A Real-Time Echo Recovery Method for High Repetition Rate Coding Synthetic Aperture Radar

Abstract

This invention discloses a real-time echo recovery method for high repetition rate (PRR) coded synthetic aperture radar (SAR). First, a periodic phase coding sequence is constructed, and the transmitted pulses are phase-coded in the slow-time domain within the radar transmission link. Then, based on the period of the phase coding, the received echo data is grouped at the radar receiver. Subsequently, a phase coding guidance matrix is ​​constructed, and the echo recovery weight vector is derived from this matrix. Finally, using the ambiguity signal fast recovery algorithm disclosed in this invention, real-time and accurate unambiguous signal recovery across multiple sub-mapped areas is achieved. This invention replaces traditional matrix-vector multiplication with FFT operations, increasing computational efficiency. This invention can realize real-time, online high repetition rate SAR echo recovery on platforms with limited computing resources, such as airborne or spaceborne SAR, and is of great significance for improving real-time processing in areas such as detection range, range ambiguity suppression, resistance to intermittent sampling interference, and delayed forwarding interference.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing, and in particular to a real-time echo recovery method for high repetition rate coded synthetic aperture radar. Background Technology

[0002] Synthetic Aperture Radar (SAR) is an active microwave imaging sensor mounted on a moving platform. It can penetrate clouds, rain, and fog, enabling all-weather, all-day ground-to-ground imaging, and has wide applications in both military and civilian fields. For pulse-based SAR, increasingly complex application scenarios place higher demands on the SAR system's operating modes and signal post-processing. Examples include achieving high signal-to-noise ratio (SNR) long-range detection with airborne SAR and dealing with issues such as enemy jammers intercepting radar signals in battlefield environments and then relaying and jamming our radar. A common approach to addressing these issues is to increase the radar pulse repetition frequency (PRF). On one hand, increasing the PRF results in a higher duty cycle radar signal, thereby increasing the average power of the transmitted signal and enabling the electromagnetic signal to propagate over longer distances, ultimately receiving high SNR echo signals at extremely long distances. On the other hand, for radar jammers, it is necessary to first intercept and analyze the signal transmitted by the target radar, then add an interference phase and relay it again to deceive our radar. When SAR operates at a high PRF (Radar Frequency Reception), radar jammers cannot intercept and process the transmitted signals of our radar in real time, preventing them from accurately and continuously deceiving and jamming our radar. However, a high PRF also brings many negative effects to pulse-based SAR. Because SAR cannot sample the echo signal when transmitting, a high PRF leads to frequent range obstruction, resulting in severe signal ambiguity in the received signal. Therefore, the technical challenge of improving the PRF of pulse-based SAR systems lies in how to effectively recover the signal ambiguity caused by obstruction.

[0003] Currently, many researchers have proposed solutions to address the signal ambiguity problem caused by high PRF (Pulse Repetition Rate) radar. These solutions include using multi-channel elevation radar and digital beamforming technology to improve the angular resolution in the elevation direction, thereby separating ambiguous signals. While multi-channel elevation technology can separate ambiguous signals to some extent, its performance is affected by factors such as radar antenna system defects and terrain undulations, leading to a decrease in separation performance. Furthermore, multi-channel radar systems have significantly higher system complexity and instantaneous data volume than traditional single-transmitter, single-receiver radar systems. Therefore, for highly maneuverable airborne SAR, high-precision real-time recovery of ambiguous echo signals is a major challenge. In conclusion, developing a real-time echo recovery method for high-repetition-rate SAR is an urgent problem to be solved for traditional single-transmitter, single-receiver SAR systems. Summary of the Invention

[0004] This invention provides a real-time echo recovery method for high repetition rate coded synthetic aperture radar to solve the problem of high-precision real-time echo recovery in high repetition rate SAR systems.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for real-time echo recovery of high repetition rate coded synthetic aperture radar includes the following steps:

[0007] 5): Construct a periodic phase coding sequence to perform phase coding on the transmitted pulse in the slow time domain;

[0008] 6): Based on the period of the phase encoding sequence in step 1), the echo signal is grouped into data to construct the echo vector;

[0009] 7): Based on the phase encoding sequence in step 1), construct the phase encoding guidance matrix, and derive the echo recovery weight vector through the phase encoding guidance matrix;

[0010] 8): Real-time and accurate unambiguous signal recovery of multiple sub-mapped areas is achieved through a fast ambiguity signal recovery algorithm.

