【摘 要】
：
由于系统线性化时存在忽略项,扩展卡尔曼滤波成为一种次优的梯度下降算法.当滤波方程病态时,求解存在发散倾向,且估计量为有偏的,而非最优估计.即使动态系统噪声为高斯噪声时
【机 构】
：
长安大学地质工程与测绘工程学院,武汉大学测绘学院
论文部分内容阅读
由于系统线性化时存在忽略项,扩展卡尔曼滤波成为一种次优的梯度下降算法.当滤波方程病态时,求解存在发散倾向,且估计量为有偏的,而非最优估计.即使动态系统噪声为高斯噪声时,残差也不是高斯噪声.在伪距定位中,由于线性化后的伪距方程是局部解,这有可能丢失正确解值.针对GPS动态导航扩展卡尔曼滤波定位问题,基于美国Bancroft的全局性非线性"闭合式求解"最小二乘算法(Bancroft算法),本文提出一种闭合式GPS非线性代数解的卡尔曼滤波法(两步算法).该方法将GPS滤波问题中的空间与时间分离,较好地解决了非线性GPS动态定位求解问题,且获得稳定可靠的动态定位解.
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