CN114740433A - Time synchronization method based on compressed sensing under influence of multipath effect - Google Patents

Time synchronization method based on compressed sensing under influence of multipath effect Download PDF

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CN114740433A
CN114740433A CN202210451893.6A CN202210451893A CN114740433A CN 114740433 A CN114740433 A CN 114740433A CN 202210451893 A CN202210451893 A CN 202210451893A CN 114740433 A CN114740433 A CN 114740433A
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time synchronization
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CN114740433B (en
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窦衡
李也
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier transforms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04J—MULTIPLEX COMMUNICATION
    • H04J3/00—Time-division multiplex systems
    • H04J3/02—Details
    • H04J3/06—Synchronising arrangements
    • H04J3/0602—Systems characterised by the synchronising information used
    • H04J3/0617—Systems characterised by the synchronising information used the synchronising signal being characterised by the frequency or phase

Abstract

The invention belongs to the technical field of radar time synchronization, and particularly relates to a time synchronization method based on compressed sensing under the influence of multipath effect. In the multipath time delay estimation process, the selected atoms are secondarily screened by adopting a backtracking idea, and the time synchronization precision is improved compared with an OMP algorithm and an ROMP algorithm. And when the method solves the least square problem, a conjugate gradient method is preferentially adopted to replace the least square method, and compared with the SP algorithm, the inversion operation is not needed, so that the algorithm time complexity is reduced, the operation time is shortened, the resource utilization rate is improved, and the space occupation is reduced.

Description

Time synchronization method based on compressed sensing under influence of multipath effect
Technical Field
The invention belongs to the technical field of radar time synchronization, and particularly relates to a time synchronization method based on compressed sensing under the influence of multipath effect.
Background
The unmanned aerial vehicle has no replaceable function in modern wars, and one of key technologies for realizing light weight and cooperative work of formation of multiple unmanned aerial vehicles during high-precision time synchronization. After the time synchronization is carried out on the unmanned aerial vehicle receiving array radar system, the receiving end can obtain the starting moment of the transmitting signal, and meanwhile, the time synchronization is also the premise of data acquisition and distance measurement. Because unmanned aerial vehicle receiving array radar system requires to realize signal level coherent, this has higher requirement to the time synchronization precision, consequently, the reason that analysis unmanned aerial vehicle receiving array radar system produced the time synchronization error, establish corresponding time synchronization error model in view of the above, and it is very necessary to finally realize time synchronization, and it can effectively promote unmanned aerial vehicle to the control of space target, survey and discrimination ability, promotes unmanned aerial vehicle radar system's reliability.
In practical situations, each receiving radar of the receiving array radar system of the unmanned aerial vehicle is affected by multipath effects, and due to the fact that the time delay interval between each path is small, the multipath time delay estimation method with low resolution can easily mistake the multipath time delay parameter into the time delay parameter of a single path, so that time delay estimation errors are caused, and further estimation of time synchronization errors is affected, and therefore the multipath time delay is accurately estimated in the first step of achieving time synchronization. At present, algorithms for multipath delay estimation mainly include classical algorithms such as a Maximum Likelihood (ML) algorithm, a cross-correlation method, a Multiple Signal Classification (MUSIC) algorithm and the like, but the classical algorithms need a large amount of independent statistical data for multipath delay estimation, have high requirements on measurement data and a Signal-to-noise ratio, and are difficult to accurately estimate multipath delay under the condition of low Signal-to-noise ratio, so that in order to reduce the requirements on the measurement data and accurately estimate the multipath delay under the condition of low Signal-to-noise ratio, it is necessary to research the multipath delay estimation algorithm.
In recent years, a Compressed Sensing (CS) technique is not controlled by nyquist sampling theorem, and can accurately recover a signal with only a small amount of measurement data, so that many discussions and researches of experts have been initiated. Therefore, the principle of compressed sensing is utilized, the method is applied to the multipath delay estimation field, the accuracy of multipath delay estimation is improved under the conditions of low signal-to-noise ratio and few fast beats, and the improvement of time synchronization accuracy has profound research significance and practical value.
Disclosure of Invention
The invention aims to solve the problems that the time synchronization precision of an OMP algorithm and an ROMP algorithm is not high, the time synchronization of an SP algorithm is high in algorithm time complexity and long in running time due to inversion operation, and the like. In the multipath time delay estimation process, the method adopts the backtracking idea to carry out secondary screening on the selected atoms, and compared with an OMP algorithm and an ROMP algorithm, the time synchronization precision is improved. And when the method solves the least square problem, a conjugate gradient method is preferentially adopted to replace the least square method, and compared with the SP algorithm, the inversion operation is not needed, so that the algorithm time complexity is reduced, the operation time is shortened, the resource utilization rate is improved, and the space occupation is reduced.
