CN102087362A

CN102087362A - Kalman filtering, digital phase-locked loop filtering and signal tracking methods and devices

Info

Publication number: CN102087362A
Application number: CN2009102414034A
Authority: CN
Inventors: 邓中亮; 邹德君; 杨磊; 刘华东; 黄建明; 韩可; 徐连明
Original assignee: Beijing University of Posts and Telecommunications
Current assignee: Beijing University of Posts and Telecommunications
Priority date: 2009-12-08
Filing date: 2009-12-08
Publication date: 2011-06-08
Anticipated expiration: 2029-12-08
Also published as: CN102087362B

Abstract

The invention discloses Kalman filtering, digital phase-locked loop filtering and signal tracking methods and Kalman filtering, digital phase-locked loop filtering and signal tracking devices, and belongs to the field of wireless communication. The signal tracking method comprises the following steps of: processing the received signal to obtain a receiving signal, and extracting a current observed value from the receiving signal, wherein the receiving signal comprises receiving carriers and receiving codes; and judging whether speed, accelerated speed or jerk of a receiver is greater than a preset threshold value, if so, determining that the carrier is positioned in a high dynamic environment, and performing signal tracking by utilizing the Kalman filtering, otherwise, determining that the carrier is positioned in a non-high dynamic environment, and performing signal tracking by utilizing a digital phase-locked loop. The invention also discloses a signal tracking device, a Kalman filtering method, a Kalman filtering device, a digital phase-locked loop filtering method and a digital phase-locked loop filtering device. The provided methods make the receiver adaptively switch a carrier tracking method, reduce the loop error, improve a refresh rate of a positioning result, and fulfill the aim of continuously tracking and locking the signal in a complex dynamic environment.

Description

Kalman filtering, digital phase-locked loop filtering and signal tracking method and device

Technical Field

The invention relates to the field of wireless communication, in particular to a Kalman filtering, digital phase-locked loop filtering and signal tracking method and device.

Background

The key part in the baseband signal processing module of the satellite navigation receiver is a carrier tracking and code tracking method, after a GPS satellite signal is received, carrier Doppler frequency shift values and code phase estimated values in an input signal can be obtained, at the moment, the receiver can enter a tracking state, and a tracking loop is used for tracking code phase changes and carrier Doppler changes, so that accurate synchronization of a local reproduction signal of the receiver and the input signal is realized, accurate observed quantity and navigation message information are extracted, and receiver navigation resolving is carried out.

The carrier tracking and code tracking methods in the prior art mainly include two methods:

the first method is to process the received signal and then carry out Kalman filtering to obtain a new observation value, and then estimate the current value of the signal according to the previous estimation value and the last observation data, and is particularly suitable for real-time data processing.

The second method is to process the received signal and then complete the precise carrier stripping by reproducing the precise carrier frequency and phase through a digital phase-locked loop. The specific method comprises the following steps: an input signal and a local carrier frequency are mixed to obtain an in-phase signal I and a quadrature signal Q, a phase difference between a carrier signal and the local carrier signal is obtained after the in-phase signal I and the quadrature signal Q pass through a low-pass filter and a phase discriminator, and the phase difference signal controls a local carrier signal NCO (digital controlled oscillator) to generate a new carrier.

After analyzing the prior art, the inventors found that the prior art has at least the following disadvantages:

although the Kalman filtering can predict the variation trend of a high dynamic signal in real time, the calculation amount is large, the calculation complexity is high, and the requirement of high-speed calculation cannot be met in a high dynamic environment.

The loop bandwidth of the digital phase-locked loop is fixed and cannot be adjusted in a self-adaptive mode along with the change of the signal-to-noise ratio, and the lock is lost due to the fact that the Doppler change rate exceeds the loop bandwidth under the condition that the carrier speed changes suddenly or the acceleration changes suddenly.

Disclosure of Invention

The embodiment of the invention provides a Kalman filtering method, a digital phase-locked loop filtering method and a digital phase-locked loop signal tracking device. The technical scheme is as follows:

a kalman filtering method, comprising:

processing a received signal to obtain a received signal, and extracting an observation value from the received signal, wherein the received signal comprises a received carrier and a received code;

updating the state value of the received signal;

correcting the updated state value by using the observed value to obtain a final estimated value of the current state; the observed value is obtained by an observation equation,

the observation equation is shown in the following formula,

x (k) is the state value at time k, r_I(k) Is an in-phase component, r_Q(k) Is the quadrature component, a is the amplitude of the received signal, d (k) takes the value ± 1, θ (k) is L^TX(k)，L＝[1，0，0，0]^TN' (k) is an observation noise vector;

the final estimate of the state is used to control the NCO to generate a new signal.

