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First we look at two recently reported techniques for finding the ML-PDA track estimate which improves computational efficiency by one order of magnitude.
The first joint likelihood term on the right hand side implies that all measurements belong to the clutter; thus, it does not correct the track estimate.
Scenario 2: d input data of MTT from sensor S1, e input data of MTT from sensor S2, f the fusion center output - the final target track estimate.
Scenario 1: a input data of MTT from sensor S1, b input data of MTT from the sensor S2, c the fusion center output - the final target track estimate.
The full track estimate is shown here providing a single view of the entire trip.
We ran a sensitivity analysis to assess how many random walks were necessary to obtain a reliable and stable track estimate.
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To further illustrate the importance of TSSG estimation for subsequently forming position and track estimates, Figure 11 depicts two snapshots of the TSSG heat maps after the KF prediction and correction steps at times k=2 and 3.
Figure 7 shows the track estimates of the proposed APPF-CBMeMBer algorithm.
Figuree problem of vehicle tracking, the importance density used in the projective particle filter is therefore more suitable for drawing samples, compared to the prior density used in the standard particle filter.
Next we review a method for validating ML-PDA track estimates based on the Neyman-Pearson lemma which gives improved reliability in track validation over previous methods.
It is possible however to utilize each target's movement model to develop other (more effective) data association schemes and refine the track estimates as well.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com