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However, the performance of DSRC based rear end collision warning systems suffers due to uncertain measurement errors and cause high rates of false alarms.
In such problems, the initial conditions are typically estimated from a very uncertain measurement process: it is very difficult to estimate the exact fault deformation or the initial position and velocity of a landslide.
The problem at hand is to perform high-resolution reconstruction of the power spatial spectrum pattern (SSP) of the wavefield scattered from the extended remotely sensed scene via space-time adaptive processing of finite recordings of the imaging radar/SAR data distorted in a stochastic uncertain measurement channel.
At their core, these devices rely on inexpensive, and often uncertain, measurement technologies.
The modification is based on the model assumption of uncertain measurement times.
The results clearly show the advantage of the application of the MTU-PF in cases of uncertain measurement times.
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Then, GNN associated uncertain measurements with known tracks.
A data association method, either Global Nearest Neighbor or Multiple Hypothesis Tracking, was employed to associate uncertain measurements to known tracks.
In the case of uncertain measurements obtained from a family of quasi-identical structures, parameters need to be modeled randomly.
Vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments.
This paper deals with model validation in structural dynamics for a family of quasi-identical structures in the context of uncertain measurements.
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