A method of filtering with a digital phase-locked loop, comprising:

processing a received signal to obtain a received signal, wherein the received signal comprises a received carrier and a received code;

calculating a phase or frequency difference between a local signal and the received signal;

the local signal comprises a local carrier and a local code;

performing loop filtering on the phase difference or the frequency difference by using loop filtering parameters;

the loop filtering parameters are obtained by using a controllable root algorithm;

the NCO is controlled by the phase or frequency difference to generate a new local signal.

A method of signal tracking, comprising:

judging whether the speed, the acceleration or the jerk of the receiver is greater than a preset threshold value, if so, enabling the carrier to be in a high dynamic environment, and performing signal tracking by using Kalman filtering; if not, the carrier is in a non-high dynamic environment, and signal tracking is carried out by utilizing a digital phase-locked loop.

The observation equation in Kalman filtering is

X (k) is the state value at time k, r_I(k) Is an in-phase component, r_Q(k) Is the quadrature component, a is the amplitude of the received signal, d (k) takes the value ± 1, θ (k) is L^TX(k)，L＝[1，0，0，0]^TAnd n' (k) is an observation noise vector.

And the loop filter parameters of the digital phase-locked loop are obtained by using a controllable root algorithm.

A kalman filtering apparatus, comprising:

the device comprises a first processing module, a second processing module and a control module, wherein the first processing module is used for processing a received signal to obtain a received signal and extracting an observation value from the received signal, and the received signal comprises a received carrier and a received code;

the updating module is used for updating the state value of the received signal;

the observation module is used for correcting the updated state value by using the observation value to obtain a final estimation value of the current state; the observed value is obtained by an observation equation,

the observation equation is shown in the following formula,

and the first generation module is used for controlling the NCO to generate a new signal by utilizing the final estimated value of the state.

An apparatus for filtering using a digital phase-locked loop, comprising:

the second processing module is used for processing the received signal to obtain a received signal, wherein the received signal comprises a received carrier and a received code;

a difference module, configured to calculate a phase difference or a frequency difference between a local signal and the received signal;

the local signal comprises a local carrier and a local code;

the loop filtering module is used for performing loop filtering on the phase difference or the frequency difference by using loop filtering parameters;

and the second generation module is used for controlling the NCO to generate a new local signal by utilizing the phase difference or the frequency difference.

An apparatus for signal tracking, comprising:

the processing module is used for processing the received signals to obtain received signals and extracting observation values from the received signals, wherein the received signals comprise received carriers and received codes;

the judging module is used for judging whether the speed, the acceleration or the jerk of the receiver is greater than a preset threshold value, if so, a Kalman filtering module used for enabling the carrier to be in a high dynamic environment and utilizing Kalman filtering to track signals is triggered; and if not, triggering a digital phase-locked loop module which is used for tracking the signal by using the digital phase-locked loop when the carrier is in a non-high dynamic environment.

The observation equation in the Kalman filtering module is

And the loop filter parameters of the digital phase-locked loop module are obtained by using a controllable root algorithm.

The technical scheme provided by the embodiment of the invention has the beneficial effects that:

according to the invention, through a carrier tracking and code tracking method of self-adaptive switching according to the dynamic characteristics of a carrier, when the carrier is in a high dynamic environment, a receiver utilizes a sigma point Kalman filtering method which is provided by the scheme and can self-adaptively adjust the bandwidth and improves an observation equation to track the carrier; and when the carrier is in a non-high dynamic environment, the carrier is switched to a high-efficiency digital phase-locked loop based on a continuous controllable root for tracking, so that the error of the loop is reduced, the refresh rate of a positioning result is improved, and the aim of continuously tracking the signal without losing the lock in a complex dynamic environment is fulfilled.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a flowchart of a method for carrier tracking according to embodiment 1 of the present invention;

fig. 2 is a schematic structural diagram of a kalman filtering apparatus provided in embodiment 2 of the present invention;

fig. 3 is a schematic structural diagram of an apparatus for digital phase-locked loop filtering according to embodiment 2 of the present invention;

fig. 4 is a schematic structural diagram of a signal tracking apparatus provided in embodiment 2 of the present invention;

fig. 5 is a flowchart of a code tracking method according to embodiment 3 of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

For ease of understanding, kalman filtering is described herein:

kalman filtering is used primarily to predict system states that can only be observed indirectly or inaccurately by the system itself. The kalman filtering method specifically includes:

the system state equation is as follows:

X_k+1＝Φ_kX_k+G_kf_k+w_k

the above equation is derived from a 4 th order linear state equation, the first three of the selected state vectors being for the carrier and the other for the C/a code. Wherein x is_pIs the phase difference between the real carrier and the locally reproduced carrier;

is the doppler shift of the carrier;

is the doppler shift rate of change of the carrier; x is the number of_tThe difference between the real code phase and the local reproduction code phase is shown, and the unit is the number of the C/A code base codes; delta T_kIs the time difference of the two calculations; k represents the fourth measurement; omega_NcokIs the angular frequency offset on an intermediate frequency basis at the time of local carrier replication; f. of_ckWhen the local receiver reproduces the C/A code, the code rate is offset on the basis of 1.023 MHz; w is a_kIs a 4 x 1 process noise vector, mainly contains the influence of relative motion of the satellite and the receiver,noise that is not synchronized between the code and carrier due to ionospheric fluctuations. In addition, reference 1540 in the equation is the ratio of the L1 carrier to the C/A code frequency.

The system's observation equation is as follows:

Z_k＝H_kX_k+U_kf_k+v_k

the above formula is a system observation equation established by derivation, and the observed quantity is delta phi_kAnd Δ τ_k。Δφ_kIs the kth pre-detection integration time DeltaT_kAverage phase difference between the real carrier and the locally reproduced carrier; delta tau_kIs the kth pre-detection integration time DeltaT_kIn the method, the average phase difference between the real C/A code and the local reproduction code is expressed by the number of the base codes. v. of_kIs a 2 x 1 observation noise vector. The actual observations come from the output of the loop discriminator:

<math><mrow><mi>Δ</mi><msub><mover><mtext>φ</mtext><mo>~</mo></mover><mi>k</mi></msub><mo>=</mo><mi>arctan</mi><mo>[</mo><msub><mi>Q</mi><mi>Pk</mi></msub><mo>/</mo><msub><mi>I</mi><mi>Pk</mi></msub><mo>]</mo></mrow></math>

(base code)

Wherein, I_Ek、I_Pk、I_Lk、Q_Ek、Q_Pk、Q_LkThe leading, immediate, and lagging values of the k-th pre-detection integration of branch I, Q, respectively.

The loop filtering process is as follows:

J_{k} = P_{k, k - 1} H_{k}^{T} {[H_{k} P_{k, k - 1} H_{k}^{T} + {\hat{R}}_{k - 1}]}^{- 1}

{\hat{X}}_{k} = {\hat{X}}_{k, k - 1} + J_{k} [Z_{k} - H_{k} {\hat{X}}_{k, k - 1} - U_{k} {\hat{f}}_{k - 1}]

P_k＝[I-J_kH_k]P_k，k-1

wherein,

and is

E {w_{k} v_{l}^{T}} = 0 .

The ordinary kalman filter is used to estimate the state of a linear system, and for the state of a nonlinear system, an extended kalman filter is used for estimation, and the algorithm principle of Extended Kalman (EKF) is described in detail as follows:

taking GPS satellites as an example, the received digital intermediate frequency signal is as follows:

r(k)＝Ad(k)c[(1+ζ)(k-τ)]cos(ω_ck+φ₀)+n(k)

when the loop integration time is T_sThe carrier phase θ (k) can be developed as follows:

ω₁(k+1)＝ω₁(k)+T_sω₂(k)+η₃(k)

ω₂(k+1)＝ω₂(k)+η₄(k)

wherein ω is₀(k)，ω₁(k)，ω₂(k) Is the derivative of each order of the carrier phase, representing the frequency, the frequency of change and the second derivative of the frequency, eta, respectively_i(i ═ 1, 2, 3, 4) is an expansion remainder, and represents the driving noise. Because the phase is estimated simultaneously with the frequency estimation, the frequency estimation error can be reduced, and meanwhile, for meeting the requirement of a high dynamic environment, the first derivative and the second derivative of the frequency are estimated together, and then the state vector is expressed as:

X(k)＝[θ(k)，ω₀(k)，ω₁(k)，ω₂(k)]^T

the available state equation is shown as the formula,

X(k+1)＝ФX(k)+η(k)

wherein eta (k) ═ eta₁(k)，η₂(k)，η₃(k)，η₄(k)]^TWhich is a driving noise vector, generally considered as white gaussian noise, the covariance matrices Q of the system transfer matrices Φ and η (k) can be represented as,

