Long-time accumulation method for GNSS-based passive source moving target based on multi-frame processing

Abstract

GNSS external source moving target based on multi-frame processing long time accumulation method, improve signal gain, reduce the influence of distance walk in signal energy accumulation process. The method first divides the continuous reference signal and target echo signal by equal length and non-overlapping method, and divides the long signal into multiple sub-frames, then carries out coherent accumulation in single frame and uses Keystone transform to correct distance walk, carries out non-coherent accumulation between multiple frames, realizes long time energy accumulation. Under the same processing time, compared with the conventional accumulation method, the method can reduce the influence of distance walk and improve the signal gain.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, specifically to a long-term accumulation method for moving targets of GNSS external radiation sources based on multi-frame processing. Background Technology

[0002] With the gradual opening of my country's low-altitude airspace and the widespread use of aircraft, the types and range of low-altitude targets are constantly expanding, and low-altitude target detection technology is receiving increasing attention. Passive radar, also known as external radiation source radar, is one of the important means of low-altitude target detection. GNSS signals are a high-quality third-party signal source for passive radar. However, due to its signal characteristics, using GNSS satellites as an external radiation source for target detection presents several problems. On the one hand, because GNSS signals have long transmission times and large signal attenuation, the power is extremely weak when it reaches the ground receiver. Moreover, satellites of the same system use the same carrier frequency to transmit signals, and the received reference signal inevitably contains other satellite signals, making it difficult to directly obtain a pure reference signal. The impurity of the reference signal reduces detection performance. At the same time, the already weak satellite signal becomes even weaker after being reflected by the target, and is also subject to interference from various ground clutter and direct waves, which drown out target information and make it impossible to extract useful information. On the other hand, the GNSS signal becomes even weaker after being reflected by the target and is generally submerged in noise and clutter. With the development of modern technology, the speed and maneuverability of moving targets such as aircraft and ground vehicles have been greatly improved. Therefore, researching methods to correct the cross-range cell movement and Doppler diffusion of moving targets while extending the coherent accumulation time as much as possible to improve the signal-to-noise ratio has become the key to improving the low-altitude target detection technology of external radiation source radar.

[0003] Improving the target signal-to-noise ratio requires long-term accumulation. In traditional methods, target echoes experience distance migration and Doppler migration due to target movement, reducing accumulation gain and impacting target detection performance. Traditional Keystone transform-based coherent accumulation algorithms offer good migration correction, but the interpolation-based Keystone transform is computationally complex and time-consuming. While it can compensate for distance migration, it's difficult to apply in practical engineering.

[0004] Compared with existing technologies:

[0005] Technical comparison with patent CN115902811A "A GNSS External Radar Moving Target Imaging Method Based on Segmented Secondary Accumulation":

[0006] Patent CN115902811A aims to improve the efficiency of moving target imaging algorithms during the long-term accumulation of GNSS external radiation source radar echo signals, while our method aims to provide a way to improve the efficiency of long-term accumulation of GNSS external radiation source radar echo signals.

[0007] Patent CN115902811A mainly focuses on processing signals within a three-dimensional matrix, requiring the processing of the target Doppler frequency and Doppler modulation frequency. Our proposed method, however, processes signals in two dimensions: fast time and slow events, only requiring the processing of the Doppler frequency.

[0008] The accumulation scheme used in patent CN115902811A is based on the RFT signal processing method, which is essentially a scheme to reduce parameter search. In our proposed processing method, we mainly use the Keystone method, which is essentially to reduce the number of operations in the distance movement correction process.

