CN104076373A

CN104076373A - Receiver carrier wave tracking implementation method and system based on multi-information fusion assistance

Info

Publication number: CN104076373A
Application number: CN201310108167.5A
Authority: CN
Inventors: 陈彦均; 于敦山; 路卫军; 崔小欣; 黄永灿
Original assignee: Peking University
Current assignee: Peking University
Priority date: 2013-03-27
Filing date: 2013-03-27
Publication date: 2014-10-01

Abstract

The invention discloses a receiver carrier wave tracking implementation method based on multi-information fusion assistance. According to the method, the radial speed of a satellite relative to a user is calculated according to information such as a satellite orbit and the position and speed of the user, and the Doppler frequency shift of a received carrier wave is predicated, wherein the information is obtained after a receiver is positioned; the actually measured value of the Doppler frequency shift of the carrier wave is output by a tracking loop in the receiver; correction is carried out on measurement remnants of the Doppler frequency shift through the Kalman filtering method, and the corrected optimal estimated value of the Doppler frequency shift is fed back to the tracking loop. According to the loop design method, dynamic influences caused due to the fact that the satellite moves relative to the user, a loop filter can be designed to work under a narrow noise bandwidth, the loop filtering effect is enhanced, and a system integrates robustness and high sensitivity. According to the Kalman filtering method adopted in the method and system, gain can be adjusted dynamically, optimal estimation of feedback of the Doppler frequency shift is achieved, and therefore the method and system have high commercial value.

Description

A carrier tracking method and system based on multi-information fusion assistance

技术领域technical field

本发明涉及一种接收机载波跟踪实现方法，具体是基于定位解算所得卫星轨道和用户位置、速度等信息的跟踪环路设计方法和系统。The invention relates to a receiver carrier tracking implementation method, in particular to a tracking loop design method and system based on satellite orbit, user position, speed and other information obtained through positioning calculation.

背景技术Background technique

随着GPS导航定位系统在各行业的广泛应用，人们对于提高GPS接收机跟踪卫星信号的鲁棒性和灵敏度以及改善GPS测量值的质量提出了越来越高的要求。在接收机的设计中，噪声带宽的设计与上述性能指标息息相关。噪声带宽越窄，由于越少频率成分的噪声被允许进入环路，环路滤波效果就越好，环路对信号的跟踪也就越精确。然而噪声带宽并不能任意地小。由于高动态应力会引起接收信号载波频率和相位的大幅度变动，进而引起频率和相位跟踪误差的激烈振荡，所以噪声带宽必须大到能够容忍容忍由于高动态应力引起的载波频率和相位的正常波动，以保证环路对信号的持续精确跟踪；否则，若噪声带宽太小，则由高动态应力所致的载波频率和相位变化中有用的高频信号成分会同噪声一起被滤除，这破坏了接收信号的真实性，导致环路失锁，鲁棒性变差。传统的载波跟踪环路设计方法采取平衡考虑低噪声和高动态要求后折中选取一个通常较大的噪声带宽值实现二阶或三阶载波环路，其根本问题是较大的噪声带宽提高了环路的噪声量，使环路的噪声性能变差，信号跟踪的精确度减低，不能实现信号跟踪的高灵敏度和鲁棒性。With the wide application of GPS navigation and positioning system in various industries, people put forward higher and higher requirements for improving the robustness and sensitivity of GPS receiver tracking satellite signals and improving the quality of GPS measurement values. In the design of the receiver, the design of the noise bandwidth is closely related to the above performance indicators. The narrower the noise bandwidth, the better the loop filtering effect and the more accurate the loop will track the signal because less frequency component noise is allowed to enter the loop. However, the noise bandwidth cannot be arbitrarily small. Since high dynamic stress will cause large changes in the carrier frequency and phase of the received signal, which will cause severe oscillations in frequency and phase tracking errors, the noise bandwidth must be large enough to tolerate normal fluctuations in carrier frequency and phase due to high dynamic stress , to ensure the continuous and accurate tracking of the signal by the loop; otherwise, if the noise bandwidth is too small, the useful high-frequency signal components in the carrier frequency and phase changes caused by high dynamic stress will be filtered out together with the noise, which destroys the The authenticity of the received signal causes the loop to lose lock and the robustness deteriorates. The traditional carrier tracking loop design method adopts a balanced consideration of low noise and high dynamic requirements, and then selects a usually larger noise bandwidth value to realize the second-order or third-order carrier loop. The fundamental problem is that the larger noise bandwidth increases the The amount of noise in the loop deteriorates the noise performance of the loop, reduces the accuracy of signal tracking, and cannot achieve high sensitivity and robustness of signal tracking.

