A Novel Smooth Variable Structure Smoother for Robust Estimation

Chen, Yu; Xu, Luping; Yan, Baixing; Liu, Cong

doi:10.3390/s20061781

Cited by 6 publications

(5 citation statements)

References 56 publications

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“…This is one of the possible representations of the smoother error dynamics. On rearranging, this does not yield a form similar to Equation (11), and hence the dynamics of the smoother error over increasing lags are analyzed using the trace of error-covariance of the smoother directly. Simulations carried out over different problems indicate that during the initial few lags, the smoother attempts to capture information from previous lagged estimates, and once this estimate converges, higher-lag estimates are not very helpful.…”

Section: Adaptive Lag Selection Mechanismmentioning

confidence: 99%

“…Chen et al. proposed a smooth variable structure smoother that is based on a smooth variable structure filter and aims at overcoming the uncertainties in the system model [ 11 ]. Recently, Alenlov and Olsson proposed a design framework by which the particle filter’s lineage degeneracy problem can be solved using a fixed-lag smoother [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Kim proposed finite memory-based fixed-lag smoothers that use only the most recent finite measurements in the window, which is less complex than the traditional fixed-lag smoother [10]. Chen et al proposed a smooth variable structure smoother that is based on a smooth variable structure filter and aims at overcoming the uncertainties in the system model [11]. Recently, Alenlov and Olsson proposed a design framework by which the particle filter's lineage degeneracy problem can be solved using a fixed-lag smoother [12].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Lag Smoother for State Estimation

Poddar

2022

Sensors

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Fixed-lag smoothing has been used across different disciplines for offline analysis in many applications. With rising computational power and parallel processing architectures, fixed-lag smoothers are increasingly integrated into online processing system with small delays. This delay is directly related to the lag-length used in system design, which needs to be chosen appropriately. In this work, an adaptive approach is devised to choose an appropriate lag-length that provides a good trade-off between accuracy and computational requirements. The analysis shown in this paper for the error dynamics of the fixed-lag smoother over the lags helps in understanding its saturation over increasing lags. In order to provide the empirical results, simulations are carried out over a second-order Newtonian system, single-axis attitude estimation, Van der Pol’s oscillator, and three-axis attitude estimation. The simulation results demonstrate the performance achieved with an adaptive-lag smoother as compared to a fixed-lag smoother with very high lag-length.

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Section: Adaptive Lag Selection Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Lag Smoother for State Estimation

Poddar

2022

Sensors

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“…7, the OSPA distance values achieved by the proposed method at the 11 th , 21 th , and 31 th scans are higher than those in the other scans. This fact can be attributed to the least-square criterion adopted in trajectory smoothing: A larger positional error is unavoidable when the target is maneuvering because of the mismatch between the smoothing model and actual target motion state [48]. The smoothing model trades off positional error between target in straight line and in maneuvering.…”

Section: A Computer Simulationsmentioning

confidence: 99%

A Track-Before-Detect Algorithm for UWB Radar Sensor Networks

Yan

Giorgetti

2021

Preprint

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Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by multistatic radar network with localization errors of 25.4 cm and 19.7 cm, and detection rate of the two targets is 88.75%, and no false alarm trajectory.

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“…The ISVSF proposed here also can be applied in systems with states undergoing sudden changes. However, our former work [41] proposed a non real-time smoother, which is also applied in maneuvering target tracking. The rest of the paper is structured as follows: The review of the SVSF will be presented in Section 2, the elaborate explanation of the proposed ISVSF algorithm in Section 3, the simulation process and an analysis in Section 4 and conclusions of the main findings in this paper in Section 5.…”

Section: Introductionmentioning

confidence: 99%

An Improved Smooth Variable Structure Filter for Robust Target Tracking

Chen

Wang

et al. 2021

Remote Sensing

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As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.

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A Novel Smooth Variable Structure Smoother for Robust Estimation

Cited by 6 publications

References 56 publications

Adaptive Lag Smoother for State Estimation

Adaptive Lag Smoother for State Estimation

A Track-Before-Detect Algorithm for UWB Radar Sensor Networks

An Improved Smooth Variable Structure Filter for Robust Target Tracking

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