KR20170111231A - Beamforming based gnss receiver and operating method thereof - Google Patents

Abstract

A satellite navigation receiver includes an array antenna, a sample data extractor for collecting sample data from a signal received at the array antenna, and a controller for determining a cost function based on the cross-correlation matrix of the received signal based on the sample data, And a beamforming weight vector determiner for determining a beamforming weight vector of the array antenna that maximizes the cost function.

Description

Field of the Invention [0001] The present invention relates to a beamforming-based GNSS receiver,

The present invention relates to a global navigation satellite system (GNSS) receiver based on beamforming and more particularly to a GNSS based on blind beamforming technology having an anti-jamming function for jamming signals having self- Receiver.

The satellite navigation system uses the CDMA (Code Division Multiple Access) scheme, which is robust to the jamming signal according to the spread code gain. However, since the strength of the satellite navigation signal is very weak, it is easily disturbed by the jamming signal having strong signal strength . In order to solve this problem, adaptive beamforming technology, which is a technique for minimizing the antenna gain in the direction of the jamming signal in the array antenna and blocking the reception of the jamming signal, has been studied.

On the other hand, when there is no previous navigation information (location and time) or time-lapse and the previous navigation information can not be trusted, the satellite navigation receiver can operate in a cold start mode to acquire the satellite signal . In this case, since the satellite navigation receiver must steer the beam in the satellite direction without information on the satellite direction, a blind beam forming technique capable of automatically steering the beam in the satellite direction using only the received signal is required.

SCORE (Spectral self-coherence restoral) technique is applicable to a signal having autocorrelation characteristics. It is an antenna beam which amplifies a desired signal and removes an interference signal even if there is no information on the direction of a desired signal. It is a technique of forming a pattern. The SCORE technique creates a cost function to maximize the autocorrelation characteristics of the desired signal and calculates a weight vector of the array antenna that maximizes the cost function.

However, this method has the problem that the jamming signal having no autocorrelation characteristic can be removed and the jamming signal having the same autocorrelation characteristic as the GNSS signal structure can not be removed. In addition, a cross-correlation matrix of sample data is used to calculate a weight vector. When the time at which a message bit is transitioned to sample data is included, the autocorrelation property of the signal in the cross-correlation matrix of the sample data disappears There is a problem.

A Self-Coherence Anti-Jamming GPS Receiver, IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 53, NO. 10, OCTOBER 2005

According to one aspect, a satellite navigation receiver includes an array antenna, a sample data extractor for collecting sample data from a signal received at the array antenna, and a cost function based on the cross-correlation matrix of the received signal based on the sample data. And a beamforming weight vector determiner for determining a beamforming weight vector of the array antenna that maximizes the cost function.

In one embodiment, the sample data extractor determines a sample data point of view based on a sum of eigenvalues of the cost function calculated for each C / A code period during a period corresponding to a message bit length of the GPS signal.

In one embodiment, the sample data extracting unit determines a point of time corresponding to a sum having a maximum value among a sum of eigenvalues of the cost function calculated for each of the C / A code periods, as a collection point of the sample data. In one embodiment, the sample data extracting unit may calculate a time point corresponding to a sum exceeding the predetermined threshold value when the sum of eigenvalues of the cost function calculated for each C / A code cycle exceeds a predetermined threshold value And is determined as the collection point of the sample data.

In one embodiment, the beamforming weight vector determining section includes: a main beam former coefficient arithmetic section for multiplying a first part of the sample data by a main beam former coefficient to generate a beam former output signal; And a sub-beam former coefficient operation unit for multiplying a sub-beam former coefficient by a sub-beam former coefficient to generate a reference signal. In one embodiment, the beamforming weight vector determining unit determines the cost function using a sub-beam former coefficient that minimizes an error between the beam former output signal and the reference signal.

In one embodiment, the beamforming weight vector determiner may identify an eigenvalue corresponding to a GPS signal component among eigenvalues of the cost function, and calculate a beamforming weight vector based on an eigenvector corresponding to the eigenvalue corresponding to the GPS signal component, The vector is determined. In one embodiment, the beamforming weight vector determining unit identifies, as a eigenvalue corresponding to the GPS signal component, an eigenvalue having a magnitude within a predetermined reference range out of eigenvalues of the cost function. In one embodiment, the satellite navigation receiver further includes an antenna controller for controlling the array antenna using the beamforming weight vector.

According to another aspect, a satellite navigation receiving method includes the steps of collecting sample data from a signal received at an array antenna, determining a cost function based on the cross-correlation matrix of the received signal based on the sample data, And determining a beamforming weight vector of the array antenna that maximizes the cost function.

In one embodiment, the step of collecting the sample data includes determining a time point of collection of sample data based on a sum of eigenvalues of the cost function calculated for each C / A code period during a period corresponding to a message bit length of the GPS signal .

In one embodiment, the step of determining the time of collecting the sample data may include calculating a time point corresponding to a sum having a maximum value among a total of eigenvalues of the cost function calculated for each C / A code cycle, . ≪ / RTI > In one embodiment, the step of determining the time of collection of the sample data further comprises the step of, if the sum of the eigenvalues of the cost function calculated for each C / A code period exceeds a predetermined threshold, And determining a point of time corresponding to the total sum as the point of time when the sample data is collected.

In one embodiment, the step of determining the beamforming weight vector comprises multiplying a first portion of the sample data by a main beamformer coefficient and then summing to generate a beamformer output signal, Multiplying the sub-beam former coefficients by the sum of the sub-beam former coefficients to produce a reference signal. In one embodiment, determining the beamforming weight vector comprises determining the cost function using a sub-beam former coefficient that minimizes an error between the beam former output signal and the reference signal.