众所周知，动态应力产生的影响主要是指卫星与接收机相对运动带来的多普勒频移以及接收机时钟的钟漂。其中接收机时钟的钟漂通常是未知而且无法预测的，而卫星与接收机相对运动的多普勒效应反映的是信号发射源与信号接收机之间距离的变化快慢，是卫星相对接收机运行速度在信号入射方向上的投影，这是可以求解出的量。本发明采用的方法正是利用定位后所得的卫星轨道与用户位置、速度等信息来预测卫星与接收机相对运动产生的多普勒频移，并通过卡尔曼滤波算法消除该多普勒频移的影响，从而能够设计载波环路工作在更窄的噪声带宽下，降低了载波环路的噪声，提升了载波环路信号跟踪的精确度，达到高灵敏度跟踪的效果。As we all know, the impact of dynamic stress mainly refers to the Doppler frequency shift caused by the relative motion between the satellite and the receiver and the clock drift of the receiver clock. Among them, the clock drift of the receiver clock is usually unknown and unpredictable, and the Doppler effect of the relative motion between the satellite and the receiver reflects the speed of the distance between the signal transmitter and the signal receiver, which is the relative motion of the satellite to the receiver. The projection of the velocity on the incident direction of the signal, which is a quantity that can be solved for. The method adopted in the present invention is to use information such as the satellite orbit and user position and velocity obtained after positioning to predict the Doppler frequency shift generated by the relative motion between the satellite and the receiver, and eliminate the Doppler frequency shift through the Kalman filter algorithm Therefore, the carrier loop can be designed to work in a narrower noise bandwidth, which reduces the noise of the carrier loop, improves the accuracy of carrier loop signal tracking, and achieves the effect of high-sensitivity tracking.

发明内容Contents of the invention

本发明的技术目的在于提供一种基于多信息融合辅助的载波跟踪方法与系统，利用接收机定位后所得到的卫星轨道与用户位置速度等信息计算卫星相对用户的径向速度，进而预测接收载波的多普勒频移，同时由接收机载波环路得到多普勒频移的实际测量值，再采用卡尔曼滤波的方法对多普勒频移测量残余进行校正处理，将校正后的最优多普勒频移反馈给跟踪环路，从而在满足高动态的前提下减小信号跟踪环路的滤波带宽，进而降低环路中的测量噪声，提高信噪比，本发明的系统整体框图可参考附图1。The technical purpose of the present invention is to provide a carrier tracking method and system based on multi-information fusion assistance, which uses information such as satellite orbit and user position velocity obtained after receiver positioning to calculate the radial velocity of the satellite relative to the user, and then predicts the received carrier At the same time, the actual measurement value of Doppler frequency shift is obtained by the carrier loop of the receiver, and then the Kalman filtering method is used to correct the Doppler frequency shift measurement residue, and the corrected optimal The Doppler frequency shift is fed back to the tracking loop, thereby reducing the filter bandwidth of the signal tracking loop under the premise of satisfying high dynamics, thereby reducing the measurement noise in the loop and improving the signal-to-noise ratio. The overall block diagram of the system of the present invention can be Refer to accompanying drawing 1.

为达到上述目的，本发明的技术方案包括三部分内容，具体为：In order to achieve the above object, the technical solution of the present invention comprises three parts, specifically:

第一部分是得到卫星相对接收机运动的径向速度，从而能预测由卫星运动产生的多普勒频率。运行速度为v^(s)的卫星发射频率为f，相应波长为λ的载波(如L1信号)，而接收机的速度为v，那么该接收机接收到的卫星信号的多普勒频移为：The first part is to obtain the radial velocity of the satellite's motion relative to the receiver, so that the Doppler frequency generated by the satellite's motion can be predicted. A satellite with a running speed of v ^(s) transmits frequency f, and the corresponding carrier wave (such as L1 signal) with a wavelength of λ, and the speed of the receiver is v, then the Doppler frequency shift of the satellite signal received by the receiver is :

${f f}_{d d} = = \frac{((v v - - {v v}^{((s the s))})) \cdot &Center Dot; {l l}^{((s the s))}}{λ λ} \approx \approx - - \frac{(({v v}^{((s the s))} - - v v)) \cdot \cdot {l l}^{((s the s))}}{λ λ} = = \frac{((- - \overset{\cdot &Center Dot;}{r r}))}{λ λ} = = \frac{- - u u}{λ λ} - - - - - - ((11))$

其中l^(s)为卫星在接收机处的单位观测矢量，代表卫星与接收机几何距离r对时间的导数，当卫星与接收机相对远离时，值为正，为负。u为卫星相对用户径向速度的大小。where l ^(s) is the unit observation vector of the satellite at the receiver, Represents the derivative of the geometric distance r between the satellite and the receiver with respect to time, when the satellite and the receiver are relatively far away, value is positive, is negative. u is the magnitude of the radial velocity of the satellite relative to the user.