Q = q T_{s} [\begin{matrix} T_{s}^{6} / 252 & T_{s}^{5} / 72 & T_{s}^{4} / 30 & T_{s}^{3} / 24 \\ T_{s}^{5} / 72 & T_{s}^{4} / 20 & T_{s}^{3} / 8 & T_{s}^{2} / 6 \\ T_{s}^{4} / 30 & T_{s}^{3} / 8 & T_{s}^{2} / 3 & T_{s} / 2 \\ T_{s}^{3} / 24 & T_{s}^{2} / 6 & T_{s} / 2 & 1 \end{matrix}]

after despreading and orthogonal decomposition of the digital intermediate frequency signal, the instantaneous signal of the kth input carrier tracking can be represented as:

r_I(k)＝Ad(k)sin(θ(k))+n_I(k)

r_Q(k)＝Ad(k)cos(θ(k))+n_Q(k)

wherein r is_I(k) Is an in-phase component, r_Q(k) Is the quadrature component, d (k) takes the value ± 1, θ (k) ═ L^TX(k)，L＝[1，0，0，0]^T，n_I(k)，n_Q(k) To observe the noise vector. For GPS C/A, the above equation is typically expressed as:

the above equation is the observation equation of the extended kalman filter.

Wherein n (k) ═ n_I(k)，n_Q(k)]^TTo observe the vector noise, its covariance matrix is

I denotes a 2 × 2 unit array.

And then, obtaining an observation value by using the observation equation and the state value, extracting the phase difference between the local carrier and the receiving carrier from the observation value, and controlling the carrier NCO to generate a new carrier by using the phase difference.

Example 1

In order to adapt to a complex dynamic environment, a carrier tracking method can be switched by a tracking loop according to the change of the dynamic environment, the bandwidth of the loop has certain self-adaptive adjustment capability, the loop has wider bandwidth in a high dynamic environment and smaller bandwidth in a non-high dynamic environment, and the capturing performance and the steady-state tracking performance of the loop are considered at the same time, the embodiment of the invention provides a Kalman filtering method, which comprises the following steps:

processing the received signal to obtain a received signal, and extracting an observation value from the received signal, wherein the received signal comprises a received carrier and a received code;

there are various methods for extracting the observation value, and the embodiment of the present invention is not particularly limited.

Updating the state value of the received signal;

the observation equation is shown in the following formula,

the final estimate of this state is used to control the NCO to generate a new signal.

The embodiment of the invention provides a filtering method by using a digital phase-locked loop, which comprises the following steps:

processing the received signal to obtain a received signal, wherein the received signal comprises a received carrier and a received code;

calculating a phase difference or a frequency difference between a local signal and the received signal;

the local signal comprises a local carrier and a local code;

the loop filter parameters are obtained by using a controllable root algorithm;

the NCO is controlled by this phase or frequency difference to generate a new local signal.

The embodiment of the invention also provides a signal tracking method, which comprises the following steps:

Judging whether the speed, the acceleration or the jerk of the receiver is greater than a preset threshold value, if so, the carrier is in a high dynamic environment, and tracking signals by using Kalman filtering; if not, the carrier is in a non-high dynamic environment, and signal tracking is carried out by utilizing a digital phase-locked loop.

The observation equation in the Kalman filtering is

The loop filter parameters of the digital phase-locked loop are obtained by using a controllable root algorithm.

Preferably, an embodiment of the present invention provides a carrier tracking method, and referring to fig. 1, the method specifically includes:

101: the receiver receives satellite signals, down-converts the received satellite signals to intermediate frequency signals (4Mbps) through a radio frequency front end and A/D conversion, and digitizes the intermediate frequency signals to obtain intermediate frequency digital signals, namely received signals; multiplying the intermediate frequency digital signal by a locally generated C/A code, multiplying the multiplied product by the in-phase component and the orthogonal component of a local carrier to obtain I, Q paths of signals, namely observation signals, and extracting a current observation value from the observation signals;

it should be noted that, when performing code tracking, after obtaining an intermediate frequency digital signal, multiplying the intermediate frequency digital signal by the in-phase and quadrature components of the local carrier, respectively, and then multiplying by the locally generated C/a code to obtain an observation signal, and extracting the current observation value from the observation signal.

Where the received signal includes, but is not limited to, a received carrier wave extracted from the signal. In the present embodiment, the carrier tracking method and the code tracking method are collectively referred to as a signal tracking method.

102: judging whether the speed, the acceleration or the jerk of the receiver is greater than a preset threshold value, if so, the receiver is in high dynamic state, executing step 103, and if not, the receiver is in non-high dynamic state, executing step 106;

the motion characteristic of the receiver is determined, that is, the motion characteristic of the carrier of the determination signal, and since the positioning is to position the receiver, or to receive a chip, which can be embedded into any moving carrier, the receiver is positioned, that is, to position the moving carrier.

The dynamic characteristics of the receiver are determined by information of a sensor (an accelerometer, a gyroscope, or the like) of the receiver, including but not limited to velocity, acceleration, jerk, or the like.

The threshold may be set according to an actual situation, and the embodiment of the present invention is not particularly limited.

