阅读说明：本技术 一种无人机蜂群组网中sc-fde信号联合测距方法 (SC-FDE signal joint ranging method in unmanned aerial vehicle swarm networking ) 是由 闫朝星 付林罡 刘同领 王圆圆 于 2020-11-06 设计创作，主要内容包括：本发明提供一种无人机蜂群组网中SC-FDE信号联合测距方法,本方法对2倍过采样SC-FDE信号进行联合定时同步与测距估计,首先对2倍过采样信号,然后联合较短前导段数据估计粗测距信息,从而完成数据链系统中两个通信终端间准确测距。本方法可以较低复杂度实现测距性能的有效提升,只需2倍采样实现联合定时同步与测距,适用于采样率有限的宽带SC-FDE通信系统,信号包含前导段与数据段,采样率为2倍,适用于宽带自组网通信系统；基于2倍采样信号对应2路信号分别差分相关,比较得到相关度量较高的采样信号作为精测距序列,该差分相关不受频偏影响；本方计算式不受频偏影响,还可有效提升无人机蜂群组网系统任意两无人机节点之间的测距精度。(The invention provides a SC-FDE signal joint ranging method in an unmanned aerial vehicle swarm network, which carries out joint timing synchronization and ranging estimation on 2 times of oversampled SC-FDE signals, firstly estimates coarse ranging information on the 2 times of oversampled signals and then combines with shorter front-end data, thereby completing accurate ranging between two communication terminals in a data chain system. The method can effectively improve the ranging performance with lower complexity, realizes the joint timing synchronization and ranging only by 2 times of sampling, is suitable for a broadband SC-FDE communication system with limited sampling rate, has signals comprising a front section and a data section, has the sampling rate of 2 times, and is suitable for a broadband ad hoc network communication system; respectively carrying out differential correlation on 2 paths of signals corresponding to 2 times of sampling signals, and comparing to obtain a sampling signal with higher correlation measurement as a fine ranging sequence, wherein the differential correlation is not influenced by frequency offset; the calculation mode is not affected by frequency deviation, and the distance measurement precision between any two unmanned aerial vehicle nodes of the unmanned aerial vehicle swarm networking system can be effectively improved.)

1. An SC-FDE signal joint ranging method in unmanned aerial vehicle swarm networking is characterized in that: the method comprises the following steps:

s1, transmitting and receiving signals: unmanned aerial vehicle node A in the unmanned aerial vehicle bee colony sends SC-FDE sampling signal x (k) to unmanned aerial vehicle node B: x (k) ═ x (kT)_s)，

Wherein T is symbol periodPeriod T of_ST/2 is a sampling clock, s (N) is a data symbol with a length N at the transmitting end, N is greater than or equal to 1 and less than or equal to N, s (N) includes a preamble section { a (1), a (2),.., a (p) } and a data section { b (1), b (2),., b (q) }, the sum of the lengths of which is N: p + q ═ N; k is more than or equal to 1 and less than or equal to 2N, and g (t) is a filter; τ T is time delay; z (t) is white Gaussian noise;

s2, obtaining 2 times of over-sampled signals: dividing the sampled signal x (k) into an odd sequence y₁And even sequence y₂And deriving a correlation metric R_μ(n) correlating said R with said R_μ(n) carrying out differential correlation and comparison to obtain a sequence with high correlation metric, and obtaining 2 times of oversampling signals y (k) and n by taking the sequence with high correlation metric as a reference_F；

S3, obtaining the estimation of the fine ranging: accumulating, real-obtaining and angle-operating the 2 times of over-sampled signals y (k) to obtain the estimation of the fine ranging

S4, obtaining rough distance measurement information estimation: n is to be_FSpeed and time processing to obtain coarse ranging information estimate

S5, obtaining ranging information: the accurate distance estimate between the drone node a and the drone node B is:the accurate distance estimation is ranging information.

