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In many multiobject tracking applications, including radar and sonar tracking, after prefiltering the received signal, measurement data is typically structured in cells. The cells, e.g., represent different range and bearing values. However, conventional multiobject tracking methods use so-called point measurements. Point measurements are provided by a preprocessing stage that applies a threshold or detector and breaks up the cell’s structure by converting cell indexes into, e.g., range and bearing measurements. We here propose a Bayesian multiobject tracking method that processes measurements that have been thresholded but are still cell-structured. We first derive a likelihood function that systematically incorporates an adjustable detection threshold which makes it possible to control the number of cell measurements. We then propose a Poisson Multi-Bernoulli (PMB) filter based on the likelihood function for cell measurements. Furthermore, we establish a link to the conventional point measurement model by deriving the likelihood function for point measurements with amplitude information (AM) and discuss the PMB filter that uses point measurements with AM. Our numerical results demonstrate the advantages of the proposed PMB filter for thresholded cell measurements compared to the conventional PMB filter for point measurements with and without AM.
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Camera-Based Modal Fingerprinting of Cavity Resonances in a Photonic Crystal Nanobeam
Utilizing distinct features in the leaky region of k-space as ‘modal fingerprints’, we demonstrate resonant mode identification in a photonic crystal nanobeam via infrared camera measurements with a ~19dB detection SNR improvement over transmission measurements.
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- Award ID(s):
- 1809937
- PAR ID:
- 10188102
- Date Published:
- Journal Name:
- Conference on Lasers and Electro-Optics
- Page Range / eLocation ID:
- SW3F.6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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