Report
Coherent long-time integration and Bayesian detection with Bernoulli track-before-detect
| العنوان: | Coherent long-time integration and Bayesian detection with Bernoulli track-before-detect |
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| المؤلفون: | Murat Uney (14275829), Paul Horridge (14275832), Bernard Mulgrew (14275835), Simon Maskell (14275841) |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Signal Processing and Analysis, Bernoulli filter, Coherent detection, track-before-detect, long-time integration, staring array radar |
| الوصف: | We consider the problem of detecting small and manoeuvring objects with staring array radars. Coherent processing and long-time integration are key to addressing the undesirably low signal-to-noise/background conditions in this scenario and are complicated by the object manoeuvres. We propose a Bayesian solution that builds upon a Bernoulli state space model equipped with the likelihood of the radar data cubes through the radar ambiguity function. Likelihood evaluation in this model corresponds to coherent long-time integration. The proposed processing scheme consists of Bernoulli filtering within expectation maximisation iterations that aims at approximately finding complex reflection coefficients. We demonstrate the efficacy of our approach in a simulation example. |
| نوع الوثيقة: | report |
| اللغة: | unknown |
| Relation: | https://figshare.com/articles/preprint/Coherent_long-time_integration_and_Bayesian_detection_with_Bernoulli_track-before-detect/21747497 |
| DOI: | 10.36227/techrxiv.21747497.v1 |
| الاتاحة: | https://doi.org/10.36227/techrxiv.21747497.v1 |
| Rights: | CC BY-NC-SA 4.0 |
| رقم الانضمام: | edsbas.3A8E0146 |
| قاعدة البيانات: | BASE |
| DOI: | 10.36227/techrxiv.21747497.v1 |
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