For an unmanned aerial vehicle receiving array radar system with one transmitting and multiple receiving, a unit radar 1 is defined to transmit a chirp signal x (t), and a receiving unit radar of a Q part is provided, so that due to the influence of multipath effect, the signal expression received by the receiving unit radar of the Q part is as follows:
Figure BDA0003618960410000021
in the formula, Q represents the number of multipath components, and the number is the number of radar receiving units; lambda [ alpha ]q,iThe fading coefficients of different paths reaching the q-th receiving unit radar; tau isq,iPropagation delay (including time synchronization error of the q-th receiving unit radar) required to reach different paths of the q-th receiving unit radar, nq(t) is white Gaussian noise.
Theoretically:
τq,i＝τq,i_real-κq (2)
wherein τ is τ when i ≠ qq,i_realThe representation unit radar 1 transmits a chirp signal, and a multipath signal reaches the section q of the unit radar to receive after being reflected or scattered by the unit radar of the i partReal propagation delay caused by the process without time synchronization error, in particular when i equals q, τq,i_realThe real transmission time delay without time synchronization error caused by the section q unit radar receiving the direct wave signal when the unit radar 1 transmits the linear frequency modulation signal is shown. In this context, the true propagation delay without time synchronization error is defined to be known; kappa typeqThe time synchronization error of the q-th unit radar is compared with that of the reference unit radar, wherein the unit radar 1 is defined as the reference unit radar.
After the reception signal of the q-th element radar is obtained, it can be seen from equation (2) that the propagation delay τ is estimatedq,iAnd using the known true propagation delay value tauq,i_realThe estimated time synchronization error value of the q unit radar compared with the reference unit radar can be obtained by the formula (3)
Figure BDA0003618960410000031
Figure BDA0003618960410000032
in the formula ,
Figure BDA0003618960410000033
is an estimate of the propagation delay.
From the above, the key to the time synchronization of the receiving array radar system of the unmanned aerial vehicle affected by the multipath effect is to estimate the multipath time delay. A multipath time delay estimation model based on compressed sensing is established below.
The discrete fourier transform of the signal received by the q-th receiving unit radar includes:
Figure BDA0003618960410000034
in the formula ,Sq(k) Is s isq(t) discrete fourier transform; x (k) is the discrete Fourier transform of x (n); n is a radical of hydrogenq(k) Is Gaussian white noise nq(n) a discrete fourier transform; k is the number of sampling points.
Changing equation (4) into vector form, then:
Sq＝X′·Πq·λq+Nq＝Γq·λq+Nq (5)
in the formula ：
Sq＝[sq(0),sq(1),...,sq(K-1)]T，
X′＝diag(|X(0)|,|X(1)|,...,|X(K-1)|)，
Πq＝[χ(τq,1),χ(τq,2),...,χ(τq,Q)]，
Figure BDA0003618960410000035
λq＝[λq,1,λq,2,...,λq,Q]T，
Γq＝X′·Πq，
Nq＝[Nq(0),Nq(1),...,Nq(K-1)]T
in particular, X' is called a signal vector containing information of the transmitted signal, ΓqReferred to as steering vectors containing multipath propagation delays.
The whole time domain is equally divided into P parts, i.e.
Figure BDA0003618960410000041
Defining any one of the time delays
Figure BDA0003618960410000042
Where i is 1,2, …, P, which corresponds to a potential path to the q-th receiving unit radar. Then the sparse vector
Figure BDA0003618960410000043
Each row of (a) also corresponds to a potential path to the q-th receiving unit radar. In particular, it is possible to use, for example,in order to highlight the sparseness of the signal, the path potentially present to the q-th receiver radar should be much larger than the path actually present to the q-th receiver radar, i.e. P>>Q，
Figure BDA0003618960410000044
Also known as sparse coefficients.
After the division mode of the time domain is determined, a guide vector gamma containing the multipath transmission time delayqAlso from the K x Q order to the K x P order, i.e.
