CN105530703A - A GSM-R Interference Source Location Method Based on Direction of Arrival Estimation - Google Patents

Abstract

The invention belongs to the railway radio communication net interference source positioning technology and the array signal processing technology field, and relates to a GSM-R interference source positioning method based on a wave arrival direction estimate value. The GSM-R interference source positioning method comprises steps of adopting a GSM-R base station reception antenna to perform DOA estimation on an interference signal incoming wave direction of the interference signal, performing space diversity on the GSM-R base station to construct two groups of antenna arrays, obtaining a DOA estimation result of the interference signal, respectively establishing a linear equation of the connection line between the interference source and two GSM-R base stations to obtain the intersection point of the two straight lines; and the intersection is the position of the interference source. The beneficial effects are that the invention does not need to receive signal frequency spectrum data and is portable; the invention does not need network management system data, the cellular district planning data and the road measurement data and thus is independent; the invention does not need to use the directional antenna to perform step-by-step positioning and is fast; and the invention does not need obtain the environment path loss model in advance and is more flexible.

Description

A GSM-R Interference Source Location Method Based on Direction of Arrival Estimation

technical field

The invention belongs to the field of GSM-R (GlobalSystemforMobileCommunications-Railway) interference source location technology and array signal processing technology, in particular to a GSM-R interference source location method based on DOA (direction of arrival) estimation of interference signals.

Background technique

GSM-R is a comprehensive dedicated wireless communication system specially designed for railway communication, developed by the International Union of Railways (UIC) and the European Telecommunications Standards Institute (ETSI) for the new generation of railway wireless communication in Europe. GSM-R is based on GSM technology, combined with the needs of railway communication, added multiple priority, emergency call, group call, broadcast call and other related services. The GSM-R system is the development trend of railway communication in my country. It can realize the collection and transmission of various mobile information resources of railways, and provide a communication platform for modern railway dispatching, command and control. cost. In 2003, the Ministry of Railways selected GSM-R as the technical system for the future integrated digital dispatching network of my country's railways. The introduction of the GSM-R system has promoted the development of my country's railway construction, accelerated the pace of my country's railway information construction, improved the competitiveness of my country's railways, and contributed an important force to the country's economic and technological strength.

The GSM-R frequency band mainly works in the range of 885-889/930-934MHz, with a total bandwidth of 4MHz. This frequency band is exclusively used by the GSM-R system. However, due to the popularization of electronic equipment, the electromagnetic environment in life has become more and more complex. Since the two systems share frequency bands or frequency bands are similar, electromagnetic interference between systems has become inevitable. For a relatively complex electromagnetic environment, there are a variety of wireless network components, including mobile communication service systems, dedicated mobile radio equipment and paging/broadcasting systems, namely civil mobile systems, trunking systems and broadcasting systems, and even some unauthorized illegal For wireless networks, the antennas of these different networks send out various wireless signals of different frequencies, different amplitudes, and different semaphores, so it is easy to cause mutual interference between networks in space.

At present, base stations and antennas with various channels, regional authorization or unauthorized authorization are laid along the railways in our country. Although the relevant departments of China have stipulated that the GSM-R system will exclusively occupy the 4M bandwidth along the railway, the signals emitted by other stations such as China Mobile, China Unicom, China Telecom and radio stations may cause adjacent frequency interference and intermodulation interference of the GSM-R system, and its illegal Co-channel interference caused by unlicensed base stations or antennas. These wireless interference signals will affect the normal reception and transmission of GSM-R, and bring problems such as call drop, poor call quality, and channel congestion to mobile communications in the coverage area of the base station, which will lead to a decrease in the efficiency of railway operations and even affect railway safety. Operation, the safety of passengers and the development of the country have brought hidden concerns. Therefore, an effective GSM-R interference source location method is urgently needed to eliminate the influence of the interference source on the GSM-R communication system in time to ensure the safety of railway transportation.

At this stage, the methods for locating interference sources mainly include: using spectrum analysis to locate external interference; using network management system data, cell planning data and test data to locate interference sources; using directional antennas to gradually approach positioning; using The electromagnetic wave power propagation model and the path loss model locate the interference source. At present, there is no method for estimating the DOA of the direction of arrival of the interference signal by array signal processing to locate the interference source.