In one embodiment, the step of determining the beamforming weight vector comprises the steps of: identifying an eigenvalue corresponding to a GPS signal component from eigenvalues of the cost function; and determining an eigenvector corresponding to the eigenvalue corresponding to the GPS signal component And determining the beamforming weight vector. In one embodiment, identifying the eigenvalues corresponding to the GPS signal component includes identifying eigenvalues having magnitudes within a predetermined reference range from among the eigenvalues of the cost function as eigenvalues corresponding to the GPS signal components . In one embodiment, the method further comprises controlling the array antenna using the beamforming weight vector.

1 schematically illustrates a portion of a GNSS receiver in accordance with one embodiment.
2 schematically shows the structure of the GPS L1 C / A signal.
3 is a flowchart illustrating a process of determining a beamforming weight vector in a satellite navigation receiving method according to an embodiment.
4A to 4D show beam pattern simulation results according to one embodiment.
5 shows a beam pattern simulation result according to an embodiment.
6A and 6B show the beam pattern simulation results according to one embodiment.
Figures 7A and 7B illustrate sample data collected according to one embodiment.
Figure 8 illustrates exemplary sample data collected according to one embodiment.
FIG. 9 shows a beam pattern simulation result according to an embodiment.
10 is a flowchart for explaining a process of extracting sample data in a satellite navigation receiving method according to an embodiment.
11 is a schematic diagram for explaining a process of extracting sample data in a satellite navigation receiving method according to an embodiment.
12A and 12B illustrate a total sum of eigenvalues of a cost function when a jamming signal is applied.
FIG. 13 shows a beam pattern simulation result according to an embodiment.
14A and 14B illustrate the sum of the eigenvalues of the cost function when the jamming signal is applied.
15 shows a beam pattern simulation result according to an embodiment.
16A and 16B illustrate the sum of the eigenvalues of the cost function when the jamming signal is applied.
17 shows a beam pattern simulation result according to an embodiment.

Specific structural or functional descriptions of embodiments are set forth for illustration purposes only and may be embodied with various changes and modifications. Accordingly, the embodiments are not intended to be limited to the particular forms disclosed, and the scope of the disclosure includes changes, equivalents, or alternatives included in the technical idea.

The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ", or" having ", and the like, are used to specify one or more of the described features, numbers, steps, operations, elements, But do not preclude the presence or addition of steps, operations, elements, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the meaning of the context in the relevant art and, unless explicitly defined herein, are to be interpreted as ideal or overly formal Do not.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the rights is not limited or limited by these embodiments. Like reference symbols in the drawings denote like elements.

Embodiments relate to the use of autocorrelation characteristics of GPS signals in an array antenna based receiver using a GNSS including a Global Positioning System (GPS) to identify eigenvalues associated with GPS signal components through eigenvalue analysis of cost functions, And determining the beamforming coefficients of the array antenna using only the eigenvectors corresponding to the eigenvalues identified as GPS signal components. That is, the embodiments can use the eigenvectors corresponding to the GPS signal component through the size analysis of the eigenvalue values of the cost function, rather than using only the eigenvectors corresponding to the eigenvalue maximum value of the cost function. Therefore, an anti-jamming function can be provided to a jamming signal having autocorrelation characteristics.

Jamming signals with autocorrelation properties include intelligent smart jamming signals such as relay signals, meaconing signals, and GPS spoofing signals as well as simple jamming signals such as continuous waves, as well as GPS signals. can do. Embodiments can provide anti-jamming functions for smart jamming signals as well as simple jamming signals such as continuous waves.

In addition, in the actual situation where there are various time-of-arrival (TOA) of the navigation reception period for GPS visible satellites, sample data without bit inversion interval is extracted from signals received from GPS visible satellites . By using the sample data having no bit inversion period as described above, it is possible to solve the problem that the autocorrelation characteristic disappears due to the bit inversion in the cross-correlation matrix.

1 schematically illustrates a portion of a GNSS receiver in accordance with one embodiment. 1, a GNSS receiver according to an embodiment may include an array antenna 110, a main beam former coefficient operation unit 120, and a sub beam member coefficient operation unit 130. [ The GNSS receiver may further include a sample data collecting unit (not shown) for sampling the signal received from the array antenna 110 and transmitting the sampled signal to the main beam former calculator 120 and the sub beam former calculator 130.

The GNSS receiver according to an exemplary embodiment is applicable to all GNSS signals including GPS, and will be described below with reference to a GPS L1 C / A (Coarse / Acquisition) signal.

Referring to FIG. 2, the structure of the GPS L1 C / A signal is schematically shown. The GPS L1 C / A signal is a civil signal and uses a C / A code with a PRN (pseudorandom noise) code. The cycle of the C / A code is 1 ms and the 1 bit consists of 20 C / A codes. That is, the GPS L1 message bit is composed of 20 C / A code block units, and the bits can be inverted every 20 ms. Since the C / A code is repeatedly transmitted every 1ms period, the GPS L1 C / A signal has autocorrelation characteristics between C / A codes repeated 20 times in 1ms cycle within the same bit. However, the bits can be inverted every 20 ms, and autocorrelation characteristics do not appear between the C / A codes having different code bits. Therefore, in order to prevent the autocorrelation property from disappearing, it is necessary to prevent the bit inversion section from being included in the sample data at the time of extracting the sample data.

Referring again to FIG. 1, the M array antennas 110 may be arranged at half-wave intervals of a GPS L1 C / A signal. The sample data x (n) collected during the time corresponding to one cycle of the C / A code (for example, 1 ms) from the signal received from the array antenna 110 is sampled by the main beam former coefficient arithmetic unit 120, Multiplied by the former coefficient, and then added to generate a beam former output signal z (n).