[0011] Preferably, step 1) is implemented as follows: based on the flight parameters of the synthetic aperture radar, the order of the ambiguity signal appearing within the same receiving window is calculated, denoted as M; subsequently, a phase coding sequence with a period of M is constructed, denoted as... in This represents the Mth phase-coded value in a phase-coded sequence period, expressed as: Where θ M This represents the phase angle corresponding to the Mth phase code value, in radians, ranging from 0 to 2π. Then, the phase code values ​​in the phase code sequence Φ are tuned according to the pulse transmission order to the baseband transmitted signal s. TIn (τ), where, For a linear frequency modulated signal, K represents the frequency modulation slope, and τ represents the fast time step.

[0012] Preferably, step 2) is implemented as follows: assuming there are M mapping areas, each with a point target, the nth echo signal of the synthetic aperture radar is represented as s(τ,n).

[0013]

[0014] Where, Φ n-m+1 Let f0 represent the (n-m+1)th phase code value in the coded sequence Φ, f0 represent the radar carrier frequency, n∈[0,+∞) represent the sequence number of the transmitted pulse and the received echo, and τ represent the phase code value. m Represents the instantaneous two-way slant range delay of the m-th point target;

[0015] In the slow time domain, we define each M echo signals as a group and construct the echo vector s(τ,k)=[s(τ,kM),…,s(τ,kM+M-1)] T , where k∈[0,+∞) represents the echo vector composed of the kth group of echo data.

[0016] Preferably, the implementation process of step 3) is as follows:

[0017] 3.1) Assume the phase code value modulated by the nth transmitted pulse is Based on the phase encoding sequence Φ from step 1), construct the phase encoding guidance matrix X(Φ), which is expressed as:

[0018]

[0019] The echo vector s(τ,k) can then be expressed as:

[0020] s(τ,k)=X(Φ)y(τ,k)

[0021] Where y(τ,k) represents the unambiguous echo signal vectors of multiple real sub-mapped areas, expressed as:

[0022] y(τ,k)=[y1(τ),y2(τ),…,y M (τ)] T

[0023] Among them, y M (τ) represents the unambiguous echo signal of the Mth mapping area, expressed as:

[0024]

[0025] 3.2) By left-multiplying the vector s(τ,k) by the inverse matrix X(Φ), we can obtain the vector s(τ,k). -1(Φ) Solve for y(τ,k), the process of which is expressed as:

[0026]

[0027] in, This represents the solution value of y(τ,k), which is the unambiguous signal vector of the multiple sub-mapped areas to be recovered. Represented as:

[0028]

[0029] in, These represent the unambiguous echo signals of the 1st to Mth sub-mapped areas obtained from the solution;

[0030] The matrix X(Φ) is inverted beforehand to save real-time computing resources: the inverse of the phase encoding guidance matrix X(Φ) is denoted as the weight vector matrix X. -1 (Φ); where matrix X(Φ) conforms to the definition of a circular matrix, then X -1 (Φ) is still a cyclic matrix, represented as:

[0031]

[0032] Where x(M) represents X -1 The Mth element in the first column vector of (Φ);

[0033] 3.3) Extracting the circular matrix X -1 Perform an M-point FFT operation on the first column vector x(Φ) to obtain the echo recovery weight vector.

[0034] Preferably, the implementation process of step 4) is as follows:

[0035] 4.1) For the weight vector matrix X in step 3.2), -1 (Φ) is analyzed, and the circular matrix X is further analyzed. -1 (Φ) is represented as:

[0036] X -1 (Φ)=F -1 ΛF

[0037] Among them, F -1 Λ represents the inverse FFT matrix, F represents the FFT matrix, matrix Λ represents diag(Fx(Φ)), and x(Φ) is the value of X. -1 The first column vector in (Φ);

[0038] The Fx(Φ) term in matrix Λ represents the M-point FFT operation performed on vector x(Φ), the result of which has already been obtained in step S3.3, i.e., the echo recovery weight vector.

