UAV positioning method for external emitter sources by integrating synchronized signal block scanning and multi-station ranging

Abstract

The present invention discloses a method for positioning an unmanned aerial vehicle (UAV) with an external radiation source that integrates synchronization signal block scanning and multi-station ranging, including receiving synchronization signal block signals transmitted by a base station at multiple stations to obtain spatial beam scanning information; determining the synchronization signal block number by calculating the reference signal receiving power and delay; dividing the space to be measured into uniform subspaces, calculating the delay and synchronization signal block number of the target relative to each receiving station at different subspaces, and obtaining a dictionary matrix for sparse reconstruction; using the synchronization signal block number and multi-station delay information, positioning the target through a Bayesian compressed sensing method. The present invention can effectively detect targets within the coverage area of ​​the base station signal, and is effective and superior for detecting the target position. Combined with the division of the beam scanning area and the received signal delay information, the detection effect is more accurate than that estimated by delay measurement alone, and can be applied to the positioning detection of aerial targets such as UAVs and small aircraft.

Description

Technical Field

[0001] The present invention relates to the field of signal analysis and processing technology, and specifically to a method for positioning unmanned aerial vehicles (UAVs) with external radiation sources by integrating synchronization signal block scanning and multi-station ranging. The method specifically relates to a target detection technology utilizing signals, and is a 5G unmanned aerial vehicle (UAV) positioning technology that integrates synchronization signal block scanning beam direction and multi-station ranging. The present invention is used for positioning aerial UAVs or small aircraft by 5G base stations. Background Art

[0002] 5G communication technology is a technology that has developed rapidly in recent years, with the characteristics of high speed, low latency and high connection density. At present, China has built the world's largest and most technologically advanced 5G independent networking network. As of the end of March 2022, China's 5G network has covered all prefecture-level cities and county towns in the country, and more than 80% of townships and towns. Based on such a huge 5G network, the present invention has a good application foundation. Synchronization Signal Block (SSB) is a specific signal used to establish downlink synchronization in 5G communication. Since the transmission frequency used by 5G signals is high, beamforming is usually used to increase the signal coverage range. At the same time, the coverage angle of each beam is limited, so beam scanning is used to cover the entire cell. A periodic beam scan transmits synchronization signal blocks with different numbers in different directions. Based on this, the target area can be detected according to the signal strength of SSBs with different numbers in the target area.

[0003] Bayesian compressed sensing is a signal reconstruction method based on Bayesian statistical theory and is an important development direction in compressed sensing technology. Traditional compressed sensing methods typically use sparse representation models to sample and reconstruct signals during signal sampling. However, this method requires certain conditions to ensure the accuracy and stability of the reconstruction. Bayesian compressed sensing, on the other hand, uses Bayesian statistical models to describe the distribution characteristics of signals, enabling more accurate signal sampling and reconstruction.

[0004] Compared to traditional radar ranging methods that rely on signal delay, this method also uses the strength of the different SSB signals reflected by the target to determine the target's location. This narrows the search range and is more efficient than searching the entire space directly. Furthermore, this method incorporates Bayesian compressed sensing for position estimation, resulting in a more accurate estimate of the target's location. Summary of the Invention

[0005] In response to the shortcomings of the existing technology in detecting the position of aerial targets using signal base stations, the present invention provides a 5G external radiation source UAV positioning technology that integrates the synchronization signal block scanning beam direction and multi-station ranging. Combined with the target's SSB signal strength information and signal delay information, the Bayesian compressed sensing method is applied to improve the accuracy of position measurement.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for locating a UAV using an external radiation source by integrating synchronization signal block scanning and multi-station ranging, comprising the following steps:

[0007] A three-dimensional coordinate system is established near the base station, with the horizontal center direction of the beam with SSB number 0 as the positive direction of the x-axis. Simulate a target to be measured, with the base station located at the origin of the coordinates and the jth receiving station located at a j It is assumed that the positions of the transmitting source and the receiving station are known and remain stationary.

[0008] S1. Multiple stations receive the synchronization signal block signal transmitted by the base station and obtain spatial beam scanning information;

[0009] S2. Determine the synchronization signal block number by calculating the reference signal received power and delay;

[0010] S3, dividing the space to be measured into uniform subspaces, calculating the delay and synchronization signal block number of the target relative to each receiving station in different subspaces, and obtaining a dictionary matrix for sparse reconstruction;

[0011] S4. Using the synchronization signal block number and multi-station delay information, the target is located through the Bayesian compressed sensing method.