In order to utilize the autocorrelation characteristic of the GPS L1 C / A signal, sample data is collected at a time point when the time (mP) times the integer (m) times the number of samples P corresponding to the C / A code 1 cycle time is delayed . The sample data x (n-mP) collected during a period corresponding to one cycle of the C / A code is multiplied by the sub-beam former coefficient by the sub-beam former coefficient calculator 130 and then added to generate a reference signal d (n) do. Here, the collection period of the sample data is selected as one cycle of the C / A code, but it is not necessarily limited to one period or 1 ms, but may be selected to be shorter or longer.

The received signal x (n) can be expressed by the following equation (1).

Here, s (n) denotes a GPS signal component, i (n) denotes a jamming signal component, and v (n) denotes a noise component. Multipath signal components are not considered.

The delayed received signal x (n-mP) can be expressed by the following equation (2).

In addition, the beam former output signal z (n) and the reference signal d (n) can be expressed by the following equations (3) and (4), respectively.

Here, (·) ^H means a Hermitian transpose matrix.

The covariance of each of the beam former output signal z (n) and the reference signal d (n) can be expressed by Equations (5) and (6) using Equations (3) and (4) .

로 정의할 수 있고, 교차상관 행렬

는

로 정의할 수 있다. Here, E {·} means average. Then, the covariance matrix R _xx

, And a cross-correlation matrix

The

.

로 분해가 가능하며 수학식 8과 같이 나타낼 수 있다.On the other hand, assuming that the intensity of the jamming signal is much greater than the GPS signal intensity and that the number of array antennas (M) is sufficiently large such that all the steering vectors are orthogonal, the covariance matrix Rxx is estimated by eigenvalue decomposition

And can be expressed by Equation (8).

이다.here,

to be.

의 신호 세기를 갖는 잡음 및 재밍 서브스페이스의 고유벡터, U_s는

의 신호 세기를 갖는 잡음 및 GPS 신호 서브스페이스의 고유벡터, 그리고 U_v는

의 신호 세기를 갖는 잡음 서브스페이스를 의미한다. 그리고,

및

는 각각, 재밍신호, GPS 신호, 잡음의 세기를 의미한다.U _i , is

Eigenvector, U _s of the noise and jamming subspace having a signal strength

And the eigenvector of the GPS signal subspace, with U _v =

Quot; noise subspace ". And,

And

Denote the jamming signal, the GPS signal, and the strength of the noise, respectively.

는 EVD (eigenvalue decomposition)에 의해

로 분해가 가능하며, 수학식 9와 같이 나타낼 수 있다.Similarly, a cross-correlation matrix

By eigenvalue decomposition (EVD)

And can be expressed by Equation (9).

이다.here,

to be.

는 교차상관 행렬

에서 재밍 신호의 세기를 의미한다. 즉,

는 재밍 신호의 자기상관 특성의 정도를 나타내므로 SCORE 기술의 성능을 결정하는 중요한 성분이다.

Is a cross-correlation matrix

The strength of the jamming signal is referred to as " In other words,

Shows the degree of autocorrelation of the jamming signal and is an important component for determining the performance of the SCORE technique.

이고, 비용함수를 최대화시키는 가중치 벡터(

)를 산출하여 이를 빔포밍 계수로 이용한다. 즉, SCORE 기술은 일반적으로 자기상관 특성을 갖는 신호의 교차상관 행렬(

)을 최대화시키는 가중치 벡터(

)를 산출한다.The cost function of the SCORE technique, on the other hand,

And a weight vector that maximizes the cost function (

) Is calculated and used as a beam forming coefficient. In other words, the SCORE technique is generally called a cross-correlation matrix of signals having autocorrelation properties

) ≪ / RTI >

).

로 나타낼 수 있으며, 이를 최소화시키는 부 빔포머 계수

을 비용함수에 대입하여 정리하면 비용함수는 다음의 수학식 10과 같이 나타낼 수 있다.The error between the beam former output signal and the reference signal is

, And the sub-beam former coefficient

Is substituted into the cost function, the cost function can be expressed by the following Equation (10).

이다. here,

to be.

)로 결정하면, 다음의 수학식 11과 같이 나타낼 수 있다.In Equation (10), the eigenvector corresponding to the maximum eigenvalue in the general eigenvalue problem is defined as a weight vector

), The following expression (11) can be obtained.

In Equation (11), a solution can be obtained by determining the eigenvector corresponding to the maximum eigenvalue value (? _Max ) as the weight vector (w). In other words, when the weighting vector w corresponding to the maximum eigenvalue value? _Max is applied to Equation 10, the cost function C (w) calculates the maximum cost value and the maximum cost value is the maximum eigenvalue value? _Max ).

라고 정의하자. 수학식 8에서

를 얻을 수 있고, 수학식 9에서

를 얻을 수 있으므로, 이를 A 에 대입하여 정리하면 다음의 수학식 12와 같이 나타낼 수 있다.On the other hand, in Equation (11)

. In Equation (8)

And in Equation 9,

Can be obtained by substituting this into A and summarizing it as shown in the following Equation (12).

이다.here,

to be.

및

는 각각 재밍, GPS 신호 및 잡음의 고유치들을 의미하고, 이들은 자기상관값과 교차상관값간의 신호세기 비율에 의해 결정된다. 한편,

는 고유치 크기의 내림차 순에 의해 각각 수학식 13 및 수학식 14와 같이 나타낼 수 있다.

And

Mean eigenvalues of jamming, GPS signal and noise, respectively, which are determined by the signal intensity ratio between the autocorrelation value and the cross-correlation value. Meanwhile,

Can be represented by Equation (13) and Equation (14) according to the descending order of the eigenvalue size, respectively.

Here, diag {} denotes a diagonal matrix, K denotes the number of jamming signals, and Q denotes the number of GPS signals.

