基于多源信息融合的车辆航姿估计技术研究
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摘要
随着道路交通的行驶高速化和车流密集化，车辆的行驶安全问题已成为全球共同关注的焦点。车辆的安全控制系统通过实时获取车体角度信息对发动机及制动系统进行主动干预控制，能有效避免因车辆侧滑、侧翻和侧向碰撞引起的道路交通事故。在该类车辆安全控制系统中，实时准确地获取车辆行驶过程中的航向姿态信息是首要解决的问题。由于车辆环境的各种电磁干扰以及传感器本身的测量误差问题，单独采用某一种传感器难以得到准确的航姿信息。针对上述问题，论文对车辆航姿估计系统中的航姿更新解算、传感器误差建模与信息融合等关键技术进行了深入分析与研究，在统一参考坐标系框架下，采用粒子滤波融合算法对MARG（Magnetic，Angular rate and Gravity）中陀螺、磁强计与加速度计的样本信息进行有效融合，估计并解算出车辆最优航姿信息。论文重点针对粒子滤波融合算法的样本集贫化与粒子退化问题，提出了采样、建议分布与重采样三个阶段的优化策略，以提高粒子滤波的单变量系统模型状态估计精度以及车辆多维航姿系统的估计精度。论文的主要工作有以下4个方面：
1.研究了一种基于改进入侵式野草优化的粒子滤波采样策略（MIWOSIR）。首先，改进入侵式野草优化算法（MIWO）是通过调整入侵式野草优化算法的种群迭代步长来提高算法的寻优精度与收敛速度。其次，将MIWO融入到粒子滤波采样阶段，使粒子以自身权值在附近搜索空间动态繁衍，优胜劣汰出最优粒子集，指导粒子向后验概率的局部高似然域运动，增加了采样后样本的多样性。论文分析证明了MIWO算法的Markov全局收敛性，并通过Sphere、Rosenbrock、Griewank和Rastrigin四种测试函数的对比实验，验证了MIWO算法相比于入侵式野草优化算法具有更好的寻优速度与寻优精度。最后，通过单变量非静态增长模型与强非线性空间模型进行对比实验，验证了MIWOSIR算法采样后粒子多样性明显提高，相比其他粒子滤波优化算法有效减缓样本集贫化问题，在不影响算法运行效率的同时提高了状态估计精度。
2.构造了一种基于Fourier-Hermite RTS的粒子滤波建议分布函数。该方法采用一簇Fourier-Hermite RTS平滑算法构造粒子的重要性密度函数，主要适用于系统状态的离线估计。算法通过Fourier-Hermite Kalman前向滤波将系统方程线性化至指定阶Taylor展式，以提高非线性系统模型的数学精度。且在后向平滑递推过程中充分考虑所有量测信息，增加了重要性密度与后验概率密度的重叠度。此外，将随机摄动理论引入重采样，以改善重采样后粒子多样性。通过分时恒定模型与强非线性模型的对比仿真，验证了该算法在长时间不变量与强非线性状态的动态跟踪中具有较强的跟踪能力。
3.提出了一种基于遗传算法和入侵式野草优化的粒子滤波重采样与采样策略。遗传重采样过程中，根据有效粒子数量选择较优粒子个体，以特定区间随机数做交换率进行样本交叉繁殖，使用马尔可夫链蒙特卡罗移动加高斯白噪声做粒子变异繁殖并使用快速MH（Metropolis-Hastings）抽样算法选取粒子，使粒子有效分布到状态后验概率的局部模式上，维持重采样后粒子的多模式。并将入侵式野草优化应用于采样过程，以保持采样粒子随机性与有效性。通过单变量非静态增长模型与强非线性空间模型的对比仿真，该策略采样后的粒子权值分布明显提高，有效克服了粒子退化问题，具有较高的状态估计精度。
4.构建了一种基于多源信息融合的车辆航姿估计系统。系统采用MARG传感器测量车辆的旋转运动角速率、直线运动加速度与地磁信息，将地理坐标系作为时空基准坐标系，建立了基于低运算复杂度MRPs（Modified Rodrigues Parameters）参数的车辆运动学模型，并由乘性MRPs误差矢量与角速率误差矢量建立状态误差模型，由加速度误差矢量和地磁误差矢量建立观测误差模型，搭建以改进入侵式野草优化粒子滤波模型为核心的带反馈最优融合估计结构，实现非线性航姿观测信息的实时最优关联。融合过程中，将陀螺的连续测量值作为车辆的短期航姿基准，加速度计和磁强计的测量值作为车辆的长期航姿辅助校正信息，利用改进入侵式野草优化粒子滤波算法计算陀螺漂移估计误差以及航姿估计误差。通过车辆航姿估计的模拟对比实验与实车对比试验，结果表明，相比基于EKF的车辆航姿估计方法和基于SIR的车辆航姿估计方法，论文所提方法具有较高的航姿精度。
数值仿真实验与车辆航姿估计试验结果表明，论文提出的基于采样、建议分布与重采样优化的粒子滤波算法在克服样本集贫化与粒子退化方面具有显著效果，提高了算法的状态估计精度与车辆航姿估计精度。该研究成果对于基于多源信息融合的车辆航姿估计系统的工程应用与推广具有重要参考价值。
With the development of society economy, the highway traffic tends to drive highspeed and flow intensively, and the vehicle safety issue is focused worldwide. Thevehicle stability control system can effectively avoid accidents caused by vehiclerollover, sideslip and side collision, which actively intervenes and controls the engineand the brake system. However, obtaining the vehicle attitude angle is a key issue todetermine the accuracy and stability of the control system. Due to the environmentnoise and the measurement error, it can not obtain the optimal attitude angle by usingonly a sensor. In allusion to the problems mentioned above, the paper uses themulti-sensor information fusion method to provide the accurate vehicle attitudes. Thekey techniques such as the dynamic attitude solution, the error modeling of sensor andthe information fusion method are discussed in depth. Within the framework of theproposed particle filter, the output information from the gyro, the accelerometer and themagnetometer are fusioned under the unified reference coordinate system. The paperfocuses on the sample dilution and the degeneracy problem of particle filter andproposes the optimized method on the sampling, the proposal distribution and theresampling to improve the estimated accuracy of the state and the vehicle attitude. Maincontributions of the paper are shown as follows:
1. The MIWOSIR is suggested in this paper. Firstly, the MIWO is presented that isbeneficial to get the optimal value in the global scope by adding an envelope of thestandard deviation for improving the convergence rate of the IWO. Then the MIWOSIRincorporates the MIWO into sampling process of the particle filter, enables the particlesto reproduce dynamically by their own fitness in nearby space and optimize the particlepopulation of optimal weight. Though MIWOSIR, particles are moved towards regionswhere they had larger value of posterior density. The Markov chain is applied forproving the global convergence of the MIWO algorithm and4benchmark functions areused to verify the strong global search ability. Simulation results demonstrate thatMIWOSIR has higher estimation accuracy and operational efficiency than otheroptimized particle filters.
2. The Fourier-Hermite Rauch-Tung-Striebel based particle filter is proposed. Inthe algorithm, a bank of Fourier-Hermite RTS smoother is employed for generating theimportance density function to impel the probabilities approximating to the true state forobtaining prediction samples with higher precision. Because all observation informationis introduced into the state transition function, consequently the suggested proposaldistribution has much more overlap regions with the real posterior distribution. Besidesthat, the stochastic perturbation re-sampling is introduced that can ameliorate the particle diversity after re-sampling and reduce the computation in a certain extent. Thenew algorithm is tested on three classic non-linear and non-Gaussian models withpromising results compared with existing ones.
3. The particle filter using genetic algorithm and invasive weed optimization ispresented. In the re-sampling process, the degeneracy problem is relieved by applying aselection operator of genetic algorithm to choose the optimal particles iteratively, andthen the crossover and mutation operation are implemented for the particles which arenot selected so that the diversity of particles is maintained. Meanwhile, IWO makes thenewest observations into sampling process, and optimizes the particle population withthe optimal weight. Simulation results demonstrate that the proposed method has higherestimation accuracy.
4. The paper proposes the heading and attitude determination of the vehicle usingmulti-source information fusion method. It uses MARG sensor to measure the angularrate, acceleration and geomagnetic information of the vehicle, and regardes the localCartesian coordinate system as the reference coordinate system. The state error model isbuilt with the multiplicative MRPs and the angular rate error vector, the observationerror model is built with the the acceleration vector and geomagnetic vector, and anoptimal estimation fusion structure with feedback by using the improved invasive weedoptimized particle filter is built. The system takes the continuous measurement of thegyro as the attitude reference information of vehicle in short-term, simultaneouslymakes the measurement of the accelerometer and the magnetometer be the attitudecorrection information of the gyro in long term. The improved invasive weed optimizedparticle filter is used for correcting the attitude error and the gyro drift to establish anaccurate model of the attitude determination estimator, which achieve an optimalassociation of the nonlinear attitude observation. By comparing with the traditionalattitude estimation methods in experiment, the proposed method improves the vehicleattitude estimation accuracy.