103: updating each state value (carrier phase, frequency, first and second derivatives of frequency) in the received signal;

specifically, the following processes 103b to 103b are processes for updating each state value in the received signal, and the processes specifically include:

103 a: initializing the received signal, giving an initial state estimate

Covariance matrix S with initial estimate_0|0；

Wherein, the initialized objects are the initial state estimation value and the initial estimation covariance matrix of the received signal, and the initial state estimationThe values are 4 x 1 matrices, the values are all 0, and the covariance matrix isTs is integration time of pre-detection, in the embodiment of the invention, Ts is 1ms, and q is 1000.

103 b: determining a sigma point set and updating a state;

the sigma point set determination is a mathematical modeling process, and is obtained by a weighted statistical linear regression method, and specifically comprises the following steps: selecting a plurality of points according to random variable prior distribution, calculating the values of the points by using a nonlinear function, and then realizing linearization of the random variable nonlinear function by using a linear regression technology, wherein the selected plurality of regression points are sigma points, namely a sigma point set is determined.

The object of the state update is the carrier phase, the frequency and the first and second derivatives of the frequency of the received signal, and the state update specifically includes: using state x at time k_kGlobal-based estimation

And the square root of the covariance of the estimation error S_k|kGenerating 2n +1 sigma points, and calculating one-step predicted value of stateAnd square root of prediction error covariance S_k+1|kThe algorithm is detailed in the following formula:

X_{k} = [{\hat{X}}_{k}, {\hat{X}}_{k} + \sqrt{n + r} S_{k}, {\hat{X}}_{k} - \sqrt{n + k} S_{k}]

X_k+1|k＝Φ(X_k)

λ＝α²(n+k)-n

i＝1，2，...2n

S_{k + 1 | k}^{0} = qr {[\sqrt{W_{1}^{c}} (X_{1 : 2 n, k + 1 | k} - {\hat{X}}_{k + 1 | k}), \sqrt{Q}]}

S_{k + 1 | k} = choludate (S_{k + 1 | k}^{0}, X_{0, k} - {\hat{X}}_{k + 1 | k}, W_{0}^{c})

the state update described above corresponds to the state at time k, and the state update proceeds to the next round at time k + 1.

104: correcting the updated state value by using the observed value to obtain a final estimated value of the current state;

specifically, step 104 is a process of measurement update, with Z_k+1To pair

Make a correction toBased on the measurement information Z by the time k +1^kAnd the observed value Z_k+1Final estimate of stateAnd the square root S of the corresponding estimation error_k+1|k+1。

In particular, byTo pairCorrecting to obtain k +1 time based on measurement z^kAnd the observed value

Final estimate of state

And the square root of the corresponding estimation error

By using

To pair

Correcting to obtain k +1 time based on measurement z^kAnd the observed value

Final estimate of stateAnd the square root of the corresponding estimation error

By using

(3. ltoreq. i. ltoreq.N) pairs

Correcting to obtain k +1 time based on measurement z^kAnd the observed value

Final estimate of state

And the square root of the corresponding estimation error

Finally, the measurement information z based on the system at the moment of k +1 is obtained^k+1The final estimate of the state and the corresponding estimation error are:

{\hat{x}}_{k + 1 | k + 1} = {\hat{x}}_{k + 1 | k + 1}^{N}

{\hat{P}}_{k + 1 | k + 1} = {(S_{k + 1 | k + 1}^{N})}^{T} S_{k + 1 | k + 1}^{N}

the specific algorithm is shown as the following formula:

S_{k + 1 | k} = chouludate (S_{k + 1 | k}^{0}, y_{0, k + 1 | k} - Z_{k + 1 | k}, W_{0}^{(c)})

y_k+1|k＝H(X_k+1|k)

K_{k + 1} = (P_{xy (k + 1 | k)} / S_{y (k + 1 / k)}^{T}) / S_{y (k + 1 | k)}

{\hat{X}}_{k + 1 | k + 1} = {\hat{X}}_{k + 1 | k} + K_{k + 1} (Z_{k + 1} - Z_{k + 1 | k})

U＝K_k+1S_y(k+1|k)

S_k+1|k+1＝choludate(S_k+1|k，U，-1)

where α determines the dispersion of sigma points around the mean in the above equation, usually a small positive number (e.g., 1e-4 ≦ α ≦ 11), k is a scaling factor, usually set to 0 during state estimation, β is another scaling factor used to incorporate a priori knowledge of the distribution of random variables (for Gaussian distributions with optimal values of 2), QR { } represents the QR decomposition of the matrix, cholepdate { } represents the correction of the Cholesky decomposition of the matrix, i.e., if S is the Cholesky decomposition of the original matrix P, the correction matrix is

Cholesky of (c) is decomposed into S ═ choleupdate { S, u, ± v }.