Figure BDA0003618960410000045
Thereby resulting in a sparse basis matrix. Wherein the content of the first and second substances,
Figure BDA0003618960410000046
then, the estimation model of multipath propagation delay based on compressed sensing is:
Figure BDA0003618960410000047
in the formula, phi is an observation matrix; y isq＝Φ·SqIs an observation vector under an observation matrix phi;
Figure BDA0003618960410000048
sparse coefficients to be solved;
Figure BDA0003618960410000049
is a perceptual matrix.
As can be seen from the basic theory of the compressive sensing technology, the sparse coefficient can be obtained by solving the formula (6)
Figure BDA00036189604100000410
Then according to
Figure BDA00036189604100000411
And
Figure BDA00036189604100000412
in one-to-one correspondence withThe relationship can estimate the multipath transmission delay.
Figure BDA00036189604100000413
The specific solution comprises the following steps:
inputting: perception matrix thetaqObservation vector YqDegree of sparsity Q of signal, maximum number of iterations W, allowable error
Figure BDA00036189604100000414
Initial vector
Figure BDA00036189604100000415
And (3) outputting: sparse vectors
Figure BDA0003618960410000051
Description of the drawings: t denotes the t-th iteration, rtRepresents the residual error, Ω, generated by the t-th iterationtRepresenting the perceptual matrix theta selected by the t-th iterationqA set of indices (i.e., column sequence numbers) in (1);
Figure BDA0003618960410000052
denotes thetaqColumn i, AtIs expressed in terms of ΩtIndex value of from thetaqThe selected column set, i.e. atom set.
Initialization: defining an initial residual r0＝YqInitial index set
Figure BDA0003618960410000053
Initial set of atoms
Figure BDA0003618960410000054
The iteration time t is 1;
a: the residual r generated by the t-1 th iteration is calculated according to the formula (8)t-1Projection values (i.e., inner products) at the perceptual matrix:
Figure BDA0003618960410000055
b: sorting the components of the projection value obtained by the formula (8) according to the absolute value, finding out Q column vectors with the maximum absolute value, and forming a set J by the corresponding column serial numbers (index values)0；
c: update index set omegat＝Ωt-1∪J0Set of atoms At＝At-1∪θqi, wherein i∈J0；
d: calculation using conjugate gradient method
Figure BDA0003618960410000056
Wherein the initial vector in the conjugate gradient method is
Figure BDA0003618960410000057
If the result calculated using the conjugate gradient method diverges, it is recalculated using equation (9):
Figure BDA0003618960410000058
e: calculating the absolute value of the atom selected in the fourth step by utilizing the concept of backtracking, leaving the Q components with the maximum absolute values, and recording the corresponding column sequence numbers thereof, wherein the column sequence numbers form a set
Figure BDA0003618960410000059
Corresponding perception matrix thetaqThe set of Q column vectors in (A) is denoted as
Figure BDA00036189604100000510
Updating index collections
Figure BDA00036189604100000511
Collection of atoms
Figure BDA00036189604100000512
f: updating residual r of the t-th iteration by using equation (10)t：
Figure BDA00036189604100000513
g: let t equal t +1, if k>W or residual
Figure BDA00036189604100000514
Ending the iteration and entering the step h, otherwise, continuing the iteration and returning to the step a;
h: to obtain
Figure BDA0003618960410000061
The value is obtained from the last iteration
Figure BDA0003618960410000062
And in accordance with
Figure BDA0003618960410000063
And
Figure BDA0003618960410000064
estimating the multipath time delay according to the one-to-one correspondence relationship;
and (4) calculating a time synchronization error estimated value according to the formula (3), and compensating the time synchronization error of the received signal so as to realize time synchronization.
According to the method, on the basis of a Subspace tracking method (SP), a conjugate gradient method is preferentially adopted to replace a least square method to solve the problem of least square, so that the problems of high algorithm time complexity, long operation time, space occupation and the like caused by inversion operation in the process of solving by using the least square method are effectively solved, and the resource utilization rate is improved. Compared with Orthogonal Matching Pursuit (OMP) algorithm and Regularized Orthogonal Matching Pursuit (ROMP) algorithm, the method provided by the invention has higher time synchronization precision because a backtracking mechanism is adopted to carry out secondary screening on the selected atoms.
The beneficial effects of the invention are as follows: compared with an OMP algorithm and an ROMP algorithm, the method improves the time synchronization precision, reduces the algorithm time complexity and improves the operation speed and the space resource utilization rate.
Drawings
FIG. 1 is a schematic diagram of estimating multipath delay for 4 receiving unit radars using the method of the present invention, wherein (a) is a diagram of estimating multipath propagation delay for the first receiving unit radar; (b) a multipath transmission delay estimation diagram of the radar of the second receiving unit; (c) a multipath transmission delay estimation diagram of a radar of a third receiving unit; (d) a graph is estimated for the multipath propagation delay for the fourth receiving unit radar.
