Iet Signal Processing 

About: Iet Signal Processing is an academic journal published by Institution of Engineering and Technology. The journal publishes majorly in the area(s): Computer science & Filter (signal processing). It has an ISSN identifier of 1751-9675. It is also open access. Over the lifetime, 1429 publications have been published receiving 16736 citations. The journal is also known as: Signal processing, IET & Institution of Engineering and Technology signal processing.

Unreliability of frequency-domain approximation in recognising chaos in fractional-order systems

Abstract: The effects of using frequency-domain approximation in numerical simulation of fractional-order systems are analytically studied. The main aim in the study is to determine the number, location and stability property of the equilibriums in a fractional-order system and its frequency-based approximating counterpart. The comparison shows that the original fractional-order system and its frequency-based approximation may differ from each other in some or all issues considered in the study. Unfortunately, these differences can lead to wrong consequences in some special cases such as detecting chaos in the fractional-order systems. It is shown that using the frequency-domain approximation methods can conceal chaotic behaviour for a chaotic fractional-order system or display chaos for a non-chaotic one. Therefore one should be careful and conservative in using these methods to recognise chaos in the fractional-order systems.

Doppler signatures of radar backscattering from objects with micro-motions

Abstract: Radar backscattering from objects with micro-motions is subject to Doppler modulations that help determine dynamic properties of objects and provide useful information about the objects. Doppler modulations represented by joint time-frequency analysis provide useful time-varying Doppler characteristics and, thus, add additional time-dimension information to exploit motion characteristics. The author discusses how to simulate radar backscattering from objects with rigid body motions and objects with non-rigid body motions, and how to analyse, interpret and characterise Doppler signatures of objects that undergo these micro-motions. Precession heavy top and human locomotion are used as examples of rigid and non-rigid body motions, respectively. Radar micro-Doppler signatures derived from these motions illustrate the potential of the joint time-frequency analysis for exploiting kinetic and dynamic properties of objects.

Parameter estimation algorithms for dynamical response signals based on the multi-innovation theory and the hierarchical principle

Abstract: In this study, the authors consider the parameter estimation problem of the response signal from a highly non-linear dynamical system. The step response experiment is taken for generating the measured data. Considering the stochastic disturbance in the industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. Furthermore, a hierarchical identification algorithm is derived by means of the decomposition technique and interaction estimation theory. Regarding to the coupled parameter problem between subsystems, the authors put forward the scheme of replacing the unknown parameters with their previous parameter estimates to realise the parameter estimation algorithm. Finally, several examples are provided to access and compare the behaviour of the proposed identification techniques.

Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises

Abstract: By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input–output data The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels The simulation test results indicate that the proposed algorithms are effective

Cubature quadrature Kalman filter

Abstract: In this correspondence, the authors develop a novel method based on spherical radial cubature and Gauss-Laguerre quadrature rule for non-linear state estimation problems. The proposed filter, referred as cubature quadrature Kalman filter (CQKF) would be able to overcome inherent disadvantages associated with the earlier reported cubature Kalman filter (CKF). The theory and formulation of CQKF has been presented. Using two well-known non-linear examples, the superior performance of CQKF has been demonstrated. Owing to computational efficiency (compared to the particle and grid-based filter) and enhanced accuracy compared to the extended Kalman filter and the CKF, the developed algorithm may find place in on-board real life applications.