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Multi-Source Interactive Resilient Fusion Algorithm Based on RIEKF
Signal Processing · Submitted 2 Mar 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experimental results show that the algorithm presented in this paper, without using any sensor fault diagnosis and isolation logic, without setting any complex detection timing and thresholds, demonstrates a resilience advantage, thereby enhancing the adaptability of the state estimation system in complex environments.
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As the number of heterogeneous redundant sensors on unmanned aerial vehicle (UAV) increases, onboard sensors require a more rational and efficient credibility evaluation system and a resilient fusion framework to achieve the essence of seamless sensor group switching. A simple and efficient sensor credibility evaluation system is proposed to guide the selection of the optimal multi-source sensor submodel combination, thereby providing key model prior knowledge for multi-source resilient fusion. Furthermore, a multi-model interactive resilient fusion framework based on RIEKF is proposed, utilizing the defined sensor credibility indexes to guide the design of the model transition probability matrix, thereby reducing the sensitivity of submodel weights to fusion stability and solving the problem of the model transition matrix lacking a basis for adjustment. Model weights are updated in real time through credibility prior information and submodel posterior probabilities, thus leveraging the adaptive resilience advantage between models to achieve seamless switching between submodels in complex environments. Experimental results show that the algorithm presented in this paper, without using any sensor fault diagnosis and isolation logic, without setting any complex detection timing and thresholds, demonstrates a resilience advantage, thereby enhancing the adaptability of the state estimation system in complex environments.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | As the number of heterogeneous redundant sensors on unmanned aerial vehicle (UAV) increases, onboard sensors require a more rational and efficient credibility evaluation system and a resilient fusion framework to achieve the essence of seamless sensor group switching. | background 0.99 | 0.07 | 0.05 | asserted 0.97 |
| s1 | A simple and efficient sensor credibility evaluation system is proposed to guide the selection of the optimal multi-source sensor submodel combination, thereby providing key model prior knowledge for multi-source resilient fusion. | contribution 0.79 | 0.90 | 0.07 | asserted 1.00 |
| s2 | Furthermore, a multi-model interactive resilient fusion framework based on RIEKF is proposed, utilizing the defined sensor credibility indexes to guide the design of the model transition probability matrix, thereby reducing the sensitivity of submodel weights to fusion stability and solving the problem of the model transition matrix lacking a basis for adjustment. | method 0.59 | 0.90 | 0.12 | asserted 1.00 |
| s3 | Model weights are updated in real time through credibility prior information and submodel posterior probabilities, thus leveraging the adaptive resilience advantage between models to achieve seamless switching between submodels in complex environments. | method 0.97 | 0.75 | 0.14 | asserted 1.00 |
| s4 | Experimental results show that the algorithm presented in this paper, without using any sensor fault diagnosis and isolation logic, without setting any complex detection timing and thresholds, demonstrates a resilience advantage, thereby enhancing the adaptability of the state estimation system in complex environments. | result 0.94 | 0.95 | 0.79 | asserted 1.00 |
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Original abstract
As the number of heterogeneous redundant sensors on unmanned aerial vehicle (UAV) increases, onboard sensors require a more rational and efficient credibility evaluation system and a resilient fusion framework to achieve the essence of seamless sensor group switching. A simple and efficient sensor credibility evaluation system is proposed to guide the selection of the optimal multi-source sensor submodel combination, thereby providing key model prior knowledge for multi-source resilient fusion. Furthermore, a multi-model interactive resilient fusion framework based on RIEKF is proposed, utilizing the defined sensor credibility indexes to guide the design of the model transition probability matrix, thereby reducing the sensitivity of submodel weights to fusion stability and solving the problem of the model transition matrix lacking a basis for adjustment. Model weights are updated in real time through credibility prior information and submodel posterior probabilities, thus leveraging the adaptive resilience advantage between models to achieve seamless switching between submodels in complex environments. Experimental results show that the algorithm presented in this paper, without using any sensor fault diagnosis and isolation logic, without setting any complex detection timing and thresholds, demonstrates a resilience advantage, thereby enhancing the adaptability of the state estimation system in complex environments.
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The system state $^(m_j)χ_k-1$ and error state covariance $^(m_j)P_k-1$ of the $j$th submodel Sensor measurements $^(i)Z_k$ in the $j$th submodel System state transition matrix $Φ_t,t+1$, predicted state covariance matrix $Q_0$, measurement covariance matrix $^(i)R_k$, noise excitation matrix $^(i)G_k$ The system state $^(m_j)χ_k$ and error state covariance $^(m_j)P_k$ of the $j$th submodel Sensor measurement residual statistic $^(i)q_k$ in the $j$th submodel Right-invariant error state one-step prediction: $dχ^R_t,t+1 ⟵Φ_t,t+1 dχ^R_t$ One-step covariance update: $P_t,t+1 ⟵Φ_t,t+1 P_t Φ_t,t+1^⊤+ G_0 Q_0 G_0^⊤$ Compute KF gain: $K_t+1 ⟵P_t,t+1 H^R,⊤_t+1(H^R_t+1 P_t,t+1 H^R,⊤_t+1 )^-1$ State update: $dχ^R_t+1 ⟵dχ^R_t,t+1 + K_t+1 (z_k - H^R_t+1 dχ^R_t,t+1 )$ Covariance update: $P_t+1 ⟵(I - K_t+1 H^R_t+1)P_t,t+1$ The Invariant Extended Kalman Filter (IEKF) algorithm has gained widespread att
Semi-Aerodynamic Model Aided Invariant Kalman Filtering for UAV Full-State Estimation · Sensor Submodel Update Based on Right-Invariant Kalman Filter · 0.80
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