2. The SC-FDE signal joint ranging method in unmanned aerial vehicle swarm networking according to claim 1, wherein the SC-FDE signal joint ranging method comprises the following steps: step S2 includes:

s21, grouping sampling signals: 1 st to 2 nd_TThe sampling signals x (k) are represented as: { x (1), x (2), x (3), x (4), … …, x (2N)_T-1),x(2N_T) H, mixing 2N_TA sampling signalx (k) is divided into odd sequences y₁And even sequence y₂：

y₁＝{x(1),x(3),x(5),……,x(2N_T-1)},

y₂＝{x(2),x(4),x(6),……,x(2N_T)}，

The odd sequence y₁And the even sequence y₂All lengths are N_T；

S22, obtaining a correlation metric: the odd sequence y₁Item y of₁(n) with the even sequence y₂Item y of₂(n) obtaining the correlation metric R according to the following formula_μ(n)：

Wherein, mu is 1,2, alpha is roll-off coefficient;

s23, obtaining n_FUpdating n to n +1, returning to step S21 and step S22 to calculate R_μ(n +1) up to n_F＝arg{R_μ(n) ≧ λ }, wherein arg represents the value of n for which the extraction satisfies a condition;

s24, obtaining 2 times of over-sampled signals: comparison of R₁(n_F) And R₂(n_F) Taking 2N +1 data sequences backward to obtain the 2 times oversampled signal y (k) with the large sequence as a reference point.

3. The SC-FDE signal joint ranging method in unmanned aerial vehicle swarm networking according to claim 1, wherein the SC-FDE signal joint ranging method comprises the following steps: in step S3:

where c is the speed of light, about 3X 10⁸M/s; re is the real part and angle is the calculated angle.

4. The method of claim 1An SC-FDE signal joint ranging method in unmanned aerial vehicle swarm networking is characterized in that: in step S4:

Technical Field

The invention relates to the technical field of digital information transmission, in particular to an SC-FDE signal joint ranging method in unmanned aerial vehicle swarm networking.

Background

In the unmanned plane swarm networking, because the attitude change and the cooperative task execution environment between the networked unmanned planes are complex, satellite navigation signals are shielded and are often in a weak information state or the navigation signals are interfered, the relative position information of the swarm formation is difficult to obtain. Therefore, relative distance and position information between airborne data terminals need to be acquired through networking communication signals, and therefore task efficiency of unmanned aerial vehicle colonies and survivability of a combat system are improved.

A frequency domain nonlinear estimation algorithm insensitive to jitter of code loop tracking of spread spectrum codes is designed in ' Wangbuyun ', a frequency domain nonlinear estimation algorithm for pseudo code ranging, and radio engineering 2007 '; the method based on the PN sequence is researched in the literature of Changchun university of science and technology, 2012, which is characterized by interference resistance, multipath fading resistance, good concealment and the like, but the ranging accuracy of the traditional pseudo-random code ranging technology depends on the chip rate.

An SC-FDE signal (single carrier domain balance) adopted in the unmanned plane swarm networking system has the characteristics of low peak-to-average ratio, multipath interference resistance and the like. The research literature on the ranging of the system signal is less related, and the invention patent of an author, namely 'Yan star, handle bar, Zan Ming, a ranging method, a device and a recording medium based on joint estimation, 2018, Chinese invention patent, 201810622433.9', provides a ranging method based on joint estimation, and the accurate ranging is carried out based on the sum of absolute values of over-sampled signals, but the sampling rate of the method can be calculated by 4 times of over-sampling. Another invention patent "Yan entitled" timing synchronization method based on SC-FDE burst signal 2 times oversampling "2020, chinese invention patent, 202018001719.3" relates to a timing synchronization method during 2 times oversampling. But no ranging method has been designed for the 2 x oversampled SC-FDE signal. Bandwidth is usually limited to 56MHz in the software radio chip AD9361, and the sampling rate is limited to 122.88MHz, that is, a wideband signal is oversampled at most by 2 times, and 4 times of oversampling cannot be obtained, and a joint timing synchronization and ranging method needs to be studied for 2 times of oversampling of the wideband signal.

Disclosure of Invention

The invention aims to solve the problem of researching a combined timing synchronization and ranging method for broadband signal oversampling 2 times in an unmanned aerial vehicle swarm network, and provides an SC-FDE signal combined ranging method in the unmanned aerial vehicle swarm network. The method only needs 2 times of sampling to realize the joint timing synchronization and the distance measurement, and is suitable for a broadband SC-FDE communication system with limited sampling rate.