)를 산출한다.In the conventional SCORE technique, the weight vector of the array antenna is calculated so that the autocorrelation characteristic of the desired signal is maximized under a special condition that the jamming signal has no correlation with the desired signal. However, in this case, all of the common jamming signals are considered. In this case, the eigenvalues of all the signal components are analyzed as shown in Equation (12), and eigenvalues corresponding to the GPS signal intensity are selected through Equation (14) Then, the eigenvectors corresponding to these are extracted, and finally the weight vector

).

As can be seen from Equation (12), when the weight vector is calculated by applying the conventional SCORE technique in the case of a jamming signal having a correlation characteristic with a desired signal, the gain may be amplified in the direction of the jamming signal.

Because the GPS signal has a robust nature to the jamming signal according to the spreading code gain, the jamming signal that directly affects the GPS signal typically has a signal strength that is 20 to 30 dB greater than the strength of the GPS signal. For example, a GPS signal typically has a signal strength on the order of -160 dBW +/- 3dB, but a simple jamming signal may have a signal strength greater than 20-30 dB above the strength of the GPS signal. On the other hand, in the case of a smart jamming signal having the same structure as that of the GPS signal, the GPS only signal can be transmitted so as to have a signal strength slightly higher than the intensity of the GPS signal at the deemed receiver (about 5 dB) The intensity of the signal varies depending on the radius of influence of the GPS signal, but is transmitted so as to have a relatively larger signal intensity than the GPS signal alone.

Utilizing the fact that there is such a difference between the strength of the GPS signal and the intensity of the jamming signal, all eigenvalue values in equation (12) can be analyzed to identify components corresponding to each eigenvalue value.

When all the eigenvalue values in the cost function of Equation 12 are analyzed, the eigenvalue values corresponding to the GPS signals have similar magnitudes to each other. However, since the intensity of the jamming signal is 20 to 30 dB or more greater than the intensity of the GPS signal, The eigenvalue values having a relatively larger value as compared to the eigenvalue values corresponding to the GPS signal. In addition, in the case of a GPS signal, it is practically difficult to generate a GPS signal only in various satellite directions such as an actual GPS satellite. Therefore, in general, do. The strength of the GPS simulant signal for each satellite can be transmitted about 5dB higher than the actual GPS signal, but in this case the signal strength received in the GPS simulcast direction may be very high because it is the sum of all GPS simulant signals .

The eigenvalues corresponding to the GPS signal and the eigenvalues corresponding to the jamming signal can be identified through analysis of the eigenvalue values using the characteristics of the jamming signal intensity. If there is no jamming signal or no correlation, eigenvalues corresponding to the jamming signal will not exist in the cost function, so that the eigenvalues corresponding to the GPS signal can be similarly identified. That is, all the eigenvalue values in the cost function of the SCORE technique can be analyzed, among which the eigenvalues corresponding to the GPS signals can be identified and the corresponding eigenvectors can also be identified.

Setting of the threshold value is required to identify the eigenvalue value corresponding to the GPS signal. For example, a first threshold value corresponding to the lower limit of the eigenvalue value corresponding to the GPS signal and a second threshold value corresponding to the upper limit of the eigenvalue value corresponding to the GPS signal may be set. The first threshold value and the second threshold value may vary depending on conditions such as the received GPS signal and the number of array antennas, and therefore may be determined in advance through a test. When the first threshold value and the second threshold value are set, the eigenvalue that is larger than the first threshold value and smaller than the second threshold value may be determined as a eigenvalue corresponding to the GPS signal according to the comparison result of the eigenvalue values of the cost function and the threshold values .

)를 결정할 수 있다. GPS 신호의 고유치 값에 대응하는 고유벡터에 기초하여 빔포밍 가중치 벡터(

)를 적용함으로써, GPS 신호 방향의 빔은 증폭시키고 재밍 신호 방향의 빔은 감소시키는 적응형 빔포밍을 통해 재밍 신호의 상관 특성 유무와 관계 없이 다양한 종류의 재밍 신호에 대하여 항재밍 기능을 제공할 수 있다.When an eigenvalue value corresponding to the GPS signal is identified in this manner, a beamforming weight vector

Can be determined. Based on the eigenvector corresponding to the eigenvalue of the GPS signal, the beamforming weight vector

), It is possible to provide an anti-jamming function for various kinds of jamming signals regardless of the correlation characteristic of the jamming signal through the adaptive beamforming that amplifies the beam in the GPS signal direction and reduces the beam in the jamming signal direction have.

)를 산출할 수 있다. 예를 들어, GPS 신호 성분의 최대 고유치 값에 대응하는 고유벡터를 가중치 벡터(

)로 사용하거나, 복수의 가중치 벡터의 평균을 구해서 빔포밍 가중치 벡터(

)로 사용할 수 있다. 또한, 추출된 GPS 신호 성분의 고유벡터를 방향 성분에 해당하는 4개의 가중치 벡터를 제한 조건으로 설정하고 MMSE(Minimum Mean-Squared Error)의 해를 구하는 식을 이용하여 가중치 벡터(

)를 산출할 수 있다. MMSE 해를 구하는 식은 다음의 수학식 15와 같이 나타낼 수 있다. Therefore, after calculating all the eigenvalues of Equation (12), eigenvectors corresponding to the eigenvalues of the GPS signal components existing in the threshold range are used according to various methods to obtain the final beamforming weight vector

) Can be calculated. For example, the eigenvector corresponding to the maximum eigenvalue value of the GPS signal component is referred to as a weight vector

), Or averaging a plurality of weight vectors to obtain a beamforming weight vector (

). In addition, the eigenvectors of the extracted GPS signal components are set as constraint conditions of four weight vectors corresponding to the direction components, and a solution of the minimum mean-squared error (MMSE)

) Can be calculated. The equation for obtaining the MMSE solution can be expressed by the following equation (15).

이고, f=[11....1]^T 이다.Here, the direction component of the GPS signal

, And f = [11 .... 1] ^T.