由(1)式可知假设k时刻的多普勒频移为f_k，则k+1时刻的多普勒频移预测值为It can be known from formula (1) that assuming that the Doppler frequency shift at time k is f _k , then the predicted Doppler frequency shift at time k+1 is

${f f}_{k k + + 11} = = {f f}_{k k} + + Δ Δ {f f}_{d d} = = {f f}_{k k} + + \frac{Δu Δ u}{λ λ} - - - - - - ((22))$

第二部分是得到接收机的实际测量多普勒频率。接收机载波跟踪环路由混频器，相关与相干积分器，鉴别器，环路滤波器和载波数控振荡器(DCO)组成，采用I/Q解调法来帮助完成对输入信号的载波剥离与鉴相的任务。输入数字中频信号经过I/Q混频，相关和相干积分等预检操作后得到两路相干积分能量送至载波环鉴别器。环路滤波器对鉴别器输出的相位差φ_e或者频率差f_e进行滤波，得到滤波结果即频率调节字M送至DCO调节其输出相位和频率。根据DCO工作原理可知此时相对于中频的多普勒频移为The second part is to get the actual measured Doppler frequency of the receiver. The carrier tracking loop of the receiver is composed of a mixer, a correlation and coherent integrator, a discriminator, a loop filter and a carrier digitally controlled oscillator (DCO). It uses I/Q demodulation to help complete the carrier stripping and Appraisal task. The input digital intermediate frequency signal undergoes pre-check operations such as I/Q mixing, correlation and coherent integration to obtain two coherent integration energies and send them to the carrier loop discriminator. The loop filter filters the phase difference φ _e or the frequency difference f _e output by the discriminator, and the filtering result is obtained, that is, the frequency adjustment word M is sent to the DCO to adjust its output phase and frequency. According to the working principle of DCO, it can be known that the Doppler frequency shift relative to the intermediate frequency at this time is

${f f}_{o o} = = \frac{{Mf mf}_{c c}}{22^{N N}} - - - - - - ((33))$

其中f_c为采样时钟频率，N为数控振荡器的位数，f_o即为接收机的实际测量多普勒频率。Among them, f _c is the sampling clock frequency, N is the number of digits of the numerically controlled oscillator, and f _o is the actual measurement Doppler frequency of the receiver.

第三部分是卡尔曼滤波，得到最优估计多普勒频移进行反馈。在这里我们引入一个离散控制过程的系统模型，将卫星相对用户径向速率的变化量作为系统控制变量。由于反馈的最优多普勒频移已经包含卫星相对用户运动引入的动态，所以接收机跟踪通道内的环路滤波器可以改用窄噪声带宽参数，从而达到提高跟踪灵敏度的效果。The third part is Kalman filtering, which obtains the optimal estimated Doppler frequency shift for feedback. Here we introduce a system model of a discrete control process, and take the variation of the satellite relative to the radial velocity of the user as the system control variable. Since the optimal Doppler frequency shift of the feedback already includes the dynamics introduced by the relative motion of the satellite to the user, the loop filter in the receiver tracking channel can be changed to a narrow noise bandwidth parameter, so as to achieve the effect of improving the tracking sensitivity.

本发明的技术效果在于：相对于传统的二阶或三阶载波跟踪环路，本发明将卫星运动带来的多普勒频移反馈至跟踪环路，消除了动态应力产生影响中的可预测部分，使跟踪环路实现了高鲁棒性。同时由于消除了卫星相对用户运动的动态影响，接收机环路可以设计在窄噪声带宽下工作，降低了载波环路的噪声，提升了跟踪灵敏度。本文采用的卡尔曼滤波方法能动态地调节增益值，实现对反馈多普勒频移的最优估计，因而具有较高商用价值。The technical effect of the present invention is that, compared with the traditional second-order or third-order carrier tracking loop, the present invention feeds back the Doppler frequency shift brought about by the satellite movement to the tracking loop, eliminating the predictability of dynamic stress. part, making the tracking loop achieve high robustness. At the same time, due to the elimination of the dynamic influence of the satellite relative to the user's motion, the receiver loop can be designed to work in a narrow noise bandwidth, which reduces the noise of the carrier loop and improves the tracking sensitivity. The Kalman filtering method used in this paper can dynamically adjust the gain value to realize the optimal estimation of the feedback Doppler frequency shift, so it has high commercial value.

