CN114884790B - Doppler frequency shift synchronization method based on four-dimensional track prediction - Google Patents
Doppler frequency shift synchronization method based on four-dimensional track prediction Download PDFInfo
- Publication number
- CN114884790B CN114884790B CN202210507253.2A CN202210507253A CN114884790B CN 114884790 B CN114884790 B CN 114884790B CN 202210507253 A CN202210507253 A CN 202210507253A CN 114884790 B CN114884790 B CN 114884790B
- Authority
- CN
- China
- Prior art keywords
- time
- aircraft
- value
- frequency
- state
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000001914 filtration Methods 0.000 claims abstract description 24
- 125000004122 cyclic group Chemical group 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 34
- 230000005540 biological transmission Effects 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 12
- 230000001133 acceleration Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000033001 locomotion Effects 0.000 claims description 5
- 230000007704 transition Effects 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000007476 Maximum Likelihood Methods 0.000 claims description 3
- 238000005314 correlation function Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 abstract description 20
- 238000005516 engineering process Methods 0.000 description 7
- 239000013256 coordination polymer Substances 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2647—Arrangements specific to the receiver only
- H04L27/2655—Synchronisation arrangements
- H04L27/2657—Carrier synchronisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The application discloses a Doppler frequency shift synchronization method based on four-dimensional track prediction, and belongs to the technical field of communication. Firstly, the actual running track of the aircraft to be tested is observed and recorded, the actual running track is represented by a linear hybrid model, and four-dimensional tracks of the aircraft are initialized and predicted based on a Kalman filtering mode, so that a system state estimated value of the aircraft at the moment t is obtained. And then obtaining average Doppler frequency shift according to the average system state estimated value of the aircraft in a time window, pre-compensating the received signal, and performing time-frequency synchronization on the compensated received signal by using a cyclic prefix and a synchronization signal. And finally, demodulating the received signal with the time-frequency synchronization completed, obtaining information of the received signal, feeding back the information to the predicted initial track, correcting the information, and predicting the next round of four-dimensional track according to corrected data. The application meets the requirement of accurate and rapid time-frequency synchronization with the ground station when the aircraft runs at high speed, and saves the cost.
Description
Technical Field
The application relates to the technical field of communication, in particular to a Doppler frequency shift synchronization method based on four-dimensional track prediction.
Background
With the rapid development of civil aviation, the current narrow-band communication system cannot meet the increasing aviation communication service demand, the upgrading of the current narrow-band communication system of the civil aviation industry to the broadband communication system is a development target of the next stage of industry, and the national civil aviation bureau also publishes a 'national civil aviation new generation aviation broadband communication technology roadmap' in 2021, wherein the 'new generation aviation broadband communication technology' mainly comprises 5G aeroMACS, 5G LDACS, 5G ATG, 5G public networks and the like based on the 5G communication technology of China.
The 5G L band digital aviation communication system (L-band Digital Aeronautical Communications System, LDACS) technology is mainly applied to an air-ground communication scene in an airplane route stage, works in an L band and is based on an OFDM technology. However, the flight speed of the aircraft in the course stage is fast and can reach 1000km/h, so that a large Doppler frequency shift is caused, the existing receiving equipment cannot receive signals with excessive frequency shift, and how to solve the key problem in the 5G LDACS technology under the condition of large Doppler frequency shift is realized.
Disclosure of Invention
The application provides a Doppler frequency shift synchronization method based on four-dimensional track prediction, which aims to solve the problem of signal synchronization in a large Doppler frequency shift scene when LDACS is applied and promote the practical application of civil aviation broadband communication service.
The Doppler frequency shift compensation method based on four-dimensional track prediction comprises the following specific steps:
firstly, aiming at an aircraft to be tested, observing and recording the actual running track of the aircraft, and representing the actual running track by using a linear hybrid model;
the linear hybrid model includes an observation equation and a state equation, which are expressed as follows:
the equation of state: x (t) =AX (t-1) +BU (t-1) +w (t)
Observation equation: z (t) =H (t) X (t) +n (t)
Wherein X (t) is a real state vector at time t, and comprises a real three-dimensional coordinate of the aircraft, component speeds in three directions and accelerations in three directions. X (t-1) is the true state at time t-1; a is a real state transition matrix; w (t) is the noise matrix at the current time; u (t-1) represents a true control vector at time t-1; b is a real control matrix representing the change of converting the input real control vector into a state vector;
z (t) is the real observation value of the system at the moment t; n (t) represents real observation noise at the time t, the mean value of the real observation noise is 0, and the covariance is R; h (t) represents the real observation matrix at time t.
