Radiation source positioning method based on radio spectrum monitoring big data processing

Abstract

An emitter positioning method based on spectrum monitoring big data processing comprises the following steps: station monitoring data obtaining, multi-station spectrum monitoring data-based emitter direction finding, multi-station spectrum monitoring data-based emitter cross positioning, and emitter continuous positioning.

Claims

1. An emitter positioning method for big data processing based on spectrum monitoring, characterized in that, comprising the following steps of: S1: Obtaining station monitoring data based on electromagnetic spectrum monitoring data of all stations in a certain area, selecting a monitoring station set Ø that can monitor a signal of a target emitter, and collecting accurate geolocation of all stations of the monitoring station set for a time interval and obtaining monitoring data of all the stations at the same time interval, dividing the monitoring station set Ø into multiple sub-region monitoring station sets Ø.sub.n based on a degree of geographic dispersion, where n≥2; S2: Emitter direction finding based on multi-station spectrum monitoring data for the station set Ø.sub.k, where 1≤k≤n, select monitoring data corresponding to a certain monitoring time interval t of all stations in the station set, according to the geolocation and signal strength of an emitter of all the stations, a direction in which the target signal strength of the emitter descends along a geographic gradient is the estimated direction of the signal of the target emitter; S3: Emitter cross-positioning based on multi-station spectrum monitoring data repeating step S2, calculating the direction of the signal of the target emitter corresponding to all the station sets Ø.sub.n, utilizing cross positioning method to estimate and obtain the geolocation of the target emitter; S4: Continuous positioning of the emitter returning to step S2, obtaining monitoring data corresponding to a new time period, and repeating steps S2-S4 and realizing continuous positioning of the target emitter.

2. The emitter positioning method for big data processing based on spectrum monitoring according to claim 1, characterized in that, in the step S1, the multiple sub-region monitoring station sets meets the following regulations: if the sub-region monitoring station set is relatively more dispersed geographically, then the accuracy of the cross-positioning method to estimate the geolocation of the emitter in a subsequent step is facilitated to improve; allowing a station to belong to different sub-region monitoring station sets; setting the stations in the sub-region monitoring station as dense as possible, the larger the number of stations, the better the accuracy of the estimation of the target emitter.

3. The emitter positioning method for big data processing based on spectrum monitoring according to claim 1, characterized in that, in the step S2, the direction in which the target signal intensity of the emitter descends geographically is determined by the following method: for all the stations in the station set Ø.sub.k, where 1≤k≤n, a relationship between the target signal intensity of the emitter and the position of the station can be defined as the signal intensity function of the target emitter f(p.sub.i), where p.sub.i is a position of a station i, f(p.sub.i) can also be expressed as a ternary function f(x.sub.i, y.sub.i, z.sub.i), where x.sub.i, y.sub.i, z.sub.i are the longitude, latitude, and height of the station i respectively; utilizing the station set Ø.sub.k as a training data set to calculate a gradient corresponding to a data set, which can be obtained by using batch gradient descent (Batch Gradient) and other methods, and obtaining a gradient vector v.sub.k of the radiation intensity function of the target emitter corresponding to the station set Ø.sub.k as the station position changes.

4. The emitter positioning method for big data processing based on spectrum monitoring according to claim 1, characterized in that, in the step S3, utilizing cross positioning method to estimate and obtain the geolocation of the target emitter comprises the steps of: for the station set Ø.sub.k, 1≤k≤n, calculate the gradient descent vector v.sub.k of the signal intensity function of the target emitter corresponding to each station set as the station position changes respectively, where 1≤k≤n; when k=2, the vector v.sub.k has an intersection point p in the space position, and a geolocation corresponding to p is the estimated geolocation of the target emitter, where 1≤k≤2, when 2<k, there will be multiple intersection points p.sub.j, 1≤j≤k, take p=(Σ.sub.j=1.sup.k x.sub.j, Σ.sub.j=1.sup.k y.sub.j, Σ.sub.j=1.sup.k z.sub.j)/k, where (x.sub.j, y.sub.j, z.sub.j) is the longitude, latitude, and height corresponding to the intersection points p.sub.j.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 is an illustration of the steps of the emitter positioning method for big data processing based on spectrum monitoring of the present invention.

(2) FIG. 2 is an illustration of an application scenario for the emitter positioning method for big data processing based on spectrum monitoring of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

(3) In order to make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the typical embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein may be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without inventive work shall fall within the protection scope of the present invention.

(4) The present invention mainly is a target emitter positioning by using the regional SMN data, which is beneficial to the more reasonable and efficient utilization of the monitoring data of multiple stations. FIG. 1 is an illustration of the steps of a positioning method of emitter for big data processing based on spectrum monitoring of the present invention. FIG. 2 is an illustration of an application scenario for the positioning method of emitter for big data processing based on spectrum monitoring of the present invention.

(5) In the scenario as shown in FIG. 2, the position of point p is the target position of the emitter, and the black circle in the scenario is the spectrum monitoring station, and the spectrum monitoring data of all stations can be obtained. The steps by using monitoring data to automatically geolocate the target emitter are as follows:

(6) According to the characteristics of the emitter signal, screen out the SMN node set that can monitor the signal of the target emitter, Q=Q.sub.1∪Q.sub.2;

(7) Divide the SMN node set Q into two sub-region SMN node sets Ø.sub.1, Ø.sub.2, where Ø.sub.1 contains 10 monitoring stations and Ø.sub.2 contains 13 monitoring stations.

(8) Take the sub-region SMN node sets Ø.sub.1, Ø.sub.2 as the training data set to calculate the gradient corresponding to the data set, the batch gradient descent method can be used to calculate the gradient descent vector v.sub.1, v.sub.2 of the signal intensity function of the target emitter corresponding to the sets Ø.sub.1, Ø.sub.2 with the different station positions;

(9) Calculate the intersection of vectors v.sub.1 and v.sub.2, and obtain the intersection position p, where is the estimated position of target emitter.

(10) Because when using the training data set to estimate the gradient, there is no requirement for the data in the training data set, so there may be some stations with the same detection data in different sub-region SMN node sets. However, the monitoring data in different sub-region SMN node sets should be as different as possible, so that the gradient descent vectors estimated by different sub-region SMN node sets will be different, and the position obtained by the cross positioning method is more accurate. Similarly, the sub-regional SMN node sets should be relatively more geographically dispersed, which is conducive to improving the accuracy of cross positioning estimation of the emitter position. In addition, when using the training data set to estimate the gradient, the stations included in the sub-region SMN node set should be as dense as possible, the larger the number, the more the accuracy of the gradient estimation, which will affect the accuracy of the estimation of the target emitter.