The invention provides an SC-FDE signal joint ranging method in unmanned aerial vehicle swarm networking, which comprises the following steps:

s1, transmitting and receiving signals: unmanned aerial vehicle node A in the unmanned aerial vehicle bee colony sends SC-FDE sampling signal x (k) to unmanned aerial vehicle node B: x (k) ═ x (kT)_s)，

Wherein T is a symbol period, T_ST/2 is a sampling clock, s (N) is a data symbol with a length N at the transmitting end, N is greater than or equal to 1 and less than or equal to N, s (N) includes a preamble section { a (1), a (2),.., a (p) } and a data section { b (1), b (2),., b (q) }, the sum of the lengths of which is N: p + q ═ N; k is more than or equal to 1 and less than or equal to 2N, and g (t) is a filter; τ T is time delay; z (t) is white Gaussian noise;

s2, obtaining 2 times of over-sampled signals: dividing the sampled signal x (k) into an odd sequence y₁And even sequence y₂And deriving a correlation metric R_μ(n) correlating the metrics R_μ(n) carrying out differential correlation and comparison to obtain a sequence with high correlation metric, and obtaining 2 times of oversampling signals y (k) and n by taking the sequence with high correlation metric as a reference_F；

S3, obtaining the estimation of the fine ranging: accumulating, acquiring real and angle operation for 2 times of the over-sampled signals y (k) to obtain the estimation of the fine distance measurement

S4, obtaining rough distance measurement information estimation: n is to be_FSpeed and time processing to obtain coarse ranging information estimate

S5, obtaining ranging information: the accurate distance estimation between drone node a and drone node B is:the accurate distance estimation is the ranging information.

The SC-FDE signal joint ranging method in the unmanned aerial vehicle swarm network, which is disclosed by the invention, is a preferable mode, and the step S2 comprises the following steps:

s21, grouping sampling signals: 1 st to 2 nd_TThe sampled signals x (k) are represented as: { x (1), x (2), x (3), x (4), … …, x (2N)_T-1),x(2N_T) H, mixing 2N_TThe sampled signals x (k) being divided into odd sequences y₁And even sequence y₂：

y₁＝{x(1),x(3),x(5),……,x(2N_T-1)},

y₂＝{x(2),x(4),x(6),……,x(2N_T)}，

Odd number sequence y₁And even sequence y₂All lengths are N_T；

S22, obtaining a correlation metric: will be odd sequence y₁Item y of₁(n) and the even sequence y₂Item y of₂(n) obtaining the correlation metric R according to the following formula_μ(n)：

Wherein, mu is 1,2, alpha is roll-off coefficient;

s23, obtaining n_FUpdating n to n +1, returning to step S21 and step S22 to calculate R_μ(n +1) up to n_F＝arg{R_μ(n) ≧ λ }, wherein arg represents the value of n for which the extraction satisfies a condition;

s24, obtaining 2 times of over-sampled signals: comparison of R₁(n_F) And R₂(n_F) Taking 2N +1 data sequences backward to obtain 2 times of oversampled signal y (k) with the large sequence as a reference point.

The SC-FDE signal joint ranging method in the unmanned aerial vehicle swarm network, as an optimal mode, comprises the following steps of S3:

where c is the speed of light, about 3X 10⁸M/s; re is the real part and angle is the calculated angle.

The SC-FDE signal joint ranging method in the unmanned aerial vehicle swarm network, as an optimal mode, comprises the following steps of S4:

the above purpose of the invention is realized by the following technical scheme:

an SC-FDE signal combined ranging method in unmanned aerial vehicle swarm networking mainly comprises the following steps:

step one, unmanned plane node A is at T in unmanned plane bee colony₀Sending SC-FDE signals at any moment, and relaying to respond to the unmanned aerial vehicle node B at T₀+T₁After receiving the signal at time T₀+T₁+T₂Forwarding out all the time, the receiving end is at T₀+T₁+T₂+T₃And receiving the signals at the moment and performing parameter estimation, ranging and other processing on the signals. The SC-FDE signal transmitted between node a and node B of the drone is x (k) ═ x (kT)_s)，1≤k is less than or equal to 2N and is expressed as:

wherein T is a symbol period, T_ST/2 is a sampling clock;

s (N) is a data symbol with the length of the sending end N, N is more than or equal to 1 and less than or equal to N, s (N) comprises a leading segment { a (1), a (2),.. multidot.,. a (p) } and a data segment { b (1), b (2.,. b (q) }, the sum of the lengths of the leading segment and the data segment is N: p + q ═ N;

g (t) is a filter; τ T is time delay; z (t) is white Gaussian noise.

And (II) respectively carrying out differential correlation on 2 paths of signals based on the 2 times of oversampling signals x (k), and comparing to obtain correlation metrics.