Step two, based on Kalman filtering, combining a linear hybrid model, carrying out initialization prediction on four-dimensional tracks of the aircraft to be detected to obtain system state estimated values of the aircraft at all moments;
the method comprises the following steps:
step 201, calculating a system state predicted value X at time t according to the real state vector and the real control vector at time t-1 p (t)；
X p (t)＝A*X p (t-1)+B*U(t-1)
Step 202, using the system real observation value Z (t) and the system state prediction value X p (t) calculating covariance P - (t)；
P - (t)＝AP(t-1)A T +Q
Q represents the covariance matrix of the noise matrix w (t). P (t-1) represents the covariance of the real state vector and its optimal estimate at time t-1;
the optimal estimate of the true state value is: and estimating the observation noise to obtain an approximate real state value.
Step 203, using the real observed value Z (t-1) at time t-1 and the system state predicted value X p Covariance P of (t-1) - (t-1) updating parameters of Kalman filtering to obtain Kalman filtering gain K:
K＝P - (t-1)H T (HP - (t-1)H T +R) -1
step 204, li Yongka Kalman filtering gain K and system state prediction value X at time t p (t) calculating a system state estimation value at the time t
In step 205, the real value at time t and the covariance P (t) of the optimal estimation are updated, and the process returns to step 201 to perform kalman filtering at the next time to obtain the system state estimation value at the next time.
P(t)＝(1-KH)*P - (t-1)
And thirdly, obtaining average Doppler frequency shift according to the average system state estimated value of the aircraft in a time window, and pre-compensating the received signal.
The method comprises the following steps:
step 301, taking the current time t as the center, taking the time period h before and after the time t as a time window, and recording the estimated value of the system state as the slaveTo->Averaging to obtain an average system state estimate value within the time window of +.>
Step 302, estimating a value according to the average system stateObtaining the average velocity v of the aircraft in the time window, and carrying the average velocity v into a Doppler frequency shift formula to obtain the Doppler frequency shift f d 。
Doppler frequency shift calculation formula: f (f) d ＝f m cos(θ)
θ is the included angle between the relative motion speed direction and the connecting line of the receiving end; f (f) m Indicating the maximum doppler shift frequency of the signal,f c is the carrier center frequency and C is the speed of light.
Step 303, doppler shift f d The pre-compensation is used for receiving signals to obtain pre-compensated received signals;
assuming that the transmission signal is s (n), the reception signal r (n) is:
r(n)＝s(n-n d )exp(j*2π*n*Δf*Ts)
where j is a constant, ts is a sampling period, Δf is the total frequency offset of the signal during transmission, n d Is the time offset of the signal during transmission.
The precompensated received signal is: r' (n) =s (n-n) d )exp(j*2π*(Δf-f d )*n*Ts)
And step four, after Doppler frequency shift precompensation, the frequency offset of the received signal is time-frequency synchronized by using the cyclic prefix and the synchronizing signal.
The frequency offset of the signal is divided into an integer multiple of the frequency offset (IFO), a fractional multiple of the frequency offset (FFO), and a residual frequency offset.
(1) Integer multiple frequency offset IFO
Firstly, performing sliding correlation calculation on a plurality of OFDM symbols by using cyclic prefix, wherein the point of obtaining the maximum peak value is the starting point of one OFDM symbol, and the time offset n in the transmission process is obtained d ；
And then, the OFDM symbol time coarse synchronization and the integer frequency multiplication partial synchronization of the frequency domain of the system are completed by using a maximum likelihood algorithm.
The correlation function R (n) of the received signal R (n) is:
wherein d represents a timing pointer, N represents the number of subcarriers of an OFDM symbol, and L is the CP length;
integer multiple frequency offset estimation epsilon IFO The method comprises the following steps:
wherein d ml Is the start time of the OFDM symbol.
