中文摘要：

提出了一种基于改进卡尔曼滤波和遗传算法的室内定位方法。首先利用共轭梯度收敛法计算稳态卡尔曼滤波器的增益值和离散时间卡尔曼滤波器的Riccati方程的解,该算法利用逼近自回归模型建立一步预测方程,所有非线性方程都可化为该线性方程求解。新方法利用卡尔曼滤波预测目标在下一时刻可能出现的位置,以该位置为中心建立该点的邻域,以预测目标坐标范围为模板,并且基于欧氏距离公式原则建立适应度函数,候选区的中心坐标为参数编码,结合遗传算法进行定位,对适应度函数通过泰勒级数展开式进一步优化定位坐标。实验结果表明,这种方法稳定性好,收敛速度快,有效消除噪声干扰,得到比较准确的位置坐标。

英文摘要：

This paper proposes a new indoor localization method based on improved Kalman filter and genetic algorithm. Firstly, it makes use of conjugate gradient convergence method to calculate the steady-state Kalman filter gain value and the Riceati equation solution of discrete-time Kalman filter. The algorithm uses approximate autoregressive model to establish one-step prediction equation. All nonlinear equations can be translated into linear equations. This new method adopts Kalman filter to predict the possible location of the target at the next moment. It takes the location as center position and takes the range of predicted target coordinates as a template and establishes a neighborhood of point. It conducts fitness function based on euclidean distance formula. Center coordinates of the candidate area are regarded as parameter coding. It combines genetic algorithm through Taylor series to make further improvement on indoor location coordinates. Finally, experiment results show that this new method has a good stability and convergence speed, and can effectively eliminate the noise and get more accurate location coordinates.