Abstract
Abstract A new method to smooth the target hybrid state with Gaussian mixture measurement likelihood-integrated track splitting (GMM-ITS) in the presence of clutter for a high pulse repetition frequency (HPRF) radar is proposed. This method smooths the target state at fixed lag N and considers all feasible multi-scan target existence sequences in the temporal window of scans in order to smooth the target hybrid state. The smoothing window can be of any length N. The proposed method to smooth the target hybrid state at fixed lag is also applied to the enhanced multiple model (EMM) tracking algorithm. Simulation results indicate that the performance of fixed lag smoothing GMM-ITS significantly improves false track discrimination and root mean square errors (RMSEs).
Highlights
It is well known that, when a Pulse-Doppler radar operates in high pulse repetition frequency (HPRF) mode, the target range information is ambiguous due to the aliasing range [1]
The Gaussian mixture measurement likelihoodintegrated track splitting algorithm (GMM-ITS) [4] and the enhanced multiple model algorithm (EMM) (it incorporates the track quality measure in a multiple model algorithm (MM) [3]) are investigated in [5] for singletarget tracking in clutter using an HPRF radar; both algorithms are capable of trajectory estimation and false track discrimination
In GMM-ITS algorithm, the non-linear measurement likelihood is approximated by a Gaussian mixture of measurement components, which corresponds to the ambiguous measurement components due to the aliasing target range in the application of an HPRF radar
Summary
It is well known that, when a Pulse-Doppler radar operates in high pulse repetition frequency (HPRF) mode, the target range information is ambiguous due to the aliasing range [1]. The authors in [3] present a multiple model algorithm to eliminate the range ambiguity problem in an HPRF radar These references estimate the trajectory state without providing a track quality measure for false track discrimination (FTD). This paper presents a new method to smooth the target hybrid state at fixed lag N It applies the proposed smoothing algorithm on both GMM-ITS and EMM algorithms to obtain the smoothing benefits for an HPRF radar. Khan et al EURASIP Journal on Advances in Signal Processing (2015) 2015:47 the smoothing interval This technique considers all feasible multi-scan target existence events and smoothed state estimates (using the augmented state GMM-ITS update) calculated at all intermediate scans in the smoothing interval in order to smooth the target hybrid state at fixed lag N.
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