[0009] The GNSS external radiation source radar moving target imaging method proposed in patent CN115902811A performs all operations in the range frequency domain, aiming to reduce the computational load of moving target Doppler parameter estimation. In contrast, our proposed long-term accumulation method for GNSS external radiation source moving targets based on multi-frame processing processes the signal in both the time and range dimensions, aiming to improve the signal-to-noise ratio enhancement effect and efficiency. Summary of the Invention

[0010] To address the above problems, this invention proposes a long-term accumulation method for moving targets from GNSS external radiation sources based on multi-frame processing. This method combines Keystone transform implemented using multi-frame processing interpolation and employs linear frequency modulated Z-transform (Chirp-Z transform, CZT) with spiral sampling to perform Z-transform on the sampling points, reducing the computational load of Keystone transform. Compared to other methods, this effectively eliminates the influence of distance movement and effectively concentrates target energy, enhancing the target signal-to-noise ratio.

[0011] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0012] A long-term accumulation method for moving targets from GNSS external radiation sources based on multi-frame processing includes the following steps, characterized by:

[0013] (1) The continuous reference signal and target echo signal are divided into equal-length, non-overlapping segments. Segments, each segment is [length missing] The long signal is divided into multiple subframes, and the reference signal and echo signal are divided into matrices composed of fast time dimension and slow time dimension, respectively.

[0014] (2) Perform FFT transformation in the fast time dimension on each echo signal and the reference signal to transform the target echo into a two-dimensional plane of frequency-slow time domain. Perform matched filtering operation through the range-Doppler processing method to obtain the time delay-Doppler mutual ambiguity function.

[0015] (3) Estimate the target speed by the relationship between Doppler frequency and target speed, and then estimate the target distance travel and echo signal delay;

[0016] (4) The CZT algorithm is used to perform Keystone transformation on the matched filter result along the slow time dimension to correct the signal and compensate for distance travel.

[0017] (5) Perform an IFFT transformation on the Keystone transformation in the distance dimension to obtain the distance in the time domain-slow time dimension, so that the distance of the target is corrected to the initial position when it has not moved.

[0018] (6) Perform FFT transformation on the slow time dimension signal to obtain the range-Doppler detection result. Based on this, perform coherent accumulation to concentrate the target echo energy and enhance the signal-to-noise ratio.

[0019] As a further improvement of the present invention, step (1) specifically includes:

[0020] (1.1) Continuously length of The reference signal and the target echo signal are divided into equal-length, non-overlapping segments. Segments, each segment is [length missing] This divides the long signal into multiple subframes;

[0021] (1.2) Divide both the reference signal and the echo signal into... The matrix, Represents the fast time dimension. Representing the slow time dimension, ignoring noise, the reference signal and echo signal can be expressed in the fast and slow time dimensions as follows:

[0022]

[0023]

[0024] In the formula, , These are the reference signal and the echo signal, respectively. , Indicates fast time and slow time. , These represent the complex amplitude values ​​of the reference signal and the target echo, respectively. The complex envelope of the reference signal, The initial distance between the target and the receiving antenna. To detect the speed of the target, The speed of electromagnetic wave propagation. This represents the Doppler frequency of the echo signal when the reference signal is used as a reference.

[0025] As a further improvement of the present invention, step (2) specifically includes:

[0026] (2.1) Perform an FFT transformation in the fast time dimension on each echo signal and the reference signal to transform the target echo into a two-dimensional plane of frequency-slow time domain. The frequency domain representation of the signal is as follows;

[0027]

[0028]

[0029] In the formula, It is a fast time frequency; Represents a frequency domain signal;

[0030] (2.2) The time delay-Doppler cross-ambiguity function is obtained by performing matched filtering using the distance-Doppler processing method in the following formula:

[0031]

[0032] In the formula, .

[0033] As a further improvement of the present invention, step (3) specifically includes:

[0034] (3.1) Calculate the segmented data length and sampling frequency using the Doppler frequency and Doppler element relationship:

[0035]

[0036]

[0037] In the formula, This indicates the length of each data segment. Number of signal segments Sampling frequency, Number the Doppler channels;

[0038] (3.2) Estimate the target velocity based on the relationship between Doppler frequency and target velocity. The relationship between Doppler frequency and target velocity and the target velocity are as follows:

[0039]

[0040]

[0041] In the formula, For Doppler frequency, The speed of the target motion;

[0042] (3.3) Using the first echo segment in which no distance travel occurred, estimate the distance travel and time delay in each echo segment using the following formula:

[0043]

[0044]

[0045] In the formula, This represents the distance traveled in each echo segment. For the corresponding delay, .