[0039] Based on the above analysis, the solution expression for vector y(τ,k) in step 3.2) is updated as follows:

[0040]

[0041] Perform an M-point FFT operation on the echo vector s(τ,k) to solve for the Fs(τ,k) term, and denote the result as a vector. 4.2) Transform the vector sum vector Performing a dot product yields the term ΛFs(τ,k) in the above equation, and the result is denoted as a vector. The process is represented as follows:

[0042]

[0043] By analyzing vectors Perform an inverse FFT operation at point M to solve for the vector. The above process can be represented as follows:

[0044]

[0045] This completes the accurate recovery of unambiguous echo signal vectors from multiple sub-mapped areas.

[0046] Beneficial effects:

[0047] This invention comprises several steps: phase encoding of the transmitted pulse in the slow time domain; data grouping of the echo data to construct an echo vector; construction of an echo recovery weight vector; and rapid recovery of the ambiguous signal. This invention enables real-time and accurate recovery of echo ambiguity caused by obstruction in high-repetition-rate SAR systems, which is of great significance for improving radar performance. Attached Figure Description

[0048] Figure 1 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] This invention provides a real-time echo recovery method for high repetition rate coded synthetic aperture radar (SAR) to solve the echo ambiguity problem caused by obstruction in practical applications of high repetition rate SAR.

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] like Figure 1 As shown in the figure, this embodiment discloses a real-time echo recovery method for high repetition rate coded SAR, the method comprising:

[0053] Step S1: Construct a periodic phase coding sequence to perform phase coding on the transmitted pulse in the slow time domain;

[0054] Step S2: Based on the period of the phase encoding sequence in step S1, the echo signal is grouped into data to construct the echo vector.

[0055] Step S3: Based on the phase encoding sequence in step S1, construct the phase encoding guidance matrix, and derive the echo recovery weight vector through the phase encoding guidance matrix.

[0056] Step S4: Real-time and accurate unambiguous signal recovery of multiple sub-mapped areas is achieved through a fast ambiguity signal recovery algorithm.

[0057] Specifically, in step S1, constructing a periodic phase-coded sequence to perform phase coding on the transmitted pulse in the slow-time domain includes:

[0058] Based on the SAR flight parameters, the order of the resulting ambiguous signal is pre-calculated, denoted as M. Subsequently, a phase-coded sequence with a period of M is constructed, denoted as... in The values ​​can be equal, but the period of the phase encoding sequence Φ must be M. Then, during the signal transmission phase, the phase codes in the phase encoding sequence Φ are tuned to the pulse transmission sequence in the baseband transmitted signal s. Tx In (τ), τ represents the fast time point.

[0059] Specifically, in step S2, the step of grouping the echo signal into data and constructing an echo vector based on the period of the phase encoding sequence in step S1 includes:

[0060] The SAR echo signal can be represented as s(τ,n), where n∈[0,+∞) represents the echo index. Assuming there are M mapping areas, each with a point target, the i-th echo signal from the synthetic aperture radar is represented as...

[0061]

[0062] Where, Φ n-m+1 Let f0 represent the (n-m+1)th phase code value in the coded sequence Φ, f0 represent the radar carrier frequency, n∈[0,+∞) represent the sequence number of the transmitted pulse and the received echo, and τ represent the phase code value.m This represents the instantaneous two-way slant range delay of the m-th point target.

[0063] In the slow time domain, we define each M echo signals as a group and construct the echo vector s(τ,k)=[s(τ,kM),…,s(τ,kM+M-1)] T , where k∈[0,+∞) represents the echo vector composed of the kth group of echo data.