3 is a flowchart illustrating a process of determining a beamforming weight vector in a satellite navigation receiving method according to an embodiment. The method shown in FIG. 3 may be performed, for example, by a beamforming weight vector determination unit included in the receiver of FIG.

In step 310, the satellite navigation receiving method may collect sample data from the beam former output signal. For example, it is possible to receive the output signal side sample data x (n) collected during the time corresponding to one cycle of the C / A code from the array antenna 110, and calculate the number of samples (N-mP) collected during the time corresponding to one cycle of the C / A code at the point of time when the time mP times the integer m of the sampling signal P is delayed. Side sample data signal x (n-mP) is multiplied by the sub-beam former coefficient, and the output signal side sample data x (n) is multiplied by the main beam former coefficient to be summed to generate the beam former output signal z And then generates a reference signal d (n). Here, the collection period of the sample data is selected as one cycle of the C / A code, but it is not necessarily limited to one cycle, and can be selected to be shorter or longer.

)를 계산할 수 있다. 빔포머 출력 신호와 기준 신호 간의 오차를 최소화시키는 부 빔포머 계수를 비용함수에 대입하여 정리하면 비용함수는 수학식 10과 같이 표현된다.In step 320, the satellite navigation receiving method calculates a cost function C (

) Can be calculated. When the sub-beam former coefficient that minimizes the error between the beam former output signal and the reference signal is substituted into the cost function, the cost function is expressed by Equation (10).

In step 330, the satellite navigation receiving method may identify a eigenvalue corresponding to a GPS signal component from among all the eigenvalue values of the cost function. For this purpose, an analysis based on the relationship between the GPS signal and the signal strength of the jamming signal can be used. For example, eigenvalue values having magnitudes within a predetermined reference range may be identified as eigenvalues corresponding to GPS signal components. Thresholds (upper and lower limits) that define a reference range that identifies the eigenvalue value corresponding to the GPS signal component may be predetermined through testing. On the other hand, when there is no jamming signal or no correlation property, eigenvalue values corresponding to the jamming signal will not exist in the cost function, so that a eigenvalue corresponding to the GPS signal can be similarly identified

)를 결정할 수 있다. 예를 들어, GPS 신호 성분의 비용함수의 최대 고유치 값에 대응하는 고유벡터를 가중치 벡터(

)로 사용하거나, 복수의 가중치 벡터의 평균을 구해서 빔포밍 가중치 벡터(

)로 사용할 수 있다. 또한, GPS 신호의 방향 성분에 해당하는 가중치 벡터를 제한 조건으로 설정하고 MMSE의 해를 구하는 식을 이용하여 가중치 벡터(

)를 산출할 수 있다. 빔포밍 가중치 벡터(

)를 적용함으로써, GPS 신호 방향의 빔은 증폭시키고 재밍 신호 방향의 빔은 감소시키는 적응형 빔포밍을 통해 재밍 신호의 상관 특성 유무와 관계 없이 다양한 종류의 재밍 신호에 대하여 항재밍 기능을 제공할 수 있다.In step 340, based on the eigenvector corresponding to the GPS signal component, the beamforming weight vector

Can be determined. For example, the eigenvector corresponding to the maximum eigenvalue of the cost function of the GPS signal component is referred to as a weight vector

), Or averaging a plurality of weight vectors to obtain a beamforming weight vector (

). In addition, the weight vector corresponding to the direction component of the GPS signal is set as the limiting condition, and the weight vector (

) Can be calculated. Beamforming weight vector (

), It is possible to provide an anti-jamming function for various kinds of jamming signals regardless of the correlation characteristic of the jamming signal through the adaptive beamforming that amplifies the beam in the GPS signal direction and reduces the beam in the jamming signal direction have.

4A to 4D show results of simulation of a beam pattern after calculating a weight vector using only eigenvectors corresponding to the eigenvalue maximum value of the cost function according to an embodiment.

In the simulation, four GPS satellite signals are received from ten ULA (Uniform Linear Array) antennas at a signal strength of -157 dBW in the direction of -50, -10, 30, and 60 degrees, and two jamming signals are received in directions of 15 and 45 degrees , And the noise intensity was set to -142 dBW (2 MHz bandwidth), and 2,046 sample data corresponding to 1 ms was used.

In FIG. 4A, a jamming signal having no correlation characteristic is received at a jamming to signal ratio (JSR) of 30 dB. In FIG. 4B, a continuous wave jamming signal is received at a 30 dB JSR. In FIG. 4C, four smart jamming signals having the same C / A code as the four GPS satellite signals are synthesized with 0 dB of JSR and received at 15 and 45 degrees, respectively. In FIG. 4D, the same conditions as in FIG. 4C are applied, but four smart jamming signals are synthesized at 30 dB each.

As shown in FIGS. 4A to 4D, the simulation result using the weight vector according to Equation (13) successfully nullifies the jamming signal direction only when a jamming signal having no correlation characteristic is received (FIG. 4A) When a jamming signal with characteristics is received (Figs. 4B to 4D), a beam is also formed in the direction of the jamming signal.

FIG. 5 illustrates a result of simulating a beam pattern after calculating eigenvectors corresponding to a GPS signal component by analyzing magnitudes of eigenvalue values of a cost function according to an embodiment, calculating weight vectors using the eigenvectors corresponding to GPS signal components, and FIG.

The simulation conditions in Fig. 5 were set the same as those in Fig. 4A. Four GPS satellite signals were received in the -50, -10, 30, and 60 degrees directions and two continuous wave jamming signals were received in the JSRs of 50 dB in the 15 and 45 degrees directions.