Detailed Description
The invention is described in detail below with reference to the drawings and simulation examples to prove the applicability of the invention.
The unmanned aerial vehicle receiving array radar system adopts a one-transmitting and multi-receiving mode, a unit radar 1 is defined to transmit linear frequency modulation signals, and specific parameters of the linear frequency modulation signals are shown in a table 1.
TABLE 1 transmitting Unit Radar parameter meanings and Default values thereof
Figure BDA0003618960410000065
Figure BDA0003618960410000071
Defining the number of receiving unit radars to be 4, the time synchronization error value of each receiving unit radar compared with the transmitting unit radar is a random value between 10ns and 20ns, and here: kappa1＝19.233ns，κ2＝13.891ns，κ3＝11.736ns，κ4＝14.915ns。
Table 2 shows the multipath propagation delay of each receiving unit radar without time synchronization error. When i is not equal to q, the transmission time delay which is not contained with time synchronization error and is required in the process that the signal reaches the q-th receiving unit radar after being transmitted or scattered by the ith receiving unit radar is shown after the signal is transmitted by the transmitting unit radar. Specifically, when i is q, the transmission delay time without a time synchronization error required in the process of the signal transmitted by the transmitting unit radar to the direct wave of the q-th receiving unit radar is represented.
TABLE 2 multipath propagation delay (not including time synchronization error) for each receiving unit radar
Figure BDA0003618960410000072
Table 3 shows the multipath propagation delay including the time synchronization error of each receiving unit radar calculated by equation (2), which is also a parameter to be estimated later. And when i is not equal to q, the transmission time delay containing the time synchronization error is required in the process that the signal reaches the q-th receiving unit radar after being transmitted or scattered by the ith receiving unit radar after the signal is transmitted by the transmitting unit radar. Specifically, when i is q, the transmission delay including the time synchronization error required in the process of the signal transmitted by the transmitting unit radar to the direct wave of the q-th receiving unit radar is represented.
TABLE 3 multipath propagation delay (including time synchronization error) for each receiving unit radar
Figure BDA0003618960410000081
The signal-to-noise ratio SNR is defined as 10 dB. In the experiment, when the SNR is 10dB, if the time synchronization accuracy of each receiving unit radar is within 1ns, the experiment is successful.
The method of the invention is used for estimating the multipath transmission time delay of 4 receiving unit radars, thereby calculating the time synchronization error estimation value of each receiving unit radar.
The multipath delay estimation diagram for estimating 4 receiving unit radars by using the method of the invention is shown in figure 1:
table 4 shows the estimated multipath propagation delay values of the radars of the respective receiving units estimated by the method of the present invention. When i ≠ q, table 4 shows estimated transmission delay values estimated in the process that the signal transmitted by the transmitting unit radar reaches the qth receiving unit radar after passing through the ith receiving unit radar. Specifically, when i is q, table 4 shows estimated propagation delay values estimated in the process of a direct wave from a signal transmitted by the transmitting unit radar to the qth receiving unit radar.
TABLE 4 multipath propagation delay estimate for each receiving unit radar
Figure BDA0003618960410000091
After the multipath transmission delay estimation value of each receiving unit radar is obtained by the method, the time synchronization error estimation value can be estimated by using the formula (3) according to the known real transmission delay value without the time synchronization error.
The time synchronization error estimation value and the time synchronization accuracy of each receiving unit radar are calculated and shown in table 5:
TABLE 5 comparison table for radar time synchronization error true value and estimated value of each receiving unit
Figure BDA0003618960410000092
From the above table, it can be seen that: the method of the invention can accurately estimate the multipath transmission time delay of each receiving unit radar, and further estimate the time synchronization error value according to the formula (3). Under the condition that SNR is 10dB, the time synchronization precision is within 1ns, the experimental requirement is met, and the algorithm has practicability.