(2) Fractional frequency offset FFO
Dividing the received PSS signal p (n) into front and rear sections, and performing conjugate multiplication on the PSS signal p (n) with no local deviation to obtain decimal frequency bias epsilon FFO ：
(3) Residual frequency offset
And performing frequency offset adjustment on the residual frequency offset by training and using a phase-locked loop.
And fifthly, demodulating the received signal with the time-frequency synchronization, acquiring speed information, position information and aircraft intention of the received signal, returning to the step two, feeding back to the predicted initial track, correcting the predicted initial track, and carrying out the next round of four-dimensional track prediction according to corrected data.
The application has the advantages that:
1. the application meets the requirement of accurate and rapid time-frequency synchronization for communication with the ground station when the aircraft generates large Doppler frequency shift under high-speed operation on the basis of four-dimensional track prediction.
2. The Doppler frequency shift is estimated by adopting four-dimensional track prediction, and the requirement on the time-frequency synchronization capability of the equipment is reduced and the cost is saved by pre-compensating the Doppler frequency shift.
3. The method demodulates the signal after the time-frequency synchronization is finished, acquires the position state information of the aircraft and the intention of the aircraft, feeds back the information to the prediction process, and simultaneously completes the rapid convergence of the time-frequency synchronization and the correction of four-dimensional track prediction.
Drawings
FIG. 1 is a schematic diagram of a Doppler shift compensation method based on a four-dimensional track according to the present application;
fig. 2 is a flow chart of a doppler shift compensation method based on a four-dimensional track according to the present application.
Detailed Description
The implementation of the present application will be further described with reference to the drawings and examples.
Under the air-ground communication scene of civil aviation communication, the aircraft base station is in wireless connection with the fixed ground base station, so that real-time communication is realized. When the LDACS is applied to an air-ground communication scene, the Doppler frequency shift is large because the flight speed of the airplane is 800-1200 km/h, normal equipment is difficult to receive, and the Doppler frequency shift needs to be pre-compensated.
Compared with the traditional three-dimensional flight path, the four-dimensional flight path prediction adopted by the application is a new generation air control technology, the four-dimensional flight path also considers the time dimension on the basis of considering the longitude and latitude three-dimensional flight path of the aircraft, the whole flight process is represented by longitude and latitude height and time, the time of the flight passing through the waypoint can be accurately controlled, the information such as the position and speed of the aircraft at any moment can be accurately obtained, and the Doppler frequency shift is well compensated.
The existing aircraft track prediction method mainly comprises two main categories, namely prediction by utilizing aircraft performance parameters, meteorological data and aircraft intention, and a parameter-free estimation method based on Kalman filtering or machine learning and the like without a large number of parameters.
A Doppler frequency shift compensation method based on four-dimensional flight paths applies civil aviation broadband communication LDACS to time-frequency synchronization when an airborne CPE and a ground station are in communication, as shown in figures 1 and 2, the method comprises the following specific steps:
firstly, aiming at an aircraft to be tested, observing and recording the actual running track of the aircraft, and representing the running track through a linear hybrid model;
the linear hybrid model includes an observation equation and a state equation, which are expressed as follows:
the equation of state: x (t) =AX (t-1) +BU (t-1) +w (t)
Observation equation: z (t) =H (t) X (t) +n (t)
Where X (t) is a true state vector describing the states of the aircraft, including the true three-dimensional coordinates of the aircraft, the component speeds in three directions, and the accelerations in three directions. X (t) is the true state at the current time t, and X (t-1) is the true state at time t-1; a is a real state transition matrix; w (t) is the noise matrix at the current time; u is a real control vector, and U (t-1) represents the real control vector at the time t-1; b is a real control matrix representing the change of converting the input real control vector into a state vector;
z (t) is the real observation value of the system at the moment t; n (t) represents real observation noise at the time t, the mean value of the real observation noise is 0, and the covariance is R; h is the real observation matrix, and H (t) represents the real observation matrix at time t.