The numerical simulations and the experiments for vehicle heading and attitudeestimation show that the optimized strategies of the paritlce filter in this papereffectively eliminate the particle degeneration and particle impoverishment, whichimproves the estimation accuracy of the vehicle heading and attitude. It has animportant reference for application and popularization of the vehicle heading andattitude determination system using multi-source information fusion.
1.研究了一种基于改进入侵式野草优化的粒子滤波采样策略（MIWOSIR）。首先，改进入侵式野草优化算法（MIWO）是通过调整入侵式野草优化算法的种群迭代步长来提高算法的寻优精度与收敛速度。其次，将MIWO融入到粒子滤波采样阶段，使粒子以自身权值在附近搜索空间动态繁衍，优胜劣汰出最优粒子集，指导粒子向后验概率的局部高似然域运动，增加了采样后样本的多样性。论文分析证明了MIWO算法的Markov全局收敛性，并通过Sphere、Rosenbrock、Griewank和Rastrigin四种测试函数的对比实验，验证了MIWO算法相比于入侵式野草优化算法具有更好的寻优速度与寻优精度。最后，通过单变量非静态增长模型与强非线性空间模型进行对比实验，验证了MIWOSIR算法采样后粒子多样性明显提高，相比其他粒子滤波优化算法有效减缓样本集贫化问题，在不影响算法运行效率的同时提高了状态估计精度。
2.构造了一种基于Fourier-Hermite RTS的粒子滤波建议分布函数。该方法采用一簇Fourier-Hermite RTS平滑算法构造粒子的重要性密度函数，主要适用于系统状态的离线估计。算法通过Fourier-Hermite Kalman前向滤波将系统方程线性化至指定阶Taylor展式，以提高非线性系统模型的数学精度。且在后向平滑递推过程中充分考虑所有量测信息，增加了重要性密度与后验概率密度的重叠度。此外，将随机摄动理论引入重采样，以改善重采样后粒子多样性。通过分时恒定模型与强非线性模型的对比仿真，验证了该算法在长时间不变量与强非线性状态的动态跟踪中具有较强的跟踪能力。
3.提出了一种基于遗传算法和入侵式野草优化的粒子滤波重采样与采样策略。遗传重采样过程中，根据有效粒子数量选择较优粒子个体，以特定区间随机数做交换率进行样本交叉繁殖，使用马尔可夫链蒙特卡罗移动加高斯白噪声做粒子变异繁殖并使用快速MH（Metropolis-Hastings）抽样算法选取粒子，使粒子有效分布到状态后验概率的局部模式上，维持重采样后粒子的多模式。并将入侵式野草优化应用于采样过程，以保持采样粒子随机性与有效性。通过单变量非静态增长模型与强非线性空间模型的对比仿真，该策略采样后的粒子权值分布明显提高，有效克服了粒子退化问题，具有较高的状态估计精度。
4.构建了一种基于多源信息融合的车辆航姿估计系统。系统采用MARG传感器测量车辆的旋转运动角速率、直线运动加速度与地磁信息，将地理坐标系作为时空基准坐标系，建立了基于低运算复杂度MRPs（Modified Rodrigues Parameters）参数的车辆运动学模型，并由乘性MRPs误差矢量与角速率误差矢量建立状态误差模型，由加速度误差矢量和地磁误差矢量建立观测误差模型，搭建以改进入侵式野草优化粒子滤波模型为核心的带反馈最优融合估计结构，实现非线性航姿观测信息的实时最优关联。融合过程中，将陀螺的连续测量值作为车辆的短期航姿基准，加速度计和磁强计的测量值作为车辆的长期航姿辅助校正信息，利用改进入侵式野草优化粒子滤波算法计算陀螺漂移估计误差以及航姿估计误差。通过车辆航姿估计的模拟对比实验与实车对比试验，结果表明，相比基于EKF的车辆航姿估计方法和基于SIR的车辆航姿估计方法，论文所提方法具有较高的航姿精度。
数值仿真实验与车辆航姿估计试验结果表明，论文提出的基于采样、建议分布与重采样优化的粒子滤波算法在克服样本集贫化与粒子退化方面具有显著效果，提高了算法的状态估计精度与车辆航姿估计精度。该研究成果对于基于多源信息融合的车辆航姿估计系统的工程应用与推广具有重要参考价值。
With the development of society economy, the highway traffic tends to drive highspeed and flow intensively, and the vehicle safety issue is focused worldwide. Thevehicle stability control system can effectively avoid accidents caused by vehiclerollover, sideslip and side collision, which actively intervenes and controls the engineand the brake system. However, obtaining the vehicle attitude angle is a key issue todetermine the accuracy and stability of the control system. Due to the environmentnoise and the measurement error, it can not obtain the optimal attitude angle by usingonly a sensor. In allusion to the problems mentioned above, the paper uses themulti-sensor information fusion method to provide the accurate vehicle attitudes. Thekey techniques such as the dynamic attitude solution, the error modeling of sensor andthe information fusion method are discussed in depth. Within the framework of theproposed particle filter, the output information from the gyro, the accelerometer and themagnetometer are fusioned under the unified reference coordinate system. The paperfocuses on the sample dilution and the degeneracy problem of particle filter andproposes the optimized method on the sampling, the proposal distribution and theresampling to improve the estimated accuracy of the state and the vehicle attitude. Maincontributions of the paper are shown as follows:
1. The MIWOSIR is suggested in this paper. Firstly, the MIWO is presented that isbeneficial to get the optimal value in the global scope by adding an envelope of thestandard deviation for improving the convergence rate of the IWO. Then the MIWOSIRincorporates the MIWO into sampling process of the particle filter, enables the particlesto reproduce dynamically by their own fitness in nearby space and optimize the particlepopulation of optimal weight. Though MIWOSIR, particles are moved towards regionswhere they had larger value of posterior density. The Markov chain is applied forproving the global convergence of the MIWO algorithm and4benchmark functions areused to verify the strong global search ability. Simulation results demonstrate thatMIWOSIR has higher estimation accuracy and operational efficiency than otheroptimized particle filters.
2. The Fourier-Hermite Rauch-Tung-Striebel based particle filter is proposed. Inthe algorithm, a bank of Fourier-Hermite RTS smoother is employed for generating theimportance density function to impel the probabilities approximating to the true state forobtaining prediction samples with higher precision. Because all observation informationis introduced into the state transition function, consequently the suggested proposaldistribution has much more overlap regions with the real posterior distribution. Besidesthat, the stochastic perturbation re-sampling is introduced that can ameliorate the particle diversity after re-sampling and reduce the computation in a certain extent. Thenew algorithm is tested on three classic non-linear and non-Gaussian models withpromising results compared with existing ones.
3. The particle filter using genetic algorithm and invasive weed optimization ispresented. In the re-sampling process, the degeneracy problem is relieved by applying aselection operator of genetic algorithm to choose the optimal particles iteratively, andthen the crossover and mutation operation are implemented for the particles which arenot selected so that the diversity of particles is maintained. Meanwhile, IWO makes thenewest observations into sampling process, and optimizes the particle population withthe optimal weight. Simulation results demonstrate that the proposed method has higherestimation accuracy.
4. The paper proposes the heading and attitude determination of the vehicle usingmulti-source information fusion method. It uses MARG sensor to measure the angularrate, acceleration and geomagnetic information of the vehicle, and regardes the localCartesian coordinate system as the reference coordinate system. The state error model isbuilt with the multiplicative MRPs and the angular rate error vector, the observationerror model is built with the the acceleration vector and geomagnetic vector, and anoptimal estimation fusion structure with feedback by using the improved invasive weedoptimized particle filter is built. The system takes the continuous measurement of thegyro as the attitude reference information of vehicle in short-term, simultaneouslymakes the measurement of the accelerometer and the magnetometer be the attitudecorrection information of the gyro in long term. The improved invasive weed optimizedparticle filter is used for correcting the attitude error and the gyro drift to establish anaccurate model of the attitude determination estimator, which achieve an optimalassociation of the nonlinear attitude observation. By comparing with the traditionalattitude estimation methods in experiment, the proposed method improves the vehicleattitude estimation accuracy.
The numerical simulations and the experiments for vehicle heading and attitudeestimation show that the optimized strategies of the paritlce filter in this papereffectively eliminate the particle degeneration and particle impoverishment, whichimproves the estimation accuracy of the vehicle heading and attitude. It has animportant reference for application and popularization of the vehicle heading andattitude determination system using multi-source information fusion.
引文
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