CN113824484A - Data processing method of PAF phased array receiver - Google Patents

Abstract

The invention discloses a data processing method for a PAF phased array receiver, which relates to the technical field of data processing. The specific steps are: constructing a model: constructing a beam forming model according to a multi-beam model; calculating beam forming SNR and gain: according to any receiving Obtain the SNR beamforming output signal from the relationship between the signal of interest in the channel and the interference and noise signals; calculate the total gain of the array according to the relationship between the SNR beamforming output and the input; optimize the beamforming algorithm: According to the beamforming SNR and gain , determine the minimum mean square error s; the present invention improves the overall efficiency of the radio reception processing system through the determination of the minimum mean square error and the distortion-free response beam synthesis.

Description

A PAF Phased Array Receiver Data Processing Method

technical field

The invention relates to the technical field of data processing, and more particularly to a data processing method of a PAF phased array receiver.

Background technique

The field of view of a radio telescope is an important indicator of the telescope's ability to survey the sky, which represents the range of the observable sky area at any given moment. For a single-aperture radio telescope, the field of view and resolution can be expressed by the half-beam power width: HPBW=1.02λ/D, where λ is the observation wavelength and D is the diameter of the telescope. Large-diameter radio telescopes can obtain higher resolution and sensitivity by increasing the diameter D, but at the same time, the field of view of the telescope decreases with the increase of the diameter, resulting in a reduction in the area of the observed sky per unit time. For observations such as pulsar or transient source search, molecular spectral line surveys, etc., the observation area of the same size, the telescope with a small field of view will take more observation time. Aperture and field of view seem to be an unavoidable contradiction for large-aperture telescopes. However, the advent of multi-beam receivers has broken this situation.

Phased Array Feed (PAF) is a multi-beam receiver technology that has been vigorously developed in radio astronomy in recent years. PAF uses a small antenna as a feed and places it on the focal plane of the radio telescope, and forms multiple synchronized beams through electronic scanning, which can increase the field of view of the telescope and improve the efficiency of sky surveys. At the same time, these densely overlapping beams also Continuous sky coverage can be formed, and multiple flexible observation modes can be achieved through real-time beamforming. Beam synthesis directly affects the sensitivity, system noise and observation efficiency of the entire system. Therefore, it is urgent to obtain an optimization method for beam synthesis to ensure the receiving efficiency of the radio receiving system. It is an urgent problem for those skilled in the art to solve.

SUMMARY OF THE INVENTION

In view of this, the present invention provides a data processing method for a PAF phased array receiver, which overcomes the defects of the prior art.

In order to achieve the above object, the present invention provides the following technical solutions:

A PAF phased array receiver data processing method, the specific steps are:

Build a model: build a beamforming model based on the multi-beam model;

Calculate the beamforming SNR and gain: Obtain the SNR beamforming output signal according to the relationship between the signal of interest in any receive channel and the interference and noise signals; calculate the total gain of the array according to the relationship between the SNR beamforming output and the input;

Optimized beamforming algorithm: According to the beamforming SNR and gain, determine the minimum mean square error s.

Optionally, the response of the array elements in the receiving array to the plane wave is:

After introducing the time difference of arrival, the transformation is:

为起始相位。Among them, M is the array element relative to the mth reference array element; s is the minimum mean square error; k is the unit vector of the signal propagation direction; x is the position coordinate of the array element; c is the speed of light; t is the time; τ is the wave Arrival time difference; A is the amplitude response; ω ₀ is the signal frequency;

is the starting phase.

Optionally, the response of the receiving array to the plane wave is:

Optionally, the model of the synthetic beam is:

或

or

为第i个阵元的加权共轭转置；i为相对阵元编号。in,

is the weighted conjugate transpose of the i-th array element; i is the relative array element number.

Optionally, the SNR output of beamforming is:

为阵元的信噪比。in,

is the signal-to-noise ratio of the array element.

Optionally, when the noise output of each array element is uncorrelated, the signal output of interest is specifically:

M is the number of array elements; W is the weight; S is the signal of interest.

Optionally, when the noise outputs of each array element are not correlated, the interference and noise signal outputs are specifically:

N is the interference and noise signal, N～(0, R _nn ); R _nn is the standard deviation.

Optionally, when determining the minimum mean squared error s, estimate the minimum variance s and the minimum variance according to the largest possible approximation ML

It can be seen from the above technical solutions that, compared with the prior art, the present disclosure provides a data processing method for a PAF phased array receiver. By optimizing the beamforming algorithm and determining the minimum mean square error s, there is no Distorted response beamforming improves the overall efficiency of the radio reception processing system.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only It is an embodiment of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without creative work.