Step two, based on a Kalman filtering mode, combining a linear hybrid model to perform initialization prediction on four-dimensional tracks of the aircraft to obtain a system state estimated value at the moment t of the aircraft
The initialization predictor for the four-dimensional track of the aircraft is set to 0. The real state X (t) of the system cannot be obtained due to the presence of noise, but the real observed value Z (t) obtained by observation and the predicted value X obtained by prediction p (t) weighting to obtain a system state estimate approaching the true valueWhen Kalman filtering is used at this time, the state of the next moment can be calculated only by the parameters which are input currently. Wherein, the noise matrix w (t) of the system has the greatest influence on the accuracy of the Kalman filtering.
The specific process is as follows:
step 201, calculating a system state prediction value X at time t according to the real state value and the real control vector at time t-1 p (t); and calculates the real observation value Z (t) and the system state predicted value X by using the covariance P (t-1) of the real state value at the time t-1 and the optimal estimation thereof p Covariance P of (t) - (t)；
X p (t)＝A*X p (t-1)+B*U(t-1)
P - (t)＝AP(t-1)A T +Q
Q represents the covariance matrix of gaussian noise.
Step 202, using the real observed value Z (t-1) at time t-1 and the system state predicted value X p Covariance P of (t-1) - (t-1) updating parameters of Kalman filtering to obtain Kalman filtering gain K:
K＝P - (t-1)H T (HP - (t-1)H T +R) -1
step 203, li Yongka Kalman filtering gain K and system state prediction value X at time t p (t) calculating a system state estimate at time tAnd updates the true value at time t and the covariance P (t) of the optimal estimate, and returns to step 201 to perform kalman filtering at the next time.
P(t)＝(1-KH)*P - (t-1)
The process is automatically triggered when the observed value Z (t) is received each time, and ADS-B message information on the aircraft is input into a Kalman filter as the observed value in the actual sailing process.
And thirdly, obtaining average Doppler frequency shift according to the average system state estimated value of the aircraft in a time window, and pre-compensating the received signal.
In a new generation of air traffic control system based on four-dimensional track operation, there is a concept of estimated arrival time and actual arrival time, a time interval of a period of time before and after a certain point of time is defined as a time window, the estimated arrival time and the actual arrival time are different, but the arrival within the time window is considered to be accurate, and more accurate prediction can control the shorter the time window that the aircraft arrives at a certain position.
The method comprises the following steps:
step 301, in order to reduce the error as much as possible, taking the current time t as the center, taking the time period h before and after the time t as a time window, and recording the estimated value of the system state in the time period h as the slaveTo->Averaging to obtain an average system state estimate value within the time window of +.>
Step 302, estimating a value according to the average system stateObtaining the average velocity v of the aircraft in the time window, and carrying the average velocity v into a Doppler frequency shift formula to obtain the Doppler frequency shift f d 。
Doppler frequency shift calculation formula: f (f) d ＝f m cos(θ)
θ is the included angle between the relative motion speed direction and the connecting line of the receiving end; f (f) m Indicating the maximum doppler shift frequency of the signal,f c is the carrier center frequency and C is the speed of light.
Step 303, using the doppler shift to precompensate the received signal, thereby obtaining a precompensated received signal;
initially, assuming that the transmission signal is s (n), the reception signal r (n) is:
r(n)＝s(n-n d )exp(j*2π*n*Δf*Ts)
wherein j is a constant,ts is the sampling period, Δf is the total frequency offset of the signal during transmission, n d Is the time offset of the signal during transmission.
The precompensated received signal is: r' (n) =s (n-n) d )exp(j*2π*(Δf-f d )*n*Ts)
And step four, after Doppler frequency shift precompensation, the frequency offset of the received signal is time-frequency synchronized by using the cyclic prefix and the synchronizing signal.
The cyclic prefix CP is a part of the end of an OFDM symbol added to the symbol, and has good autocorrelation.
The frequency offset of the received signal is divided into an integer multiple of the frequency offset (IFO), a fractional multiple of the frequency offset (FFO), and a residual frequency offset for the subcarrier spacing.
(1) Integer multiple frequency offset IFO
Firstly, performing sliding correlation calculation on a plurality of OFDM symbols by using cyclic prefix, wherein the point of obtaining the maximum peak value is the starting point of one OFDM symbol, and the time offset n in the transmission process is obtained d ；
And then, the OFDM symbol time coarse synchronization and the integer frequency multiplication partial synchronization of the frequency domain of the system are completed by using a maximum likelihood algorithm.
