357 research outputs found
Accurate detection of moving targets via random sensor arrays and Kerdock codes
The detection and parameter estimation of moving targets is one of the most
important tasks in radar. Arrays of randomly distributed antennas have been
popular for this purpose for about half a century. Yet, surprisingly little
rigorous mathematical theory exists for random arrays that addresses
fundamental question such as how many targets can be recovered, at what
resolution, at which noise level, and with which algorithm. In a different line
of research in radar, mathematicians and engineers have invested significant
effort into the design of radar transmission waveforms which satisfy various
desirable properties. In this paper we bring these two seemingly unrelated
areas together. Using tools from compressive sensing we derive a theoretical
framework for the recovery of targets in the azimuth-range-Doppler domain via
random antennas arrays. In one manifestation of our theory we use Kerdock codes
as transmission waveforms and exploit some of their peculiar properties in our
analysis. Our paper provides two main contributions: (i) We derive the first
rigorous mathematical theory for the detection of moving targets using random
sensor arrays. (ii) The transmitted waveforms satisfy a variety of properties
that are very desirable and important from a practical viewpoint. Thus our
approach does not just lead to useful theoretical insights, but is also of
practical importance. Various extensions of our results are derived and
numerical simulations confirming our theory are presented
Efficient active SLAM based on submap joining
This paper considers the active SLAM problem where a robot is required to cover a given area while at the same time performing simultaneous localization and mapping (SLAM) for understanding the environment and localizing the robot itself. We propose a model predictive control (MPC) framework, and the minimization of uncertainty in SLAM and coverage problems are solved respectively by the Sequential Quadratic Programming (SQP) method. Then, a decision making process is used to control the switching of two control inputs. In order to reduce the estimation and planning time, we use Linear SLAM, which is a submap joining approach. Simulation results are presented to validate the effectiveness of the proposed active SLAM strategy
Dual Quaternions as Constraints in 4D-DPM Models for Pose Estimation
This work was partially financed by Plan Nacional de Investigacion y Desarrollo (I+D), Comision Interministerial de Ciencia y Tecnologia (FEDER-CICYT) under the project DPI2013-44227-R.MartÃnez BertÃ, E.; Sánchez Salmerón, AJ.; Ricolfe Viala, C. (2017). Dual Quaternions as Constraints in 4D-DPM Models for Pose Estimation. Sensors. 17 (8)(1913):1-16. https://doi.org/10.3390/s17081913S11617 (8)191
Digital Twin Technology Enabled Proactive Safety Application for Vulnerable Road Users: A Real-World Case Study
While measures, such as traffic calming and advance driver assistance
systems, can improve safety for Vulnerable Road Users (VRUs), their
effectiveness ultimately relies on the responsible behavior of drivers and
pedestrians who must adhere to traffic rules or take appropriate actions.
However, these measures offer no solution in scenarios where a collision
becomes imminent, leaving no time for warning or corrective actions. Recently,
connected vehicle technology has introduced warning services that can alert
drivers and VRUs about potential collisions. Nevertheless, there is still a
significant gap in the system's ability to predict collisions in advance. The
objective of this study is to utilize Digital Twin (DT) technology to enable a
proactive safety alert system for VRUs. A pedestrian-vehicle trajectory
prediction model has been developed using the Encoder-Decoder Long Short-Term
Memory (LSTM) architecture to predict future trajectories of pedestrians and
vehicles. Subsequently, parallel evaluation of all potential future
safety-critical scenarios is carried out. Three Encoder-Decoder LSTM models,
namely pedestrian-LSTM, vehicle-through-LSTM, and vehicle-left-turn-LSTM, are
trained and validated using field-collected data, achieving corresponding root
mean square errors (RMSE) of 0.049, 1.175, and 0.355 meters, respectively. A
real-world case study has been conducted where a pedestrian crosses a road, and
vehicles have the option to proceed through or left-turn, to evaluate the
efficacy of DT-enabled proactive safety alert systems. Experimental results
confirm that DT-enabled safety alert systems were succesfully able to detect
potential crashes and proactively generate safety alerts to reduce potential
crash risk.Comment: 19 pages, 9 figures, submitted to the Transportation Research Board
2024 TRB Annual Meetin
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