弱信号下基于卡尔曼滤波导航接收机载波恢复算法研究

发布时间：2018-06-25 03:48

本文选题：导航接收机 + 载波恢复　；参考：《国防科学技术大学》2014年硕士论文

【摘要】：随着卫星导航技术的不断发展和应用,人们对导航服务性能的需求不断提升,特别是弱信号条件下的服务性能。由于信号强度较低,在弱信号下载波跟踪环路容易失锁,不能实现稳定的跟踪。针对该问题,本文研究了弱信号下的卡尔曼滤波载波恢复算法。论文的研究工作主要从以下四个方面展开:(1)总结归纳了传统的载波恢复方法,详细介绍了载波恢复环路中影响性能的鉴相器、鉴频器和环路滤波器。针对传统的载波恢复模型在弱信号容易出现失锁的问题,本文在传统的锁相环载波恢复模型的基础上,利用卡尔曼滤波器替代了其中的环路滤波器,建立了卡尔曼滤波载波恢复模型。(2)相位卡尔曼滤波的原始输入为鉴相器的输入,鉴别结果的好坏及卡尔曼滤波积分时间直接决定了卡尔曼滤波的性能。目前的研究主要针对给定积分时间的跟踪性能问题,没有针对卡尔曼滤波积分时间优化选择的研究。本文建立了鉴相器的噪声模型,结合鉴相器噪声模型和卡尔曼滤波载波恢复模型,以稳态卡尔曼滤波相位标准差最小为准则针对积分时间进行优化设计。针对相位卡尔曼滤波载波恢复算法在低载噪比下容易发生载波周跳的问题,本文提出了一种控制新息幅度的周跳抑制方法。(3)在高动态应用中,传统的鉴相器无法满足高动态跟踪需求,需要使用鉴频器对频率进行鉴别,并作为卡尔曼滤波器的原始观测量。频率卡尔曼滤波也需要针对积分时间进行优化设计。本文研究了鉴频器的噪声模型,结合鉴频器噪声模型和卡尔曼滤波频率估计模型,以稳态卡尔曼滤波频率标准差最小为准则针对积分时间进行优化设计。(4)结合本文中提出的积分时间优化,利用稳态卡尔曼滤波器作为环路滤波器,本文给出了一种应用协处理器的跟踪环路设计方案。设计硬件加速器和相关值预处理的方式增强了协处理器的性能。该方案简化了基带处理芯片设计,可直接应用于导航接收机基带芯片开发。
[Abstract]:With the development and application of satellite navigation technology, the demand for navigation service performance is increasing, especially under the condition of weak signal. Because of the low signal intensity, it is easy to lose the lock in the weak signal download wave tracking loop and can not achieve stable tracking. To solve this problem, the Kalman filter carrier recovery algorithm for weak signals is studied in this paper. The main work of this paper is as follows: (1) the traditional carrier recovery methods are summarized, and the phase discriminator, frequency discriminator and loop filter which affect the performance of the carrier recovery loop are introduced in detail. Aiming at the problem that the traditional carrier recovery model is prone to lose lock in weak signal, the Kalman filter is used to replace the loop filter on the basis of the traditional phase-locked loop carrier recovery model. The carrier recovery model of Kalman filter is established. (2) the original input of phase Kalman filter is the input of phase discriminator. The performance of Kalman filter is directly determined by the quality of discriminating result and the integral time of Kalman filter. The current research focuses on the tracking performance of given integral time, and there is no research on the optimal selection of Kalman filter integral time. In this paper, the noise model of the phase discriminator is established. Combining the noise model of the phase discriminator and the Kalman filter carrier recovery model, the integration time is optimized based on the minimum standard deviation of the steady-state Kalman filter phase. Aiming at the problem that phase Kalman filter carrier recovery algorithm is easy to occur carrier cycle slip at low carrier / noise ratio, a cycle slip suppression method to control the amplitude of innovation is proposed in this paper. (3) in high dynamic applications, The traditional phase discriminator can not meet the high dynamic tracking requirement, so it is necessary to use frequency discriminator to identify the frequency and to be the original observation of Kalman filter. The frequency Kalman filter also needs to be optimized for integral time. In this paper, the noise model of the discriminator is studied, which combines the noise model of the discriminator and the Kalman filter frequency estimation model. The minimum standard deviation of steady-state Kalman filter frequency is taken as the criterion to optimize the integration time. (4) combined with the integration time optimization proposed in this paper, the steady-state Kalman filter is used as the loop filter. This paper presents a design scheme of tracking loop with coprocessor. The design of hardware accelerator and correlation value preprocessing enhances the performance of the coprocessor. This scheme simplifies the design of baseband processing chip and can be directly applied to the development of baseband chip of navigation receiver.
【学位授予单位】：国防科学技术大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN965.5

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