Claims (1)

1. A time synchronization method based on compressed sensing under the influence of multipath effect defines that in an unmanned aerial vehicle receiving array radar system with one transmitting unit and multiple receiving units, a transmitting unit radar transmits a linear frequency modulation signal x (t), a receiving unit radar of a Q part is provided, and due to the influence of multipath effect, a signal expression received by the receiving unit radar of the Q part is as follows:
Figure FDA0003618960400000011
q represents the number of multipath components and the number of receiving unit radars; lambda [ alpha ]q,iThe fading coefficients of different paths reaching the q-th receiving unit radar; t is tq,iThe transmission time delay required for reaching different paths of the q-th receiving unit radar comprises the time synchronization error of the q-th receiving unit radar, nq(t) is white gaussian noise;
tq,i＝tq,i_real-κq
wherein, when i ≠ q, τq,i_realThe real transmission time delay without time synchronization error caused in the process that the transmitting unit radar transmits linear frequency modulation signals, and multipath signals reach the q-th unit radar after being reflected or scattered by the i-th unit radar to receive; when i ═ q, tq,i_realThe method comprises the steps that a transmitting unit radar transmits a linear frequency modulation signal, real transmission time delay without time synchronization errors is caused in the process that a q-th unit radar receives a direct wave signal, and the real transmission time delay without the time synchronization errors is defined to be known; kappaqThe time synchronization error of the qth unit radar is compared with that of the reference unit radar, and the reference unit radar is a transmitting unit radar;
the time synchronization method is used for carrying out time synchronization by estimating multipath time delay and is characterized by comprising the following steps:
s1, obtaining the receiving signal of the q unit radar and obtaining the transmission time delay tauq,iAnd using the known true propagation delay value tauq,i_realObtaining the estimated time synchronization error value of the q unit radar compared with the reference unit radar
Figure FDA0003618960400000012
Figure FDA0003618960400000013
wherein ,
Figure FDA0003618960400000014
is a transmission delay estimation value;
s2, establishing a multi-path time delay estimation model based on compressed sensing, which specifically comprises:
performing discrete Fourier transform on a signal received by a q-th receiving unit radar:
Figure FDA0003618960400000021
wherein ,Sq(k) Is s isq(t) discrete Fourier transform, X (k) is x (N), Nq(k) Is Gaussian white noise nq(n) discrete fourier transform, K being the number of sampling points, into vector form:
Sq＝X′·Πq·λq+Nq＝Γq·λq+Nq
wherein ：
Sq＝[sq(0),sq(1),...,sq(K-1)]T
X′＝diag(|X(0)|,|X(1)|,...,|X(K-1)|)
Πq＝[χ(τq,1),χ(τq,2),...,χ(τq,Q)]
Figure FDA0003618960400000022
λq＝[λq,1,λq,2,...,λq,Q]T
Γq＝X′·Πq
Nq＝[Nq(0),Nq(1),...,Nq(K-1)]T
x' is defined as a signal vector containing information of the transmitted signal, ΓqA steering vector containing multipath transmission time delay;
the whole time domain is equally divided into P parts, i.e.
Figure FDA0003618960400000023
Defining any one of the time delays
Figure FDA0003618960400000024
Corresponds to a potential path to the q-th receiving unit radar, where i is 1,2, …, P, then the sparse vector is
Figure FDA0003618960400000025
Each row of the same corresponds to a potential path to reach the q-th receiving unit radar;
a steering vector gamma containing multipath transmission time delayqFrom K × Q to K × P, i.e.:
Figure FDA0003618960400000026
thereby obtaining a sparse basis matrix, wherein
Figure FDA0003618960400000027
Then, the established multipath propagation delay estimation model based on compressed sensing is as follows:
Figure FDA0003618960400000031
wherein ,Yq＝Φ·SqFor an observation vector under the observation matrix phi,
Figure FDA0003618960400000032
sparse coefficients to be solved;
s3, solving the multipath transmission time delay estimation model based on the compressed sensing to obtain the sparse coefficient
Figure FDA0003618960400000033
The method specifically comprises the following steps:
defining a perception matrix
Figure FDA0003618960400000034
The sparsity of the signal is the number Q of multipath components, the maximum iteration number W, and the allowable error
Figure FDA0003618960400000035
Initial vector
Figure FDA0003618960400000036
Initial residual r0＝YqInitial index set
Figure FDA0003618960400000037
Initial set of atoms
Figure FDA0003618960400000038
The iteration time t is 1; the solution is performed in an iterative manner as follows:
a. computing the residual r generated by the t-1 th iterationt-1Projection values on the perceptual matrix:
Figure FDA0003618960400000039
