阅读说明：本技术 一种基于分布式无人机移动监测的干扰源直接定位方法 (Interference source direct positioning method based on distributed unmanned aerial vehicle mobile monitoring ) 是由 李建峰 赵高峰 何益 晋本周 张小飞 于 2020-06-23 设计创作，主要内容包括：本发明公开了一种基于分布式无人机移动监测的干扰源直接定位方法,首先,提取L(L≥4)个无人机监测节点接收的来自未知辐射源的辐射信号y；其次,对提取的辐射源信号进行离散傅里叶变换,得到信号频域表达式Y；接着,将多个监测站的频域数据进行合成,并建立和干扰源位置直接相关的代价函数；然后,多个无人机监测平台移动后重新监测,结合多次移动监测数据形成合成的代价函数；最后,基于相关法找出未知干扰源的位置。本发明采用多无人机监测平台,解决了传统频谱监测平台易受到地面因素影响的问题,并对数据进行分布式移动监测,实现了多无人机协同,运用直接定位方法对干扰源进行定位,提升了对干扰源的定位精度和鲁棒性。(The invention discloses an interference source direct positioning method based on distributed unmanned aerial vehicle movement monitoring, which comprises the following steps of firstly, extracting radiation signals y from unknown radiation sources received by L (L is more than or equal to 4) unmanned aerial vehicle monitoring nodes; secondly, performing discrete Fourier transform on the extracted radiation source signal to obtain a signal frequency domain expression Y; then, synthesizing the frequency domain data of the monitoring stations, and establishing a cost function directly related to the position of the interference source; then, after the plurality of unmanned aerial vehicle monitoring platforms move, monitoring is carried out again, and a synthesized cost function is formed by combining multiple times of moving monitoring data; and finally, finding out the position of the unknown interference source based on a correlation method. According to the invention, the multi-unmanned aerial vehicle monitoring platform is adopted, so that the problem that the traditional spectrum monitoring platform is easily influenced by ground factors is solved, distributed mobile monitoring is carried out on data, cooperation of multiple unmanned aerial vehicles is realized, the interference source is positioned by using a direct positioning method, and the positioning precision and robustness of the interference source are improved.)

1. A method for directly positioning an interference source based on distributed unmanned aerial vehicle mobile monitoring is characterized by comprising the following steps:

(1) extracting radiation signals y from unknown radiation sources received by L frequency spectrum monitoring base stations, wherein (L is more than or equal to 4);

(2) performing discrete Fourier transform on the extracted radiation source signal to obtain a signal frequency domain expression Y;

(3) synthesizing the frequency domain data of the monitoring stations, and establishing a cost function directly related to the position of the interference source;

(4) the unmanned aerial vehicle monitoring platforms are monitored again after moving, and a synthesized cost function is formed by combining multiple moving monitoring data;

(5) gridding an interference source monitoring area to obtain a series of grid position points; and solving the synthesized cost function based on a correlation method to obtain a grid point meeting the cost function, wherein the grid point is the position of the target interference source.

2. The method for directly positioning the interference source based on the distributed unmanned aerial vehicle mobile monitoring according to claim 1, wherein the radiation signal y in the step (1) is:

y₁(t)＝α₁s(t-τ₁)+n₁(t)

y_l(t)＝α_ls(t-τ_l)+n_l(t)

y_L(t)＝α_Ls(t-τ_L)+n_L(t)

wherein, y_l(t) denotes a reception signal of the l-th base station, s (t) is a transmission source signal, α_lAnd τ_lAttenuation coefficient and propagation delay, n, of signals received by the l-th base station_lAnd (t) is the noise component (1 ≦ L ≦ L) of the received signal of the corresponding base station.

3. The method for directly positioning the interference source based on the distributed unmanned aerial vehicle mobile monitoring as claimed in claim 1, wherein the signal frequency domain expression in step (2) is as follows:

wherein, Y_l(w) is the frequency domain version of the signal received by the l base station, S (w) represents the discrete Fourier transform of the signal s (t), N_lAnd (w) is the frequency domain noise component (1 ≦ L ≦ L) of the corresponding signal.

