IET Signal Processing (IET SIGNAL PROCESS)
IET Signal Processing publishes novel contributions in signal processing including: advances in single and multi-dimensional filter design and implementation; linear and nonlinear, fixed and adaptive digital filters and multirate filter banks; statistical signal processing techniques and analysis; classical, parametric and higher order spectral analysis; signal transformation and compression techniques, including time-frequency analysis; system modelling and adaptive identification techniques; machine learning based approaches to signal processing; Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques; theory and application of blind and semi-blind signal separation techniques; signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals; direction-finding and beamforming techniques for audio and electromagnetic signals; analysis techniques for biomedical signals; baseband signal processing techniques for transmission and reception of communication signals; signal processing techniques for data hiding and audio watermarking.
Current impact factor: 0.91
Impact Factor Rankings
|2016 Impact Factor||Available summer 2017|
|2014 / 2015 Impact Factor||0.911|
|2013 Impact Factor||0.691|
|2012 Impact Factor||0.714|
|2011 Impact Factor||0.561|
|2010 Impact Factor||0.741|
|2009 Impact Factor||0.794|
|2008 Impact Factor||0.762|
Impact factor over time
|Website||IET Signal Processing website|
|Other titles||Signal processing|
|Material type||Internet resource|
|Document type||Journal / Magazine / Newspaper, Internet Resource|
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Publications in this journal
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ABSTRACT: In this study, the effects of noisy links are investigated on the steady-state performance of mobile adaptive networks with diffusion least mean-squares strategies. The authors derive theoretical relations which explain how the steady-state performance metrics, including the steady-state network mean-square deviation and steady-state velocity mean-square-error is affected by noisy links. The provided analysis relies on the spatial-temporal energy conservation argument. The proposed simulation results reveal that although the noisy links degrade the performance of mobile adaptive networks; however, for suitably chosen combination coefficients the mobile adaptive network with noisy links provides a bounded estimation error. Finally, the proposed simulations verify that the derived theoretical analysis closely matches the actual steady-state performance observed in a network.
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ABSTRACT: The problem of distributed target tracking using time of arrival and received signal strength with unknown path loss exponent (PLE) is studied. The PLE is modelled as a Markov chain with three states and an adaptive gridding strategy is adopted to adjust the PLE recursively in a bounded interval. Therefore, the target tracking model is formulated as a jump Markov non-linear system. The interacting multiple model () estimator is applied to derive the target state estimates for each sensor and the covariance intersection approach is used to fuse sensor-based estimates in a distributed fashion. Simulation results show a promising performance for the proposed filter.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.