[0046] As a further improvement of the present invention, step (4) specifically includes:

[0047] (4.1) Parameter initialization, so that , , ,but , ;according to Select the smallest positive integer. ,satisfy ;

[0048] in, The radius of the initial sampling point. The phase of the initial sampling point; The extension rate of the sampling path. This indicates the angle between adjacent sampling points. Indicates the radius of the sampling point. Indicates elongation;

[0049] (4.2) will , Padding with zeros Take a sequence of points and then perform an FFT:

[0050]

[0051]

[0052] (4.3) For the sequence , Formed into Take a sequence of points and then perform an FFT:

[0053]

[0054]

[0055] (4.4) and Perform an IFFT after multiplication and take the first part. Using points as weights, we obtain the Keystone transformation result:

[0056]

[0057]

[0058]

[0059] In the formula, This is the expression for the result after Keystone transformation.

[0060] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0061] This invention discloses a long-term energy accumulation method for moving targets with external GNSS radiation sources based on multi-frame processing, which improves signal gain and reduces the impact of range travel during signal energy accumulation. The method first segments the continuous reference signal and target echo signal into equal-length, non-overlapping segments, dividing the long signal into multiple subframes. Then, coherent accumulation is performed within a single frame, and Keystone transform is used to correct range travel. Non-coherent accumulation is performed between multiple frames to achieve long-term energy accumulation. Compared to conventional accumulation methods, this method reduces the impact of range travel and improves signal gain within the same processing time. Attached Figure Description

[0062] Figure 1 This is a flowchart of one embodiment of the present invention. Detailed Implementation

[0063] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0064] like Figure 1 As shown, the long-term accumulation method for moving targets from GNSS external radiation sources based on multi-frame processing according to the present invention includes:

[0065] (1) The continuous reference signal and target echo signal are divided into equal-length, non-overlapping segments. Segments, each segment is [length missing] The long signal is divided into multiple subframes, and the reference signal and echo signal are divided into matrices composed of fast time dimension and slow time dimension, respectively.

[0066] Step (1) specifically includes:

[0067] (1.1) Continuously length of The reference signal and the target echo signal are divided into equal-length, non-overlapping segments. Segments, each segment is [length missing] This divides the long signal into multiple subframes;

[0068] (1.2) Divide both the reference signal and the echo signal into... The matrix, Represents the fast time dimension. Representing the slow time dimension, ignoring noise, the reference signal and echo signal can be expressed in the fast and slow time dimensions as follows:

[0069]

[0070]

[0071] In the formula, , These are the reference signal and the echo signal, respectively. , Indicates fast time and slow time. , These represent the complex amplitude values ​​of the reference signal and the target echo, respectively. The complex envelope of the reference signal, The initial distance between the target and the receiving antenna. To detect the speed of the target, The speed of electromagnetic wave propagation. This represents the Doppler frequency of the echo signal when the reference signal is used as a reference.

[0072] (2) Perform FFT transformation in the fast time dimension on each echo signal and the reference signal to transform the target echo into a two-dimensional plane of frequency-slow time domain. Perform matched filtering operation through the range-Doppler processing method to obtain the time delay-Doppler mutual ambiguity function.

[0073] Step (2) specifically includes:

[0074] (2.1) Perform an FFT transformation in the fast time dimension on each echo signal and the reference signal to transform the target echo into a two-dimensional plane of frequency-slow time domain. The frequency domain representation of the signal is as follows;

[0075]

[0076]

[0077] In the formula, It is a fast time frequency; Represents a frequency domain signal;

[0078] (2.2) The time delay-Doppler cross-ambiguity function is obtained by performing matched filtering using the distance-Doppler processing method in the following formula:

[0079]

[0080] In the formula, .

[0081] (3) Estimate the target speed by the relationship between Doppler frequency and target speed, and then estimate the target distance travel and echo signal delay;

[0082] Step (3) specifically includes:

[0083] (3.1) Calculate the segmented data length and sampling frequency using the Doppler frequency and Doppler element relationship:

[0084]

[0085]

[0086] In the formula, This indicates the length of each data segment. Number of signal segments Sampling frequency, Number the Doppler channels;

[0087] (3.2) Estimate the target velocity based on the relationship between Doppler frequency and target velocity. The relationship between Doppler frequency and target velocity and the target velocity are as follows:

[0088]

[0089]

[0090] In the formula, For Doppler frequency, The speed of the target motion;

[0091] (3.3) Using the first echo segment in which no distance travel occurred, estimate the distance travel and time delay in each echo segment using the following formula:

[0092]

[0093]

[0094] In the formula, This represents the distance traveled in each echo segment. For the corresponding delay, .

[0095] (4) The CZT algorithm is used to perform Keystone transformation on the matched filter result along the slow time dimension to correct the signal and compensate for distance travel.

[0096] Step (4) specifically includes:

[0097] (4.1) Parameter initialization, so that , , ,but , ;according to Select the smallest positive integer. ,satisfy ;

[0098] in, The radius of the initial sampling point. The phase of the initial sampling point; The extension rate of the sampling path. This indicates the angle between adjacent sampling points. Indicates the radius of the sampling point. Indicates elongation;

[0099] (4.2) will , Padding with zeros Take a sequence of points and then perform an FFT:

[0100]

[0101]

[0102] (4.3) For the sequence , Formed into Take a sequence of points and then perform an FFT:

[0103]

[0104]

[0105] (4.4) and Perform an IFFT after multiplication and take the first part. Using points as weights, we obtain the Keystone transformation result:

[0106]

[0107]

[0108]

[0109] In the formula, This is the expression for the result after Keystone transformation.

[0110] (5) Perform an IFFT transformation on the Keystone transformation in the distance dimension to obtain the distance in the time domain-slow time dimension, so that the distance of the target is corrected to the initial position when it has not moved.

[0111] (6) Perform FFT transformation on the slow time dimension signal to obtain the range-Doppler detection result. Based on this, perform coherent accumulation to concentrate the target echo energy and enhance the signal-to-noise ratio.

[0112] The signal-to-noise ratio improvement effect of this invention will have a small deviation depending on the selection of different data samples. Simulation experiments have shown that when the signal sampling frequency... =10MHz; total signal accumulation time 6s, single frame accumulation time 3ms, total 2000 subframes; initial target distance 1350m, radial uniform motion at 80m / s; target echo to direct wave signal strength ratio -60dB; noise to direct wave signal strength ratio -30dB. Calculations show that the signal-to-noise ratio (SNR) of the target signal peak to the average noise is 17.927dB; and the SNR of the target signal peak to the average noise is 21.842dB. Therefore, the designed method improves the accumulation gain by 3.915dB compared to conventional accumulation methods.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any modifications or equivalent changes made based on the technical essence of the present invention shall still fall within the scope of protection claimed by the present invention.