[0064] Specifically, in step S3, constructing a phase-encoding guidance matrix based on the phase-encoding sequence in step S1, and deriving the echo recovery weight vector through the phase-encoding guidance matrix, specifically includes:

[0065] Step S3.1 Assume the phase encoding value modulated by the nth transmitted pulse is Based on the phase encoding sequence Φ from step one, the phase encoding guidance matrix X(Φ) is constructed, which can be expressed as:

[0066]

[0067] The echo vector s(τ,k) can then be expressed as:

[0068] s(τ,k)=X(Φ)y(τ,k)

[0069] Where y(τ,k) represents the unambiguous echo signal vectors of multiple real sub-mapped areas, which can be expressed as:

[0070] y(τ,k)=[y1(τ),y2(τ),…,y M (τ)] T

[0071] Among them, y M (τ) represents the unambiguous echo signal of the Mth mapping area, which can be expressed as:

[0072]

[0073] Step S3.2 As analyzed in step S3.1, solving for y(τ,k) can be done by left-multiplying the vector s(τ,k) by the inverse matrix X(Φ). -1 (Φ) is achieved, and this process can be represented as:

[0074]

[0075] in, This represents the solution value of y(τ,k), which is the unambiguous signal vector of the multiple sub-mapped areas to be recovered. It can be represented as:

[0076]

[0077] in, These represent the unambiguous echo signals of the 1st to Mth sub-mapped zones obtained from the solution.

[0078] It is known that matrix X(Φ) does not change with the pulse group number k. Therefore, matrix X(Φ) can be inverted in advance to save real-time computing resources. The inverse of the phase encoding guidance matrix X(Φ) can be denoted as the weight vector matrix X. -1 (Φ).

[0079] Here, matrix X(Φ) clearly conforms to the definition of a circular matrix, then X -1 (Φ) is still a cyclic matrix, which can be represented as:

[0080]

[0081] Where x(M) represents X -1 The Mth element in the first column vector of (Φ).

[0082] Step S3.3 Extract the weight vector matrix X -1 Perform an M-point FFT operation on the first column vector x(Φ) to obtain the echo recovery weight vector. For use in subsequent steps.

[0083] Specifically, in step S4, the real-time and accurate unambiguous signal recovery of multiple sub-mapped areas using the ambiguity signal fast recovery algorithm includes:

[0084] Step S4.1 Adjust the weight vector matrix X from step S3.2. -1 (Φ) can be analyzed further to further refine the circular matrix X. -1 (Φ) is represented as:

[0085] X -1 (Φ)=F -1 ΛF

[0086] Among them, F -1 Λ represents the inverse FFT matrix, F represents the FFT matrix, matrix Λ represents diag(Fx(Φ)), and x(Φ) is the value of X. -1 The first column vector in (Φ).

[0087] The Fx(Φ) term in matrix Λ represents the M-point FFT operation performed on vector x(Φ), the result of which has already been obtained in step S3.3, i.e., the echo recovery weight vector.

[0088] Based on the above analysis, the expression for solving vector y(τ,k) in step S3.2 can be updated to:

[0089]

[0090] According to the associative law of matrix multiplication, we can first calculate the Fs(τ,k) term in the above equation, that is, perform an M-point FFT operation on the echo vector s(τ,k), and denote the result as a vector.

[0091] Step S4.2 again utilizes the associative law of matrix multiplication. The ΛFs(τ,k) term in the above equation can be obtained by transforming the vector... sum vector The result is obtained by performing a dot product, and denoted as a vector. The process can be represented as follows:

[0092]

[0093] Finally, vectors The solution can be obtained by working with the vector The above process can be represented as follows: (The inverse FFT operation is performed at point M.)

[0094]

[0095] This completes the accurate recovery of unambiguous echo signal vectors from multiple sub-mapped areas. Because the computational complexity of the FFT operation is far less than that of matrix-vector multiplication, the above echo recovery process is guaranteed to be real-time.