The correlation function R (n) of the received signal R (n) is:
wherein d represents a timing pointer, N represents the number of subcarriers of an OFDM symbol, and L is the CP length;
integer multiple frequency offset estimation epsilon IFO The method comprises the following steps:
wherein d ml Is the start time of the OFDM symbol.
(2) Fractional frequency offset FFO
The PSS (primary synchronization signal ) is used to estimate the fractional frequency offset, the received PSS signal p (n) is divided into front and back sections, and the two sections are conjugated and multiplied with the local unbiased PSS signal s (n), so that the fractional frequency offset epsilon FFO The method comprises the following steps:
(3) Residual frequency offset
In practical cases, after FFO and IFO estimation are completed, some residual frequency offset still exists, and the residual frequency offset is subjected to frequency offset adjustment by training and using a phase-locked loop.
And fifthly, demodulating the received signal with the time-frequency synchronization, acquiring speed information, position information and aircraft intention of the received signal, returning to the step two, feeding back to the predicted initial track, correcting the predicted initial track, and carrying out the next round of four-dimensional track prediction according to corrected data.
In the general equation of the linear hybrid model, Z (t) is the observed value at the current time, and noise n (t) exists, but the secondary error is from the measuring instrument and cannot be estimated. The noise matrix w (t) in the state equation can be estimated by demodulating the real value of the state at the previous time and the estimated value of the state at the previous time, so that the prediction is more accurate, and the pre-compensation estimation of the Doppler frequency shift is more accurate.
Examples
The Doppler shift compensation method based on the four-dimensional flight path comprises the following specific steps in the embodiment:
firstly, considering a real observation equation and a real state equation, and completing prediction of an initial track based on Kalman filtering;
the state equation and the observation equation are respectively:
X(t)＝AX(t-1)+BU(t-1)+w(t)
Z(t)＝H(t)X(t)+n(t)
in the state information received by the receiving end, the speed and the position of the aircraft at the last moment are known, and the true state value representing the last moment can be known. In the state equation, a noise term w (t) exists in the system state equation X (t), when the time goes to the k moment, the actual state value at the k-1 moment is known, the predicted value is also recorded, and the difference between the two can be compared at the moment, so that the noise level can be obtained. Considering that the noise is gaussian noise, the mean is 0 but covariance exists, at which point the noise can be calculated by calculating the covariance matrix.
In the actual process, the covariance matrix of the noise may be continuously changed, and in view of this point, the covariance matrix at the previous moment may be differentiated to calculate the change rate of covariance demonstration, so as to complete the estimation of the covariance matrix at the next moment, and then the covariance matrix is brought into the state equation, so that a better result can be obtained.
The prediction of the initial track is completed based on Kalman filtering, and the specific process is as follows:
the state matrix of the aircraft is an n-dimensional column vector, and X is set T (t)＝[v x ,v y ,v z ,p x ,p y ,p z ,a x ,a y ,a z ]；
Wherein [ v ] x ,v y ,v z ]Is the velocity component in three directions of xyz of the aircraft, [ p ] x ,p y ,p z ]Is the three position coordinates of the aircraft, [ a ] x ,a y ,a z ]Is the acceleration component of the aircraft in three directions.
For the state transition matrix a, a is an n×n matrix, the state of the aircraft at the next moment can be estimated according to the existing state of the aircraft, and the state transition matrix a is obtained according to a motion formula:
wherein b=1/2 k 2 K is the time interval between the last time and the current time.
The control matrix B is used to input the aircraft for various human intervention movements, such as steering and landing control actions. The control matrix is typically an n-dimensional column vector and has values only on three terms of acceleration. For simplicity, the control vector is set here to an n-order unit array.
The noise matrix w (t) is a 9-dimensional column vector, the content of which is consistent with that of X (t), represents noise on the position, velocity and acceleration components, and is Gaussian noise with the mean value of 0 and the covariance matrix of Q.
The observations Z (t) during the kalman filtering process are a three-dimensional column vector, recording the observations of the xyz coordinate position of the aircraft.
