Single station passive and active cooperative positioning method for moving ship based on relaxation transformation

Abstract

The application provides a single-station moving ship active and passive cooperative positioning method based on relaxation transformation. The method comprises the following steps: a single-station synthetic aperture radar receives a plurality of frames of radiation pulse signals from a ship, obtains a pulse repetition interval sum, a time difference of arrival measurement value and a Doppler phase measurement value of the ship, and further determines a passive measurement matrix of the ship; based on the pulse repetition interval sum and the speed and position of the ship, a theoretical measurement matrix of the ship is determined to construct a ship positioning model, a weighted least square method and a semi-positive definite relaxation transformation algorithm are used for solving, and a predicted position and a predicted speed of the ship are obtained; a radar wave is emitted to the predicted position of the ship to obtain a scene echo of the ship; a matched filter is designed to coarsely focus the scene echo and extract a peak value signal, the peak value signal is inversely focused and inverted, and a ship echo signal is obtained; the ship is imaged by using the echo signal, and an actual position of the ship is determined according to an imaging result.

Description

Technical Field

[0001] This application relates to the field of radar detection and target localization technology, and in particular to a single-station moving vessel active-passive cooperative localization method based on relaxation transform. Background Technology

[0002] Currently, traditional active detection technologies, such as using satellite-based monostatic synthetic aperture radar (SAR), process two-dimensional high-resolution images of the observed scene containing stationary targets, providing range and azimuth information. However, monostatic SAR suffers from azimuth offset when imaging moving targets such as ships, leading to distortion of ship position information. Furthermore, the radiation signals emitted by radars carried by the target ship in the same frequency band can interfere with the SAR, causing the loss of ship position information within the SAR image.

[0003] Currently, there are two main types of methods for ship relocation using single-station SAR: the first is the Doppler center frequency estimation method, and the second is the ship wake method. The Doppler center frequency estimation method estimates the Doppler intermediate frequency of the ship and its adjacent sea surface particles within the SAR image, and then estimates the ship's radial velocity based on the Doppler intermediate frequency difference between the ship and the SAR image, thus achieving relocation. The ship wake method utilizes the representation of the wake generated by the ship's motion in the SAR image to achieve ship relocation.

[0004] Passive detection technology is unaffected by the ship's motion, achieving localization of moving ships by analyzing the received signals from shipborne radiation sources or the algebraic relationship between their parameters and the ship's position and velocity. Currently, passive localization methods for moving ships primarily rely on constructing an optimized model using the SAR time difference of arrival (TDOA) and frequency difference of arrival (FDOA) of radiated pulse signals received from multiple stations. Single-station passive localization methods for moving ships face significant challenges in retrieving the radiation source's motion parameters due to the changing state of the radiation source corresponding to the received signal parameters over time. All of the above passive detection methods assume continuous operation of the platform's receiving window, contradicting the one-transmit-one-receive operation of SAR; therefore, there are currently no results regarding single-station SAR passive localization of moving ships. Summary of the Invention

[0005] In view of this, this application provides a single-station active-passive cooperative localization method for moving ships based on relaxation transform. It solves the problems of azimuth offset and position loss caused by shipborne radar interference when traditional single-station SAR actively detects moving ships; and the problem that existing relocation methods rely on ship SAR images, which cannot achieve accurate ship positioning in complex scenarios.

[0006] The first aspect of this application provides a single-station active-passive cooperative positioning method for a moving vessel based on relaxation transform, comprising the following steps: S1, a single-station synthetic aperture radar receives multiple frames of radiated pulse signals from a target vessel along the azimuth direction; the multiple frames of radiated pulse signals are processed to obtain the sum of pulse repetition intervals, the time difference of arrival measurement, and the Doppler phase measurement of the target vessel, wherein each frame of radiated pulse signal contains multiple pulses; S2, the passive measurement matrix of the target vessel is determined based on the time difference of arrival measurement and the Doppler phase measurement; S3, the theoretical measurement matrix of the target vessel is determined based on the sum of pulse repetition intervals and the relationship between the velocity constraint value and the position constraint value of the target vessel; a positioning model of the target vessel is constructed based on the theoretical measurement matrix and the passive measurement matrix; S4, after linearizing the positioning model using the weighted least squares method, the model is transformed into a convex optimization positioning model using a semidefinite relaxation transform algorithm. S5. A single-station synthetic aperture radar (SAR) uses a time-division multiplexing mechanism to periodically transmit radar waves towards the predicted position while receiving the radiated pulse signal from the target vessel to obtain the radar echo of the predicted position. Within a preset error range of the predicted position, the radar echo is framed to obtain the scene echo of the target vessel. The direction of the transmitted radar beam can be dynamically adjusted according to the real-time predicted position and speed. S6. A matched filter is designed based on the predicted speed and Doppler phase measurements. The scene echo is coarsely focused using the matched filter to obtain a focused echo. The peak signal in the focused echo is extracted, and inverse focusing inversion is performed on the peak signal to obtain the echo signal of the target vessel. S7. The Doppler phase information contained in the echo signal is used to image the target vessel, and the actual position of the target vessel is determined based on the imaging results.