4. The method for directly positioning the interference source based on the distributed unmanned aerial vehicle mobile monitoring as claimed in claim 1, wherein the step (3) comprises the following steps:

(31) synthesizing all monitoring data to obtain

Wherein Φ (α) is diag (α)₁,...,α_L) For a diagonal matrix containing all attenuation coefficients, the time delay τ_lIs only related to the position p of the interferer source, and is therefore denoted τ_l(p)，

(32) assuming a total of K frequency points, w, after discrete Fourier transform₁,w₂,...,w_KThen, a cost function directly related to the position p is established as:

the position of the radiation source is solved by adopting a direct correlation method, because the correlation mainly lies in the compensation of the phase, and phi (alpha) is a real number and is irrelevant to the frequency point, the position can be simplified as follows:

5. the method for directly positioning the interference source based on the distributed unmanned aerial vehicle mobile monitoring according to claim 1, wherein the step (4) is realized by the following formula:

wherein H is the number of times that many unmanned aerial vehicles monitor platform moves, Y_h(w) composite frequency domain signals measured at the h-th monitoring position for multiple unmanned aerial vehicle platforms, a_h(w_kAnd p) is a delay factor vector corresponding to the H position, wherein H is more than or equal to 1 and less than or equal to H.

Technical Field

The invention belongs to the technical field of interference source positioning, and particularly relates to an interference source direct positioning method based on distributed unmanned aerial vehicle mobile monitoring.

Background

With the development of technologies such as mobile internet, internet of things and the like, the space electromagnetic environment is increasingly complex. Communication interference and electromagnetic leakage events caused by illegal interference sources such as pseudo base stations, black broadcasting and satellite interferometers and abnormal radiation sources formed by equipment faults, misoperation and the like are increasingly frequent, great harm is caused, and severe challenge is brought to radio management. However, the traditional frequency spectrum monitoring equipment is greatly influenced by a complex ground environment, has poor flexibility and is limited in practical application; meanwhile, limited load and endurance also enable the single unmanned aerial vehicle to be limited in monitoring. Therefore, the research of the air-based platform positioning based on the distributed unmanned aerial vehicle has very important practical significance.

The traditional spectrum monitoring device mostly adopts a two-step positioning method, such as: a positioning method based on Received Signal Strength (RSS), a positioning method based on Angle of arrival (AOA), or a positioning method based on Time Difference of arrival (TDOA). These positioning methods have a number of significant drawbacks: once errors occur in the accuracy of parameter estimation and mathematical modeling, the positioning accuracy is greatly influenced, the relevance of data acquired by each station is lost, and the problem of parameter ambiguity exists in a multi-target positioning scene. Meanwhile, due to the influence of various obstacles in the space, a signal can form a multipath effect in the transmission process, which has a great influence on the accurate positioning of an interference source. The invention provides a direct positioning algorithm based on distributed multi-unmanned aerial vehicle monitoring data fusion, which utilizes the air neutrality, the distributivity and the mobility of unmanned aerial vehicles to perform data fusion, and improves the positioning precision and the robustness.

Disclosure of Invention

The purpose of the invention is as follows: the invention provides an interference source direct positioning method based on distributed unmanned aerial vehicle mobile monitoring, which realizes the cooperation of multiple unmanned aerial vehicles, solves the problem that the traditional spectrum monitoring platform is easily influenced by ground factors, performs distributed mobile monitoring on data and improves the positioning precision of the interference source.

The technical scheme is as follows: an interference source direct positioning method based on distributed unmanned aerial vehicle mobile monitoring comprises the following steps:

(1) extracting radiation signals y from unknown radiation sources received by L frequency spectrum monitoring base stations, wherein (L is more than or equal to 4);

(2) performing discrete Fourier transform on the extracted radiation source signal to obtain a signal frequency domain expression Y;

(3) synthesizing the frequency domain data of the monitoring stations, and establishing a cost function directly related to the position of the interference source;

(4) the unmanned aerial vehicle monitoring platforms are monitored again after moving, and a composite cost function is formed by combining multiple times of moving monitoring data;

(5) gridding an interference source monitoring area to obtain a series of grid position points; and solving the synthesized cost function based on a correlation method to obtain a grid point meeting the cost function, wherein the grid point is the position of the target interference source.