[0012] Preferably, in step S1, for the 5G signal frequency band within the 3GHz-6GHz frequency band, there are 8 consecutive SSBs in a synchronization signal burst, and the SSBs are transmitted in beams in different directions in space through a beam scanning method.

[0013] Preferably, the SSB in each direction has a unique number, and this information is used to divide the circle centered on the base station on the horizontal plane into 8 uniform sector areas for subsequent positioning operations.

[0014] Preferably, the synchronization signal block number of the target area in step S2 is obtained by the following steps:

[0015] (2a) Perform OFDM demodulation on the received signal. Assuming the received signal is X, the frequency domain signal is obtained by fast Fourier transform:

[0016]

[0017] Then remove the cyclic prefix of the signal to obtain signal Y:

[0018]

[0019] where N CP is the cyclic prefix length, N SC is the number of subcarriers; then the minimum mean square error algorithm is used to eliminate the influence of signal transmission in the channel, that is, the demodulated signal is obtained:

[0020]

[0021]

[0022] in is the estimated channel frequency response, H(k) is the actual channel frequency response;

[0023] (2b) Measure the received signal and obtain the delay information t; obtain the SSS signal resource element index based on the demodulated signal, extract the SSS signal from each SSB signal, and obtain the RSRP by calculating its average power sss ; Then obtain the resource element index of the PBCH-DMRS signal, extract the PBCH-DMRS signal from each SSB, calculate its average power to obtain RSRP PBCH ; For each SSB, calculate its reference signal received power:

[0024]

[0025] Get the SSB number with the largest power and record it as idx.

[0026] Preferably, the specific steps in step S3 include:

[0027] (3a) Establish a three-dimensional space coordinate system with the base station as the origin, and take the center direction of the beam with SSB number 0 as the positive direction of the x-axis; let the height of the scene space to be measured be H, the interval between the center points of each subspace be d, measure the circular area to be measured with the base station as the center, and the measurement radius be R; divide the point set on the radius of the circle with the base station as the center Where N1 = [R / d]; then the distance r from each point to the center of the circle i The area is divided into intervals d on the arc of radius; the coordinates of each point on the arc are:

[0028]

[0029] in

[0030] The total number of subspaces that can be divided is:

[0031]

[0032] (3b) The compressed sensing sparse reconstruction process requires a dictionary matrix containing the parameters of each subspace; let the coordinate vector of the center point of each subspace be Q i =[x i ,y i ,z i ], any parameter vector is:

[0033] S i =[d 1i ,d 2i ,d 3i ,k i ] T

[0034] where d ji The distance from this point to the center of the circle plus the distance to the jth receiving station:

[0035] d ij =||Q i ||2+||Q i -a j ||2

[0036] a j is the coordinate vector of the jth receiving station; k i is the number of the subspace covered by the specific SSB signal:

[0037]

[0038] This gives us the dictionary matrix:

[0039] Φ=[s1,s2,s3...s M ].

[0040] Preferably, step S4 specifically includes the following steps:

[0041] (4a) The sum of the distances from the target to the receiving station and the base station is d = t * c, where c is the speed of light. This operation is performed for each receiving station, and combined with the region number idx measured in step S2, the measurement matrix is:

[0042] v=[d1,d2,d3,idx] T

[0043] (4b) According to the Bayesian compressed sensing theorem, the parameter matrix θ of the estimated target position satisfies the following formula:

[0044] v=Φθ+n

[0045] Where n is approximately subject to zero-mean Gaussian random variable noise, assuming its accuracy is α0;

[0046] Apply a prior function to the accuracy of the noise:

[0047] p(α0|c,d)=Γ(α0|c,d)

[0048] Where c and d are the hyperparameters of the gamma prior distribution; then place the prior on the matrix θ:

[0049]

[0050] in is a Gaussian density function with zero mean and an accuracy of α i , continue to impose a prior on α:

[0051]

[0052] Then the total prior function of θ is:

[0053]

[0054] Given the measurement matrix v, the likelihood density function of the parameter matrix θ and α0 is expressed as:

[0055]

[0056] (4c) Given v, α, and α0, the posterior of θ is expressed as a multivariate normal distribution with mean and covariance:

[0057] μ=α0∑Φ T v

[0058] ∑=(A+α0Φ T Φ) -1

[0059] Where A=diag(α1,α2,α3,...,α N ), the estimation of α and α0 is realized by the following formula:

[0060]

[0061] in ∑ ii is the i-th diagonal element in ∑, and:

[0062]

[0063] By continuously iterating and calculating the four equations in (4c) until convergence, the mean value μ is obtained and used as the estimated value of the parameter matrix θ; the actual position of the target is effectively estimated based on the subspace position corresponding to the maximum weight in θ.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. The present invention simultaneously utilizes the beam scanning information and signal delay information of the 5G synchronization signal block to locate the target. Compared with traditional radar delay positioning, it improves the positioning accuracy. In addition, the 5G signal transmission power is lower than the radar power, which reduces power consumption and improves performance.