b. sorting the components of the obtained projection values according to the absolute value, finding out Q column vectors with the maximum absolute value, and forming a set J by the corresponding column sequence numbers0；
c. Update index set omegat＝Ωt-1∪J0Set of atoms At＝At-1∪θqi, wherein i∈J0；
d. Calculation using conjugate gradient method
Figure FDA00036189604000000310
Wherein the initial vector in the conjugate gradient method is
Figure FDA00036189604000000311
If the result calculated using the conjugate gradient method diverges, it is recalculated:
Figure FDA00036189604000000312
e. calculating the absolute value of the atom selected in the step d by utilizing the backtracking idea, leaving the Q components with the maximum absolute value, and recording the corresponding column sequence numbers thereof, wherein the column sequence numbers form a set
Figure FDA00036189604000000313
Corresponding perception matrix thetaqThe set of Q column vectors in (A) is denoted as
Figure FDA00036189604000000314
Updating index collections
Figure FDA00036189604000000315
Collection of atoms
Figure FDA00036189604000000316
f. Updating residual r of the t-th iterationt：
Figure FDA00036189604000000317
g. Let t equal t +1, if k>W or residual
Figure FDA00036189604000000318
Ending iteration and entering step h, otherwise, continuing iteration and returning to the step a;
h. to obtain
Figure FDA00036189604000000319
The value is obtained from the last iteration
Figure FDA00036189604000000320
At the same time according to
Figure FDA00036189604000000321
And
Figure FDA00036189604000000322
the multipath transmission delay can be estimated according to the one-to-one correspondence relationship:
and S5, obtaining a time synchronization error estimation value according to the obtained multipath transmission time delay and the time synchronization error estimation formula in the step S1, and compensating the time synchronization error of the received signal so as to realize time synchronization.
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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5742635A (en) * 1994-05-05 1998-04-21 Sanconix, Inc. Enhanced time of arrival method
CN102253388A (en) * 2011-06-23 2011-11-23 哈尔滨工程大学 Collaborative detection method for multiple unmanned underwater vehicles on basis of compressed sensing
CN104392412A (en) * 2014-10-23 2015-03-04 西安电子科技大学 Compressed sensing signal recovery method based on orthogonal matching pursuit evolution
CN106291452A (en) * 2016-09-12 2017-01-04 电子科技大学 A kind of direction of arrival estimation method based on enhanced greedy algorithm
CN107741579A (en) * 2017-11-15 2018-02-27 中国矿业大学(北京) TOA mine object localization methods based on the reconstruct of compressed sensing subspace
CN109039960A (en) * 2018-06-26 2018-12-18 东南大学 A kind of underwater sound condition of sparse channel estimation variable step degree of rarefication Adaptive matching method for tracing
CN109165416A (en) * 2018-07-30 2019-01-08 广东工业大学 Conjugate gradient iteration hard -threshold radiation energy method for solving based on backtracking
US20190109713A1 (en) * 2017-10-06 2019-04-11 Stealthpath, Inc. Methods for internet communication security
US11249195B1 (en) * 2020-11-29 2022-02-15 Kiomars Anvari Exchange of time of day between two IoT devices
CN114362849A (en) * 2022-01-18 2022-04-15 重庆邮电大学 Signal detection method based on compressed sensing in generalized RASK system

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5742635A (en) * 1994-05-05 1998-04-21 Sanconix, Inc. Enhanced time of arrival method
CN102253388A (en) * 2011-06-23 2011-11-23 哈尔滨工程大学 Collaborative detection method for multiple unmanned underwater vehicles on basis of compressed sensing
CN104392412A (en) * 2014-10-23 2015-03-04 西安电子科技大学 Compressed sensing signal recovery method based on orthogonal matching pursuit evolution
CN106291452A (en) * 2016-09-12 2017-01-04 电子科技大学 A kind of direction of arrival estimation method based on enhanced greedy algorithm
US20190109713A1 (en) * 2017-10-06 2019-04-11 Stealthpath, Inc. Methods for internet communication security
CN107741579A (en) * 2017-11-15 2018-02-27 中国矿业大学(北京) TOA mine object localization methods based on the reconstruct of compressed sensing subspace
CN109039960A (en) * 2018-06-26 2018-12-18 东南大学 A kind of underwater sound condition of sparse channel estimation variable step degree of rarefication Adaptive matching method for tracing
CN109165416A (en) * 2018-07-30 2019-01-08 广东工业大学 Conjugate gradient iteration hard -threshold radiation energy method for solving based on backtracking
US11249195B1 (en) * 2020-11-29 2022-02-15 Kiomars Anvari Exchange of time of day between two IoT devices
CN114362849A (en) * 2022-01-18 2022-04-15 重庆邮电大学 Signal detection method based on compressed sensing in generalized RASK system

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