附图说明Description of drawings

附图1为本发明系统整体框图。Accompanying drawing 1 is the overall block diagram of the system of the present invention.

附图2为本发明实现流程图。Accompanying drawing 2 is the realization flowchart of the present invention.

附图3为本发明求解卫星相对于用户的径向速率u(t)的原理图。Accompanying drawing 3 is the schematic diagram of the present invention for solving the radial velocity u(t) of the satellite relative to the user.

附图4为本发明使用的载波环设计图。Accompanying drawing 4 is the design diagram of the carrier loop used in the present invention.

附图5为本发明一种基于多信息融合辅助的载波跟踪装置结构示意图。Fig. 5 is a schematic structural diagram of a carrier tracking device based on multi-information fusion assistance in the present invention.

具体实施方式Detailed ways

本发明的基本思想是根据由卫星相对用户运动速度预测出的多普勒频移和接收机内部载波跟踪环路输出的多普勒频移实际测量值来搭建卡尔曼滤波模型；对所得测量残余进行处理校正，得到校正后的最优多普勒频移估计值反馈给跟踪环路进行调节，使接收机实时掌握卫星的最新运动状态，从而准确地复制出将要接收到的卫星信号载波相位(或频率)。The basic idea of the present invention is to set up the Kalman filter model according to the Doppler frequency shift predicted by the satellite relative to the user's motion speed and the actual measured value of the Doppler frequency shift output by the carrier tracking loop inside the receiver; After processing and correction, the corrected optimal Doppler frequency shift estimation value is fed back to the tracking loop for adjustment, so that the receiver can grasp the latest motion state of the satellite in real time, thereby accurately replicating the carrier phase of the satellite signal to be received ( or frequency).

以下结合附图和具体实施例详细描述本发明所提供的基于多信息融合辅助的载波跟踪方法与系统，但不构成对本发明的限制。The multi-information fusion-assisted carrier tracking method and system provided by the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, but this does not constitute a limitation to the present invention.

附图2为本发明的具体实施流程图，如附图2所示，具体包括以下四个阶段：第一阶段：在接收机稳定定位后，根据卫星星历中的卫星运行轨道信息和用户位置速度信息，计算出卫星相对于用户的径向速度大小u(t)，进而得到多普勒频移预测值。Accompanying drawing 2 is the concrete implementation flow chart of the present invention, as shown in accompanying drawing 2, specifically comprise following four stages: the first stage: after the stable positioning of the receiver, according to the satellite orbit information and the user position in the satellite ephemeris Velocity information, calculate the radial velocity u(t) of the satellite relative to the user, and then obtain the predicted value of Doppler frequency shift.

附图3为本发明求解卫星相对于用户的径向速率u(t)的原理图，由附图3可知，在接收机完成定位后，从卫星星历和定位结果我们可以得到接收机位置坐标向量[x y z]^T和速度向量v＝[v_x v_y v_z]^T，卫星n的位置坐标向量[x⁽ⁿ⁾ y⁽ⁿ⁾ z⁽ⁿ⁾]^T以及卫星的速度向量 $v^{(s)} {[\begin{matrix} v_{x}^{(s)} & v_{y}^{(s)} & v_{z}^{(s)} \end{matrix}]}^{T},$ 根据上述信息求解卫星相对于用户的径向速率u(t)的步骤如下：Accompanying drawing 3 is the schematic diagram of the present invention to solve the radial velocity u (t) of satellite relative to the user, as can be seen by accompanying drawing 3, after receiver completes positioning, we can obtain receiver position coordinates from satellite ephemeris and positioning results Vector [x y z] ^T and velocity vector v=[v _x v _y v _z ] ^T , the position coordinate vector [x ⁽ⁿ⁾ y ⁽ⁿ⁾ z ⁽ⁿ⁾ ] ^T of satellite n and the velocity vector of satellite $v^{(the s)} {[\begin{matrix} v_{x}^{(the s)} & v_{the y}^{(the s)} & v_{z}^{(the s)} \end{matrix}]}^{T},$ The steps to calculate the radial velocity u(t) of the satellite relative to the user based on the above information are as follows:

步骤1.接收机到卫星n的几何距离r⁽ⁿ⁾为Step 1. The geometric distance r ⁽ⁿ⁾ from the receiver to the satellite n is