Further, the radiation signal y in step (1) is:

y₁(t)＝α₁s(t-τ₁)+n₁(t)

y_l(t)＝α_ls(t-τ_l)+n_l(t)

y_L(t)＝α_Ls(t-τ_L)+n_L(t)

wherein, y_l(t) denotes a reception signal of the l-th base station, s (t) is a transmission source signal, α_lAnd τ_lAttenuation coefficient and propagation delay, n, of signals received by the l-th base station_lAnd (t) is the noise component (L is more than or equal to 1 and less than or equal to L) of the received signal of the corresponding base station.

Further, the signal frequency domain expression in step (2) is:

wherein, Y_l(w) is the frequency domain version of the signal received by the first base station, S (w) represents the discrete Fourier transform of the signal s (t), N_lAnd (w) is the frequency domain noise component (1 ≦ L ≦ L) of the corresponding signal.

Further, the step (3) includes the steps of:

(31) synthesizing all monitoring data to obtain

Wherein Φ (α) is diag (α)₁,...,α_L) For a diagonal matrix containing all attenuation coefficients, the time delay τ_lIs only related to the position p of the interferer source, and is therefore denoted τ_l(p)，

Time delay factor vectors corresponding to different monitoring positions;

(32) assuming a total of K frequency points, w, after discrete Fourier transform₁,w₂,...,w_KThen, a cost function directly related to the position p is established as:

the position of the radiation source is solved by adopting a direct correlation method, because the correlation mainly lies in the compensation of the phase, and phi (alpha) is a real number and is irrelevant to the frequency point, the position can be simplified as follows:

further, the step (4) is realized by the following formula:

wherein H is the number of times that many unmanned aerial vehicles monitor platform moves, Y_h(w) is the composite frequency domain signal measured by multiple UAV platforms at the h-th monitoring location, a_h(w_kAnd p) is a delay factor vector corresponding to the H position, wherein H is more than or equal to 1 and less than or equal to H.

Has the advantages that: compared with the prior art, the invention has the beneficial effects that: the problem that a traditional frequency spectrum monitoring platform is easily affected by ground factors is solved by adopting a plurality of unmanned aerial vehicle monitoring platforms, distributed mobile monitoring is carried out on data, cooperation of the plurality of unmanned aerial vehicles is realized, an interference source is positioned by using a direct positioning method, and positioning accuracy and robustness of the interference source are improved; and the data processing of the invention is based on the conventional processing method, and is easy to realize in engineering.

Drawings

FIG. 1 is a flow chart of the present invention;

fig. 2 is a view of a positioning scene of multiple drones according to the present invention;

FIG. 3 is a positioning scatter diagram without considering signal multipath effect according to the method of the present invention;

FIG. 4 is a positioning scatter diagram considering signal multipath effect according to the method of the present invention;

FIG. 5 is a graph comparing the performance of the method of the present invention with that of the TDOA location method.

Detailed Description

The technical scheme of the invention is further explained in detail by combining the attached drawings:

the invention provides an interference source direct positioning method based on distributed unmanned aerial vehicle mobile monitoring, which is specifically realized as follows as shown in figure 1:

step 1: and extracting L (L is more than or equal to 4) radiation signals y from unknown radiation sources received by the spectrum monitoring base stations.

The positioning scenario shown in fig. 2 assumes the presence of a radiation source signal s (t) at a position p (x, y, z). There are L monitoring stations (L is more than or equal to 4) which can receive signals, and the signal received by the base station L is marked as y_l(t), L is more than or equal to 1 and less than or equal to L; then the received signal expressions of the L base stations are:

y₁(t)＝α₁s(t-τ₁)+n₁(t)

y_l(t)＝α_ls(t-τ_l)+n_l(t)

y_L(t)＝α_Ls(t-τ_L)+n_L(t)

in the above formula, α_lAnd τ_lAttenuation coefficient and propagation delay, n, of signals received by the l-th base station_lAnd (t) is the noise component (1 ≦ L ≦ L) of the received signal of the corresponding base station.

Step 2: and carrying out Discrete Fourier Transform (DFT) on the extracted radiation source signal to obtain a signal frequency domain expression Y.