1) 1 st to 2 nd_TThe sampling signal x (k) is expressed as

{x(1),x(2),x(3),x(4)……,x(2N_T-1),x(2N_T),x(2N_T)}

Divide it into 2 pieces with length N_TIs an odd sequence y₁And even sequence y₂：

y₁＝{x(1),x(3),x(5),……,x(2N_T-1)},

y₂＝{x(2),x(4),x(6),……,x(2N_T)}。

2) By y_μ(n), μ ═ 1,2 calculating and obtaining a correlation metric R_μ(n): roll-off coefficient of alpha

3) Updating n to n +1, calculating new R through steps 1) and 2)_μ(n+1)。

4) During the updating of n, by comparing R_μ(n) determining n from the threshold lambda_F＝arg{R_μ(n) is more than or equal to lambda }, arg represents that the value of n meeting the condition is extracted, and the threshold lambda is set through simulation analysis according to the requirement.

5) For odd number sequence y₁(n) and the even sequence y₂N of (n)_FValue, comparison metric R₁(n)、R₂And (N) taking the larger sequence as a reference point, and taking 2N +1 data sequences backwards to obtain a corresponding 2-time oversampling signal y (k).

And (iii) calculating the 2N +1 length y (k) obtained in the step (ii), wherein the sequence numbers of the 2N +1 data are k equal to 0, …,2N, so as to obtain the estimation of the fine ranging:

wherein, the braces { } contain 2 summation terms, the 1 st term uses 2N samples, and the 2 nd term uses 2N-1 samples; c is the speed of light, about 3X 10⁸M/s; re is the operation of the real part and angle is the operation of calculating the angle.

Step (IV) of subjecting n obtained in step (II) to_FObtaining coarse ranging information estimatesCombining the fine ranging information estimation in step (three)Completing accurate distance estimation between two unmanned aerial vehicle nodes A and B in an unmanned aerial vehicle swarm:

any other two nodes in the unmanned aerial vehicle swarm can obtain ranging information between every two nodes in the same way.

The invention has the following advantages:

(1) the invention relates to a SC-FDE signal joint distance measurement method in unmanned plane swarm networking, which is suitable for an unmanned plane swarm adopting SC-FDE, wherein the signal comprises a front lead section and a data section, the sampling rate is 2 times, and the method is suitable for a broadband ad hoc network communication system;

(2) the invention relates to a SC-FDE signal joint ranging method in unmanned aerial vehicle swarm network, which is characterized in that 2 paths of signals are respectively differentially correlated based on 2 times of sampling signals, and the sampling signals with higher correlation measurement are obtained through comparison and serve as a precise ranging sequence, wherein the differential correlation is not influenced by frequency deviation;

(3) the SC-FDE signal joint distance measurement method in the unmanned aerial vehicle swarm networking is based on 2 times of over-sampled signal precise distance measurement information estimation, the calculation formula is not influenced by frequency deviation, and the distance measurement precision between any two unmanned aerial vehicle nodes of the unmanned aerial vehicle swarm networking system can be effectively improved.

Drawings

Fig. 1 is a flowchart of an embodiment 1-3 of a SC-FDE signal joint ranging method in an unmanned aerial vehicle swarm networking;

fig. 2 is a flowchart of an embodiment 2-3 of a SC-FDE signal joint ranging method in an unmanned aerial vehicle swarm networking;

fig. 3 is a schematic diagram of an unmanned aerial vehicle swarm networking time slot in an embodiment 3 of an SC-FDE signal joint ranging method in an unmanned aerial vehicle swarm networking;

fig. 4 shows ranging error deviation value performance of embodiment 3 of the SC-FDE signal joint ranging method in the drone swarm networking;

fig. 5 is a ranging error root mean square error performance diagram of embodiment 3 of the SC-FDE signal joint ranging method in the drone swarm networking.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.