Claims

1. A long-term accumulation method for moving targets from GNSS external radiation sources based on multi-frame processing, comprising the following steps, characterized in that: (1) The continuous reference signal and target echo signal are divided into equal-length, non-overlapping segments. Segments, each segment is [length missing] The long signal is divided into multiple subframes, and the reference signal and echo signal are divided into matrices composed of fast time dimension and slow time dimension, respectively. (2) Perform FFT transformation in the fast time dimension on each echo signal and the reference signal to transform the target echo into a two-dimensional plane of frequency-slow time domain. Perform matched filtering operation through the range-Doppler processing method to obtain the time delay-Doppler mutual ambiguity function. (3) Estimate the target speed by the relationship between Doppler frequency and target speed, and then estimate the target distance travel and echo signal delay; (4) The CZT algorithm is used to perform Keystone transformation on the matched filter result along the slow time dimension to correct the signal and compensate for distance travel. (5) Perform an IFFT transformation on the Keystone transformation in the distance dimension to obtain the distance in the time domain-slow time dimension, so that the distance of the target is corrected to the initial position when it has not moved. (6) Perform FFT transformation on the slow time dimension signal to obtain the range-Doppler detection result. Based on this, perform coherent accumulation to concentrate the target echo energy and enhance the signal-to-noise ratio.

2. The long-term accumulation method for moving targets from GNSS external radiation sources based on multi-frame processing according to claim 1, characterized in that, Step (1) specifically includes: (1.1) Continuously length of The reference signal and the target echo signal are divided into equal-length, non-overlapping segments. Segments, each segment is [length missing] This divides the long signal into multiple subframes; (1.2) Divide both the reference signal and the echo signal into... The matrix, Represents the fast time dimension. Representing the slow time dimension, ignoring noise, the reference signal and echo signal can be expressed in the fast and slow time dimensions as follows: ； ； In the formula, , These are the reference signal and the echo signal, respectively. , Indicates fast time and slow time. , These represent the complex amplitude values ​​of the reference signal and the target echo, respectively. The complex envelope of the reference signal, The initial distance between the target and the receiving antenna. To detect the speed of the target, The speed of electromagnetic wave propagation. This represents the Doppler frequency of the echo signal when the reference signal is used as a reference.

3. The long-term accumulation method for moving targets from GNSS external radiation sources based on multi-frame processing according to claim 2, characterized in that, Step (2) specifically includes: (2.1) Perform an FFT transformation in the fast time dimension on each echo signal and the reference signal to transform the target echo into a two-dimensional plane of frequency-slow time domain. The frequency domain representation of the signal is as follows; ； ； In the formula, It is a fast time frequency; Represents a frequency domain signal; (2.2) The time delay-Doppler cross-ambiguity function is obtained by performing matched filtering using the distance-Doppler processing method in the following formula: ； In the formula, .

4. The long-term accumulation method for moving targets from GNSS external radiation sources based on multi-frame processing according to claim 3, characterized in that, Step (3) specifically includes: (3.1) Calculate the segmented data length and sampling frequency using the Doppler frequency and Doppler element relationship: ； ； In the formula, This indicates the length of each data segment. Number of signal segments Sampling frequency, Number the Doppler channels; (3.2) Estimate the target velocity based on the relationship between Doppler frequency and target velocity. The relationship between Doppler frequency and target velocity and the target velocity are as follows: ； ； In the formula, For Doppler frequency, The speed of the target motion; (3.3) Using the first echo segment in which no distance travel occurred, estimate the distance travel and time delay in each echo segment using the following formula: ； ； In the formula, This represents the distance traveled in each echo segment. For the corresponding delay, .

5. The long-term accumulation method for moving targets from GNSS external radiation sources based on multi-frame processing according to claim 4, characterized in that, Step (4) specifically includes: (4.1) Parameter initialization, so that , , ,but , ;according to Select the smallest positive integer. ,satisfy ; in, The radius of the initial sampling point. The phase of the initial sampling point; The extension rate of the sampling path. This indicates the angle between adjacent sampling points. Indicates the radius of the sampling point. Indicates elongation; (4.2) will , Padding with zeros Take a sequence of points and then perform an FFT: ； ； (4.3) For the sequence , Formed into Take a sequence of points and then perform an FFT: ； ； (4.4) and Perform an IFFT after multiplication and take the first part. Using points as weights, we obtain the Keystone transformation result: ； ； ； In the formula, This is the expression for the result after Keystone transformation.

Images