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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Osadciw, L.A. Yanjun Yan |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Electrical Engineering and Computer Science, Syracuse University, NY 13244, USA (Osadciw, L.A.; Yanjun Yan) |
| Abstract | Power spectrum estimation from radar data is essential for target detection. For instance, microburst causes detrimental effects on airplane performance, and hence its detection is critical. We compare auto-regression (AR), Periodogram, Kaiser windowed Periodogram, and multiple-signal-classification (MUSIC) methods for microburst clutter spectrum estimation. Given a long train of returned signal, we are able to segment the signal to obtain multiple estimations of the parameters, which leads to a more accurate estimation after coefficient averaging. The estimated power spectrum is then integrated for clutter magnitude calculation to determine whether a target is present at certain cell. The magnitude of clutters in an ensemble from a wide region spatially or through time temporally is used to estimate the clutter map. We choose a K-distribution mixture model over the traditional Rayleigh distribution to better approximate the tail structure of the distribution to minimize the false alarm rate. We show that Dimension-Adaptive Particle Swarm Optimization (DA-PSO) is robust to sample size in estimating the K-distribution mixture model, which is desirable for real-time implementations. |
| Starting Page | 1668 |
| Ending Page | 1672 |
| File Size | 300544 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424458257 |
| ISSN | 10586393 |
| e-ISBN | 9781424458271 |
| DOI | 10.1109/ACSSC.2009.5469793 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-01 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Radar clutter Particle swarm optimization Spectral analysis Object detection Airplanes Radar detection Multiple signal classification Parameter estimation Probability distribution Robustness |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Computer Networks and Communications |
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