[0007] Furthermore, processing the multi-frame radiation pulse signal includes compressing the bandwidth of the multi-frame radiation pulse signal.

[0008] Furthermore, the sum of the pulse repetition intervals of the target vessel is the sum of the time intervals between each frame of the multi-frame radiation pulse signal and the sum of the time intervals between multiple pulses in each frame of the radiation pulse signal.

[0009] Furthermore, the time difference of arrival measurement includes: taking the first pulse in the first frame of the multi-frame radiation pulse signal as the reference time; taking the time when the first pulse of each frame of the multi-frame radiation pulse signal arrives at the monostation synthetic aperture radar as the arrival time; and subtracting the total pulse repetition interval from the arrival time and then subtracting the reference time to obtain the time difference of arrival measurement.

[0010] Furthermore, the Doppler phase measurement includes the peak phase of the first pulse in each frame of the multi-frame radiation pulse signal calculated using a non-uniform Fourier transform.

[0011] Furthermore, the target vessel's positioning model includes: achieving it by constructing a maximum likelihood estimate with the error between the theoretical measurement matrix and the passive measurement matrix as the objective function; the maximum likelihood estimate of the objective function is constrained by the target vessel's velocity constraint value and position constraint value; both the velocity constraint value and the position constraint value are determined by the physical relationship between the monostation synthetic aperture radar and the target vessel.

[0012] Furthermore, the theoretical measurement matrix includes the theoretical time difference of arrival measurement value and the theoretical Doppler phase value of the target vessel; wherein, the theoretical Doppler phase value is related to the carrier frequency of the radiation pulse signal, the speed of light, the coordinates of the satellite geocentric-geo-fixed coordinate system, and the velocity constraint value and position constraint value of the target vessel, and the theoretical time difference of arrival measurement value is related to the coordinates of the satellite geocentric-geo-fixed coordinate system, the speed of light, and the velocity constraint value and position constraint value of the target vessel.

[0013] Further, in step S6, the scene echo is coarsely focused by a matched filter to obtain a focused echo, and the peak signal in the focused echo is extracted. This includes matching the matched filter with the phase characteristics in the scene echo to coarsely focus the scene echo to obtain a focused echo, so that the actual position of the target ship forms a peak signal in the focused echo.

[0014] Furthermore, the peak signal is inversely focused to obtain the echo signal of the target vessel. This includes: inverse focusing is the reverse operation of coarse focusing, and the echo signal of the target vessel is derived from the peak signal through inverse focusing.

[0015] Furthermore, the scene echo of the target vessel includes a mixture of signals from the area where the target vessel is located and the areas where other vessels besides the target vessel are located.

[0016] The active-passive cooperative positioning method for a single-station moving vessel based on relaxation transform provided in this application can achieve the following technical effects:

[0017] (1) By subtracting the arrival time and approximately compensating for the radiation source signal parameters within the transmission window, the problem of positioning moving ships under incomplete single-station SAR observations is solved, and the dependence of existing single-station moving ship positioning methods on radiation source transmission time and complete signal reception is reduced.

[0018] (2) By combining TDOA and Doppler phase to construct a non-convex localization model, and applying weighted least squares method and semidefinite relaxation algorithm to convert the non-convex localization model into a convex optimization problem, the problems of local optimal solutions and complex computational costs caused by the non-convex localization model can be solved.

[0019] (3) This method uses the positioning results of moving ships under single-station SAR passive detection as prior information, completes ship echo detection and extraction through coarse focusing and peak search, and uses the Doppler phase in the extracted ship echo to image the ship, realizing active and passive cooperative positioning. This solves the dependence of existing ship relocation methods on the visibility of ships in SAR images, and can achieve accurate positioning of target ships under the influence of complex sea conditions, ship three-dimensional motion, interference and other factors. Attached Figure Description

[0020] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0021] Figure 1 A flowchart illustrating a single-station moving vessel active-passive cooperative positioning method based on relaxation transform according to an embodiment of this application is shown. Detailed Implementation

[0022] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0023] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0024] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0025] This application provides a single-station moving vessel active-passive cooperative positioning method based on relaxation transform, which solves the problems of low positioning accuracy of active detection of vessel bearing offset and passive detection, and reduces positioning complexity.

[0026] Figure 1 A flowchart illustrating a single-station moving vessel active-passive cooperative positioning method based on relaxation transform according to an embodiment of this application is shown.

[0027] like Figure 1As shown, the active-passive cooperative positioning method for a single-station moving vessel based on relaxation transform according to this embodiment includes steps S1 to S7.

[0028] In step S1, the monostation synthetic aperture radar receives multiple frames of radiated pulse signals from the target vessel along the azimuth direction. The multiple frames of radiated pulse signals are processed to obtain the sum of the pulse repetition intervals, the time difference of arrival measurement, and the Doppler phase measurement of the target vessel. Each frame of radiated pulse signal contains multiple pulses.

[0029] In this embodiment, processing the multi-frame radiation pulse signal includes compressing the bandwidth of the multi-frame radiation pulse signal.