[0066] 2. The present invention can directly obtain the area number and signal delay while receiving the signal, with simple operation, high calculation efficiency and fast speed.

[0067] 3. The present invention applies Bayesian compressed sensing theory to estimate position, which can greatly reduce the sampling rate and improve information storage efficiency and calculation speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0069] In the attached figure:

[0070] Figure 1 It is a schematic diagram of the overall process of the present invention;

[0071] Figure 2 It is a schematic diagram of a simulation scenario of the present invention;

[0072] Figure 3 Schematic diagram of received power of SSB reference signals with different numbers according to the present invention;

[0073] Figure 4 This is a schematic diagram of the method error comparison in scenario 1 of the present invention;

[0074] Figure 5 This is a schematic diagram of the method error comparison in scenario 2 of the present invention. DETAILED DESCRIPTION

[0075] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0076] Example: First refer to Figure 2 As shown, a three-dimensional coordinate system is established in space, with the horizontal center direction of the beam with SSB number 0 as the positive x-axis direction. It includes a base station, three receivers located at positions a1, a2, and a3, and a target to be measured. The positions of the base station and the receiver are all known.

[0077] like Figure 1As shown, the present invention integrates the synchronization signal block scanning beam direction and multi-station ranging of the 5G external radiation source UAV positioning technology, which uses the synchronization signal blocks and delay information measured by multiple receiving stations to measure the position of the target, and specifically includes the following steps:

[0078] Step 1: Multiple stations receive synchronization signal block signals transmitted by the base station and obtain spatial beam scanning information. For the test scenario of the present invention, a synchronization signal burst typically contains eight consecutive SSBs. The planar space is evenly divided into eight regions based on the number of beams for subsequent positioning operations.

[0079] Step 2: Determine the synchronization signal block number by calculating the reference signal received power (RSRP) and delay. The SSB number of the target area can be obtained by the following steps:

[0080] (2a) Perform OFDM demodulation on the received signal. Assuming the received signal is X, the frequency domain signal can be obtained by fast Fourier transform:

[0081]

[0082] Then remove the cyclic prefix of the signal to obtain signal Y:

[0083]

[0084] where N CP is the cyclic prefix length, N SC is the number of subcarriers. Then, the minimum mean square error algorithm is used to eliminate the influence of signal transmission in the channel, and the demodulated signal can be obtained:

[0085]

[0086]

[0087] in is the estimated channel frequency response, and H(k) is the actual channel frequency response.

[0088] (2b) Measure the received signal and obtain the delay information t. According to the demodulated signal, the SSS signal resource element index is obtained, and the SSS signal is extracted from each SSB signal, and the RSRP is obtained by calculating its average power. sss Then obtain the resource element index of the PBCH-DMRS signal, extract the PBCH-DMRS signal from each SSB, and calculate its average power to obtain RSRP PBCH For each SSB, calculate its reference signal received power:

[0089]

[0090] Draw the reference signal received power diagram of each SSB signal and get the following Figure 3 The SSB with the largest power is numbered and recorded as idx.

[0091] Step 3: Divide the measured space into uniform subspaces, calculate the target's delay relative to each receiving station and the synchronization signal block number in different subspaces, and obtain the dictionary matrix for sparse reconstruction.

[0092] (3a) In the scene space to be measured in the present invention, assuming that the height is 64m, the interval between the center points of each subspace is 8m, and the measurement area to be measured with the base station as the center of the circle has a measurement radius of 64m.

