Event-triggered identification of FIR systems with binary-valued output observations

Diao, Jing-Dong; Guo, Jin; Sun, Changyin

doi:10.1016/j.automatica.2018.09.024

Cited by 26 publications

(14 citation statements)

References 30 publications

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“…In practice, in order to reduce time costs, it is necessary to make trade-offs between the quality of analog-to-digital conversion and the computational complexity of processing digital samples. In the extreme case, binary quantization methods are used to convert signals into a digital code [8][9][10][11][12][13][14].…”

Section: Estimation Of the Power Spectral Density Based On Binary-sign Stochastic Quantizationmentioning

confidence: 99%

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Digital Spectral Analysis by means of the Method of Averag Modified Periodograms Using Binary-Sign Stochastic Quantization of Signals

Yakimov

2021

Prib. metody izmer.

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The method of averaging modified periodograms is one of the main methods for estimating the power spectral density (PSD). The aim of this work was the development of mathematical and algorithmic support, which can increase the computational efficiency of signals digital spectral analysis by this method.The solution to this problem is based on the use of binary-sign stochastic quantization for converting the analyzed signal into a digital code. A special feature of this quantization is the use of a randomizing uniformly distributed auxiliary signal as a stochastic continuous quantization threshold (threshold function). Taking into account the theory of discrete-event modeling the result of binary-sign quantization is interpreted as a chronological sequence of instantaneous events in which its values change. In accordance with this we have a set of time samples that uniquely determine the result of binary-sign quantization in discrete-time form. Discrete-event modeling made it possible to discretize the process of calculating PSD estimates. As a result, the calculation of PSD estimates was reduced to discrete processing of the cosine and sine Fourier transforms for window functions. These Fourier transforms are calculated analytically based on the applied window functions. The obtained mathematical equations for calculating the PSD estimates practically do not require multiplication operations. The main operations of these equations are addition and subtraction. As a consequence, the time spent on digital spectral analysis of signals is reduced.Numerical experiments have shown that the developed mathematical and algorithmic support allows us to calculate the PSD estimates by the method of averaging modified periodograms with a high frequency resolution and accuracy even for a sufficiently low signal-to-noise ratio. This result is especially important for spectral analysis of broadband signals.The developed software module is a problem-oriented component that can be used as part of metrologically significant software for the operational analysis of complex signals.

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Section: Estimation Of the Power Spectral Density Based On Binary-sign Stochastic Quantizationmentioning

confidence: 99%

“…At time intervals, the boundaries of which are determined by the counts (12), the values for z(t) are equal to "-1" or "+1". Therefore, the integrals in (10) and (11) can be represented as a sum of integrals:…”

Section: Estimation Of the Power Spectral Density Based On Binary-sign Stochastic Quantizationmentioning

confidence: 99%

Digital Spectral Analysis by means of the Method of Averag Modified Periodograms Using Binary-Sign Stochastic Quantization of Signals

Yakimov

2021

Prib. metody izmer.

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“…However, for recent years, the quantized or binary-valued observations have attracted much attention in systems and control community, since such kind of sensors are usually with low complexity and with much less operational cost in comparison with the traditional ones, and hence, they are more attractive for applications [19]. In most of these works the centralized estimation and control problems with quantized or binary-valued observations are concerned, see [20]- [23] and references therein. The distributed consensus of multi-agent systems with quantized communications is studied in [24], [32].…”

Section: Introductionmentioning

confidence: 99%

Distributed System Identification for Linear Stochastic Systems with Binary Sensors

Fu¹,

Chen²,

Zhao³

2021

Preprint

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The problem of distributed identification of linear stochastic system with unknown coefficient θ * over timevarying networks is considered. For estimating θ * , each agent in the network can only access the input and the binary-valued output of the local system. Compared with the existing works on distributed optimization and estimation, the binary-valued local output observation considered in the paper makes the problem challenging. By assuming that the agent in the network can communicate with its adjacent neighbours, a stochastic approximation based distributed identification algorithm is proposed, and the consensus and convergence of the estimates are established. Finally, a numerical example is given showing that the simulation results are consistent with the theoretical analysis.

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“…A number of centralized estimators with event-triggered measurement schedulers are proposed in the literature, such as [13,14] for state estimation of dynamical systems, and [15][16][17] for static parameter estimation. Regarding the event-triggered parameter estimation, the authors in [15] propose a measurement scheduler such that the asymptotic estimation performance is optimized.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Distributed Estimation With Decaying Communication Rate

He¹,

Lei²,

Wu³

et al. 2021

Preprint

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We study distributed estimation of a high-dimensional static parameter vector through a group of sensors whose communication network is modeled by a fixed directed graph. Different from existing time-triggered communication schemes, an event-triggered asynchronous scheme is investigated in order to reduce communication while preserving estimation convergence. A distributed estimation algorithm with a single step size is first proposed based on an event-triggered communication scheme with a time-dependent decaying threshold. With the event-triggered scheme, each sensor sends its estimate to neighbor sensors only when the difference between the current estimate and the last sent-out estimate is larger than the triggering threshold. Different sensors can have different step sizes and triggering thresholds, enabling the parameter estimation process to be conducted in a fully distributed way. We prove that the proposed algorithm has mean-square and almost-sure convergence respectively, under proper conditions of network connectivity and system collective observability. The collective observability is the possibly mildest condition, since it is a spatially and temporally collective condition of all sensors and allows sensor observation matrices to be time-varying, stochastic, and non-stationary. Moreover, we provide estimates for the convergence rates, which are related to the step sizes as well as the triggering thresholds. Furthermore, we prove that the communication rate is decaying to zero with a certain rate almost surely as time goes to infinity. We show that it is feasible to tune the thresholds and the step sizes such that requirements of algorithm convergence and communication rate decay are satisfied simultaneously. Numerical simulations are provided to illustrate the developed results.

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Event-triggered identification of FIR systems with binary-valued output observations

Cited by 26 publications

References 30 publications

Digital Spectral Analysis by means of the Method of Averag Modified Periodograms Using Binary-Sign Stochastic Quantization of Signals

Digital Spectral Analysis by means of the Method of Averag Modified Periodograms Using Binary-Sign Stochastic Quantization of Signals

Distributed System Identification for Linear Stochastic Systems with Binary Sensors

Event-Triggered Distributed Estimation With Decaying Communication Rate

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