The solid line is the result of simulating the beam pattern using only the maximum value among the four weight vectors corresponding to the GPS signal direction, the one-dot chain line is the result of simulating the beam pattern by averaging the four weight vectors, And the beam pattern is simulated using the MMSE solution. In each case, the gain of the GPS signal direction is amplified, and the gain of the jamming signal direction is nulled to about -70 dB.

6A and 6B show the beam pattern simulation results according to one embodiment. FIG. 5 shows a simulation result using a weight vector determined according to Equation (14). The simulation conditions in Fig. 6A are set the same as those in Fig. 4C, and the simulation conditions in Fig. 6B are set the same as in Fig. 4D. Four smart jamming signals with the same GPS C / A code as the four GPS satellites are synthesized by receiving four GPS satellite signals in directions of -50, -10, 30, and 60 degrees. 6A) or a JSR of 50 dB (in the case of FIG. 6B).

The solid line is the result of simulating the beam pattern using only the maximum value among the four weight vectors corresponding to the GPS signal direction, the one-dot chain line is the result of simulating the beam pattern by averaging the four weight vectors, And the beam pattern is simulated using the MMSE solution. It can be seen that the method using the average value of the four weight vectors or the method using the MMSE solution forms a beam pattern superior to the method using only the maximum value among the four weight vectors.

Figures 7A and 7B illustrate sample data collected according to one embodiment. Since the SCORE technique uses the cross-correlation matrix of the sample data in the cost function and operates based on the autocorrelation characteristics of the GPS signal components in the sample data, in order for the SCORE technology to operate normally, Should be maintained. For this purpose, the sample data needs to be selected in the same bit so as not to include the inversion of the message bits.

When the message bit inversion time is included in the beamformer output signal side sample data x (n) as shown in FIG. 7A, the autocorrelation characteristic in the cross-correlation matrix may disappear. Similarly, when the message bit inversion time is included in the reference signal side sample data x (n-mP) as shown in Fig. 7B, the autocorrelation characteristic may disappear in the cross-correlation matrix.

There is a method of using the length of the sample data longer than one cycle of the C / A code in order to maintain the autocorrelation property in the sample data. However, since this method considers only a signal having a specific TOA time between a specific GPS satellite and a receiver, autocorrelation characteristics can be maintained only for a signal having a corresponding TOA time. In reality, TOA time between at least 4 GPS satellites and receiver is used for GPS satellite navigation, and TOA time has various values according to various GPS satellite group arrangement at the time of positioning. In fact, when analyzing the distance between the receiver and the GPS visible satellites measured at an arbitrary point in time using the GPS simulator and the TOA time, the TOA time is distributed in the range of about 67 to 83 ms, and the TOA There is a time difference.

Figure 8 illustrates exemplary sample data collected according to one embodiment. It can be assumed that the beam former output signal side sample data x (n) and the reference signal side sample data x (n-mP) are selected as shown in FIG. 8 for the message bit structure of the given four GPS signals.

In this case, simulation results in which four GPS signals are received in the same manner as the simulation condition in FIG. 4A and two jamming signals having no autocorrelation characteristics are received in the JSR of 50 dB in the directions of 15 and 45 degrees, same.

9, the solid line is a result of simulating the beam pattern using only the maximum value among the four weight vectors corresponding to the GPS signal direction by using a method of using the length of the sample data longer than one cycle of the C / A code. As a result, the nulling function was successfully provided for the jamming signal having no autocorrelation property. However, in the case of the -50 ° direction GPS signal including the bit inversion point at 1/2 of the sample data on the beam former output signal side It can be seen that it failed to provide the beam gain. However, in the case of the GPS signal in the -10 degree direction in which the bit inversion time is included at 1/7 of the sample data on the beam former output signal side, since the remaining 6/7 part holds the same bit, Lt; RTI ID = 0.0 > beam gain < / RTI >

As a result, the method of using the length of the sample data longer than one period (1 ms) of the C / A code can be applied only to a signal having a specific TOA time, and can not be applied to all GPS visible satellite signals. Therefore, when taking the sample data used in the calculation of the cross-correlation matrix of the cost function, it is necessary to select the sample data so that the autocorrelation characteristic of the signals received from the GPS visible satellite is maintained. The sample data selection method will be further described below with reference to FIG.

10 is a flowchart for explaining a process of extracting sample data in a satellite navigation receiving method according to an embodiment. The method shown in FIG. 10 may be performed by, for example, a sample data extracting unit included in the receiver of FIG.

At step 1010, the sum of the eigenvalue values of the cost function may be computed for each C / A code period during a period corresponding to the GPS message bit length. The cross correlation matrix using the sample data including the bit inversion time has a very small eigenvalue of the cost function because the autocorrelation characteristic of the GPS signal is canceled. Therefore, when one or more GPS signals whose autocorrelation characteristics are canceled in the sample data are present, the sum of the eigenvalue values of the cost function will have a relatively small value, and the autocorrelation characteristics of all the GPS signals included in the sample data are not canceled The sum of the eigenvalues of the cost function will have a relatively large value.

In order to utilize this characteristic, for example, the sum of the eigenvalues of the cost function is calculated at an arbitrary point in time, and the eigenvalue value of the cost function is calculated over 20 times corresponding to the message bit (20ms) Can be calculated.

At step 1020, a sample data point at which autocorrelation properties of all GPS signals are not canceled out of the sum of eigenvalue values can be determined. The point of time when the calculated total sum has the maximum value among the sum of the eigenvalue values calculated over 20 times corresponding to the message bit length can be regarded as the sample data point in which the autocorrelation characteristics of all the GPS signals are not canceled. Therefore, the point in time when the calculated sum has the maximum value can be determined as the point in time of the sample data to be used in the cost function calculation.