Contents of the invention

The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art, and propose a method of using array signal processing technology to quickly locate GSM-R interference sources. The GSM-R system is mainly used for passenger dedicated lines or high-speed lines. Most of them are located in open rural areas. Electromagnetic waves mainly propagate in straight lines, and the multipath effect is not obvious. Therefore, the direction of the interference signal can be used to locate the interference source. In these areas, the GSM-R system uses space-diversity antennas, and the antennas are placed parallel to the left and right, which can be regarded as a uniform linear array antenna array placed horizontally.

Technical scheme of the present invention is: a kind of GSM-R interference source localization method based on direction of arrival estimated value, it is characterized in that, comprises the following steps:

a. Using the GSM-R base station receiving antenna to estimate the DOA of the direction of arrival of the interference signal, the specific method is: perform space diversity on the GSM-R base station antenna, construct two sets of antenna arrays, and obtain the DOA estimation result of the interference signal;

b. According to the DOA estimation result obtained in step a, establish the straight-line equations of the line connecting the interference source and the receiving antennas of the two GSM-R base stations respectively, and obtain the intersection point of these two straight lines, which is the position of the interference source.

The general technical solution of the present invention mainly adopts the array signal processing technology, uses multi-antenna arrays to estimate the DOA of the direction of arrival of the interference source signal, and estimates the direction of arrival of the same interference signal according to two groups of antenna arrays. source location.

Further, step a specifically includes the following steps:

a1. Use space diversity antennas to form a uniform linear array, and each antenna represents an array element;

a2. Perform forward/backward spatial smoothing on the uniform linear array, and construct multiple forward/backward smoothed uniform linear array sub-arrays;

a3. Establish each forward/backward smooth uniform linear array sub-array received signal model, and calculate the received signal spatial correlation matrix of each forward/backward smooth uniform linear array sub-array;

a4. Average the received signal spatial correlation matrix of each forward/backward smooth uniform linear array subarray to obtain the bidirectional spatial smooth correlation matrix of the uniform linear array;

a5. According to the obtained two-way spatial smoothing correlation matrix, the DOA estimation result of the interference signal is obtained by using the MUSIC algorithm.

The beneficial effect of the energy band of the present invention is that the present invention does not need to receive signal spectrum data, which is more convenient; it does not need related planning data such as network management system data, cell planning data, and drive test data, and is more independent; the present invention does not need to use directional antennas to carry out Step-by-step positioning is faster; the present invention does not need to obtain the path loss model of the environment in advance, which is more flexible.

Description of drawings

Fig. 1 is method flowchart of the present invention;

Fig. 2 is the uniform linear array antenna array schematic diagram that the GSM-R base station space diversity antenna of the embodiment of the present invention forms;

FIG. 3 is a schematic diagram of GSM-R interference source positioning according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a uniform linear array forward smoothing sub-array adopted in an embodiment of the present invention;

5 is a schematic diagram of a uniform linear array backward smoothing sub-array adopted in an embodiment of the present invention;

Fig. 6 is the DOA estimation result of GSM-R base station 1 to desired GSM-R signal and interference signal according to the embodiment of the present invention;

FIG. 7 shows the DOA estimation results of the desired GSM-R signal and the interference signal by the GSM-R base station 2 according to the embodiment of the present invention.

detailed description

Below in conjunction with accompanying drawing and embodiment the present invention is described in detail

As shown in Figure 1, the main solution of the GSM-R interference source location method based on the estimated direction of arrival value of the present invention is: firstly consider the possible coherence between the same-frequency signals, and adopt the space diversity GSM-R base station to receive Antennas, using the uniform linear array spatial smoothing algorithm to estimate the direction of arrival of the interference signal; then according to the estimation results of the direction of arrival of the same interference signal by two sets of antenna arrays and the geometric positions of the two sets of antenna arrays, draw two Two intersecting straight lines, the intersection point of the two straight lines is the position of the interference source.

Example

As shown in FIG. 2 , it will be described in detail by taking 10 antennas of the space diversity antenna of the GSM-R base station uniformly arranged on the same horizontal plane as an example.