Fig. 1 is the method flow schematic diagram of the present invention;

FIG. 2 is a schematic diagram of a system structure according to an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The embodiment of the present invention discloses a data processing method for a PAF phased array receiver. The steps are shown in FIG. 1 , and are specifically:

Step 1: Build a model: build a beamforming model according to the multi-beam model, the specific steps are:

Consider the response of an ideal isotropic sensor array to a single complex monochromatic plane wave as:

Substitute the arrival time difference into equation (1) to get the following equation:

为起始相位。Among them, M is the array element relative to the mth reference array element; s is the minimum mean square error; k is the unit vector of the signal propagation direction; x is the position coordinate of the array element; c is the speed of light; t is the time; τ is the wave Arrival time difference; A is the amplitude response; ω ₀ is the signal frequency;

is the starting phase.

According to formula (2), the observation vector can be obtained as:

The beamforming model is constructed according to the observation vector, and the beamforming model is:

为第i个阵元的加权共轭转置；i为相对阵元编号。in,

is the weighted conjugate transpose of the i-th array element; i is the relative array element number.

Step 2: Calculate the beamforming SNR and gain: Calculate the SNR beamforming output according to the relationship between the signal of interest in the received signal and the interference and noise signals; calculate the total gain of the array according to the relationship between the SNR beamforming output and the input;

Step 21: Given the bandwidth spectrum of any channel, provide multiple given radio wave environment broadband spectrum P(F[o], V[o]) through the radio wave environment test system of the radio astronomy station, where P is a two-dimensional array , F is the frequency, V is the power value corresponding to the frequency point, and o is the number of frequency points;

Step 22, determine whether the test parameters have changed, if the test parameters have changed, then go to step 23, if the test parameters have not changed, then go to step 212;

Step 23, selecting spectrum samples, taking O groups of spectrum samples from the multiple given radio wave environment broadband spectrums, and dividing each group of spectrum samples into Q segments according to frequency;

Step 24, calculating the standard deviation of spectral noise according to the O groups of spectral samples;

Step 25, make the adjacent value comparison discriminant value deta;

Step 26, according to the power value data in the wideband spectrum P(F[o], V[o]), calculate the adjacent value comparison initial interference and noise signal V[0];

Step 27, noise extraction, extracting the spectral noise P1(F[o], V1[o]) in the wideband spectrum P(F[o], V[o]);

Step 28, noise window division, dividing the spectral noise P1 (F[o], V1[o]) into Y windows, and the window division width of each window is B;

Step 29, calculate the median VQ[Y] and standard deviation σQ[Y] of the noise of a single window;

Step 210, calculate the signal-to-noise separation threshold VQ[Y] of each window, and obtain the signal-to-noise separation threshold V2[o] of each frequency point;

Step 211: Signal-to-noise separation, determine whether V[o]-V2[o]>0 is established, if so, it is expressed as the signal in the wideband spectrum P(F[o], V[o]), if not , then expressed as the noise in the wideband spectrum P(F[o], V[o]);

Step 212: Optimize the adjacent value comparison discriminant value and the window division width, progressively increase the value of the adjacent value comparison discriminant value deta and the value of the window division width B, and repeat steps 26 to 211 until the signal-to-noise separation accuracy rate C is counted The maximum value of , and the signal-to-noise separation result obtained at this time is the final result;

Step 213: According to the signal-to-noise separation result, the signal output of each array element is obtained as:

In the formula, i is the relative array element number, i=1,2,...,n; Wi is the weight of the _i -th array element; V _i is the signal value of the i-th array element;

According to formula (5), it can be obtained:

V _total =(W ₁ ·S ₁ +W ₂ ·S ₂ +...+W _n ·S _n )+(W ₁ ·N ₁ +W ₂ ·N ₂ +...+W _n ·N _n ) (6);

In the formula, S is the signal of interest; N is the interference and noise signal;

Assuming that the noise output of each array element is uncorrelated, then:

According to formula (7), we can get:

时，when

hour,

From this, the signal-to-noise ratio of a single channel can be obtained as:

According to equation (10), the signal-to-noise ratio output of beamforming is:

According to equation (11), the total gain of this array is obtained as:

Step 3: Optimize the beamforming algorithm: Determine the minimum mean square error s according to the beamforming SNR and gain; the specific steps are:

若R_nn是不等于

执行步骤32；Step 31: Determine if R _nn is equal to

If R _nn is not equal to

Go to step 32;

Among them, I is the identity matrix;

Step 32: Define the observation vector according to the beamforming SNR and the beamforming formula, and obtain z=a(θ)s+N, where z=M×1; a(θ) is the direction vector, which is a known condition; N～CN(0,R _nn ), R _nn is the standard deviation; s is the minimum mean square error,

Step 33: Minimize E{|w ^H z| ₂ }, so that w ^H a(θ)=1, H is the conjugate transpose; ensure that the signal of interest will not be lost;

Step 34: Find the largest approximate possible ML and estimate the smallest mean squared error s.

则增益G＝M。If R _nn is equal to

Then the gain G=M.

The maximum approximation possible ML can be obtained by two methods, which are agreed in advance, brought into the solution space as a condition or obtained before solving, and searched for the global optimal solution.

This embodiment also includes a PAF phased array receiver data processing system, the structure is shown in Figure 2, including: a signal acquisition and preprocessing unit, a beam synthesis unit, and a multi-beam processing unit;

The signal acquisition and preprocessing unit is used to acquire array element signals of a preset number of channels, and preprocess the array element signals;

The beam synthesizing unit synthesizes the preprocessed array element signals into beams with a preset number of beams;

The multi-beam processing unit calculates and optimizes the synthesized beams to determine the minimum mean square error.

The signal preprocessing unit includes a digital down-conversion module and a channelization module, wherein:

a digital down-conversion module for digital mixing and filtering of the array element signal, including a frequency synthesizer, a mixer, a filter and a down-sampler,

Frequency synthesizer, used to generate cosine and sine signals of fixed frequency from each array element signal;

a mixer, configured to mix the cosine and sine signals to generate a mixed signal;

a filter for filtering out unwanted signals in the mixed signal;

The down-sampler is used to extract signals according to preset conditions, and output in-phase and quadrature complex signals;

The channelization module is used for receiving the in-phase and quadrature two-way complex signals and channelizing them.

This embodiment also includes a computer-storable medium on which a computer program is stored, and when the program is executed by a processor, implements the steps in the described data processing method for a PAF phased array receiver.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

The above description of the disclosed embodiments enables any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (8)

1. a PAF phased array receiver data processing method, is characterized in that, concrete steps are: Build a model: build a beamforming model based on the multi-beam model; Calculate the beamforming SNR and gain: Obtain the SNR beamforming output signal according to the relationship between the signal of interest in any receive channel and the interference and noise signals; calculate the total gain of the array according to the relationship between the SNR beamforming output and the input; Optimized beamforming algorithm: According to the beamforming SNR and gain, determine the minimum mean square error s. 2. a kind of PAF phased array receiver data processing method according to claim 1, is characterized in that, the response of array element in receiving array to plane wave is:

After introducing the time difference of arrival, the transformation is:

Among them, M is the array element relative to the mth reference array element; s is the minimum mean square error; k is the unit vector of the signal propagation direction; x is the position coordinate of the array element; c is the speed of light; t is the time; τ is the wave arrival time difference; A is the amplitude response; ω ₀ is the signal frequency;

is the starting phase. 3. a kind of PAF phased array receiver data processing method according to claim 2 is characterized in that, the response of receiving array to plane wave is:

4. a kind of PAF phased array receiver data processing method according to claim 3, is characterized in that, the model of synthetic beam is:

in,

is the weighted conjugate transpose of the i-th array element; i is the relative array element number. 5. a kind of PAF phased array receiver data processing method according to claim 4, is characterized in that, the signal-to-noise ratio output of beam synthesis is:

in,

is the signal-to-noise ratio of the array element. 6. a kind of PAF phased array receiver data processing method according to claim 5 is characterized in that, when the noise output of each array element is irrelevant, the signal output of interest is specifically:

M is the number of array elements; W is the weight; S is the signal of interest. 7. A kind of PAF phased array receiver data processing method according to claim 5, is characterized in that, when the noise output of each array element is irrelevant, the interference and noise signal output are specifically:

N is the interference and noise signal, N～(0, R _nn ); R _nn is the standard deviation. 8. A PAF phased array receiver data processing method according to claim 5, characterized in that, when determining the minimum mean square error s, according to the maximum approximation possible ML, estimate the minimum variance s, the minimum variance

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