At this time, the sample data point at which the autocorrelation characteristics of all the GPS signals are not canceled may exist at several points in addition to the point at which the calculated sum has the maximum value. Therefore, since the calculation of the total sum of the eigenvalues is repeated 20 times, the calculation amount is large and the processing time is increased. Therefore, the threshold value for the sum of the eigenvalue values is set through the preliminary test, If the threshold value is exceeded, the iterative calculation of the sum of the eigenvalue values can be stopped and the calculated index can be determined as the sample data point in time.

At step 1030, sample data corresponding to the sample data point determined from the beam former output signal may be collected. The sample data collected in this manner is sample data that is selected in a section that does not include the bit inversion time point and in which autocorrelation characteristics of all GPS signals are not canceled. Therefore, the anti-jamming function can be effectively provided by performing the cross-correlation matrix calculation of the cost function using such sample data.

11 is a schematic diagram for explaining a process of extracting sample data in a satellite navigation receiving method according to an embodiment. It is assumed that the four GPS satellite directions are -50 degrees, -10 degrees, 30 degrees, and 60 degrees, and the structure of the message received from each direction at any point in time is as shown in FIG.

After collecting the beam former output signal side sample data x (n) and the reference signal side sample data x (n-mP), the eigenvalue value of the cost function is obtained and the eigenvalue values selected by all the eigenvalue values or the GPS signal components are summed . This process is repeated from 1 to 20 according to the search index, and the sum of the eigenvalues of the cost function corresponding to each search index can be calculated.

With respect to a given bit inversion point, in the case of a GPS signal in the -50 degree direction, bit inversion occurs at a search index of 2, and the search index 3 will have a relatively low eigenvalue value, and after the search index 4, It will have a typical eigenvalue value.

In the case of the GPS signal in the 60-degree direction, bit inversion occurs at a search index of 8, so that the search index 9 will have a relatively low eigenvalue value. Since the bit inversion does not occur after the search index 10, .

In the case of the GPS signal in the 30-degree direction, bit inversion occurs at a search index of 16, so that the search index 17 will have a relatively low eigenvalue value. Since the bit inversion does not occur after the search index 19, Value.

Also, it can be expected that the bit inversion occurs at the search index of 19 only in the case of the GPS signal in the -10 degree direction, and has a relatively low eigenvalue value up to the search index 20.

12A and 12B illustrate a total sum of eigenvalues of a cost function corresponding to each search index when a random jamming signal is applied with a JSR of 50 dB. FIG. 12A shows the sum of the eigenvalue values identified as corresponding to the GPS signal component among the eigenvalue values of the cost function, and FIG. 12B shows the sum of all the eigenvalue values of the cost function.

In the case of the random jamming signal, since there is no correlation characteristic, there is no eigenvalue value corresponding to the jamming signal. Therefore, FIGS. 12A and 12B show similar trends, and it can be seen that the sum of the eigenvalue values at both of the search indexes is 1 has the maximum value.

FIG. 13 shows a beam pattern simulation result at a search index 1 where the sum of the eigenvalue values of the cost function is the maximum value when the random jamming signal is applied with the JSR of 50 dB.

13, the solid line shows the result of simulating the beam pattern using only the maximum value among the four weight vectors corresponding to the GPS signal direction, the one-dot chain line shows the result of simulating the beam pattern by averaging the four weight vectors, And the beam pattern is simulated using the MMSE solution. It can be seen that the method using the average value of the four weight vectors or the method using the MMSE solution forms a beam pattern superior to the method using only the maximum value among the four weight vectors.

14A and 14B illustrate a total sum of eigenvalues of a cost function corresponding to each search index when a continuous wave jamming signal is applied with a JSR of 50 dB. FIG. 14A shows the sum of the eigenvalue values identified as corresponding to the GPS signal component among the eigenvalue values of the cost function, and FIG. 14B shows the sum of all the eigenvalue values of the cost function.

Since the continuous wave jamming signal is a jamming signal having a correlation characteristic and the JSR is 50 dB, the eigenvalue value corresponding to the jamming signal may have a much larger value than the eigenvalue value corresponding to the GPS signal. Due to this, the sum of all the eigenvalue values of the cost function of Fig. 14B has an overall larger value than the sum of the eigenvalue values identified as corresponding to the GPS signal component of Fig. 14A. However, Figs. 14A and 14B show similar trends in the change of the total value according to the search index, and it is seen that the tendency similar to Figs. 12A and 12B is also shown.

14A has the maximum value at the point of the search index 14, and FIG. 14B has the maximum value at the point of the search index 11. That is, there is a difference from the one having the maximum value at the search index 1 in FIGS. 12A and 12B. However, since the points where the search indexes are 1, 11, and 16 are the points where bit inversion does not occur in all the GPS signals, there is no problem that data of any index is selected. Thus, it is assumed that the difference between the points where the sum of the eigenvalue values has the maximum value appears according to the Gaussian noise characteristic.

15 shows a beam pattern simulation result at a search index 14 where the sum of the eigenvalue values of the cost function is the maximum value when the continuous wave jamming signal is applied with a JSR of 50 dB.

In FIG. 15, the solid line indicates the result of simulating the beam pattern using only the maximum value among the four weight vectors corresponding to the GPS signal direction, the dotted line indicates the result of simulating the beam pattern by averaging the four weight vectors, And the beam pattern is simulated using the MMSE solution. It can be seen that the method using the average value of the four weight vectors or the method using the MMSE solution forms a beam pattern superior to the method using only the maximum value among the four weight vectors.

16A and 16B illustrate a total sum of eigenvalues of a cost function corresponding to each search index when a smart jamming signal having the same structure as a GPS signal is applied with a JSR of 30 dB. FIG. 16A shows the sum of the eigenvalue values identified as corresponding to the GPS signal component among the eigenvalue values of the cost function, and FIG. 16B shows the sum of all the eigenvalue values of the cost function.

