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Multistatic radar system has a great potential for human detection and tracking for its fine localization precision, wide coverage and good observability. Traditional human detection and tracking are performed on the one dimensional (1-D) range profile. If targets are close or overlapped in range, it is difficult to distinguish these targets and obtain the measurements for target tracking. It can...
Probability hypothesis density (PHD) filtering, implemented using particle filters, is a Bayesian technique used to non-linearly track multiple objects. In this paper, we propose a new approach based on PHD particle filters (PHD-PF) to automatically track the number of magnetoencephalography (MEG) neural dipole sources and their unknown states. In particular, by separating the MEG measurements using...
Parameter estimation of biological signals such as the electrocardiogram (ECG) is of key clinical significance and can be used to monitor cardiac health and diagnose heart diseases. However, statistical ECG models with unknown parameters depend upon a priori parameters such as mean cardiac frequency and user-specified parameters such as the number of harmonics in the ECG model. These parameters can...
We investigate the target tracking problem of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors. Although the multi-modality framework allows for the integration of complementary information, there are many challenges to overcome, including targets with different energy returns, and information loss due to low signal-to-noise...
Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-Gaussian dynamic system parameters. As these algorithms are recursive, their real-time implementation can be computationally complex. In this paper, we analyze the bottlenecks in existing parallel PF algorithms, and we propose a new approach that integrates parallel PFs with independent Metropolis-Hastings (PPF-IMH)...
This paper investigates the influence of inverter nonlinearity on the parameter estimation in permanent magnet synchronous machines (PMSM). An estimator based on model reference adaptive system (MRAS) is firstly described for simultaneously estimating the stator winding resistance and rotor flux linkage, together with injection of a pulse of id <; 0 which is required for activating the estimation...
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