Example 1

As shown in fig. 1, a SC-FDE signal joint ranging method in an unmanned aerial vehicle swarm network is characterized in that: the method comprises the following steps:

s1, transmitting and receiving signals: unmanned aerial vehicle node A in the unmanned aerial vehicle bee colony sends SC-FDE sampling signal x (k) to unmanned aerial vehicle node B: x (k) ═ x (kT)_s)，

Wherein T is a symbol period, T_ST/2 is a sampling clock, s (N) is a data symbol with a length N at the transmitting end, N is greater than or equal to 1 and less than or equal to N, s (N) includes a preamble section { a (1), a (2),.., a (p) } and a data section { b (1), b (2),., b (q) }, the sum of the lengths of which is N: p + q ═ N; k is more than or equal to 1 and less than or equal to 2N, and g (t) is a filter; τ T is time delay; z (t) is white Gaussian noise;

s2, obtaining 2 times of over-sampled signals: dividing the sampled signal x (k) into an odd sequence y₁And even sequence y₂And deriving a correlation metric R_μ(n) correlating the metrics R_μ(n) carrying out differential correlation and comparison to obtain a sequence with high correlation metric, and obtaining 2 times of oversampling signals y (k) and n by taking the sequence with high correlation metric as a reference_F；

S3, obtaining the estimation of the fine ranging: accumulating, acquiring real and angle operation for 2 times of the over-sampled signals y (k) to obtain the estimation of the fine distance measurement

S4, obtaining rough distance measurement information estimation: n is to be_FSpeed and time processing to obtain coarse ranging information estimate

S5, obtaining ranging information: the accurate distance estimation between drone node a and drone node B is:the accurate distance estimation is the ranging information.

Example 2

As shown in fig. 1, a SC-FDE signal joint ranging method in an unmanned aerial vehicle swarm network is characterized in that: the method comprises the following steps:

s1, transmitting and receiving signals: unmanned aerial vehicle node A in the unmanned aerial vehicle bee colony sends SC-FDE sampling signal x (k) to unmanned aerial vehicle node B: x (k) ═ x (kT)_s)，

Wherein T is a symbol period, T_ST/2 is a sampling clock, s (N) is a data symbol with a length N at the transmitting end, N is greater than or equal to 1 and less than or equal to N, s (N) includes a preamble section { a (1), a (2),.., a (p) } and a data section { b (1), b (2),., b (q) }, the sum of the lengths of which is N: p + q ═ N; k is more than or equal to 1 and less than or equal to 2N, and g (t) is a filter; τ T is time delay; z (t) is white Gaussian noise;

s2, obtaining 2 times of over-sampled signals: dividing the sampled signal x (k) into an odd sequence y₁And even sequence y₂And deriving a correlation metric R_μ(n) correlating the metrics R_μ(n) carrying out differential correlation and comparison to obtain a sequence with high correlation metric, and obtaining 2 times of oversampling signals y (k) and n by taking the sequence with high correlation metric as a reference_F；

As shown in fig. 2, S21, the sampled signal packet: 1 st to 2 nd_TThe sampled signals x (k) are represented as: { x (1), x (2), x (3), x (4), … …, x (2N)_T-1),x(2N_T) H, mixing 2N_TThe sampled signals x (k) being divided into odd sequences y₁And the even sequence y 2:

y₁＝{x(1),x(3),x(5),……,x(2N_T-1)},

y₂＝{x(2),x(4),x(6),……,x(2N_T)}，

odd number sequence y₁And even sequence y₂All lengths are N_T；

S22, obtaining a correlation metric: will be odd sequence y₁Item y of₁(n) and the even sequence y₂Item y of₂(n) obtaining the correlation metric R according to the following formula_μ(n)：

Wherein, mu is 1,2, alpha is roll-off coefficient;

s23, obtaining n_FUpdate n to n +1, returnStep S21 and step S22 calculate R_μ(n +1) up to n_F＝arg{R_μ(n) ≧ λ }, wherein arg represents the value of n for which the extraction satisfies a condition;

s24, obtaining 2 times of over-sampled signals: comparison of R₁(n_F) And R₂(n_F) Taking the large sequence as a reference point, and taking 2N +1 data sequences backwards to obtain 2 times of over-sampled signals y (k);

s3, obtaining the estimation of the fine ranging: accumulating, acquiring real and angle operation for 2 times of the over-sampled signals y (k) to obtain the estimation of the fine distance measurement

Where c is the speed of light, about 3X 10⁸M/s; re is a real part and angle is a calculated angle;

s4, obtaining rough distance measurement information estimation: n is to be_FSpeed and time processing to obtain coarse ranging information estimate

S5, obtaining ranging information: the accurate distance estimation between drone node a and drone node B is:the accurate range estimate is the ranging information,

example 3

As shown in fig. 1, a SC-FDE signal joint ranging method in an unmanned aerial vehicle swarm network is characterized in that: the method comprises the following steps:

s1, transmitting and receiving signals: unmanned aerial vehicle node A in unmanned aerial vehicle bee colony is to unmanned aerial vehicle sectionPoint B transmits SC-FDE sampling signal x (k): x (k) ═ x (kT)_s)，

