scholarly journals Robotic Mapping and Localization Considering Unknown Noise Statistics

2011 ◽  
Vol 5 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Hamzah AHMAD ◽  
Toru NAMERIKAWA
Keyword(s):  
Robotic Mapping ◽  
Noise Statistics

scholarly journals System design for inferring colony-level pollination activity through miniature bee-mounted sensors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haron M. Abdel-Raziq ◽  
Daniel M. Palmer ◽  
Phoebe A. Koenig ◽  
Alyosha C. Molnar ◽  
Kirstin H. Petersen
Keyword(s):  
Data Acquisition ◽  
Honey Bees ◽  
Large Scale ◽  
Physical World ◽  
Cluttered Environments ◽  
Robotic Mapping ◽  
Large Scale Data ◽  
Trade Offs ◽  
Large Numbers ◽  
Engineered Systems

AbstractIn digital agriculture, large-scale data acquisition and analysis can improve farm management by allowing growers to constantly monitor the state of a field. Deploying large autonomous robot teams to navigate and monitor cluttered environments, however, is difficult and costly. Here, we present methods that would allow us to leverage managed colonies of honey bees equipped with miniature flight recorders to monitor orchard pollination activity. Tracking honey bee flights can inform estimates of crop pollination, allowing growers to improve yield and resource allocation. Honey bees are adept at maneuvering complex environments and collectively pool information about nectar and pollen sources through thousands of daily flights. Additionally, colonies are present in orchards before and during bloom for many crops, as growers often rent hives to ensure successful pollination. We characterize existing Angle-Sensitive Pixels (ASPs) for use in flight recorders and calculate memory and resolution trade-offs. We further integrate ASP data into a colony foraging simulator and show how large numbers of flights refine system accuracy, using methods from robotic mapping literature. Our results indicate promising potential for such agricultural monitoring, where we leverage the superiority of social insects to sense the physical world, while providing data acquisition on par with explicitly engineered systems.

scholarly journals Learning Based Signal Detection for MIMO Systems with Unknown Noise Statistics

2021 ◽  
pp. 1-1
Author(s):  
Ke He ◽  
Le He ◽  
Lisheng Fan ◽  
Yansha Deng ◽  
George K. Karagiannidis ◽  
...  
Keyword(s):  
Signal Detection ◽  
Mimo Systems ◽  
Noise Statistics

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

Photomultiplier noise statistics

1972 ◽  
Vol 5 (5) ◽  
pp. 915-930 ◽  
Author(s):  
P B Coates
Keyword(s):  
Noise Statistics

An Adaptive State Filtering Algorithm for Systems With Partially Known Dynamics

2002 ◽  
Vol 124 (3) ◽  
pp. 364-374 ◽  
Author(s):  
Alexander G. Parlos ◽  
Sunil K. Menon ◽  
Amir F. Atiya
Keyword(s):  
Neural Networks ◽  
Case Studies ◽  
Feedforward Neural Networks ◽  
Complex Case ◽  
State Equations ◽  
Estimation Errors ◽  
Filter Performance ◽  
On Line ◽  
Noise Statistics ◽  
On Line Learning

On-line filtering of stochastic variables that are difficult or expensive to directly measure has been widely studied. In this paper a practical algorithm is presented for adaptive state filtering when the underlying nonlinear state equations are partially known. The unknown dynamics are constructively approximated using neural networks. The proposed algorithm is based on the two-step prediction-update approach of the Kalman Filter. The algorithm accounts for the unmodeled nonlinear dynamics and makes no assumptions regarding the system noise statistics. The proposed filter is implemented using static and dynamic feedforward neural networks. Both off-line and on-line learning algorithms are presented for training the filter networks. Two case studies are considered and comparisons with Extended Kalman Filters (EKFs) performed. For one of the case studies, the EKF converges but it results in higher state estimation errors than the equivalent neural filter with on-line learning. For another, more complex case study, the developed EKF does not converge. For both case studies, the off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. On-line training further enhances filter performance, decoupling the eventual filter accuracy from the accuracy of the assumed system model.

scholarly journals Multiellipsoidal Mapping Algorithm

2018 ◽  
Vol 8 (8) ◽  
pp. 1239 ◽  
Author(s):  
Carlos Villaseñor ◽  
Nancy Arana-Daniel ◽  
Alma Alanis ◽  
Carlos Lopez-Franco ◽  
Javier Gomez-Avila
Keyword(s):  
Point Clouds ◽  
Mapping Algorithm ◽  
Robotic Mapping ◽  
Wide Range ◽  
Important Challenge ◽  
Memory Cost ◽  
Environmental Representation ◽  
Object Shapes ◽  
Quadratic Surfaces ◽  
Object Mapping

The robotic mapping problem, which consists in providing a spatial model of the environment to a robot, is a research topic with a wide range of applications. One important challenge of this problem is to obtain a map that is information-rich (i.e., a map that preserves main structures of the environment and object shapes) yet still has a low memory cost. Point clouds offer a highly descriptive and information-rich environmental representation; accordingly, many algorithms have been developed to approximate point clouds and lower the memory cost. In recent years, approaches using basic and “simple” (i.e., using only planes or spheres) geometric entities for approximating point clouds have been shown to provide accurate representations at low memory cost. However, a better approximation can be implemented if more complex geometric entities are used. In the present paper, a new object-mapping algorithm is introduced for approximating point clouds with multiple ellipsoids and other quadratic surfaces. We show that this algorithm creates maps that are rich in information yet low in memory cost and have features suitable for other robotics problems such as navigation and pose estimation.

Thermopower and 1/f Noise Measurements in Amorphous Silicon-Carbon Alloys

1995 ◽  
Vol 377 ◽  
Author(s):  
H. M. Dyalsingh ◽  
G. M. Khera ◽  
J. Kakalios
Keyword(s):  
Amorphous Silicon ◽  
Power Spectra ◽  
Carbon Films ◽  
Noise Power ◽  
Mobility Edge ◽  
Silicon Carbon ◽  
Noise Measurements ◽  
Noise Statistics ◽  
Hydrogenated Amorphous ◽  
Q Function

ABSTRACTThermopower, conductivity and 1/f noise measurements have been performed on a series of n-type doped hydrogenated amorphous silicon carbon films that are prepared with varying gas phase concentrations of CH4. The increased disorder at the mobility edge associated with alloying is characterized by the Q-function, which is obtained by combining thermopower and conductivity measurements, and is also reflected in the noise power spectra and noise statistics.

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