2017
DOI: 10.1007/s12555-016-0572-y
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Extended least square unbiased FIR filter for target tracking using the constant velocity motion model

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Cited by 18 publications
(17 citation statements)
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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%
“…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%
“…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%
“…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%