Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR
IEEE Transactions on Signal Processing
Target Detection and Localization Using MIMO Radars and Sonars
IEEE Transactions on Signal Processing
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing - Part II
Spatial diversity in radars-models and detection performance
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
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In this paper a Probability Hypothesis Density (PHD) filter based track-before-detect (TBD) algorithm is proposed for Multiple-Input-Multiple-Output (MIMO) radars. The PHD filter, which propagates 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. The proposed algorithm avoids any assumption on the maximum number of targets as a result of estimating the number of targets together with target states. With widely separated transmitter and receiver pairs, the algorithm utilizes the Radar Cross Section (RCS) diversity as a result of target illumination from ideally uncorrelated aspects. Furthermore, a multiple sensor TBD is proposed in order to process the received signals from different transmitter-receiver pairs in the MIMO radar system. In this model, the target observability to the sensor as a result of target RCS diversity is taken in to consideration in the likelihood calculation. In order to quantify the performance of the proposed algorithm, the Posterior Cramer-Rao Lower Bound (PCRLB) for widely separated MIMO radars is also derived. Simulation results show that the new algorithm meets the PCRLB and provides better results compared with standard Maximum Likelihood (ML) based localizations.