Specifically, in the present embodiment, Z_k+1|kIs obtained by the following observation equation,

n' (k) is the observation noise vector, as measured by d (k) squared²(k) To remove the effect of data jumps.

X (k) is the state value at time k, r_I(k) Is an in-phase component, r_Q(k) Is the quadrature component, d (k) takes the value ± 1, θ (k) ═ L^TX(k)，L＝[1，0，0，0]^TAnd A is the amplitude of the received signal;

wherein the observation equation is formed by

And trigonometric function angle multiplier sin2A ═ 2sinAcosA and cos2A ═ (cosA)²-(sinA)²Obtaining;

wherein r is_I(k) Is an in-phase component, r_Q(k) Is the quadrature component, d (k) takes the value ± 1, θ (k) ═ L^TX(k)，L＝[1，0，0，0]^T，n_I(k)，n_Q(k) To observe the noise vector.

105: and controlling the carrier NCO to generate a new carrier by using the final estimated value of the current state, and ending.

Specifically, the current state value includes, but is not limited to, θ (k +1), which is used to control the carrier NCO to generate a new carrier.

The above step 103-105 is a process of predicting the current state value by the previous state value and the current observation value, and the basic idea of the process is as follows: from the known k time state x_kGlobal-based estimation

And covariance P of the corresponding estimation error_k|kAt time k +1, state x is sequentially checked using the filter and the observed values of the sensors at time k +1_k+1Estimating to obtain estimated value based on global information

And covariance P of the corresponding error_k+1|k+1。

It should be noted that, in the above step 103-105, a modified sigma point kalman filter loop capable of adaptively adjusting bandwidth is used to perform carrier tracking, but due to the complex dynamic environment of the receiver, the dynamic characteristic of the receiver still needs to be determined after the tracking process, and the tracking method is selected according to the determination result.

It should be noted that the tracking is to make the locally generated carrier and code phase completely correspond to the actual received signal, so as to realize accurate stripping.

106: the received signal firstly enters a phase discriminator (frequency discriminator) to calculate the phase (or frequency) difference between the local carrier and the received carrier;

107: loop filtering the phase (or frequency) difference using loop filtering parameters;

the loop filter is used for filtering out clutter, noise and the like in the difference value and only keeping an effective part.

Wherein, the loop filter parameters are determined by a controllable root algorithm, in order to solve the problem in the method for designing a Digital loop by an analog loop, STEPENS S.A. and THOMASJ.B. propose a controllable root formula for designing a DPLL (Digital PLL, all Digital phase locked loop), and the formula set provides B_nContinuous Update (CU) mode with T < 0.1 and B_nThe design method of DPLL under two conditions of Discrete Update (DU) mode with T > 0.1 can design stable DPLL with any order according to the method, and the normalization factorB_nThe limitation of T is also eliminated. The controllable root algorithm provides a new set of loop design parameters B while simplifying the high-order stable loop design_n、λ_i、

Wherein, B_nIs a single-sided equivalent noise bandwidth, λ, of the loop_iAs a function of the relative attenuation parameter,

determines the damping characteristics of the loop.

The specific process of the controllable root algorithm is as follows:

in CU mode, when the update time T is sufficiently small, the DPLL approximates an APLL (Analog PLL) assuming a system transfer function of

The system is an N-order N-type loop with the poles of the open loop transfer function all at the origin. One-sided equivalent noise bandwidth of the system is

<math><mrow><msub><mi>B</mi><mi>n</mi></msub><mo>=</mo><msubsup><mo>&Integral;</mo><mn>0</mn><mo>∞</mo></msubsup><msup><mrow><mo>|</mo><mi>H</mi><mrow><mo>(</mo><mi>j</mi><mn>2</mn><mi>πf</mi><mo>)</mo></mrow><mo>|</mo></mrow><mn>2</mn></msup><mi>dt</mi></mrow></math>

Solving the equation to obtain the loop bandwidth B under CU approximation_nAnd loop coefficient k_iThe relational expression (c) of (c). Then, solving a characteristic equation of the system

s^N+k₁s^N-1+k₂s^N-2+...+k_N＝0

Obtaining a loop coefficient k according to the characteristic equation_iAnd a characteristic root s_kThe relationship between them.

In order to establish new loop design parameters, the formula is simplified into a plurality of second-order systems and cascade of first-order systems, and the characteristic root of each second-order system is obtained as s_i＝-β_i(1±η_i) For each first order system, the characteristic root is obtained as s_i＝-2β_iWherein eta_iThe discriminant in the root equation is solved for the unary quadratic equation corresponding to the second-order subsystem, and the attenuation rate beta_iCorresponding to a/2 in the root equation.

