An innovative navigation scheme of powered descent phase for Mars pinpoint landing

Qin, Tong; Zhu, Shengying; Cui, Pingyuan; Gao, Ai

doi:10.1016/j.asr.2014.07.009

Cited by 18 publications

(5 citation statements)

References 27 publications

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“…The position of the surface beacon is assumed as (3,396,000m, 877,800m, 177,700m) and its velocity is assumed to zero in the Mars inertial frame. The initial position of orbiting beacon is optimally preselected based on the observability analysis (Qin et al, 2014).The Mars gravity acceleration is assumed to be a constant .The altitude error of the MCAV is 0.5m, and the velocity errors are all 0.03m/s. For the radio range measurement, the range errors are 50m.…”

Section: Measurement Updatementioning

confidence: 99%

“…Li et al proposed a Miniature Coherent Altimeter and Velocimeter (MCAV) and Inertial Measurement Unit (IMU) integrated navigation scheme for the powered descent phase to correct the inertial bias and drift by augmenting them into the state vector (Li et al, 2010). Qin et al adopted the measurement information of the integrated Doppler radar and the distance and velocity information between the Mars obiter and the vehicle to correct the vehicle horizontal position (Qin et al, 2014). Some other inertial navigation schemes based on the Doppler radar, the onboard light detection and ranging or the vision-aided optical sensors are tested to improve the performance of the spacecraft during the powered descent phase and to support the NASA's autonomous pinpoint landing project (Janschek et al, 2006;Busnardo et al, 2011;Amzajerdian et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…In general, the inertial biases coming from the IMU can be augmented into the state vector to estimate with the state, and some good results can be obtained (Li et al, 2010;Qin et al, 2014). Lou et al considered IMU biases, position biases of the radio beacons and the range biases for the entry vehicle and the onboard satellite during Mars entry by incorporating statistics of the biases into the system formulation, instead of only estimating them (Lou et al, 2015a), and then considered the unobservable uncertain parameters during Mars entry by using consider Kalman filter (Lou et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

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MACV/Radio integrated navigation for Mars powered descent via robust desensitized central difference Kalman filter

Lou

Liu

Pan

et al. 2017

Advances in Space Research

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An innovative integrated navigation scheme based on MCAV/Radio measurement information during Mars powered descent phase, and a robust desensitized central difference Kalman filter (DCDKF) for systems with uncertain parameters or biases are proposed to improve the navigation descent accuracy. Based on the altitude and velocity information of the Miniature Coherent Altimeter and Velocimeter (MCAV),the radio-range information is added into the integrated navigation system to correct the horizontal position error of the vehicle during the Mars powered descent phase. Based the central difference transform, the sensitivity propagation of the state estimate errors in the DCDKF is described, and a designed desensitized cost function is minimized to obtain the gain matrix of the DCDKF. The performances of the innovative navigation scheme and the proposed DCDKF are all demonstrated by two Monte Carlo simulations with the Inertial Measurement Unit biases during the Mars power descent phase.

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Section: Measurement Updatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MACV/Radio integrated navigation for Mars powered descent via robust desensitized central difference Kalman filter

Lou

Liu

Pan

et al. 2017

Advances in Space Research

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“…Safe and soft pinpoint landing (within 100 m at

from the target site [ 1 ]) on an extraterrestrial body has been a central objective since the beginning of human space exploration missions. To date, many manned and unmanned landers have successfully landed on moons (Apollo and Chang’E), planets (Curiosity and Opportunity) and asteroids (Rosetta and Hayabusa-2), with landing footprints in the scale of kilometers [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Radio Beacon/IMU/Altimeter Integrated Localization Scheme with Uncertain Initial Beacon Locations for Lunar Pinpoint Landing

Luo³

et al. 2020

Sensors

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As a growing number of exploration missions have successfully landed on the Moon in recent decades, ground infrastructures, such as radio beacons, have attracted a great deal of attention in the design of navigation systems. None of the available studies regarding integrating beacon measurements for pinpoint landing have considered uncertain initial beacon locations, which are quite common in practice. In this paper, we propose a radio beacon/inertial measurement unit (IMU)/altimeter localization scheme that is sufficiently robust regarding uncertain initial beacon locations. This scheme was designed based on the sparse extended information filter (SEIF) to locate the lander and update the beacon configuration at the same time. Then, an adaptive iterated sparse extended hybrid filter (AISEHF) was devised by modifying the prediction and update stage of SEIF with a hybrid-form propagation and a damping iteration algorithm, respectively. The simulation results indicated that the proposed method effectively reduced the error in the position estimations caused by uncertain beacon locations and made an effective trade-off between the estimation accuracy and the computational efficiency. Thus, this method is a potential candidate for future lunar exploration activities.

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“…However, the errors of the traditional autonomous navigation method, inertial navigation, are of the order of a few kilometres as the navigation errors are always accumulating and the initial errors are hard to correct (Braun and Manning, 2007). It is hard for inertial navigation systems to meet the National Aeronautics and Space Administration (NASA) precise landing requirement that the landing errors are less than 100 m (Qin et al, 2014; Wolf et al, 2004). In 2014, NASA tested the Lander Vision System on the new Mars Lander (Johnson and Golombek, 2012), Mars 2020 Lander Vision System Tested (2016) showed that visual navigation based on feature matching is feasible.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Visual Navigation based on Feature Line Correspondences for Precision Landing

Shao

et al. 2018

J. Navigation

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To satisfy the needs of precise pin-point landing missions in deep space exploration, this paper proposes a method based on feature line extraction and matching to estimate the attitude and position of a lander during the descent phase. Linear equations for a lander's motion parameters are given by using at least three feature lines on the planetary surface and their two-dimensional projections. Then, by taking advantage of Singular Value Decomposition (SVD), candidate solutions are obtained. Lastly, the unique lander's attitude and position relative to the landing site are selected from the candidate solutions. Simulation results show that the proposed algorithm is able to estimate a lander's attitude and position robustly and quickly. Without an extended Kalman filter, the average errors of attitude are less than 1°and the average errors of position are less than 10 m at an altitude of 2,000 m. With an extended Kalman filter, attitude errors are within 0·5°and position errors are within 1 m at an altitude of 247·9 m.

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An innovative navigation scheme of powered descent phase for Mars pinpoint landing

Cited by 18 publications

References 27 publications

MACV/Radio integrated navigation for Mars powered descent via robust desensitized central difference Kalman filter

MACV/Radio integrated navigation for Mars powered descent via robust desensitized central difference Kalman filter

A Distributed Radio Beacon/IMU/Altimeter Integrated Localization Scheme with Uncertain Initial Beacon Locations for Lunar Pinpoint Landing

A Novel Approach to Visual Navigation based on Feature Line Correspondences for Precision Landing

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