Because the China Railway GSM-R system works in the 885-889/930-934MHz frequency range, 900MHz is selected as a typical frequency to set the antenna element spacing, and the antenna element spacing is set to (0.5×3×10 ⁸ )/(900 ×10 ⁶ ) = 0.15 m. The distance between the array elements is much smaller than the distance between the array elements and the interference source, and it can be considered that the incident angle of the interference signal to each array element is the same. The angle between the incident direction of the signal and the normal direction of the uniform line array is θ, when the signal enters the uniform line array from the left side of the normal line θ∈(0°,90°), when the signal enters the uniform line array from the right side of the normal line Array time θ∈(-90°,0°).

As shown in Figure 3, the space diversity antenna of GSM-R base station 1 is located at coordinate A (0m, 0m), and the space diversity antenna of GSM-R base station 2 is located at coordinate B (100m, 0m). It is expected that the GSM-R transmitter Located at the coordinate O(45m,5m), the interference source is located at the coordinate C(50m,50m).

The locations of GSM-R base station 1 and GSM-R base station 2 are known during the experiment. Suppose the position of the desired GSM-R transmitter is (X ₁ , Y ₁ ), and the position of the interference source is (X ₂ , Y ₂ ).

It is expected that the incident angle of the GSM-R signal to the base station 1 is θ ₁ , and the incident angle of the interference signal to the base station 1 is θ ₂ . It is expected that the incident angle of the GSM-R signal to the base station 2 is The incident angle of the interference signal to the base station 2 is

As shown in Figure 2, the received signal data of the uniform linear array receiving antenna of base station 1 is:

$\begin{array}{c}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\left[\begin{array}{cccc}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>& \mathrm{<span\; class="notranslate">\; ...</span>}& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 10</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\end{array}\right]}^{\mathrm{<span\; class="notranslate">\; T</span>}}\\ <span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; A</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\end{array}$

Among them, s(t)=[s ₁ (t)s ₂ (t)] ^T is the desired GSM-R signal and interference signal, n(t)=[n ₁ (t)n ₃ (t)...n ₁₀ ( t)] ^T An array of 10 elements receives noise, the noise variance is σ ² , A=[a(θ ₁ )a(θ ₂ )] is the array steering matrix, a(θ _i )=[1e ^-jβi …e ^-9jβi ] ^T is the steering vector of the signal s _i (t), and β _i =2πdsinθ _i /λ is the phase difference when the signal s _i (t) arrives at two adjacent array elements.

As shown in FIG. 4 , forward spatial smoothing is adopted, and a uniform linear array with the number of array elements M=10 is used to construct P subarray combinations (P=6), and the number of array elements in each subarray is m=5.

The received signal data of the kth subarray is:

$\begin{array}{c}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\left[\begin{array}{cccc}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>& \mathrm{<span\; class="notranslate">\; ...</span>}& {\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\end{array}\right]}^{\mathrm{<span\; class="notranslate">\; T</span>}}\\ <span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; cd</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; no</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\end{array}$

Among them, C=[c(θ ₁ )c(θ ₂ )] is the array steering matrix of the kth subarray, c(θ _i )=[1e ^-jβi …e ^-j(k-1)βi ] ^T is the signal The array steering vector of the kth subarray of s _i (t), $\mathrm{D.}=\left[\begin{array}{cc}{e}^{-{\mathrm{j\&beta;}}_1}& 0\\ 0& e^{-{\mathrm{j\&beta;}}_2}\end{array}\right]$ is the phase shift matrix between adjacent subarrays, n _k (t)=[n _k (t)n _k+1 (t)…n _k+4 (t)] ^T is the array receiving noise of the kth subarray .

The received signal vector of the kth subarray The spatial correlation matrix of is:

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; cd</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; h</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; I</span>}$

Wherein, R _s =E{s(t)s ^H (t)} is the spatial correlation matrix of the signal transmitted by the interference source, and I is the identity matrix.

The forward spatial smoothing spatial correlation function can be obtained by averaging the spatial correlation functions of each forward spatial smoothing sub-array.

