AFAKF for manoeuvring target tracking based on current statistical model

Yang, Yongjian; Fan, Xing; Zhang, Zhuo; Wang, Shengda; Jiang, Nan; Huang, Jinke

doi:10.1049/iet-smt.2016.0030

Cited by 16 publications

(17 citation statements)

References 21 publications

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“…α is the aircraft manoeuvring frequency. For specific forms of F k , U k , and q cs refer to [2, 4–6]. σ a 2 is the variance of manoeuvring acceleration complying with the mended Rayleigh distribution:

σ_{a}^{2} = ({, \begin{matrix} 1em4pt \\ \frac{4 - π}{π} {(a_{\max} - {\bar{a}}_{k + 1})}^{2} {\bar{a}}_{k + 1} \geq 0, \\ \frac{4 - π}{π} {(a_{\max} + {\bar{a}}_{k + 1})}^{2} {\bar{a}}_{k + 1} < 0 . \end{matrix})

However, due to the inaccessibility of ā k+ 1 , the following approximation is implied in the CS model [29]:

{\bar{a}}_{k + 1} ≜ E false[a_{k + 1} falsefalse| Z^{k} false] ≃ E false[a_{k} falsefalse| Z^{k} false] ≜ {\hat{a}}_{k},

where Z k is the set of measurements in time 0– k .…”

Section: Analysis Of the Cs Modelmentioning

confidence: 99%

“…However, the CS model has some inherent drawbacks such as the need of pre‐setting prior parameters and the lack of adaptive adjustment capability. Though many improved CS models [4–6] have been proposed, they only focus on mending some parameters. The innate character is not touched.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the adaptive adjustment of the state covariance matrix, state noise covariance matrix, and manoeuvring frequency are all achieved. The simulation results show that the proposed algorithm performs better than the multiple‐fading‐factor SCKF [26] based on the CS model, the SCKF‐STF [28] based on the modified CS model [6] and the interacting‐multiple‐model SCKF (IMM‐SCKF) when faced with manoeuver states. Moreover, the proposed algorithm decreases the runtime by 40% compared with the IMM‐SCKF.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Strong tracking SCKF based on adaptive CS model for manoeuvring aircraft tracking

Zhang

Xie

et al. 2018

IET Radar, Sonar & Navigation

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σ_{a}^{2} = ({, \begin{matrix} 1em4pt \\ \frac{4 - π}{π} {(a_{\max} - {\bar{a}}_{k + 1})}^{2} {\bar{a}}_{k + 1} \geq 0, \\ \frac{4 - π}{π} {(a_{\max} + {\bar{a}}_{k + 1})}^{2} {\bar{a}}_{k + 1} < 0 . \end{matrix})

However, due to the inaccessibility of ā k+ 1 , the following approximation is implied in the CS model [29]:

{\bar{a}}_{k + 1} ≜ E false[a_{k + 1} falsefalse| Z^{k} false] ≃ E false[a_{k} falsefalse| Z^{k} false] ≜ {\hat{a}}_{k},

where Z k is the set of measurements in time 0– k .…”

Section: Analysis Of the Cs Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Strong tracking SCKF based on adaptive CS model for manoeuvring aircraft tracking

Zhang

Xie

et al. 2018

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…Moving source localization and tracking using TDOAs and FDOAs is a challenging problem because of the high nonlinearity inherent in the measurement equations [3][4][5]. Extended Kalman filter (EKF) is known to be the well-known nonlinear estimator for systems with white target state and measurement noises [7][8][9][10][11][12][13]. Its efficiency and ease of implementation have made this estimator an essential tool for a wide class of applications such as signal processing, target tracking, sonar, navigation, wireless communication, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Its efficiency and ease of implementation have made this estimator an essential tool for a wide class of applications such as signal processing, target tracking, sonar, navigation, wireless communication, and so on. But the EKF is easy to encounter divergence problem caused by part linearing using first-order taylor-series in nonlinear system [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%