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|Title:||Probability Hypothesis Density Filter Algorithm for Track Before Detect Applications|
|Authors:||Habtemariam, Biruk K.|
|Department:||Electrical and Computer Engineering|
|Keywords:||Electrical and Computer Engineering;Electrical and Computer Engineering|
|Abstract:||<p>Track-Before-Detect (TBD) algorithms are far more efficient over standard DetectBefore- Track (DBT) target tracking approach for tracking targets in low Signal-toNoise- Ratio (SNR) environment . With low SNR scenario the target amplitude may never be strong enough to exceed threshold value and under classical setting such cases will not lead to detection. This might be the case in spatially diversified multiple sensors network like Multiple-Input-Multiple-Output (MIMO) radars. Through letting the tracking directly on the unthresholded data, TBD techniques exploit all the information in the received measurement signal to yield detection and tracking simultaneously. With TBD framework an efficient multitarget, non-linear filtering algorithm is an issue to extract information from target dynamics. In t his thesis Probability-Hypothesis-Density (PHD) filter implementation of a recursive TBD algorithm is proposed. The PHD filter, propagating only the first-order statistical moment of the full target posterior, is a computationally efficient solution to multitarget tracking problems with varying number of targets. Furthermore a PHD filter based tracking algorithm avoids the preassumption of the maximum number of targets performing the state estimation together with number of targets.</p> <p>The PHD filter based TBD algorithm is applied to multitarget tracking with MIMO Radars. With widely-separated transmitters and receivers of MIMO system the Radar-Cross-Section (RCS) diversity can be utilized by illuminating the target from ideally uncorrelated aspects. Multiple sensor TBD is proposed in order to process the measurement signals from different multiple transmitter-receiver pairs in the MIMO Radar system. In this model target observability to the sensor as a result of target RCS diversity is taken in to consideration in the likelihood calculation. In order to provide a benchmark for testing the proposed algorithm performance, Posterior Cramer-Rao Lower Bound (PCRLB) for widely-separated MINIO radar is also presented. Monte Carlo simulations have been done on multitarget scenarios with various SNR values and target motion models. Performance evaluation on simulation results demonstrates the improved performance of the proposed tracking algorithm.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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