Observation matrix
The observation noise n (t) is a 3-dimensional column vector whose content matches the observation value Z (t), and represents the noise of the aircraft position, and is gaussian noise with a mean value of 0 and a covariance of R.
Step two, after the initialization prediction of the track is completed by Kalman filtering, doppler frequency shift compensation is carried out;
the formula for calculating the Doppler shift is:θ is relative motionAnd an included angle between the speed direction and the connecting line of the receiving and transmitting end.
Assuming that the transmission signal is s (n), the reception signal is r (n), and it can be known that r (n) =s (n-n) d ) exp (j 2 pi Δf n Ts), where Δf represents the frequency shift due to doppler shift and other factors, n d Representing the time domain deviation in the transmission.
The precompensated signal is r' (n) =s (n-n) d )exp(j*2π*(Δf-f d )*n*Ts)，f d Is an estimate of the doppler shift from the track prediction, and then only needs to perform normal time-frequency synchronization to accept the signal.
Compared with the traditional method of directly performing time-frequency synchronization, the method reduces the requirement on the time-frequency synchronization capability of a receiving end, some devices with low original frequency offset correction capability can now also receive signals with large Doppler frequency shift, the requirement on the time-frequency synchronization capability of the devices is reduced, and the manufacturing cost of the devices is also reduced. Secondly, the convergence process of time-frequency synchronization is quickened, the frequency of the signal is closer to the correct frequency through the precompensation of Doppler frequency shift, the time for synchronizing to the correct frequency is reduced, and in the aspect of synchronization maintenance, the frequency of the signal can be tracked more accurately when the aircraft is offset due to various action execution or other changes.
And thirdly, after the signal receiving is completed, the content in the received signal can be demodulated, and after the state information of the aircraft is also put into the signal, the track prediction can be further perfected through the state information, so that the Doppler frequency shift can be estimated more accurately.
Claims (3)
1. The Doppler frequency shift synchronization method based on four-dimensional track prediction is characterized by comprising the following specific steps:
firstly, aiming at an aircraft to be tested, observing and recording the actual running track of the aircraft to be tested, and representing the actual running track by using a linear hybrid model; based on Kalman filtering mode, linear hybrid model is combined to perform initialization prediction on four-dimensional flight path of aircraft to obtain system state estimated values of each moment of the aircraft
Then, according to the average system state estimation value of the aircraft in a time window, obtaining average Doppler frequency shift, and pre-compensating the received signal; performing time-frequency synchronization on the frequency offset of the received signal after Doppler frequency shift precompensation by using the cyclic prefix and the synchronization signal;
finally, demodulating the received signal with the time-frequency synchronization, obtaining speed information, position information and aircraft intention carried by the received signal, feeding back to the predicted initial track, correcting the initial track, and predicting the next round of four-dimensional track according to corrected data;
the linear hybrid model comprises an observation equation and a state equation, which are respectively expressed as follows:
the equation of state: x (t) =AX (t-1) +BU (t-1) +w (t)
Observation equation: z (t) =H (t) X (t) +n (t)
Wherein X (t) is a true state vector comprising the true three-dimensional coordinates of the aircraft, the component speeds in three directions and the accelerations in three directions; x (t) is the true state at time t, and X (t-1) is the true state at time t-1; a is a real state transition matrix; w (t) is the noise matrix at the current time; u (t-1) represents a true control vector at time t-1; b is a real control matrix representing the change of converting the input real control vector into a state vector;
z (t) is the real observation value of the system at the moment t; n (t) represents real observation noise at the time t, the mean value of the real observation noise is 0, and the covariance is R; h (t) represents the real observation matrix at time t;
the method comprises the following specific processes of:
step 301, taking the current time t as the center, taking the time period h before and after the time t as a time window, and recording the estimated value of the system state as the slaveTo->Averaging to obtain an average system state estimate value within the time window of +.>
Step 302, estimating a value according to the average system stateObtaining the average velocity v of the aircraft in the time window, and carrying the average velocity v into a Doppler frequency shift formula to obtain the Doppler frequency shift f d ；
Doppler frequency shift calculation formula: f (f) d ＝f m cos(θ)
θ is the included angle between the relative motion speed direction and the connecting line of the receiving end; f (f) m Indicating the maximum doppler shift frequency of the signal,f c is the carrier center frequency, C is the speed of light;
step 303, using the doppler shift to precompensate the received signal, thereby obtaining a precompensated received signal;
assuming that the transmission signal is s (n), the reception signal r (n) is:
r(n)＝s(n-n d )exp(j*2π*n*Δf*Ts)
wherein,ts is the sampling period, Δf is the total frequency offset of the signal during transmission, n d Is the time offset of the signal during transmission;
the precompensated received signal is: r' (n) =s (n-n) d )exp(j*2π*(Δf-f d )*n*Ts)。
2. The method for synchronizing doppler shift based on four-dimensional track prediction according to claim 1, wherein the following steps are performedBased on Kalman filtering, to obtain system state estimated values of each moment of the aircraftThe specific process is as follows:
step 201, calculating a system state predicted value X at time t according to the real state vector and the real control vector at time t-1 p (t)；
X p (t)＝A*X p (t-1)+B*U(t-1)
Step 202, using the system real observation value Z (t) and the system state prediction value X p (t) calculating covariance P - (t)；
P - (t)＝AP(t-1)A T +Q
Q represents the covariance matrix of the noise matrix w (t); p (t-1) represents the covariance of the real state vector and its optimal estimate at time t-1;
the optimal estimate of the true state value is: an approximate real state value obtained after the observation noise is estimated;
step 203, using the real observed value Z (t-1) at time t-1 and the system state predicted value X p Covariance P of (t-1) - (t-1) updating parameters of Kalman filtering to obtain Kalman filtering gain K:
K＝P - (t-1)H T (HP - (t-1)H T +R) -1
step 204, li Yongka Kalman filtering gain K and system state prediction value X at time t p (t) calculating a system state estimation value at the time t
Step 205, updating the true value at the time t and the covariance P (t) of the optimal estimation, returning to step 201 to perform Kalman filtering at the next time to obtain a system state estimation value at the next time;
P(t)＝(1-KH)*P - (t-1)。
3. the method for synchronizing doppler shift based on four-dimensional track prediction according to claim 1, wherein the frequency offset of the received signal is divided into an integer multiple of frequency offset (IFO), a fractional multiple of frequency offset (FFO) and a residual frequency offset, and the time-frequency synchronization is performed respectively by:
(1) Integer multiple frequency offset IFO
Firstly, performing sliding correlation calculation on a plurality of OFDM symbols by using cyclic prefix, wherein the point of obtaining the maximum peak value is the starting point of one OFDM symbol, and the time offset n in the transmission process is obtained d ；
Then, using maximum likelihood algorithm to complete OFDM symbol time coarse synchronization and integer frequency multiplication partial synchronization of frequency domain;
the correlation function R (n) of the received signal R (n) is:
integer multiple frequency offset estimation epsilon IFO The method comprises the following steps:
wherein d ml Is the start time of the OFDM symbol;
(2) Fractional frequency offset FFO
Dividing the received PSS signal p (n) into front and rear sections, and performing conjugate multiplication on the PSS signal p (n) with no local deviation to obtain decimal frequency bias epsilon FFO ：
(3) Residual frequency offset
And performing frequency offset adjustment on the residual frequency offset by training and using a phase-locked loop.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210507253.2A CN114884790B (en) | 2022-05-10 | 2022-05-10 | Doppler frequency shift synchronization method based on four-dimensional track prediction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210507253.2A CN114884790B (en) | 2022-05-10 | 2022-05-10 | Doppler frequency shift synchronization method based on four-dimensional track prediction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114884790A CN114884790A (en) | 2022-08-09 |
| CN114884790B true CN114884790B (en) | 2023-11-10 |
Family
ID=82676293