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; P</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span>$

Similarly, as shown in Figure 5, the autocorrelation function matrix obtained after smoothing the backward space can be obtained

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; P</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; cd</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}^{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; h</span>}}{\mathrm{<span\; class="notranslate">\; C</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; I</span>}$

The two-way spatial smoothing algorithm is to average the forward smoothing circular autocorrelation function matrix and the backward smoothing circular autocorrelation function matrix, and the two-way spatial smoothing circular autocorrelation function matrix is:

${\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

Decompose the eigenvalues of R _x (τ), the eigenvectors u ₁ , u ₂ corresponding to the two larger eigenvalues λ ₁ , λ ₂ constitute the signal subspace, and the three smaller eigenvalues λ ₃ , λ ₄ , The eigenvectors u ₃ , u ₄ , u _{5 corresponding to λ 5 constitute} the noise subspace. Define the matrix:

G＝[u ₃ u ₄ u ₅ ]

Since matrix A is a full-rank matrix and R _x is a full-rank matrix, it can be proved that:

G ^H A = G ^H [a(θ ₁ )a(θ ₂ )] = 0

That is, G ^H a(θ _i )=0. Therefore, it can be concluded that G ^H a(θ)=0 when θ is the incoming wave direction of the signal.

Estimated using MUSIC spectrum:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; u</span>}\mathrm{<span\; class="notranslate">\; S</span>}\mathrm{<span\; class="notranslate">\; I</span>}\mathrm{<span\; class="notranslate">\; C</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{{\mathrm{<span\; class="notranslate">\; a</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; GG</span>}}^{\mathrm{<span\; class="notranslate">\; h</span>}}\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&theta;</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>\frac{\mathrm{<span\; class="notranslate">\; \&pi;</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>$

When θ is the incoming wave direction of the signal, P _MUSIC obtains the maximum value, and the incoming wave direction of the signal can be obtained by searching the peak of the MUSIC spectrum.

Through experiments, the expected GSM-R signal arrival direction estimated by GSM-R base station 1 is The incoming wave direction of the interference signal is As shown in Figure 6. The direction of arrival of the expected GSM-R signal estimated by GSM-R base station 2 is The incoming wave direction of the interference signal is As shown in Figure 7.

For the interference signal, the direction of arrival of the interference signal estimated according to GSM-R base station 1 and GSM-R base station 1 is The location of GSM-R base station 2 and the direction of arrival of the interference signal estimated by GSM-R base station 2 are The equations of the straight line where the expected GSM-R signal is located can be obtained:

$\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; no</span>}{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; 12</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}\\ {\mathrm{<span\; class="notranslate">\; tan\&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; two</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\frac{{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 100</span>}}{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}\end{array}\right.$

By solving the equations, the position of the interference source (50.026m, 49.991m) can be calculated respectively.

It can be seen from the experimental results that the method of the present invention can conveniently and quickly locate the interference source only by using the direction of arrival of the interference signal detected by the GSM-R.

Claims (2)

1., based on a GSM-R interference source localization method for Mutual coupling value, it is characterized in that, comprise the following steps:

A. adopt GSM-R base station receive antenna to carry out DOA estimation to interference signal arrival bearing, concrete grammar is: carry out space diversity to GSM-R antenna for base station, builds two groups of aerial arrays, obtains the DOA estimated result to interference signal;

B. according to the DOA estimated result obtained in step a, set up the linear equation of interference source and two GSM-R base station receive antenna lines respectively, obtain the intersection point of these two straight lines, this intersection point is the position of interference source.

2. a kind of GSM-R interference source localization method based on Mutual coupling value according to claim 1, it is characterized in that, step a specifically comprises the following steps:

A1. form even linear array with the antenna of space diversity, each antenna represents an array element;

A2. even linear array is carried out front/rear to space smoothing, construct multiple front/rear to smooth uniform linear array subarray;

A3. set up each front/rear to smooth uniform linear array sub-array receive signal model, and calculate each front/rear Received signal strength spatial correlation matrix to smooth uniform linear array subarray;

A4. each front/rear Received signal strength spatial correlation matrix to smooth uniform linear array subarray is averaging, obtains the two-way space smoothing correlation matrix of even linear array;

A5. according to the two-way space smoothing correlation matrix obtained, the DOA estimated result of MUSIC algorithm acquisition to interference signal is adopted.