${r r}^{((n no))} = = | | | | {x x}^{((n no))} - - x x | | | | = = \sqrt{{(({x x}^{((n no))} - - x x))}^{22} + + {(({y the y}^{((n no))} - - y the y))}^{22} + + {(({z z}^{((n no))} - - z z))}^{22}} - - - - - - ((44))$

步骤2.单位观测矢量l $(n) = [\begin{matrix} l_{x}^{(n)} \\ l_{y}^{(n)} \\ l_{z}^{(n)} \end{matrix}] = \frac{- 1}{r^{(n)}} [\begin{matrix} x^{(n) - x} \\ y^{(n)} - y \\ z^{(n) - z} \end{matrix}] - - - (5)$ Step 2. Unit observation vector l $(no) = [\begin{matrix} l_{x}^{(no)} \\ l_{the y}^{(no)} \\ l_{z}^{(no)} \end{matrix}] = \frac{- 1}{r^{(no)}} [\begin{matrix} x^{(no) - x} \\ {the y}^{(no)} - the y \\ z^{(no) - z} \end{matrix}] - - - (5)$

步骤3.由(5)式可得卫星相对用户径向速度向量为u(t)＝(v^(s)-v)·l⁽ⁿ⁾，其速度大小u(t)＝||u(t)||。Step 3. From the (5) formula, the radial velocity vector of the satellite relative to the user can be obtained as u(t)=(v ^(s) -v)l ⁽ⁿ⁾ , and its velocity u(t)=||u(t )||.

得到径向速率u(t)后，由公式(2)即可预测下一时刻的多普勒频移。第二阶段：接收机内部载波环路对输入载波和本地复制载波进行鉴相(鉴频)滤波，输出多普勒频移实际测量值。After obtaining the radial velocity u(t), the Doppler frequency shift at the next moment can be predicted by formula (2). The second stage: the internal carrier loop of the receiver performs phase detection (frequency detection) filtering on the input carrier and the local copy carrier, and outputs the actual measured value of Doppler frequency shift.

附图4为本发明使用的载波环设计图。载波环工作流程在发明内容中已描述。由附图4可见，载波环路为闭环系统，其输入为数字中频信号，环路滤波器输出频率调节字M，每次更新后会保存在相应的寄存器中，中断后外部处理器从寄存器中读出该值并计算出当前时刻即k时的实际测量多普勒频移值Z_k。其计算公式如下所示：Accompanying drawing 4 is the design diagram of the carrier loop used in the present invention. The working process of the carrier loop has been described in the summary of the invention. As can be seen from accompanying drawing 4, the carrier loop is a closed-loop system, and its input is a digital intermediate frequency signal, and the loop filter outputs the frequency adjustment word M, which will be stored in the corresponding register after each update, and the external processor will read from the register after the interruption Read out this value and calculate the actual measured Doppler frequency shift value Z _k at the current moment k. Its calculation formula is as follows:

${Z Z}_{k k} = = {f f}_{k k} = = \frac{{Mf mf}_{c c}}{22^{N N}} - - - - - - ((66))$

其中f_c为采样时钟频率，N为载波环数控振荡器的位数，Z_k即为接收机的实际测量多普勒频率。第三阶段：利用卡尔曼滤波器校正测量残余，得到最优化多普勒估算值。Among them, f _c is the sampling clock frequency, N is the number of digits of the carrier ring numerical control oscillator, and Z _k is the actual measured Doppler frequency of the receiver. The third stage: use the Kalman filter to correct the measurement residual and obtain the optimal Doppler estimate.

首先，我们先要引入一个离散控制过程的系统。该系统可用一个线性随机微分方程来描述：First, we first introduce a system of discrete control processes. The system can be described by a linear stochastic differential equation:

X(k)＝AX(k-1)+BU(k-1)+W(k-1) (7)X(k)＝AX(k-1)+BU(k-1)+W(k-1) (7)

再加上系统的测量值：Plus the system's measurements:

Z(k)＝HX(k)+V(k) (8)Z(k)＝HX(k)+V(k) (8)

上两式子中，X(k)是k时刻的系统状态，U(k-1)是k时刻对系统的控制量。A和B是系统参数，对于多模型系统，它们为矩阵。Z(k)是k时刻的测量值，H是测量系统的参数，对于多测量系统，H为矩阵。W和V分别表示过程和测量的噪声，这里假设都为高斯白噪声，其协方差分别为Q，R(这里我们假设它们不随系统状态变化而变化)。对于满足上面的条件(线性随机微分系统，过程和测量都是高斯白噪声)，卡尔曼滤波器是最优的信息处理器。结合本单模型的具体情况，系统模型可化简为：In the above two formulas, X(k) is the state of the system at time k, and U(k-1) is the control amount of the system at time k. A and B are system parameters, and for multi-model systems, they are matrices. Z(k) is the measured value at time k, H is the parameter of the measurement system, and for a multi-measurement system, H is a matrix. W and V represent the noise of the process and the measurement respectively, and here it is assumed that they are both Gaussian white noise, and their covariances are Q and R respectively (here we assume that they do not change with the state of the system). For satisfying the above conditions (linear stochastic differential system, process and measurement are Gaussian white noise), Kalman filter is the optimal information processor. Combined with the specific situation of this single model, the system model can be simplified as:

