Target Position Estimation Aided Swarm Robotic Search under Conditions of Relative Localization Mechanism

2012 ◽  
Author(s):  
Zan Yunlong ◽  
Xue Songdong ◽  
Zeng Jianchao ◽  
Du Jing
Keyword(s):  
Target Position ◽  
Position Estimation ◽  
Relative Localization

scholarly journals Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets

2021 ◽  
Vol 13 (15) ◽  
pp. 2997
Author(s):  
Zheng Zhao ◽  
Weiming Tian ◽  
Yunkai Deng ◽  
Cheng Hu ◽  
Tao Zeng
Keyword(s):  
Multiple Input Multiple Output ◽  
Estimation Method ◽  
Target Position ◽  
Position Estimation ◽  
Least Square ◽  
Antenna Structure ◽  
Imaging Radar ◽  
Position Errors ◽  
Calibration Methods ◽  
Array Errors

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets.

scholarly journals A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 272 ◽  
Author(s):  
Ajmal Hinas ◽  
Roshan Ragel ◽  
Jonathan Roberts ◽  
Felipe Gonzalez
Keyword(s):  
Target Detection ◽  
Low Cost ◽  
Target Position ◽  
Position Estimation ◽  
Quality Data ◽  
Unmanned Aerial Systems ◽  
Modular Software ◽  
Ground Target ◽  
High Level ◽  
Aerial Systems

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

scholarly journals Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 31 ◽  
Author(s):  
Angelo Coluccia ◽  
Alessio Fascista
Keyword(s):  
Low Cost ◽  
Computational Cost ◽  
Closed Form Solution ◽  
Target Position ◽  
Form Solution ◽  
Position Estimation ◽  
Model Parameters ◽  
Time Of Arrival ◽  
Synchronization Errors ◽  
Step Algorithm

The paper addresses the problem of localization based on hybrid received signal strength (RSS) and time of arrival (TOA) measurements, in the presence of synchronization errors among all the nodes in a wireless network, and assuming all parameters are unknown. In most existing schemes, in fact, knowledge of the model parameters is postulated to reduce the high dimensionality of the cost functions involved in the position estimation process. However, such parameters depend on the operational wireless context, and change over time due to the presence of dynamic obstacles and other modification of the environment. Therefore, they should be adaptively estimated “on the field”, with a procedure that must be as simple as possible in order to suit multiple real-time re-calibrations, even in low-cost applications, without requiring human intervention. Unfortunately, the joint maximum likelihood (ML) position estimator for this problem does not admit a closed-form solution, and numerical optimization is practically unfeasible due to the large number of nuisance parameters. To circumvent such issues, a novel two-step algorithm with reduced complexity is proposed: A first calibration phase exploits nodes in known positions to estimate the unknown RSS and TOA model parameters; then, in a second localization step, an hybrid TOA/RSS range estimator is combined with an iterative least-squares procedure to finally estimate the unknown target position. The results show that the proposed hybrid TOA/RSS localization approach outperformed state-of-the-art competitors and, remarkably, achieved almost the same accuracy of the joint ML benchmark but with a significantly lower computational cost.

An Alternative Model for Target Position Estimation in Radar Processors

2007 ◽  
Vol 14 (8) ◽  
pp. 549-552 ◽  
Author(s):  
Alper Yildirim ◽  
Murat Efe ◽  
Ahmet Kemal Ozdemir
Keyword(s):  
Alternative Model ◽  
Target Position ◽  
Position Estimation
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