[0093] Divide the point set [s1,s2,s3...s N1 ], where N1 = [R / d], and then the distance r from each point to the center of the circle i The arc with radius is divided into regions at intervals d. The coordinates of each point on the arc are:

[0094]

[0095] in

[0096] The total number of subspaces that can be divided is:

[0097]

[0098] (3b) The compressed sensing sparse reconstruction process requires a dictionary matrix containing the parameters of each subspace. Let the coordinate vector of the center point of each subspace be Q i =[x i ,y i ,z i ], the parameter vector of any subspace target point is:

[0099] S i =[d 1i ,d 2i ,d 3i ,k i ] T

[0100] where d ji The distance from this point to the center of the circle plus the distance to the jth receiving station:

[0101] d ij =||Q i ||2+||Q i -a j ||2

[0102] aj is the coordinate vector of the jth receiving station. i The number of the area divided by the SSB beam where the subspace is located:

[0103]

[0104] This gives us the dictionary matrix:

[0105] Φ=[s1,s2,s3...s M ]

[0106] Step 4: Use the synchronization signal block number and multi-station delay information to locate the target through the Bayesian compressed sensing method.

[0107] (4a) The sum of the distances from the target to the receiving station and the base station is d = t * c, where c is the speed of light. This operation is repeated for each receiving station, and combined with the region number idx measured in step 2, the measurement matrix is:

[0108] v=[d1,d2,d3,idx] T

[0109] (4b) According to the Bayesian compressed sensing theorem, the parameter matrix θ of the estimated target position satisfies the following formula:

[0110] v=Φθ+n

[0111] Where n can be approximated to obey the zero-mean Gaussian random variable noise, assuming its accuracy is α0.

[0112] Apply a prior function to the accuracy of the noise:

[0113] p(α0|c,d)=Γ(α0|c,d)

[0114] Where c and d are the hyperparameters of the gamma prior distribution. Then place the prior on the matrix θ:

[0115]

[0116] Here N(θ i |0,α i -1 ) is a Gaussian density function with zero mean and an accuracy of α i , continue to impose a prior on α:

[0117]

[0118] Then the total prior function of θ is:

[0119]

[0120] Given the measurement matrix v, the likelihood density function of the parameter matrices θ and α0 can be expressed as:

[0121]

[0122] (4c) After knowing v, α and α0, the posterior of θ can be expressed as a multivariate normal distribution with mean and covariance:

[0123] μ=α0∑Φ T v

[0124] ∑=(A+α0Φ T Φ) -1

[0125] Here A=diag(α1,α2,α3,...,α N ). The estimation of α and α0 can be achieved by the following formula:

[0126]

[0127] in ∑ ii is the i-th diagonal element in ∑, and:

[0128]

[0129] By iterating the above four equations until convergence, we can obtain the mean μ and use it as an estimate of the parameter matrix θ. The actual position of the target can be effectively estimated based on the subspace position corresponding to the maximum weight in θ.

[0130] In order to verify the effectiveness and superiority of the 5G external radiation source drone positioning technology proposed by the present invention, which integrates the synchronization signal block scanning beam direction and multi-station ranging, the present invention conducted a simulation experiment and compared it with the effect of the measurement method that does not use SSB number information but only uses delay information. The experiment verifies the effectiveness of the present invention by comparing the target measurement errors in two simulated scenarios. Scenario 1: Assume that the target to be measured is exactly at the center point of the subspace, simulate signal transmission and reception, change the Gaussian channel noise in the transmission, and compare the errors between the target position measured by different methods and the true value. The results are as follows: Figure 4 As shown. Figure 4 From the data, we can find that the error measured by the target position detection method using 5G synchronization signal block beam scanning and signal delay is generally lower than the position error measured using only delay information, and higher accuracy can be achieved at a lower signal-to-noise ratio. Scenario 2: Assuming that the target is 2 meters away from the center of the nearest subspace, verify whether the target can be located at the nearest subspace, simulate signal transmission and reception, change the Gaussian channel noise in the transmission, and compare the error between the target position measured by different methods and the true value. The results are as follows: Figure 5 As shown. Figure 5 The results show that the effect of the present invention is smaller in error and greater in accuracy than the measurement method using only delay information, and higher measurement accuracy can be obtained under lower signal-to-noise ratio conditions.

[0131] In summary, this invention discloses a 5G external radiation source drone positioning technology that integrates synchronization signal block scanning beam direction and multi-station ranging. By analyzing the synchronization signal block number information and transmission delay information in 5G signal transmission, and using Bayesian compressed sensing to rationally utilize the sparse characteristics of the measured target position, the target position is estimated. Compared with traditional methods that use delay information to measure position, the present method is more accurate and effective.

[0132] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for locating unmanned aerial vehicles (UAVs) with external radiation sources by integrating synchronous signal block scanning and multi-station ranging, characterized in that: The following steps are involved: S1. Multiple stations receive the synchronization signal block signal transmitted by the base station and obtain spatial beam scanning information; S2. Determine the synchronization signal block number by calculating the reference signal received power and delay; S3, dividing the space to be measured into uniform subspaces, calculating the delay and synchronization signal block number of the target relative to each receiving station in different subspaces, and obtaining a dictionary matrix for sparse reconstruction; S4. Using the synchronization signal block number and multi-station delay information, the target is located through the Bayesian compressed sensing method.