The corresponding subsystem is over-damped;

the corresponding subsystem is critical damping;

the corresponding subsystem is under-damped; for a second-order loop, the loop is,

can be pressed between the traditional damping factorIs converted to have a decay rate beta_iWith the conventional loop natural frequency omega₀Is related to, and beta_i＝ζω_n. In determining B_nAfter that, β is selected₁As a reference attenuation parameter, and defining a new parameter λ_i，β_i＝λ_iβ₁And i ≧ 2 is referred to as the relative attenuation parameter.

To ensure the stability of the system, the parameter β₁Must be positive and only B_nAs a function of (c). Thus the N independently selectable parameters for the N-th order loop are defined as B_n、λ_i、

Due to beta₁Is greater than 0, and lambda must be used to make the loop exhibit attenuation characteristics_iIs greater than 0. Calculation formula for defining high-order loop parameters

N, in such a way that α is defined as 2, 3_iIs dimensionless.

Solving for alpha_iAnd bring it into the loop coefficient k_iAnd system parameter B_nAnd alpha_iThe loop coefficient k can be obtained by the relation between_iAnd system parameter B_n、λ_i、

The relationship between them.

The following table lists solving expressions of 1-4 order APLL loop coefficients based on a CU mode and corresponding coefficients K of a DPLL loop under the condition of standard underdamping_i＝k_iTⁱ. In the embodiment of the present invention, the filter coefficients can be correspondingly found from the following table.

108: the carrier NCO is controlled by this phase (or frequency) difference to generate a new local carrier.

It should be noted that the above process is continuously cycled in the tracking process, and finally, the phase (or frequency) difference approaches zero infinitely, that is, accurate carrier tracking is realized.

Note that the principle of code phase tracking is the same as this principle.

It should be noted that the above steps 106 to 108 are a carrier tracking process of a Phase Lock Loop (PLL) with 3 th order FLL (frequency lock loop) based on the controlled root technique, and are one of the digital PLL.

It should be noted that, in the tracking process, the receiver constantly judges the dynamic environment of the receiver through the sensor, and then switches between two tracking loops, so as to meet the requirement of continuous tracking of signals without losing lock.

According to the invention, through the carrier tracking method of the self-adaptive switching according to the dynamic characteristics of the carrier, when the carrier is in a high dynamic environment, the receiver performs carrier tracking by utilizing the sigma point Kalman filtering method which is provided by the scheme and can self-adaptively adjust the bandwidth and improves the observation equation; and when the carrier is in a non-high dynamic environment, the carrier is switched to a high-efficiency digital phase-locked loop based on a continuous controllable root for tracking, so that the error of the loop is reduced, the refresh rate of a positioning result is improved, and the aim of continuously tracking the signal without losing the lock in a complex dynamic environment is fulfilled.

Example 2

In order to adapt to a complex dynamic environment, a carrier tracking method can be switched by a tracking loop according to the change of the dynamic environment, the bandwidth of the loop has certain self-adaptive adjustment capability, the loop has wider bandwidth in a high dynamic environment and smaller bandwidth in a non-high dynamic environment, and the capturing performance and the steady-state tracking performance of the loop are considered. Referring to fig. 2, the apparatus specifically includes:

a first processing module 201, configured to process a received signal to obtain a received signal, and extract an observation value from the received signal, where the received signal includes a received carrier and a received code;

an updating module 202, configured to update a state value of the received signal;

the observation module 203 is used for correcting the updated state value by using the observation value to obtain a final estimation value of the current state; the observed value is obtained by an observation equation,

the observation equation is shown in the following formula,

a first generation module 204 is used to control the NCO to generate a new signal using the final estimate of the state.

An embodiment of the present invention provides a filtering apparatus using a digital phase-locked loop, and referring to fig. 3, the apparatus specifically includes:

a second processing module 301, configured to process a received signal to obtain a received signal, where the received signal includes a received carrier and a received code;

a difference module 302, configured to calculate a phase difference or a frequency difference between the local signal and the received signal;

the local signal comprises a local carrier and a local code;

a loop filtering module 303, configured to perform loop filtering on the phase difference or the frequency difference by using a loop filtering parameter:

the loop filter parameters are obtained by using a controllable root algorithm;

a second generating module 304, configured to control the NCO to generate a new local signal by using the phase difference or the frequency difference.