$((k k)) = = X x ((k k - - 11)) + + \frac{U u ((k k - - 11))}{λ λ} + + W W ((k k - - 11))$

$= = X x ((k k - - 11)) + + \frac{Δu Δu ((k k - - 11))}{λ λ} + + W W ((k k - - 11)) - - - - - - ((99))$

Z(k)＝X(k)+V(k) (10)Z(k)＝X(k)+V(k) (10)

在本模型中A＝1，H＝1，X(k)为k时刻的预测多普勒频移，U(k-1)为k-1时刻卫星相对于接收机径向速率变化量，这里U(k-1)＝Δu(k-1)＝u_k-1-u_k-2，u_k-1为k-1时刻的卫星相对于接收机的径向速率。Z_k为实际多普勒观测值， In this model A=1, H=1, X(k) is the predicted Doppler frequency shift at time k, and U(k-1) is the change in radial velocity of the satellite relative to the receiver at time k-1, where U(k-1)=Δu(k-1 )=u _k-1 −u _k-2 , where u _k-1 is the radial velocity of the satellite relative to the receiver at time k-1. Z _k is the actual Doppler observation value,

我们需利用上述系统的过程模型，来预测下一状态的系统。假设现在的系统状态是k，根据系统的模型，可以基于系统的上一状态而预测现在状态：We need to use the process model of the above system to predict the system in the next state. Assuming that the current system state is k, according to the system model, the current state can be predicted based on the previous state of the system:

$X x ((k k | | k k - - 11)) = = X x ((k k - - 11 | | k k - - 11)) + + \frac{Δu Δu ((k k - - 11))}{λ λ} - - - - - - ((1111))$

X(k|k-1)是利用上一状态预测的多普勒频移，X(k-1|k-1)是上一状态最优的多普勒频移。X(k|k-1) is the Doppler frequency shift predicted by the previous state, and X(k-1|k-1) is the optimal Doppler frequency shift of the previous state.

到现在为止，卡尔曼模型系统结果已经更新。接下来更新对应于X(k|k-1)的协方差。我们用P表示协方差：Until now, the Kalman model system results have been updated. Next update the covariance corresponding to X(k|k-1). We denote covariance by P:

P(k|k-1)＝P(k-1|k-1)+Q (12)P(k|k-1)＝P(k-1|k-1)+Q (12)

表达式(11)和(12)是现在状态的多普勒预测结果，由第二阶段可得现在状态的多普勒测量值。结合多普勒预测值和多普勒测量值，可以得到现在状态k的最优化多普勒估算值X(k|k)：Expressions (11) and (12) are the Doppler prediction results of the current state, and the Doppler measurement value of the current state can be obtained from the second stage. Combining the Doppler prediction value and the Doppler measurement value, the optimal Doppler estimation value X(k|k) of the current state k can be obtained:

X(k|k)＝X(k|k-1)+Kg(k)(Z(k)-X(k|k-1)) (13)X(k|k)＝X(k|k-1)+Kg(k)(Z(k)-X(k|k-1)) (13)

其中Kg为卡尔曼增益(Kalman Gain)：Where Kg is Kalman Gain:

P(k|k)＝(1-Kg(k))P(k|k-1) (15)P(k|k)＝(1-Kg(k))P(k|k-1) (15)

当系统进入k+1状态时，P(k|k)就是式子(12)的P(k-1|k-1)。这样，算法就可以自回归的运算下去，而每次求得的X(k|k)即为该时刻的最优化多普勒估算值。为了令卡尔曼滤波器开始工作，需要设置卡尔曼两个零时刻的初始值，是X(0|0)和P(0|0)。X(0|0)可以由来计算，即利用定位成功后初次求得的卫星相对接收机的运动速率来计算。对于P(0|0)，随便设一个值就可以，但一般不要取0，因为这样可能会令卡尔曼滤波器认为给定的X(0|0)是系统最优的，从而使算法不能收敛。When the system enters k+1 state, P(k|k) is P(k-1|k-1) of formula (12). In this way, the algorithm can continue the auto-regression operation, and the X(k|k) obtained each time is the optimal Doppler estimation value at that moment. In order to make the Kalman filter work, it is necessary to set the initial values of the two zero moments of Kalman, which are X(0|0) and P(0|0). X(0|0) can be given by To calculate, that is, to calculate by using the motion rate of the satellite relative to the receiver obtained for the first time after the positioning is successful. For P(0|0), you can set a value at will, but generally do not take 0, because this may make the Kalman filter think that the given X(0|0) is the optimal system, so that the algorithm cannot convergence.

