机构

• 计算技术研究所 [1]
• 长春光学精密机械与物... [1]
• 合肥物质科学研究院 [1]
• 国家授时中心 [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2024 [1]
• 2022 [1]
• 2020 [1]
• 2009 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序作者升序作者降序 A Novel Adaptive Noise Covariance Matrix Estimation and Filtering Method: Application to Multiobject Tracking 期刊论文  OAI收割 IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2024, 卷号: 9 作者:  Jiang, Chao;  Wang, Zhiling;  Liang, Huawei;  Wang, Yajun   |  收藏  |  浏览/下载：10/0  |  提交时间：2024/11/20 Covariance matrices  Noise measurement  Estimation  Correlation  Filtering  Calibration  Technological innovation  Kalman filtering  adaptive estimation  process and measurement noise covariance matrices  multiobject tracking   Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation 期刊论文  OAI收割 REMOTE SENSING, 2022, 卷号: 14, 期号: 7, 页码: 22 作者:  Xu, Hongfu;  Luo, Haiyong;  Wu, Zijian;  Wu, Fan;  Bao, Linfeng   |  收藏  |  浏览/下载：31/0  |  提交时间：2022/12/07 tightly coupled integrated navigation  measurement noise estimation  transformer  adaptive Kalman filtering   GPS/INS松耦合组合导航的自适应卡尔曼滤波算法研究 期刊论文  OAI收割 时间频率学报, 2020, 卷号: 43, 期号: 3, 页码: 222 作者:  周先林;  张慧君;  和涛;  李孝辉   |  收藏  |  浏览/下载：35/0  |  提交时间：2021/11/29 integrated navigation  adaptive Kalman filtering  innovation  noise variance  组合导航  自适应卡尔曼滤波  新息  噪声方差   Real-time motive vehicle detection with adaptive background updating model and HSV colour space (EI CONFERENCE) 会议论文  OAI收割 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, November 19, 2008 - November 21, 2008, Chengdu, China Rong-Hui Z.; Bai Y.; Hong-guang J.; Chen T. 收藏  |  浏览/下载：70/0  |  提交时间：2013/03/25 In the transportation monitor system  we set up the area of interest (AOI) of the vehicle model and adjust the size of AOI dynamically in order to track vehicle accurately. The results of experiment show that  motive vehicle detection by adopting digital image is one of key technologies. To detect motive vehicle accurately  the arithmetic proposed in the paper can suppress shadow availably  we establish an adaptive background updating model firstly. Noise is suppressed by using modality filter  detect motive vehicle accurately and satisfy real-time motive vehicle tracking. 2009 SPIE.  and we obtain binary image by using maximum entropy to choose dynamic adaptive threshold. Based on positive information of shadow and aspect feature of motive vehicle  we adopt HSV colour space and double threshold to solve the problem of vehicle shadow. According to prediction result of Kalman filtering