Performing Discrete Fourier Transform (DFT) on the received signal obtained in step 1 to obtain a frequency domain signal expression as follows:

in the above formula, Y_l(w) is the frequency domain version of the signal received by the l base station, S (w) represents the discrete Fourier transform of the signal s (t), N_lAnd (w) is the frequency domain noise component (1 ≦ L ≦ L) of the corresponding signal.

And step 3: and synthesizing the frequency domain data of the monitoring stations, and establishing a cost function directly related to the position of the interference source.

And (3) synthesizing all monitoring data according to the frequency domain signal expression obtained in the step (2) to obtain:

wherein Φ (α) is diag (α)₁,...,α_L) For a diagonal matrix containing all attenuation coefficients, the time delay τ_lIs only related to the position p of the interferer source, and is therefore denoted τ_l(p)，

Are vectors of delay factors corresponding to different monitored locations.

Suppose that the Discrete Fourier Transform (DFT) is followed by K frequency bins, w₁,w₂,...,w_KThen, the cost function directly related to the position p is established as:

the position of the radiation source is solved by adopting a direct correlation method, because the correlation mainly lies in the compensation of the phase, and phi (alpha) is a real number and is irrelevant to the frequency point, the position can be simplified as follows:

and 4, step 4: and the multiple unmanned aerial vehicle monitoring platforms monitor again after moving, and a synthesized cost function is formed by combining multiple times of moving monitoring data.

Suppose that multiple unmanned aerial vehicles monitor the platform and move H times, Y_h(w) composite frequency domain signals measured at the h-th monitoring position for multiple unmanned aerial vehicle platforms, a_h(w_kAnd p) is a delay factor vector corresponding to the H position, wherein H is more than or equal to 1 and less than or equal to H. Synthesizing the cost function after the position is moved for many times to obtain a synthesized cost function expression as

Wherein H is the number of times that many unmanned aerial vehicles monitor platform moves, Y_h(w) is the composite frequency domain signal measured by multiple UAV platforms at the h-th monitoring location, a_h(w_kAnd p) is a delay factor vector corresponding to the H position, wherein H is more than or equal to 1 and less than or equal to H.

And 5: and gridding the spatial position, and finding out the position of an unknown interference source based on a correlation method.

And gridding the monitoring area of the interference source to obtain a series of grid position points. And solving the synthesized cost function based on a correlation method to obtain a grid point meeting the cost function, wherein the grid point is the position of the target interference source.

Fig. 3 is a scatter diagram of the interference source location estimation without considering the multipath effect of the received signal by the method of the present invention. The positions of 4 unmanned planes are respectively L₁＝[969,2633,782]^T,L₂＝[568，1318,1863]^T， L₃＝[1537,1435,1957]^T，L₄＝[440,1005,1443]^TIn the unit m. The true position of the interferer is u ═ 35,170,85]^T. As can be seen from fig. 3, the present invention can effectively realize accurate positioning of the interference source under non-multipath effect.

Fig. 4 is a diagram of an interference source position estimation scatter plot under consideration of multipath effects of a received signal according to the method of the present invention. The positions of 4 unmanned planes are respectively L₁＝[894,1741,1935]^T，L₂＝[959,1996,1089]^T， L₃＝[1090,1644,519]^T，L₄＝[1729,1153,383]^TIn the unit m. The true position of the interferer is u ═ 35,170,85]^T. As can be seen from fig. 4, the present invention can effectively realize accurate positioning of the interference source under the multipath effect.

FIG. 5 is a graph comparing the performance of the method of the present invention with that of the TDOA location method. The positions of 4 unmanned planes are respectively L₁＝[1564,936,707]^T，L₂＝[1706,2947,895]^T，L₃＝[1362,1505,1367]^T， L₄＝[1705,1202,2153]^TIn the unit m. The true position of the interference source is u ═ 750,370,685]^T. The abscissa in fig. 5 is the different average signal-to-noise ratio, snr₁＝-0.3657dB，snr₂＝4.6343dB， snr₃＝9.6343dB，snr₄＝14.6343dB，snr₅19.6343dB, it can be seen from FIG. 5 that the positioning method in the present invention has a great performance improvement compared to the conventional TDOA positioning method.

The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the changes or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.