第四阶段：将最优化多普勒估算值X(k|k)转化为载波频率调节字Mk反馈到载波环DCO，其相互关系如下式所示：The fourth stage: convert the optimized Doppler estimation value X(k|k) into the carrier frequency adjustment word Mk and feed it back to the carrier loop DCO. The relationship between them is shown in the following formula:

${M m}_{k k} = = \frac{X x ((k k | | k k)) * * 22^{N N}}{{f f}_{c c}} - - - - - - ((1616))$

其中f_c为采样时钟频率，N为数控振荡器的位数。Where f _c is the sampling clock frequency, N is the number of digits of the numerically controlled oscillator.

附图5为本发明实施例的装置结构示意图，本发明可以用本装置实现，但不局限于附图5所示装置。整个装置包括：接收天线、射频模块、GPS基带处理通道、处理器以及处理软件。其中，接收天线负责信号的接收，射频模块完成信号的放大、滤波和下变频，以及信号的模数转化，并向GPS基带处理通道输出数字中频信号和采样时钟信号。GPS基带处理通道包含捕获模块、跟踪模块、位同步与帧同步模块以及数据解调模块，完成对于数字中频信号的捕获、跟踪和解调出导航电文。处理软件分为星历处理和PVT解算，多普勒预测以及卡尔曼滤波三个部分，具体算法实现可见上文有关发明实现流程各个阶段的阐述。处理软件的输入为导航电文、卫星相位测量值和环路滤波器输出的多普勒频移实测值，而输出为多普勒频移最优估计值。处理器负责运行处理软件和检测、控制GPS基带处理通道。Accompanying drawing 5 is the device structure diagram of the embodiment of the present invention, and the present invention can be realized with this device, but is not limited to the device shown in Fig. 5 . The whole device includes: receiving antenna, radio frequency module, GPS baseband processing channel, processor and processing software. Among them, the receiving antenna is responsible for receiving the signal, and the radio frequency module completes the amplification, filtering and down-conversion of the signal, as well as the analog-to-digital conversion of the signal, and outputs the digital intermediate frequency signal and sampling clock signal to the GPS baseband processing channel. The GPS baseband processing channel includes a capture module, a tracking module, a bit synchronization and frame synchronization module, and a data demodulation module to complete the capture, tracking and demodulation of digital intermediate frequency signals to generate navigation messages. The processing software is divided into three parts: ephemeris processing and PVT calculation, Doppler prediction and Kalman filtering. The specific algorithm implementation can be seen in the above descriptions of the various stages of the invention realization process. The input of the processing software is the navigation message, the satellite phase measurement value and the measured value of the Doppler frequency shift output by the loop filter, and the output is the optimal estimated value of the Doppler frequency shift. The processor is responsible for running the processing software and detecting and controlling the GPS baseband processing channel.

