Consider unobservable uncertain parameters using radio beacon navigation during Mars entry

Lou, Taishan; Fu, Huimin; Zhang, Yongbo; Wang, Zhihua

doi:10.1016/j.asr.2014.11.016

Cited by 29 publications

(20 citation statements)

References 23 publications

(34 reference statements)

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“…However, the models of practical engineerings, such as an induction motor, always have model uncertainties, and the state estimation accuracy may remarkably decrease or even lead to diverging. To degrade the adverse effects of uncertain model parameters, many technologies are proposed, such as augmented filtering, consider approach [14], H  filtering [6], and desensitized filtering [15]. The desensitized filtering is proposed based on a desensitized optimal control technique, and it is used to reduce state estimation error sensitivity respected to uncertain parameters [16].…”

Section: Introductionmentioning

confidence: 99%

“…There are desensitized extended Kalman filter [16], desensitized divided difference filter [17], desensitized unscented Kalman filtering [18], desensitized cubature Kalman filtering [19] and desensitized ensemble new Kalman filtering [20]. Then, Lou redefined sensitivities of state estimation error and redesigned a novel desensitized cost function to obtain an analytical form solution of a gain matrix, and saved the computational time [14,21]. Lou and Ishihara et al respectively considered the selecting problem of the sensitivity-weighting matrix, and given some suggestions about this problem [22,23].…”

Section: Introductionmentioning

confidence: 99%

“…Lou and Ishihara et al respectively considered the selecting problem of the sensitivity-weighting matrix, and given some suggestions about this problem [22,23]. Now, the desensitized filters are applied in many fields, such as Mars explore navigation [14,17,24], asteroid rendezvous navigation [16], hovering helicopter [19], induction motor [20,23,25].…”

Section: Introductionmentioning

confidence: 99%

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Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

Lou¹,

Han²,

Yang³

et al. 2020

Preprint

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To improve the state estimation accuracy of nonlinear induction motor with uncertain parameters, a robust desensitized rank Kalman filtering (DRKF) is proposed to reduce state estimation error sensitivities to uncertain parameters. A new sensitivity function is defined, and a novel desensitized cost function for the deterministic sampling methods is designed to obtain an optimal gain matrix. The sensitivity propagation is summarized for deterministic sampling methods. Based on the rank sample rule, the sensitivity propagation method is given, and the DRKF algorithm is derived. Two dynamic behaviors of the induction motor with two uncertain stator and rotor resistances are simulated to demonstrate that the proposed DRKF has an excellent performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

Lou¹,

Han²,

Yang³

et al. 2020

Preprint

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show abstract

“…The IMU provides three components of acceleration for the navigation system. The UHF radio provides communication between the entry vehicle and the orbiting satellites or surface beacons (e.g., a preset fixed beacon or a previous Mars lander) and measures the distance between the vehicle and an orbiter or a surface beacon for the navigation system [12,18,19,34]. At present, there are only three available Mars orbiters to provide radio communications, which are Mars Reconnaissance Orbiter (MRO), Mars Odyssey (MO), and Mars Express (MEX), and no surface radio beacons are preset on Mars.…”

Section: Measurement Modelsmentioning

confidence: 99%

“…Yu et al primarily analyzed the optimization of the Mars entry navigation beacon configuration based on the system observability by using radiometric measurements [15,16]. Lou et al considered the uncertain model parameters and the measurement biases by using consider Kalman filter (also called Schmidt-Kalman filter) [17][18][19] and analyzed the sensitivity of the parameter uncertainties based on the robust desensitized extended Kalman filter [20] under the radio beacon navigation schemes during Mars atmospheric entry.…”

Section: Introductionmentioning

confidence: 99%

Mars Atmospheric Entry Integrated Navigation with Partial Intermittent Measurements

Lou

Wang

Ding

et al. 2017

International Journal of Aerospace Engineering

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Signal degradation suffered by the vehicle is a combination brownout and blackout during Mars atmospheric entry. The communications brownout means that signal fades and blackout means that the signal is lost completely. The communications brownout and blackout periods are analyzed and predicted with an altitude and velocity profiles. In the brownout period, the range measurements between the vehicle and the orbiters are modeled as intermittent measurements with the radio signal arrival probabilities, which are distributed as a Rayleigh distribution of the electron number density around the entry vehicle. A new integrated navigation strategy during the Mars atmospheric entry phase is proposed to consider the probabilities of the radio measurements in the communications brownout and blackout periods under the IMU/beacon scenario based on the information filter with intermittent measurements. Numerical navigation simulations are designed to show the performance of the proposed navigation strategy under the integrated navigation scenario.

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Mars entry navigation under biases based on adaptive Huber divided difference filter

Xia

Zhu

Cui

et al. 2022

Adaptive Control & Signal

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Summary To address the problems of unknown disturbances and measurement outliers at Mars entry phase, three integrated navigation schemes are proposed in this study by complying with divided difference filters, that is, integrated navigation schemes based on the Huber first‐order divided difference filter (HDDF1), adaptive Huber first‐order divided difference filter (AHDDF1), and adaptive Huber second‐order divided difference filter (AHDDF2). The novel versions of predictive state estimation error covariance (PSEEC) and measurement noise covariance (MNC) are deduced by adopting the ρ function of the Huber case to modify the cost function of the standard Kalman filter. To be specific, the HDDF1 is derived by embedding the novel PSEEC and MNC in the general first‐order divided difference filter. To more specifically enhance the performance of the filter under significant biases, an adaptive forgetting factor is introduced in the HDDF1. On that basis, the AHDDF1 is derived. Likewise, the AHDDF2 is yielded. The numerical simulations are presented to verify the effectiveness of the proposed navigation schemes.

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Consider unobservable uncertain parameters using radio beacon navigation during Mars entry

Cited by 29 publications

References 23 publications

Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

Robust State Estimation of Induction Motor using Desensitized Rank Kalman Filter

Mars Atmospheric Entry Integrated Navigation with Partial Intermittent Measurements

Mars entry navigation under biases based on adaptive Huber divided difference filter

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