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首页> 外文会议>Saint Petersburg International Conference on Integrated Navigation Systems; 20060529-31; Saint Petersburg(RU) >COMPARISON OF KALMAN FILTERING AND PARTICLE FILTERING FOR INS/GPS NAVIGATION
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COMPARISON OF KALMAN FILTERING AND PARTICLE FILTERING FOR INS/GPS NAVIGATION

机译：INS / GPS导航的卡尔曼滤波和粒子滤波的比较

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The Inertial Navigation System (INS) and Global Positioning System (GPS) are widely used navigation systems in a several applications. The main reason for their using is their dimensions and weight and relatively simple implementation in navigation system. The main objective of the INS/GPS integration is to merge information from INS and GPS sensors and to provide estimates of the vehicle states with greater accuracy than information from the individual sensors. For data fusion of these sensors one of the Kalman filter based estimators (EKF, LKF, etc.) is usually used. The paper describes integration of INS and GPS navigation systems using new approach for navigation information processing with efficient particle filtering. Numerical methods known as Monte Carlo methods can be described as statistical simulation methods, where statistical simulation is defined as a method that utilizes sequences of random numbers to perform the simulation. Despite the fact that Monte Carlo methods are known for such a long time only the nowadays progress in technique allows to apply those methods on complex applications. Particle filters (Sequential Monte Carlo Methods) are simulation-based filtering methods where product of the filtering is an empirical approximation of the joint posterior distribution created on the base of samples of the state vector. The aim of particle filtering is to track a variable of interest as it evolves over time, typically with a non-Gaussian probability density function. Although, the Sequential Monte Carlo Methods require huge amount of computing, those methods are more efficient than Kalman Filtering if the system is nonlinear or if probability density function of the errors are non-Gaussian. Integrated systems have been created in simulation environment. Results of data processing using Kalman filter or Particle filter, respectively for various condition of flight have been obtained and compared. The values of the variances when determining object trajectory by described integrated system using Particle filter are approximately five times smaller than values of navigation information from individual sensors. The results of the simulation underline the correctness of the initial hypothesis and the choice of the Particle filtering for integrated navigation systems.
机译：惯性导航系统（INS）和全球定位系统（GPS）在多种应用中被广泛使用。使用它们的主要原因是它们的尺寸和重量以及在导航系统中的相对简单的实现。 INS / GPS集成的主要目标是合并来自INS和GPS传感器的信息，并提供比来自单个传感器的信息更准确的车辆状态估计。对于这些传感器的数据融合，通常使用基于卡尔曼滤波器的估计器之一（EKF，LKF等）。本文介绍了使用新方法对INS和GPS导航系统进行集成的方法，该方法用于具有有效粒子滤波的导航信息处理。可以将称为蒙特卡洛方法的数值方法描述为统计模拟方法，其中统计模拟定义为利用随机数序列执行模拟的方法。尽管事实上蒙特卡罗方法已经有很长时间了，但是只有当今的技术进步才允许将这些方法应用于复杂的应用程序。粒子过滤器（顺序蒙特卡洛方法）是基于仿真的过滤方法，其中过滤的乘积是基于状态矢量样本创建的联合后验分布的经验近似值。粒子过滤的目的是跟踪目标变量随时间的变化，通常具有非高斯概率密度函数。尽管顺序蒙特卡洛方法需要大量计算，但是如果系统是非线性的，或者如果误差的概率密度函数是非高斯的，则这些方法比卡尔曼滤波更有效。在仿真环境中已经创建了集成系统。分别获得并比较了使用卡尔曼滤波器或粒子滤波器对各种飞行条件进行数据处理的结果。当通过所描述的集成系统使用粒子滤波器确定物体轨迹时，方差值大约比来自各个传感器的导航信息的值小五倍。仿真结果强调了初始假设的正确性以及集成导航系统的粒子滤波选择。

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