Since the smart jamming signal is a jamming signal having a correlation characteristic and the JSR is 30 dB, the eigenvalue value corresponding to the jamming signal may have a much larger value than the eigenvalue value corresponding to the GPS signal. Due to this, the sum of all the eigenvalue values of the cost function of Fig. 16B has an overall larger value than the sum of the eigenvalue values identified as corresponding to the GPS signal component of Fig. 16A. However, Figs. 16A and 16B show similar trends in the change of the total value according to the search index, and it is seen that the tendency similar to Figs. 12A, 12B, 14A and 14B is also shown.

17 shows a beam pattern simulation result at a search index 4 where the sum of the eigenvalue values of the cost function is the maximum value when the smart jamming signal is applied with the JSR of 30 dB.

17, the solid line shows the result of simulating the beam pattern using only the maximum value among the four weight vectors corresponding to the GPS signal direction, the one-dot chain line shows the result of simulating the beam pattern by averaging the four weight vectors, And the beam pattern is simulated using the MMSE solution. It can be seen that the method using the average value of the four weight vectors or the method using the MMSE solution forms a beam pattern superior to the method using only the maximum value among the four weight vectors.

The embodiments described above may be implemented in hardware components, software components, and / or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, such as an array, a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced. Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (18)

Array antenna;
A sample data extracting unit for collecting sample data from a signal received from the array antenna; And
A beamforming weight vector determiner for determining a cost function based on the cross correlation matrix of the received signal based on the sample data and determining a beamforming weight vector of the array antenna for maximizing the cost function,
And a satellite navigation receiver. The method according to claim 1,
Wherein the sample data extracting section determines a sample data point of time based on a sum of eigenvalues of the cost function calculated for each C / A code period during a period corresponding to a message bit length of a GPS signal,
Satellite navigation receiver. 3. The method of claim 2,
Wherein the sample data extracting unit determines a point of time corresponding to a sum having a maximum value among a sum of eigenvalues of the cost function calculated for each C /
Satellite navigation receiver. 3. The method of claim 2,
Wherein the sample data extracting unit extracts a time point corresponding to a total sum exceeding the predetermined threshold value when the sum of eigenvalues of the cost function calculated for each C / A code period exceeds a predetermined threshold value, As a point of decision,
Satellite navigation receiver. The method according to claim 1,
Wherein the beamforming weight vector determining unit comprises:
A main beam former coefficient operation unit for multiplying a first part of the sample data by a main beam former coefficient to generate a beam former output signal; And
A sub-beam former coefficient operation unit for multiplying a second portion of the sample data by a sub-beam former coefficient and generating a reference signal by summing,
And a satellite navigation receiver. 6. The method of claim 5,
Wherein the beamforming weight vector determining unit determines the cost function using a sub-beam former coefficient that minimizes an error between the beam former output signal and the reference signal,
Satellite navigation receiver. The method according to claim 1,
Wherein the beamforming weight vector determining unit determines the beamforming weight vector based on an eigenvector corresponding to the eigenvalue corresponding to the GPS signal component and identifies an eigenvalue corresponding to a GPS signal component among eigenvalues of the cost function,
Satellite navigation receiver. 8. The method of claim 7,
Wherein the beamforming weight vector determining unit identifies an eigenvalue having a size within a predetermined reference range from eigenvalues of the cost function as a eigenvalue corresponding to the GPS signal component,
Satellite navigation receiver. The method according to claim 1,
And an antenna controller for controlling the array antenna using the beamforming weight vector.
Satellite navigation receiver. Collecting sample data from a signal received at the array antenna;
Determining a cost function based on the cross-correlation matrix of the received signal based on the sample data; And
Determining a beamforming weight vector of the array antenna that maximizes the cost function
And a satellite navigation receiving method. 11. The method of claim 10,
Wherein collecting the sample data comprises:
Determining a time of collection of sample data based on a sum of eigenvalues of the cost function calculated for each C / A code period during a period corresponding to a message bit length of the GPS signal;
Satellite navigation receiving method. 12. The method of claim 11,
Wherein the step of determining the time of collection of the sample data comprises:
Determining a point in time corresponding to a sum having a maximum value among a total of eigenvalues of the cost function calculated for each of the C / A code periods as a point of time of collecting the sample data.
Satellite navigation receiving method. 12. The method of claim 11,
Wherein the step of determining the time of collection of the sample data comprises:
Determining a point of time corresponding to a total sum exceeding the predetermined threshold as a point of time of collecting the sample data when a sum of eigenvalues of the cost function calculated for each C / A code period exceeds a predetermined threshold value / RTI >
Satellite navigation receiving method. 11. The method of claim 10,
Wherein the step of determining the beamforming weight vector comprises:
Multiplying a first portion of the sample data by a main beamformer coefficient and summing to produce a beamformer output signal; And
Multiplying a second portion of the sample data by a sub-beam former coefficient, and summing the result to generate a reference signal
And a satellite navigation receiving method. 15. The method of claim 14,
Wherein the step of determining the beamforming weight vector comprises:
And determining the cost function using a sub-beam former coefficient that minimizes an error between the beam former output signal and the reference signal.
Satellite navigation receiving method. 11. The method of claim 10,
Wherein the step of determining the beamforming weight vector comprises:
Identifying a eigenvalue corresponding to a GPS signal component from eigenvalues of the cost function; And
Determining the beamforming weight vector based on an eigenvector corresponding to eigenvalues corresponding to the GPS signal component
And a satellite navigation receiving method. 17. The method of claim 16,
Wherein identifying an eigenvalue corresponding to the GPS signal component comprises:
Identifying an eigenvalue having a magnitude within a predetermined reference range from eigenvalues of the cost function as a eigenvalue corresponding to the GPS signal component;
Satellite navigation receiving method. 11. The method of claim 10,
Further comprising: controlling the array antenna using the beamforming weight vector.
Satellite navigation receiving method.

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