2. The method for locating an external radiation source UAV by integrating synchronization signal block scanning and multi-station ranging according to claim 1 is characterized in that: In step S1, for the 5G signal frequency band within the 3GHz-6GHz frequency band, there are 8 consecutive SSBs in a synchronization signal burst, and the SSBs are transmitted in beams in different directions in space through a beam scanning method.

3. The method for locating an external radiation source UAV by integrating synchronization signal block scanning and multi-station ranging according to claim 2 is characterized in that: The SSB in each direction has a unique number. This information is used to divide the circle centered on the base station on the horizontal plane into 8 uniform sector areas for subsequent positioning operations.

4. The method for locating an external radiation source UAV by integrating synchronization signal block scanning and multi-station ranging according to claim 1 is characterized in that: The synchronization signal block number of the target area in step S2 is obtained by the following steps: (2a) Perform OFDM demodulation on the received signal. Assuming the received signal is X, the frequency domain signal is obtained by fast Fourier transform: ； Then remove the cyclic prefix of the signal to obtain signal Y: ； in is the cyclic prefix length, is the number of subcarriers; then the minimum mean square error algorithm is used to eliminate the influence of signal transmission in the channel, that is, the demodulated signal is obtained: ； ； in is the estimated channel frequency response, is the actual channel frequency response; (2b) Measure the received signal and obtain the delay information t; obtain the SSS signal resource element index based on the demodulated signal, extract the SSS signal from each SSB signal, and obtain it by calculating its average power ; Then obtain the resource element index of the PBCH-DMRS signal, extract the PBCH-DMRS signal from each SSB, and calculate its average power to obtain ; For each SSB, calculate its reference signal received power: ； Get the SSB number with the largest power and record it as idx.

5. The method for locating an external radiation source UAV by integrating synchronization signal block scanning and multi-station ranging according to claim 1 is characterized in that: The specific steps in step S3 include: (3a) Establish a three-dimensional space coordinate system with the base station as the origin, and take the center direction of the beam with SSB number 0 as the positive direction of the x-axis; let the height of the scene space to be measured be H, the interval between the center points of each subspace be d, measure the circular area to be measured with the base station as the center, and the measurement radius be R; divide the point set on the radius of the circle with the base station as the center ,in ; Then calculate the distance from each point to the center of the circle The area is divided into intervals d on the arc of radius; the coordinates of each point on the arc are: ； in ; The total number of subspaces that can be divided is: ； (3b) The compressed sensing sparse reconstruction process requires a dictionary matrix containing the parameters of each subspace; let the coordinate vector of the center point of each subspace be , any parameter vector is: ； in The distance from this point to the center of the circle plus the distance to the jth receiving station: ； is the coordinate vector of the jth receiving station; is the number of the subspace covered by the specific SSB signal: ； This gives us the dictionary matrix: 。 6. The method for locating an external radiation source UAV by integrating synchronization signal block scanning and multi-station ranging according to claim 1 is characterized in that: Step S4 specifically includes the following steps: (4a) The sum of the distances from the target to the receiving station and the base station is d = t * c, where c is the speed of light. This operation is performed for each receiving station, and combined with the region number idx measured in step S2, the measurement matrix is: ； (4b) According to the Bayesian compressed sensing theorem, the parameter matrix of the target position is estimated Satisfy the following formula: ； Where n is approximately subject to zero-mean Gaussian random variable noise, assuming its accuracy is ; Apply a prior function to the accuracy of the noise: ； Where c and d are the hyperparameters of the gamma prior distribution; then for the matrix Place priors: ； in is a Gaussian density function with zero mean, and its accuracy is , continue to Imposing a prior: ； Then we get The overall prior function is: ； Given a measurement matrix , parameter matrix and The likelihood density function is expressed as: ； (4c) In the known , and back, The posterior of is represented as a multivariate normal distribution with mean and covariance: ； ； in , and The estimation of is achieved by the following formula: ； in , yes The i-th diagonal element in , and: ； By continuously iterating and calculating the four equations in (4c) until convergence, we can get the mean The value of and as a parameter matrix estimated value of The subspace position corresponding to the maximum weight in is the effective estimation of the actual position of the target.

Images