Wherein T is a symbol period, T_ST/2 is a sampling clock, s (N) is a data symbol with a length N at the transmitting end, N is greater than or equal to 1 and less than or equal to N, s (N) includes a preamble section { a (1), a (2),.., a (p) } and a data section { b (1), b (2),., b (q) }, the sum of the lengths of which is N: p + q ═ N; k is more than or equal to 1 and less than or equal to 2N, and g (t) is a filter; τ T is time delay; z (t) is white Gaussian noise;

s2, obtaining 2 times of over-sampled signals: dividing the sampled signal x (k) into an odd sequence y₁With the even sequence y2 and deriving a correlation metric R_μ(n) correlating the metrics R_μ(n) carrying out differential correlation and comparison to obtain a sequence with high correlation metric, and obtaining 2 times of oversampling signals y (k) and n by taking the sequence with high correlation metric as a reference_F；

As shown in fig. 2, S21, the sampled signal packet: 1 st to 2 nd_TThe sampled signals x (k) are represented as: { x (1), x (2), x (3), x (4), … …, x (2N)_T-1),x(2N_T) H, mixing 2N_TThe sampled signals x (k) being divided into odd sequences y₁And even sequence y₂：

y₁＝{x(1),x(3),x(5),……,x(2N_T-1)},

y₂＝{x(2),x(4),x(6),……,x(2N_T)}，

Odd number sequence y₁And even sequence y₂All lengths are N_T；

S22, obtaining a correlation metric: will be odd sequence y₁Item y of₁(n) and the even sequence y₂Item y of₂(n) obtaining the correlation metric R according to the following formula_μ(n)：

Wherein, mu is 1,2, alpha is roll-off coefficient;

s23, obtaining n_FUpdating n to n +1, returning to step S21 and step S22 to calculate R_μ(n +1) up to n_F＝arg{R_μ(n) ≧ λ }, wherein arg represents the value of n for which the extraction satisfies a condition;

s24, obtaining 2 times of over-sampled signals: comparison of R₁(n_F) And R₂(n_F) Taking the large sequence as a reference point, and taking 2N +1 data sequences backwards to obtain 2 times of over-sampled signals y (k);

s3, obtaining the estimation of the fine ranging: accumulating, acquiring real and angle operation for 2 times of the over-sampled signals y (k) to obtain the estimation of the fine distance measurement

Where c is the speed of light, about 3X 10⁸M/s; re is a real part and angle is a calculated angle;

s4, obtaining rough distance measurement information estimation: n is to be_FSpeed and time processing to obtain coarse ranging information estimate

S5, obtaining ranging information: the accurate distance estimation between drone node a and drone node B is:the accurate range estimate is the ranging information,

fig. 3 is a schematic diagram of an unmanned plane swarm networking time slot of the SC-FDE signal joint ranging method in the unmanned plane swarm networking of the invention; A2A-1/2/3 … represents the 1/2/3 … th UAV networking time slotAfter UAV-1 broadcasts the ranging request, the nearby UAV-2, UAV-3, UAV-6 obtain the ranging information d_1,2、d_1,3、d_1,6. Similarly, d can be obtained between UAV-3 and UAV-6_3,6。

Fig. 4 shows the performance of the range error deviation value of the joint ranging method, where QPSK signal is used for simulation, oversampling is 2 times, the roll-off coefficient is α is 0.35, SC-FDE signal p is 128, q is 1024, oversampling signal N is 1152, and N is_TThe results of fig. 2 show that at a symbol rate of 50MHz and 50MHz at 1/T, the SNR is 5dB, 10dB, and 30dB, and the deviations corresponding to the ranging errors are 0.01m, 0.003m, and 0.0005m, which are approximately 0. The present algorithm is therefore an efficient ranging estimation method.

FIG. 5 shows the root mean square error performance of the combined ranging method, where the SNR is 5dB, 10dB, 30dB, the Root Mean Square Error (RMSE) of the algorithm of the present invention is 0.14m, 0.07m, 0.01m, which is much better than the square rate nonlinear (SLN) method using 4-sample computation, 0.18m, 0.1m, 0.015m, and is closer to the theoretical threshold value σ_CRB. Therefore, the algorithm has a reliable range estimation method.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.