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210507253.2A Active CN114884790B (en) | 2022-05-10 | 2022-05-10 | Doppler frequency shift synchronization method based on four-dimensional track prediction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114884790B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007071600A (en) * | 2005-09-05 | 2007-03-22 | Nec Corp | Track generation system, its error covariance matrix initial value setting device, track generation method, and its error covariance matrix initial value setting method |
| CN108303692A (en) * | 2018-01-30 | 2018-07-20 | 哈尔滨工业大学 | A kind of multi-object tracking method of solution doppler ambiguity |
| CN110266623A (en) * | 2019-06-05 | 2019-09-20 | 北京邮电大学 | A kind of LEO satellite communication systems carrier synchronization method based on 5G |
| CN111683034A (en) * | 2020-06-09 | 2020-09-18 | 中国电子科技集团公司第五十四研究所 | OFDM-based large Doppler wireless communication time-frequency synchronization method |
-
2022
- 2022-05-10 CN CN202210507253.2A patent/CN114884790B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007071600A (en) * | 2005-09-05 | 2007-03-22 | Nec Corp | Track generation system, its error covariance matrix initial value setting device, track generation method, and its error covariance matrix initial value setting method |
| CN108303692A (en) * | 2018-01-30 | 2018-07-20 | 哈尔滨工业大学 | A kind of multi-object tracking method of solution doppler ambiguity |
| CN110266623A (en) * | 2019-06-05 | 2019-09-20 | 北京邮电大学 | A kind of LEO satellite communication systems carrier synchronization method based on 5G |
| CN111683034A (en) * | 2020-06-09 | 2020-09-18 | 中国电子科技集团公司第五十四研究所 | OFDM-based large Doppler wireless communication time-frequency synchronization method |
Non-Patent Citations (2)
| Title |
|---|
| China initial 4D flight trial evaluation;Daozhong Feng, et al.;IEEE;全文 * |
| 基于扩展卡尔曼滤波的高动态多普勒频移估计;孙峰;金炜东;王博;;现代电子技术(第08期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114884790A (en) | 2022-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP2633662B1 (en) | System and method of frequency offset compensation for radio system with fast doppler shift | |
| CN110831147B (en) | Carrier frequency synchronization method suitable for millimeter wave intersatellite link | |
| CN108199994B (en) | OFDM signal frequency offset estimation method and system for wireless comprehensive measurement instrument | |
| CN102404268B (en) | Method for estimating and compensating doppler frequency offset in Rician channels in high-speed mobile environment | |
| CN107835035B (en) | Low signal-to-noise ratio short frame burst communication open-loop demodulation method and device | |
| US7764753B2 (en) | Apparatus and method for adaptive wireless channel estimation | |
| CN110798422B (en) | Low earth orbit satellite multi-carrier communication system downlink sampling frequency offset estimation and compensation method | |
| CN107645465A (en) | The method and system for being timed and positioning to radio signal | |
| JP2002505551A (en) | OFDM receiving system | |
| CN102137049B (en) | Frequency offset estimation method and device for single time slot in long term evolution (LTE) system | |
| CN108183841A (en) | Base band data processing method and system based on IEEE802.11ah in comprehensive test instrument | |
| CN111132081B (en) | Method and system for downlink synchronization of vehicle-mounted 5G equipment of high-speed train | |
| CN114884790B (en) | Doppler frequency shift synchronization method based on four-dimensional track prediction | |
| CN108075792B (en) | Determine the method, system and receiver of Error Vector Magnitude | |
| CN105072071A (en) | Sampling frequency offset compensation method based on NG-DSL system | |
| CN114786240A (en) | 5G downlink signal discontinuity tracking method | |
| CN109756435B (en) | Method for estimating frequency offset of signal | |
| CN113949608A (en) | Decision feedback demodulation system and method for VDES system | |
| CN106101039A (en) | A kind of based on the data auxiliary adjustable frequency deviation estimating method of precision | |
| EP1402698B1 (en) | Frequency correction for a multicarrier system | |
| CN103297100B (en) | A kind of doppler changing rate method of estimation for ofdm system and system | |
| CN113949612B (en) | Burst signal capturing method and system in communication between helicopter and satellite | |
| KR102396223B1 (en) | A method for estimating a frequency value and frequency estimator for aeronautical communicaiton | |
| CN112565142A (en) | Multi-mode SC-FDE burst transmission carrier synchronization method | |
| CN111090109A (en) | Satellite-borne frequency difference extraction compensation method for rapid frequency difference change |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |