Improved Tracking Algorithm for Multiple Targets

Hou, Sheng-Yun; Wu, Chen-Wei; Hung, H.S.; Chang, Shun-Hsyung

doi:10.1109/oceanskobe.2008.4530972

Cited by 4 publications

(8 citation statements)

References 8 publications

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“…where 21 W is the fan core duct outlet flow capacity, 22 W the booster inlet one, 23 W the booster outlet one, 24 W the high pressure compressor (HPC) inlet one, 4 W the combustor outlet one, 41 W the high pressure turbine (HPT) outlet one, 42 W the low pressure turbine (LPT) inlet one, 5 W the LPT outlet one, 9 W the nozzle core duct outlet one, 16 W the fan bypass duct outlet one, 19 W the bypass duct outlet one. Four cooling air streams The CLM in steady state has eight balance equations, seen in Equations (17) and (18), therefore, there are eight guess variables used to determine the engine steady operating condition, which are low pressure spool speed L n , high pressure spool speed H n , fan blade tip pressure ratio  Ft , fan blade root pressure ratio  Fr , booster pressure ratio  B , compressor pressure ratio  C , HPT pressure ratio  HT , and LPT pressure ratio  LT .…”

Section: Turbofan Engine Gas Path Performance Estimationmentioning

confidence: 99%

“…Gross compared the performance of three nonlinear estimators-the extended Kalman filter, the sigma-point Kalman filter, and the particle filter-for global positioning system/inertial navigation system (GPS/INS) sensor fusion for aircraft navigation systems approaches [38]. The EKF algorithm is used as the proposal distribution for PF importance sampling to avoid the particle degeneration in [39][40][41].…”

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confidence: 99%

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Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Lü

Huang

2013

Energies

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Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF), and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring. OPEN ACCESSEnergies 2013, 6 493

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Section: Turbofan Engine Gas Path Performance Estimationmentioning

confidence: 99%

mentioning

confidence: 99%

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Lü

Huang

2013

Energies

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“…where I n is the n × n identity matrix, T s is the inverse of the sampling frequency, 2 and u θ ∈ R 2n is the innovation vector, which is assumed to have zero-mean and covariance matrix [13], [12] …”

Section: System Model and State Equationmentioning

confidence: 99%

“…The other approach is marked by applying tracking algorithms, for example, Kalman filtering (KF), directly to arrival angles [6], [7], [8], [9], [10], [11], [12]. This approach often requires more prior information, including proper statistical modeling, knowledge about the number of sources, and reasonable initial estimates of arrival angles (which could be obtained from MUSIC-based algorithms).…”

Section: Introductionmentioning

confidence: 99%

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DOA tracking via Simultaneous Angle-Source Update (SASU)

Gao

Filos

Dai

2012

Sensor Signal Processing for Defence (SSPD 2012)

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Abstract-This paper focuses on the DOA (direction of arrival) tracking problem with uniform linear arrays. In the literature, there are two common approaches: one is to first use MUSIC based algorithms to estimate the signal subspace and then calculate the arrival angles from it; the other is to use Kalman filtering (KF) techniques to track the angles directly. This paper emphasizes on the fast movement case and hence follows the second approach. The key feature of this work is to simultaneously update the arrival angles and the source signals in the KF step by treating the source signals as a function of the arrival angles. Hence the term Simultaneous Angle-Souce Update (SASU). On the theoretical front, we show the deep connections between SASU and the two common approaches in the literature. On the more practical side, a Newton method is developed to solve the optimization problem in the KF step. Simulations demonstrate that SASU is more robust and accurate than the benchmark algorithms, especially when uncertain and fast moving sources are concerned.

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A Sequential Bayesian Algorithm for DOA Tracking in Time‐Varying Environments

Gao

Filos

et al. 2015

Chin. j. electron.

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Improved Tracking Algorithm for Multiple Targets

Cited by 4 publications

References 8 publications

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach

DOA tracking via Simultaneous Angle-Source Update (SASU)

A Sequential Bayesian Algorithm for DOA Tracking in Time‐Varying Environments

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