Extended least square unbiased FIR filter for target tracking using the constant velocity motion model

Pak, Jung Min; Kim, Pyung Soo; You, Sung Hyun; Lee, Sang Seol; Song, Moon Kyou

doi:10.1007/s12555-016-0572-y

Cited by 18 publications

(17 citation statements)

References 16 publications

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“…If the noise information is very uncertain, this approach can provide a constant performance, whereas the non-linear estimators of the existing state, such as the extended Kalman filter and particle filter, degradation of performance often in the same condition. The simulations results indicated the robustness of this approach against the uncertainty of the noise model [27].…”

Section: Related Workmentioning

confidence: 77%

Study the Characteristics of FIR filter Based on Modified Kaiser Window

Al-Ani

2017

UHDJST

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Section: Related Workmentioning

confidence: 77%

Study the Characteristics of FIR filter Based on Modified Kaiser Window

Al-Ani

2017

UHDJST

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“…As shown in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], the state-space approach has been a general method for modeling, analyzing and designing a wide range of control and estimation problems in diverse dynamic systems, and has been especially suitable for digital computation techniques. Therefore, in this paper, a general discrete-time state space model with noises is considered as follows:…”

Section: Finite Memory Structure Filter From the Conditional Density mentioning

confidence: 99%

“…Meanwhile, in contrast to the recursive infinite memory structure (IMS) filter like the traditionally used Kalman filter [9][10][11][12], the finite memory structure (FMS) filter has been known to have inherent good properties such as bounded-input, bounded-output (BIBO) stability and and more robustness against temporary uncertainties due to its processing manner of finite measurements on the most recent window [13][14][15][16]. Thus, the FMS filter has been applied successfully for various engineering problems [17][18][19][20][21][22][23]. As shown in [13][14][15][16], the FMS filter is known to have better noise suppression as the measurement window length grows.…”

Section: Introductionmentioning

confidence: 99%

Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems

Kim

2019

Applied Sciences

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In this paper, a finite memory structure (FMS) filtering with two kinds of measurement windows is proposed using the chi-square test statistic to cover nominal systems as well as temporarily uncertain systems. First, the simple matrix form for the FMS filter is developed from the conditional density of the current state given finite past measurements. Then, one of the two FMS filters, the primary FMS filter or the secondary FMS filter, with different measurement windows is operated selectively according to the presence or absence of uncertainty, to obtain a valid estimate. The primary FMS filter is selected for the nominal system and the secondary FMS filter is selected for the temporarily uncertain system, respectively. A declaration rule is defined to indicate the presence or absence of uncertainty, operate the suitable one from two filters, and then obtain the valid filtered estimate. A test variable for the declaration rule is developed using a chi-square test statistic from the estimation error and compared to a precomputed threshold. In order to verify the proposed selective FMS filtering and compare with the existing FMS filter and the infinite memory structure (IMS) filter, computer simulations are performed for a selection of dynamic systems including a F404 gas turbine aircraft engine and an electric motor. Simulation results confirm that the proposed selective FMS filtering works well for nominal systems as well as temporarily uncertain systems. In addition, the proposed selective FMS filtering is shown to be remarkably better than the IMS filtering for the temporarily uncertain system.

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“…When PF algorithm fails under the harsh conditions mentioned above, the assisting FIR filter operates to recover the main filter from failures. The FIR filter [18][19][20][21][22][23][24][25][26][27] is generally less accurate than the PF in nonlinear state estimation problems; however, it has intrinsic robustness against model uncertainty and bounded-input bounded-output (BIBO) stability. Thus, the FIR filter is appropriate for the role of the assisting filter that operates under harsh conditions.…”

Section: Introductionmentioning

confidence: 99%

“…In the CV model, the process noise covariance Q plays a critical role; however, it is a very uncertain design parameter [28]. Thus, inappropriately selected Q values may worsen localization accuracy [20,26,27]. In cases where state-space models have uncertainties, multiplemodel approaches have been commonly used [16,17,28].…”

Section: Introductionmentioning

confidence: 99%

Multiple-model hybrid particle/FIR filter for indoor localization using wireless sensor networks

Pak

2020

IEICE Electron. Express

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This letter proposes a new state estimator called the multiplemodel hybrid particle/finite-impulse-response (FIR) filter (MMHPFF) for indoor localization using wireless sensor networks. In the proposed hybrid filtering algorithm, the multiple-model particle filter has the role of the main filter, and it overcomes uncertain process noise problems arising from the use of the constant velocity (CV) motion model in indoor localization. In addition, the multiple-model FIR filter is used as an assisting filter to overcome particle filter failures owing to the sample impoverishment phenomenon. Indoor localization simulations demonstrated that the proposed MMHPFF is more accurate and reliable than conventional algorithms.

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Extended least square unbiased FIR filter for target tracking using the constant velocity motion model

Cited by 18 publications

References 16 publications

Study the Characteristics of FIR filter Based on Modified Kaiser Window

Study the Characteristics of FIR filter Based on Modified Kaiser Window

Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems

Multiple-model hybrid particle/FIR filter for indoor localization using wireless sensor networks

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