Adaptive filter of non‐linear systems with generalised unknown disturbances

Qin, Yuemei; Liang, Yan; Yang, Yanbo; Wang, Zengfu; Yang, Feng

doi:10.1049/iet-rsn.2013.0031

Cited by 6 publications

(12 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In maneuvering target tracking in the electronic countermeasures (ECMs) environment, the target maneuvering motion is usually described by Markovian switching of multiple models [2] while the sensor bias, deception jamming and linearization approximation lead to the time-varying GUDs in measurements [14]. In other words, there coexist the Markovian switching parameter and GUDs, which motivates us to formulate the following discrete-time MJLS with GUDs in measurements:…”

Section: Problem Formulationmentioning

confidence: 99%

“…As shown in [14], δ k represents a more general uncertainty (i.e. GUD): an arbitrary linear weighted sum of f 1k , f 2k and q k , where f 1k representing a class of UD with dynamic property is an linear time-varying function of…”

Section: Problem Formulationmentioning

confidence: 99%

“…Though we have ever designed UBFs for the linear systems with GUDs in state dynamics [20] or the nonlinear systems with GUDs in sensors [14], there is no result suitable to the MJLSs due to the coexistence of Markov stochastic switching and GUDs. As shown in the following section, the filter derivation turns to designing the UBF for a more general system with stochastic parameters, determining the first two moments of stochastic parameters, and hence obtaining the UBF for MJLSs via parameter substitution.…”

Section: Remark 22mentioning

confidence: 99%

“…However, all these methods for the MJLSs, including the MM methods and the LMMSE methods, never consider the presence of unknown disturbances (UDs). In fact, the UDs exist in many actual applications [14]. In target tracking, the sensor bias and deception jamming existing in sensor measurements can be modeled as the UDs to the nominal model [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the UDs exist in many actual applications [14]. In target tracking, the sensor bias and deception jamming existing in sensor measurements can be modeled as the UDs to the nominal model [14,15]. In process control, the faults or failures can also be represented by UDs to the fault-free model in fault detection and isolation [3,4].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Minimum upper-bound filter of Markovian jump linear systems with generalized unknown disturbances

et al. 2016

Self Cite

View full text Add to dashboard Cite

This paper presents the estimation problem of Markovian jump linear systems (MJLSs) with generalized unknown disturbances (GUDs). There exist multiple uncertainties including Markovian switching parameters and GUDs, along with traditional random noises. Here, the state transition of MJLS is treated as the jump from one vertex to another on a fixed polyhedron whose vertex represents a mode. Since the transition is dependent on stochastic Markovian switching parameter, a more general polytopic system with stochastic weights is considered and the corresponding upper-bound filter (UBF) is derived. Then, the MJLS with GUDs is transformed into a special case of the considered polytopic system by letting the corresponding stochastic weight as the binary value constructed by Markovian switching parameters and hence the recursive UBF is obtained. The parameters in the derived UBF are further optimized in pursuit of the minimum upper bounds of estimation error covariances. The simulation via maneuvering target tracking shows the effectiveness of the proposed filter.

show abstract

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Remark 22mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Minimum upper-bound filter of Markovian jump linear systems with generalized unknown disturbances

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

Secure state estimation for unmanned aircraft cyber‐physical systems under multiple attacks

Feng

Shu-hui

et al. 2022

Asian Journal of Control

View full text Add to dashboard Cite

By exploiting the communication infrastructure among the sensor, actuator, and control system, attackers may compromise the security of unmanned aircraft cyber‐physical systems, with techniques such as denial‐of‐service (DoS) attack, random attack, and data‐injection attack. In this paper, the data‐injection attacks for controller and sensor are modeled as the dual unknown disturbance inputs (dual‐UIs) appearing in dynamic and measurement model. The problem of secure state estimation under multiple attacks together with the DoS attack‐induced packet dropout is transformed into the problem of stochastic system state estimation under dual‐UIs together with the missing measurement. Therefore, a recursive dual‐UIs decoupling filter is proposed through minimizing the minimum upper bound of the estimation covariance. First, a Kalman‐like recursive filter scheme independent of the dual‐UIs is established to realize the unbiased estimation. Meanwhile, a lower bounded adaptive factor is introduced to depict the state related unknown dual‐UI that cannot be eliminated completely due to the randomly missing measurements. Then, the minimum upper bound of the estimation covariance is deduced via online scalar convex optimization, based on which the measurement coefficient matrix is derived under the dual‐UIs decoupling conditions. Finally, the sufficient condition for the convergence of the proposed filter is shown and the stability of the state estimate is investigated. Simulation cases of unmanned aircraft longitudinal flight control system verifies the effectiveness and superiority of the proposed method.

show abstract

Robust multi-target tracking under mismatches in both dynamic and measurement models

Zhang

Yang

Liang

2019

Aerospace Science and Technology

View full text Add to dashboard Cite

Adaptive filter of non‐linear systems with generalised unknown disturbances

Cited by 6 publications

References 30 publications

Minimum upper-bound filter of Markovian jump linear systems with generalized unknown disturbances

Minimum upper-bound filter of Markovian jump linear systems with generalized unknown disturbances

Secure state estimation for unmanned aircraft cyber‐physical systems under multiple attacks

Robust multi-target tracking under mismatches in both dynamic and measurement models

Contact Info

Product

Resources

About