结合附图5所示装置结构示意图，本发明实施流程为：GPS基带处理通道接收射频模块输出的中频信号，处理器通过总线配置基带处理通道各个模块的初始化参数，基带处理通道完成信号的捕获跟踪与同步后得到载波相位测量值与码相位测量值以及导航电文，并以固定的采样周期采样然后存储于寄存器中，同时向处理器产生中断。处理器响应中断后，通过总线读取寄存器中的相位测量值和导航电文，由处理软件中的星历处理和PVT解算部分得到卫星位置速度和用户位置速度等信息，再由多普勒预测部分预测出此时的多普勒频移值。同时GPS基带处理通道中的载波环模块对接收载波信号与本地复制载波信号进行鉴相滤波处理，并输出此时的多普勒频移实际测量值存储于寄存器中，等待处理器读走。处理软件中的卡尔曼滤波部分完成对于计算出的多普勒频移预测值和从寄存器中读到的实测值之间残差的滤波处理，并实时求解出多普勒频移的最优估计值换算成频率调节字反馈回载波环中的数控振荡器，用来调节本地复制的载波。In conjunction with the schematic diagram of the device structure shown in accompanying drawing 5, the implementation process of the present invention is: the GPS baseband processing channel receives the intermediate frequency signal output by the radio frequency module, the processor configures the initialization parameters of each module of the baseband processing channel through the bus, and the baseband processing channel completes the capture and tracking of the signal After synchronization, the measured value of the carrier phase, the code phase and the navigation message are obtained, which are sampled at a fixed sampling period and stored in the register, and an interrupt is generated to the processor at the same time. After the processor responds to the interrupt, it reads the phase measurement value and navigation message in the register through the bus, and obtains information such as satellite position and speed and user position and speed from the ephemeris processing and PVT calculation part of the processing software, and then predicts it by Doppler Partially predict the Doppler shift value at this time. At the same time, the carrier loop module in the GPS baseband processing channel performs phase detection and filtering processing on the received carrier signal and the local replicated carrier signal, and outputs the actual measured value of Doppler frequency shift at this time and stores it in the register, waiting for the processor to read it. The Kalman filtering part in the processing software completes the filtering process for the residual between the calculated Doppler frequency shift prediction value and the measured value read from the register, and solves the optimal estimation of Doppler frequency shift in real time The value is converted into a frequency adjustment word and fed back to the numerically controlled oscillator in the carrier loop to adjust the locally replicated carrier.

以上所述，仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention.

Claims

1. A carrier tracking method and system based on multi-information fusion assistance, specifically a carrier tracking loop design method based on information such as satellite orbits and user positions and speeds, is characterized in that it includes three parts: according to the position of the satellite relative to the user The radial velocity is used to predict the Doppler frequency shift at the next moment, the actual observation value of the Doppler frequency shift is obtained from the output of the loop filter in the receiver carrier loop module, and the Doppler frequency shift measurement is corrected by using the Kalman filter The residual error is thus obtained and the optimal Doppler frequency shift estimate is fed back to the loop numerically controlled oscillator. The calculation of the radial velocity of the satellite relative to the user includes the following two processes:

1) Calculate the satellite's position coordinates and running speed vector from the satellite ephemeris. The user's position coordinates and velocity vector are obtained from the positioning and speed measurement results of the user's receiver.

2) Calculate the unit observation vector of the satellite relative to the user according to the position coordinates of the satellite and the user solved in 1), and the dot product of the unit observation vector and the motion velocity vector of the satellite relative to the user is the radial velocity of the satellite relative to the user.

2. The method according to claim 1, wherein, after obtaining the radial velocity of the satellite relative to the user, the Doppler frequency shift calculation formula is used to predict the received carrier Doppler frequency shift.

3. The method according to claim 1, wherein a typical carrier loop design is adopted, and the loop filter in the receiver carrier loop module outputs the Doppler frequency shift observation value.

4. The method according to claim 1, characterized in that, the Kalman filter model of the Doppler frequency shift is set up: the satellite is used as a system control variable relative to the user's radial velocity variation to obtain the Doppler frequency shift at the next moment. Le frequency shift prediction value; take the Doppler frequency shift output by the loop filter in the receiver carrier loop module as the actual measurement value; use the Kalman filter algorithm to calculate the difference between the Doppler frequency shift prediction value and the measured value, namely The residual is measured, processed and corrected to obtain the corrected optimal Doppler shift estimate.

5. The method according to claim 1, characterized in that: the corrected optimal Doppler frequency shift estimated value is fed back to the tracking loop numerical control oscillator in real time for adjustment, so that the receiver can grasp the latest motion state of the satellite.

6. A carrier tracking device based on multi-information fusion assistance, characterized in that the device comprises: GPS baseband processing channel and processing software; wherein,

The GPS baseband processing channel is used to obtain the phase measurement value and the navigation message of each satellite, wherein the carrier loop outputs the measured value of Doppler frequency shift;

The processing software, whose input is the phase measurement value of each satellite, the navigation message and the measured value of Doppler frequency shift, is used to obtain the optimal Doppler frequency shift estimated value and feed it back to the carrier loop numerical control oscillator to adjust the copy of the local carrier.

7. device according to claim 6, is characterized in that, described processing software is divided into ephemeris processing and PVT solution, three parts of Doppler prediction and Kalman filtering, and its processing process comprises:

Calculate the position and speed information of satellites and users according to the measured values of carrier phase and code phase of each satellite and the navigation message; calculate the radial velocity of the satellite relative to the user from the obtained multi-information; calculate the predicted value of Doppler frequency shift from the obtained radial velocity ; Use the Kalman filter algorithm to correct the Doppler frequency shift measurement residual, and finally get the optimal estimate of Doppler frequency shift.