The invention provides a signal tracking device, referring to fig. 4, the device specifically includes:

a processing module 401, configured to process a received signal to obtain a received signal, and extract an observation value from the received signal, where the received signal includes a received carrier and a received code;

A judging module 402, configured to judge whether a speed, an acceleration, or an jerk of the receiver is greater than a preset threshold, and if so, trigger a kalman filtering module 403, configured to perform signal tracking by using kalman filtering, where the carrier is in a high dynamic environment; if not, a digital phase-locked loop module 404 for signal tracking using a digital phase-locked loop is triggered for the carrier in a non-high dynamic environment.

The observation equation in the Kalman filtering module 403 is

The loop filter parameters of the digital phase-locked loop module 404 are obtained using a controlled root algorithm.

According to the invention, through the carrier tracking device which is self-adaptively switched according to the dynamic characteristics of the carrier, when the carrier is in a high dynamic environment, the receiver utilizes the sigma point Kalman filtering method which is provided by the scheme and can self-adaptively adjust the bandwidth and improves the observation equation to track the carrier; and when the carrier is in a non-high dynamic environment, the carrier is switched to a high-efficiency digital phase-locked loop based on a continuous controllable root for tracking, so that the error of the loop is reduced, the refresh rate of a positioning result is improved, and the aim of continuously tracking the signal without losing the lock in a complex dynamic environment is fulfilled.

Example 3

In order to adapt to a complex dynamic environment, a carrier tracking method can be switched by a tracking loop according to the change of the dynamic environment, the loop bandwidth has certain self-adaptive adjustment capability, a wider bandwidth in a high dynamic environment, a smaller bandwidth in a non-high dynamic environment, and the acquisition performance and the steady-state tracking performance of the loop are considered, the embodiment of the invention provides a code tracking method. Referring to fig. 5, the method specifically includes:

501: the receiver receives satellite signals, processes the received signals to obtain received signals, multiplies the received signals by in-phase and quadrature components of local carriers, multiplies the in-phase and quadrature components by locally generated C/A codes to obtain observation signals, and extracts a current observation value from the observation signals;

wherein the received signal includes, but is not limited to, a received code extracted from the signal;

502: judging whether the speed, the acceleration or the jerk of the receiver is larger than a preset threshold value, if so, the receiver is in high dynamic state, executing step 503, and if not, the receiver is in non-high dynamic state, executing step 506;

503: initializing and updating a state value of a received signal;

504: correcting the updated state value in the step 503 by using the observed value obtained in the step 501 to obtain a final estimated value of the current state;

505: and controlling the code NCO to generate a new code by using the final estimated value of the current state, and ending.

506: the received signal firstly enters a phase discriminator to calculate the phase difference between the local code and the received code;

507: performing loop filtering on the phase difference by using loop filtering parameters;

wherein the loop filter parameter is obtained by using a controllable root algorithm.

508: and generating a new local code by using the phase difference control code NCO.

The principle of the code tracking method is the same as that of the carrier tracking method provided in embodiment 1, and detailed steps are not described again.

According to the code tracking method of the self-adaptive switching according to the dynamic characteristics of the carrier, when the carrier is in a high dynamic environment, the receiver performs code tracking by using the sigma point Kalman filtering method which is provided by the scheme and can adjust the bandwidth in a self-adaptive manner and improves an observation equation; and when the carrier is in a non-high dynamic environment, the carrier is switched to a high-efficiency digital phase-locked loop based on a continuous controllable root for tracking, so that the error of the loop is reduced, the refresh rate of a positioning result is improved, and the aim of continuously tracking the signal without losing the lock in a complex dynamic environment is fulfilled.

The embodiments of the present invention may be implemented by software, and the corresponding software program may be stored in a readable storage medium, for example, a hard disk, a cache, or an optical disk of a computer.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A kalman filtering method, comprising:

updating the state value of the received signal;

the observation equation is shown in the following formula,

x (k) is the state value at time k, r_I(k) Is an in-phase component, r_Q(k) Is the quadrature component, a is the amplitude of the received signal, d (k) takes the value ± 1, θ (k) is L^TX(k)，L＝[1，0，0，0]^TN' (seven) is an observation noise vector;

2. A method of filtering using a digital phase locked loop, comprising:

the local signal comprises a local carrier and a local code;

3. A method of signal tracking, comprising:

4. The method of claim 3, wherein the observation equation in Kalman filtering is

5. The method of claim 3, wherein the loop filter parameters of the digital phase locked loop are obtained using a controlled root algorithm.

6. A kalman filtering apparatus, comprising:

the observation equation is shown in the following formula,

7. A filtering apparatus using a digital phase-locked loop, comprising:

the local signal comprises a local carrier and a local code;

8. An apparatus for signal tracking, comprising:

9. The apparatus of claim 8, in which the observation equation in the kalman filtering module is

10. The apparatus of claim 8, wherein the loop filter parameters of the digital phase-locked loop module are obtained using a controllable root algorithm.