中国科学院机构知识库网格系统: 检索

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

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收藏 | 浏览/下载：7/0 | 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

The International Pulsar Timing Array second data release: Search for an isotropic gravitational wave background 期刊论文 OAI收割
Monthly Notices of the Royal Astronomical Society, 2022, 卷号: 510, 期号: 4, 页码: 4873-4887
作者: Antoniadis, J.; Arzoumanian, Z.; Babak, S.; Bailes, M.; Nielsen, A. S. B.

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收藏 | 浏览/下载：13/0 | 提交时间：2023/06/19

gravitational waves methods: data analysis pulsars: general data set limits common-spectrum process black-hole binaries millisecond pulsars solar-system spin noise astrophysics radiation signal time Astronomy & Astrophysics

Predicting the Noise Covariance With a Multitask Learning Model for Kalman Filter-Based GNSS/INS Integrated Navigation 期刊论文 OAI收割
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 卷号: 70, 页码: 13
作者: Wu, Fan; Luo, Haiyong; Jia, Hongwei; Zhao, Fang; Xiao, Yimin

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收藏 | 浏览/下载：29/0 | 提交时间：2021/12/01

Adaptive integrated navigation deep learning denoising autoencoder (DAE) Kalman filter (KF) measurement noise process noise

Weighted Broad Learning System and Its Application in Nonlinear Industrial Process Modeling 期刊论文 OAI收割
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 卷号: 31, 期号: 8, 页码: 3017-3031
作者: Chu, Fei; Liang, Tao; Chen, C. L. Philip; Wang, Xuesong; Ma, Xiaoping

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收藏 | 浏览/下载：29/0 | 提交时间：2020/09/07

Learning systems Training Heuristic algorithms Neural networks Automation Process control Control engineering Broad learning system (BLS) incremental learning algorithm noise and outliers weighted penalty factor

RL-AKF: An Adaptive Kalman Filter Navigation Algorithm Based on Reinforcement Learning for Ground Vehicles 期刊论文 OAI收割
REMOTE SENSING, 2020, 卷号: 12, 期号: 11, 页码: 25
作者: Gao, Xile; Luo, Haiyong; Ning, Bokun; Zhao, Fang; Bao, Linfeng

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收藏 | 浏览/下载：36/0 | 提交时间：2020/12/10

integrated navigation Kalman filter process noise covariance estimation reinforcement learning deep deterministic policy gradient

Automatic Calibration of Process Noise Matrix and Measurement Noise Covariance for Multi-GNSS Precise Point Positioning 期刊论文 OAI收割
MATHEMATICS, 2020, 卷号: 8, 期号: 4, 页码: 20
作者: Zhang, Xinggang; Li, Pan; Tu, Rui; Lu, Xiaochun; Ge, Maorong

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收藏 | 浏览/下载：22/0 | 提交时间：2020/09/01

EM-algorithm multi-GNSS PPP process noise observation covariance matrix extended Kalman filter machine learning

Justify role of Similarity Diffusion Process in cross-media topic ranking: an empirical evaluation 期刊论文 OAI收割
MULTIMEDIA TOOLS AND APPLICATIONS, 2017, 卷号: 76, 期号: 23, 页码: 25145-25157
作者: Pang, Junbiao; Huang, Jing; Zhang, Weigang; Huang, Qingming; Yin, Baocai

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收藏 | 浏览/下载：28/0 | 提交时间：2019/12/10

Unsupervised ranking Poisson noise Gaussian noise Similarity Diffusion Process Deconvolution

Color filtering method for CFA images based on gradient (EI CONFERENCE) 会议论文 OAI收割
International Conference on Communication Systems and Network Technologies, CSNT 2012, May 11, 2012 - May 13, 2012, Rajkot, Gujrat, India
作者:

收藏 | 浏览/下载：33/0 | 提交时间：2013/03/25

Single-sensor digital cameras capture image by covering the sensor surface with a color filter array(CFA) such that each sensor pixel only samples one of three primary color values three color is R(red) G(green) and B(blue). To render a full-color image need an interpolation process commonly referred to as CFA demosaicking is required to estimate the other two contributions for producing a full-color image. But the noise in imaging sensors not only corrupts the color filter array at the same time introduces artifacts during the color interpolation step and influence quality of images. In order to acquire high quality full-color images adopt a sort of viable and effective interpolation algorithm based on gradient at the time of removing the noise reserve image border and detail information clearly. 2012 IEEE.

A hybrid median filter for enhancing dim small point targets and its fast implementation (EI CONFERENCE) 会议论文 OAI收割
2011 International Conference on Multimedia and Signal Processing, CMSP 2011, May 14, 2011 - May 15, 2011, Guilin, Guangxi, China
Yang Q.

收藏 | 浏览/下载：22/0 | 提交时间：2013/03/25

Weak stars and middle altitude orbit satellites or high altitude orbit satellites are presented as dim point targets in images of astronomical observation. They have low intensity and occupy few pixels in image. In most situations the background of sky is always ununiform and there exists pulse noises. Thus results in targets submerging in background noises and low signal noise ratio (SNR). Median filter is an effective method for removing pulse noise. But it can blur image after modifying gray of non-noise pixels when image has low SNR. A hybrid median filter algorithm is proposed to process low SNR dim point targets image. The newly proposed method can suppress the pulse noises also can increase target $SNR$ and does not add extra fake targets. The time complexity of median filter is O(N2)(N is the widow size) and is difficult to implement in hardware. For quasi real time application a fast implementation of our hybrid median filter is proposed to reduce the time complexity to O(N). Experimental results show that the newly proposed algorithm is superior to ordinary median filter and fast median filter by comparing visual effect and performance. 2011 IEEE.

Evaluation of the operating range for ground-based infrared imaging tracking system (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
作者: Zhang Z.-D.

收藏 | 浏览/下载：48/0 | 提交时间：2013/03/25

Ground-based infrared imaging tracking system (GIITS) is of great importance for aerial target warning and guard. The operating range is one of the key performance specifications on the other which should be calculated calculate the radiation power received on the detector in order to analysis whether the output signal meets the detection requirements or not analyzed and studied during the whole GIITS design process. The operating range is mostly influenced by a few factors without considering the effect of the background radiation. By improving of the traditional method including atmospheric attenuation a new operating range calculation model of the GIITS was established based on two requirements. One is that the image size of observed target should meet the requirement of the processor signal extraction. The number of the pixel occupied by target image should be more than 9. The other is that the signal noise ratio (SNR) of the GIITS should not be less than 5 to meet the requirements of the target detection probability and spatial frequency. The SNR calculation equation in form of energy is deduced and the radiation characteristic of the observed target and background are analyzed. When evaluate the operating range of the GIITS using the new method the performance of GIITS and feature of target and background. This paper firstly makes analysis and summarization on the definite localizations of the traditional operating range equation of the GIITS. The localizations are mainly in two aspects. On one hand we should successively calculate two operating range values according to two requirements mentioned above and choose the minimum value as the analytic result. In the end the dispersion of the image and the effect of image dispersion are not considered in the traditional method an evaluation of operating range for fighter aircraft is accomplished as an example. The influence factors in every aspect on operating range were explored by the calculated result. The new operating range calculation model provides the theoretical basis for the design and applications as well as the comprehensive evaluation of a GIITS. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).