are often complex non-linear data with noise, which results in the difficulty of accurate prediction. This paper proposes a distributed deep prediction network based on a covariance intersection (CI) fusion algorithm in which the deep learning networks, such as long-term and short-term memory networks (LSTM) and gated recurrent unit networks (GRU) are fused by CI fusion algorithm to effectively develop the performance of prediction. Mo... More >

Graphical Abstract

Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data

Free Access | Research Article | 15 May 2024 | Cited: 2

Visual Feature Extraction and Tracking Method Based on Corner Flow Detection

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IECE Transactions on Intelligent Systematics | Volume 1, Issue 1: 3-9, 2024 | DOI:10.62762/TIS.2024.136895

Abstract

Frontend feature tracking based on vision is the process in which a robot captures images of its surrounding environment using a camera while in motion. Each frame of the image is then analyzed to extract feature points, which are subsequently matched between pairwise frames to estimate the robot’s pose changes by solving for the variations in these points. While feature matching methods that rely on descriptor-based approaches perform well in cases of significant lighting and texture variations, the addition of descriptors increases computational costs and introduces instability. Therefore, in this paper, a novel approach is proposed that combines sparse optical flow tracking with Shi-Tom... More >

Graphical Abstract

Visual Feature Extraction and Tracking Method Based on Corner Flow Detection

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