An adaptive-gain alpha-beta tracker combined with circular prediction for maneuvering target tracking

Kawase, T.; Tsurunosono, Hideshi; Ehara, Naoki; Sasase, Iwao

doi:10.1002/(sici)1520-6424(199912)82:12<20::aid-ecja3>3.0.co;2-j

Cited by 4 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The coefficient β for the α-β tracker is 0.05 and the obtained value by the criterion based on the best linear track fitted to radar data in a least squares sense [4] . To verify the proposed algorithm, we assume target moves every 0.001 Fig.…”

Section: ⅲ Verificationsmentioning

confidence: 99%

“…The Kalman filter shows excellent tracking performance, when the target dynamics and statistical characteristics of measurement noise are available, which is difficult to obtain in advance [4] . The disadvantage of the Kalman filter is a large amount of computational cost.…”

Section: ⅰ Introductionmentioning

confidence: 99%

“…Because of simplicity, the tracking accuracy of the α-β tracker is not guaranteed and sometimes target is lost when the target trajectory is nonlinear in noisy environment [4] . Also, the performance of the α-β tracker heavily depends on suitable choice of α and β, which make it difficult to be used for maneuvering target with AWGN (Additive White Gaussian Noise) [5] .…”

Section: ⅰ Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tracking Algorithm Based on Moving Slide Window for Manuevering Target

Bae¹,

Lee²,

Jeon³

2016

Journal of the Institute of Electronics and Information Enginee

View full text Add to dashboard Cite

In this paper, we propose a novel tracking algorithm called slide window tracker (SWT) suitable for maneuvering target. To efficiently estimate trajectory of moving target, we adopt a sliding piecewise linear window which includes past trace information. By adjusting the window parameters, the proposed algorithm is to reduce measurement noise and to track fast maneuvering target with little computational increment as compared to α-β tracker. Throughout the computer simulations, we verify outstanding tracking performance of the SWT algorithm in noisy linear and nonlinear trajectories. Also, we show that the SWT algorithm is not sensitive to initial model parameter selection, which gives large degree of freedom in applying the SWT algorithm to unknown time-varying measurement environments.

show abstract

Section: ⅲ Verificationsmentioning

confidence: 99%

Section: ⅰ Introductionmentioning

confidence: 99%

Section: ⅰ Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tracking Algorithm Based on Moving Slide Window for Manuevering Target

Bae¹,

Lee²,

Jeon³

2016

Journal of the Institute of Electronics and Information Enginee

View full text Add to dashboard Cite

show abstract

“…Another geometric approach of implementing a circular turn has been introduced by Kawase et al [17]. The circular prediction is constrained to lie on a circle which is defined from previous measurements, by calculating the center and the radius of the circle.…”

Section: • the Roll Rate (P) Is Zeromentioning

confidence: 99%

“…The prediction of the circular filter and the α-β filter are fused by a weighting scheme proposed by Kawase et al [17]. The weight is defined by the prediction error of each filter at time step k and is used in the prediction for the next time step k + 1.…”

Section: Hybrid Filtersmentioning

confidence: 99%

Circular prediction algorithms-hybrid filters

Tenne

Singh

Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)

View full text Add to dashboard Cite

The focus of this paper is on the development of a geometric algorithm as an alternative to the coordinated turn procedures. The fact that the trajectories of maneuvering targets are always smooth, has been exploited to derive an algorithms, which constrains the predicted position of the target to lie on a circle formed by prior measurements. This filter is then used in conjunction with an α-β filter to arrive at a series of filters which are tested on benchmark trajectories. The performances show promising results for the proposed filters.

show abstract

A Kalman tracker with a turning acceleration estimator

Kawase

Tsurunosono

Ehara

et al. 2000

Electron. Comm. Jpn. Pt. I

Self Cite

View full text Add to dashboard Cite

In tracking systems using phased array antennas, the simple Kalman filter has been employed due to the development of high‐speed computers in recent years. The Kalman filter performs almost perfect tracking when a target takes a straight course. However, the quality of the Kalman filter degrades significantly for maneuvering targets since the statistics of acceleration are assumed to be a white Gaussian process in the filter. The Kalman tracker with a simple input estimator and the two‐stage Kalman estimator have been proposed for maneuvering target tracking that uses the residuals to estimate the target acceleration and that requires no a priori information of the magnitude of the maneuver. However, the estimate of the acceleration might not be accurate in the case that the residues increase dramatically such as the circular turn. In this paper, a Kalman tracker with a turning acceleration estimator is proposed that estimates the turning acceleration directly from the feature of the maneuvering target trajectory. A computer simulation is performed for various target profiles to show the effectiveness of the proposed tracker as compared with conventional trackers. © 2000 Scripta Technica, Electron Comm Jpn Pt 1, 84(1): 1–11, 2001

show abstract

An adaptive-gain alpha-beta tracker combined with circular prediction for maneuvering target tracking

Cited by 4 publications

References 7 publications

Tracking Algorithm Based on Moving Slide Window for Manuevering Target

Tracking Algorithm Based on Moving Slide Window for Manuevering Target

Circular prediction algorithms-hybrid filters

A Kalman tracker with a turning acceleration estimator

Contact Info

Product

Resources

About