[0096] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time echo recovery of high repetition rate coded synthetic aperture radar, characterized in that, Includes the following steps: 1) Construct a periodic phase coding sequence to perform phase coding on the transmitted pulse in the slow time domain; 2): Based on the period of the phase encoding sequence in step 1), the echo signal is grouped into data to construct the echo vector; 3): Based on the phase encoding sequence in step 1), construct the phase encoding guidance matrix, and derive the echo recovery weight vector through the phase encoding guidance matrix; 4): Real-time and accurate unambiguous signal recovery of multiple sub-mapped areas is achieved through a fast ambiguity signal recovery algorithm; The implementation process of step 1) is as follows: Based on the flight parameters of the synthetic aperture radar, calculate the order of the fuzzy signal appearing in the same receiving window, denoted as M; Subsequently, a phase-coded sequence with a period of M is constructed, denoted as... in This represents the Mth phase-coded value in a phase-coded sequence period, expressed as: Where θ M This represents the phase angle corresponding to the Mth phase code value, in radians, ranging from 0 to 2π. Then, the phase code values ​​in the phase code sequence Φ are tuned according to the pulse transmission order to the baseband transmitted signal s. T In (τ), where, For a linear frequency modulated signal, K represents the frequency modulation slope, and τ represents the fast time step; Step 2) is implemented as follows: Assuming there are M mapping areas, and each mapping area has a point target, the nth echo signal of the synthetic aperture radar is represented as s(τ,n): Where, Φ n-m+1 f0 represents the (n-m+1)th phase code value in the coding sequence Φ, f0 represents the synthetic aperture radar carrier frequency, n∈[0,+∞) represents the sequence number of the transmitted pulse and the received echo, and τ m Represents the instantaneous two-way slant range delay of the m-th point target; In the slow time domain, we define each M echo signals as a group and construct the echo vector s(τ,k)=[s(τ,kM),…,s(τ,kM+M-1)] T , where k∈[0,+∞) represents the echo vector composed of the kth group of echo data; The implementation process of step 3) is as follows: 3.1) Assume the phase code value modulated by the nth transmitted pulse is Based on the phase encoding sequence Φ from step 1), construct the phase encoding guidance matrix X(Φ), which is expressed as: The echo vector s(τ,k) can then be expressed as: s(τ,k)=X(Φ)y(τ,k) Where y(τ,k) represents the unambiguous echo signal vectors of multiple real surveyed areas, expressed as: y(τ,k)=[y1(τ),y2(τ),…,y M (t)] T Among them, y M (τ) represents the unambiguous echo signal of the Mth mapping area, expressed as: 3.2) By left-multiplying the vector s(τ,k) by the inverse matrix X(Φ), we can obtain the vector s(τ,k). -1 (Φ) Solve for y(τ,k), the process of which is expressed as: in, This represents the solution value of y(τ,k), which is the unambiguous signal vector of the multiple surveyed areas to be recovered. Represented as: in, These represent the unambiguous echo signals of the first to Mth survey areas obtained from the solution; The matrix X(Φ) is inverted beforehand to save real-time computing resources: the inverse of the phase encoding guidance matrix X(Φ) is denoted as the weight vector matrix X. -1 (Φ); where matrix X(Φ) conforms to the definition of a circular matrix, then X -1 (Φ) is still a cyclic matrix, represented as: Where x(M) represents X -1 The Mth element in the first column vector of (Φ); 3.3) Extracting the circular matrix X -1 Perform an M-point FFT operation on the first column vector x(Φ) to obtain the echo recovery weight vector. The implementation process of step 4) is as follows: 4.1) For the circular matrix X in step 3.2), -1 (Φ) is analyzed, and the circular matrix X is further analyzed. -1 (Φ) is represented as: X -1 (Φ)=F -1 ΛF Among them, F -1 Λ represents the inverse FFT matrix, F represents the FFT matrix, matrix Λ represents diag(Fx(Φ)), and x(Φ) is the value of X. -1 The first column vector in (Φ); The Fx(Φ) term in matrix Λ represents the M-point FFT operation performed on vector x(Φ), the result of which has already been obtained in step 3.3), i.e., the echo recovery weight vector. Based on the above analysis, the solution expression for vector y(τ,k) in step 3.2) is updated as follows: Perform an M-point FFT operation on the echo vector s(τ,k) to solve for the Fs(τ,k) term, and denote the result as a vector. 4.2) Transform the vector sum vector Performing a dot product yields the term ΛFs(τ,k) in the above equation, and the result is denoted as a vector. The process is represented as follows: By analyzing vectors Perform an inverse FFT operation at point M to solve for the vector. The above process can be represented as follows: This completes the accurate recovery of unambiguous echo signal vectors from multiple surveyed areas.

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