基于量子粒子群优化容积卡尔曼滤波的LANDMARC室内定位算法

LANDMARC indoor location algorithm based on quantum particle swarm optimized cubature Kalman filtering

针对噪声环境下,基于标准容积卡尔曼滤波的LANDMARC室内定位算法因噪声特性估计不准,引起滤波性能下降而导致定位误差较大的问题,提出一种基于量子粒子群优化容积卡尔曼滤波的LANDMARC室内定位算法。该算法首先建立基于LANDMARC定位框架下的运动目标动态模型,然后引入量子粒子群优化技术对容积卡尔曼滤波中时间更新过程的状态预测值进行优化,以降低因畸变噪声引起的误差;最后将改进的容积卡尔曼滤波算法应用到运动目标状态估计中。实验结果表明,所提算法定位误差均值为0.175 m,与相同环境下传统的LANDMARC算法、基于容积卡尔曼滤波的LANDMARC算法以及基于粒子群优化容积卡尔曼滤波的LANDMARC算法相比,定位精度和稳定性均有明显提高,且运算时间比基于粒子群优化的算法少,应用在室内定位中能够得到较为真实的目标移动轨迹。

When the LANDMARC indoor localization algorithm based on standard cubature Kalman filter is applied in the noisy environment,it has the problem of the large location error due to the decrease of the filtering performance caused by the inaccurate estimation of the noise characteristics. Aiming to solve this problem,a LANDMARC indoor location algorithm based on quantum particle swarm optimization cubature Kalman filter is proposed in this paper. Firstly,a dynamic model of the moving target based on the LANDMARC localization framework is established. Then the quantum particle swarm optimization technique is introduced to optimize the state prediction during the time updating process in the cubature Kalman filter for reducing the error caused by distortion noise. Finally,the optimized cubature Kalman filter algorithm is applied to the state estimation of moving target. The experimental results show that the mean error of the proposed algorithm is 0. 175 m. Compared with the traditional LANDMARC algorithm,the LANDMARC algorithm based on the cubature Kalman filter and the LANDMARC algorithm based on the particle swarm optimization cubature Kalman filter,the localization accuracy and stability are significantly improved,and the operation time is shorter under the same environment. Therefore,the proposed algorithm can obtain a more realistic trajectory of moving target when it is applied to indoor localization.