Event-Triggered Kalman Filter and Its Performance Analysis

Li, Xiaona; Hao, Gang

doi:10.3390/s23042202

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…Substituting (32) into P ςω i (k|k) = E{ ςi (k|k) ωT i (k|k)} leads to (21), where E{ ςi (k|k) ωT i (k|k)} = 0, E{ς(k)ω T (k)} = 0, and ( 25) are used in the derivation of this equality. Thus, the proof is completed.…”

Section: Local Filtersmentioning

confidence: 99%

“…In [ 31 ], a variance-based ET mechanism is studied, and the switching Riccati equation of its estimator can be calculated offline to determine whether to transmit the measurement data. An ET mechanism based on a normal distribution is proposed in [ 32 ] to obtain a better-performing triggered mechanism. In [ 33 ], a dissipative filter is studied under the ET mechanism for MJSs subject to time-varying delays.…”

Section: Introductionmentioning

confidence: 99%

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Event-Triggered Distributed Fusion Estimator for Asynchronous Markov Jump Systems with Correlated Noises and Fading Measurements

Zhang,

Lin

2024

Sensors

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In this study, we investigate event-triggered distributed fusion estimation for asynchronous Markov jump systems subject to correlated noises and fading measurements. The measurement noises are interrelated, and they are simultaneously coupled with the system noise. The sensor samples measurements uniformly, and the sampling rates of the sensors are different. First, the asynchronous system is synchronized at state update points; then, the local filter is obtained. Furthermore, a variance-based event-triggered strategy is introduced between the local estimator and the fusion center to decrease the energy consumption of network communication. Then, a distributed fusion estimation algorithm is proposed using a matrix-weighted fusion criterion. Finally, the effectiveness of the algorithm is verified using computer simulations.

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Section: Local Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event-Triggered Distributed Fusion Estimator for Asynchronous Markov Jump Systems with Correlated Noises and Fading Measurements

Zhang,

Lin

2024

Sensors

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“…The Kalman filter (KFA) algorithm is a predictor algorithm in the form of mathematical equations to estimate a process by minimizing the value of SME (Square Mean Error), and a feedback process occurs from the sensor as an output [40,[42][43][44]. The sensor output still contains noise that interferes with the expected output results.…”

Section: 2 Implementation Of Kalman Filter Algorithm To Reduce Noisementioning

confidence: 99%

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

Pratiwi,

Fadli,

Cahyanto

et al. 2024

EEJET

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The Kalman filter algorithm is very important as a recursive algorithm method to optimize sensor output from physical parameter measurement systems, especially physics practicum demonstration systems. One of the distance parameter measurement demonstration systems used in Hooke’s law demonstration system is applied in physics practicum, the system has problems related to fluctuating or unstable sensor output. This research implements the Kalman filter algorithm on the Arduino IDE sketch to reduce noise that appears at the ultrasonic sensor output. The methodology used in this study includes the application of the Kalman filter algorithm to the Arduino IDE sketch with the variable value of the Kalman filter algorithm equation modified with a value of R=10, H=1, and Q=1, and returns the filtered Kalman out value. The Arduino output results are exported to Ms. Excel for further analysis and generate a filtered ultrasonic sensor output signal graph compared without using the Kalman filter. The ultrasonic sensor output noise filtration effectively reduces noise by showing a decrease in the mean squared error (MSE) value and obtaining the best performance of up to 89.23 %. The accuracy of Kalman filter filtration results can be seen from the calculation that the spring constant of filtered metal materials is smaller than the conventional measurement spring constant. Accurate and effective results with the implementation of the Kalman filter algorithm can be developed for the variation values of distance parameters and Kalman filter algorithm variables (R, Q, and H) with other value variations, especially variables that produce filtering curves close to straight lines. It was concluded that the Kalman filter algorithm was able to improve the performance of Hooke’s law prop system

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“…KF can adopt Gaussian distribution to filter the noise to improve the prediction accuracy for different kinds of signal predictions. For example, the combination of KF can adopt Gaussian distribution was adopted for proposing the event trigger algorithm [20], the low-precision numerical representation for efficient Gaussian estimation of high-dimensional problems [21], the adaptive tracking technique for digital global navigation satellite system [22], the resilient state estimation under sensor attacks [23], linear quadratic Gaussian control strategy for concrete caisson deployment for marine structures [24], and the localization method for continuous-wave radar carrier [25]. Thus, KF can utilize Gaussian distribution to achieve accurate latest signal prediction.…”

Section: Related Workmentioning

confidence: 99%

“…The Gaussian process can identify Gaussian distribution signals to assist KF in improving the signal estimation [18,19]. Gaussian distribution is a popular approach to identifying noise signals to estimate the latest signal, together with KF [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

FFK: Fourier-Transform Fuzzy-c-means Kalman-Filter Based RSSI Filtering Mechanism for Indoor Positioning

Tseng,

Tsaur

2023

Sensors

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As indoor positioning has been widely utilized for many applications of the Internet of Things, the Received Signal Strength Indication (RSSI) fingerprint has become a common approach to distance estimation because of its simple and economical design. The combination of a Gaussian filter and a Kalman filter is a common way of establishing an RSSI fingerprint. However, the distributions of RSSI values can be arbitrary distributions instead of Gaussian distributions. Thus, we propose a Fouriertransform Fuzzyc-means Kalmanfilter (FFK) based RSSI filtering mechanism to establish a stable RSSI fingerprint value for distance estimation in indoor positioning. FFK is the first RSSI filtering mechanism adopting the Fourier transform to abstract stable RSSI values from the low-frequency domain. Fuzzy C-Means (FCM) can identify the major Line of Sight (LOS) cluster by its fuzzy membership design in the arbitrary RSSI distributions, and thus FCM becomes a better choice than the Gaussian filter for capturing LOS RSSI values. The Kalman filter summarizes the fluctuating LOS RSSI values as the stable latest RSSI value for the distance estimation. Experiment results from a realistic environment show that FFK achieves better distance estimation accuracy than the Gaussian filter, the Kalman filter, and their combination, which are used by the related works.

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Event-Triggered Kalman Filter and Its Performance Analysis

Cited by 4 publications

References 30 publications

Event-Triggered Distributed Fusion Estimator for Asynchronous Markov Jump Systems with Correlated Noises and Fading Measurements

Event-Triggered Distributed Fusion Estimator for Asynchronous Markov Jump Systems with Correlated Noises and Fading Measurements

Implementation of Kalman filter algorithm to optimize the calculation of ultrasonic sensor distance value in Hooke law props system

FFK: Fourier-Transform Fuzzy-c-means Kalman-Filter Based RSSI Filtering Mechanism for Indoor Positioning

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