[0030] Specifically, the bandwidth of the multi-frame radiation pulse signal is compressed by converting it to the frequency domain / time-frequency domain through Fourier transform or wavelet transform, eliminating low-energy components and retaining the key high-energy spectrum.

[0031] In this embodiment, the sum of the Pulse Repetition Interval (PRI) of the target vessel is the sum of the time intervals between each frame of the multi-frame radiation pulse signal and the sum of the time intervals between multiple pulses in each frame of the radiation pulse signal.

[0032] Specifically, the sum of pulse repetition intervals It can be represented as:

[0033]

[0034] In the formula, n is the frame number and n is a positive integer; It is the sum of time intervals between multiple pulses in the n-frame radiation pulse signal, that is, the sum of PRI within the SAR receiving window, where re represents the receiving window; It is the sum of time intervals between each frame of the n+1 frame radiation pulse signal, that is, the sum of PRI within the SAR transmission window, where nre represents the transmission window.

[0035] In this embodiment, the calculation method for the time difference of arrival (TDOA) measurement includes: using the first pulse in the first frame of a multi-frame radiation pulse signal as the reference time; using the time when the first pulse of each frame of the multi-frame radiation pulse signal arrives at the monostation synthetic aperture radar as the arrival time; and subtracting the sum of the pulse repetition intervals from the arrival time and then subtracting the reference time to obtain the TDOA measurement. The above TDOA measurements constitute a time difference measurement matrix.

[0036] Specifically, assuming the first pulse in the first frame is the reference time is... The arrival time is the time when the first pulse of the nth frame of radiated pulse signal arrives at the SAR. The time difference measurement of the arrival of the first pulse in the nth frame at the SAR for:

[0037]

[0038] Where i represents the frame number.

[0039] Furthermore, the aforementioned time difference measurements constitute a time difference measurement matrix. Time difference measurement matrix It can be represented as

[0040]

[0041] Where T is the transpose of the matrix.

[0042] In this embodiment, the Doppler phase measurement value is calculated by applying a non-uniform Fourier transform to calculate the peak phase of the first pulse of each frame in the multi-frame radiation pulse signal.

[0043] Specifically, the Doppler phase measurement value of the nth frame of the radiation pulse signal It can be represented as:

[0044]

[0045] in, Represented as The radiation pulse signal of the target ship in the i-th frame at time i, and , This is a non-uniform Fourier transform.

[0046] In this embodiment, a Short-Time Fourier Transform (STFT) can also be used instead of a Non-Uniform Fourier Transform to obtain Doppler phase measurements. .

[0047] Furthermore, the above Doppler phase measurements Constructing the Doppler phase measurement matrix Doppler phase measurement matrix It can be represented as:

[0048]

[0049] In step S2, the arrival time difference measurement value calculated above is used... Doppler phase measurement Determine the passive measurement matrix of the target vessel.

[0050] Specifically, by combining the time difference measurement matrix Doppler phase measurement matrix The passive measurement matrix D of the target vessel can be obtained:

[0051]

[0052] In step S3, based on the sum of pulse repetition intervals and the relationship between the velocity constraint value and the position constraint value of the target vessel, the theoretical measurement matrix of the target vessel is determined; based on the theoretical measurement matrix and the passive measurement matrix, the positioning model of the target vessel is constructed.

[0053] In this embodiment, the positioning model of the target vessel includes: constructing a maximum likelihood estimation with the error between the theoretical measurement matrix and the passive measurement matrix as the objective function; the maximum likelihood estimation of the objective function is constrained by the velocity constraint value v and the position constraint value p of the target vessel; both the velocity constraint value v and the position constraint value p are determined by the physical relationship between SAR and the target vessel.

[0054] The theoretical measurement matrix includes the theoretical time difference of arrival measurements of the target vessel. and theoretical Doppler phase value The theoretical Doppler phase value With the carrier frequency of the radiation pulse signal The speed of light c, the satellite's geocentric coordinate system coordinates s, and the target ship's velocity constraint value v and position constraint value p are all related; the theoretical time difference of arrival measurement value is also relevant. It is related to the satellite's geocentric coordinate system coordinates s, the speed of light c, and the target ship's velocity constraint value v and position constraint value p.

[0055] Specifically, the theoretical time difference of arrival measurement value for:

[0056]

[0057] Theoretical Doppler phase value for:

[0058]

[0059] Combine the above theoretical arrival time difference measurements and theoretical Doppler phase value The theoretical measurement matrix of the target ship can be obtained. .

[0060] Specifically, theoretical measurement matrix It can be represented as:

[0061]

[0062] The satellite geocentric coordinate system is a geocentric coordinate system with the Earth's center of mass as the origin, the x-axis pointing to the intersection of the prime meridian and the equator, the z-axis pointing to the Earth's north pole, and the y-axis forming a right-handed rectangular coordinate system with the x and z axes. The coordinates of the satellite geocentric coordinate system are updated in real time as the satellite moves.

[0063] In this embodiment, the aforementioned velocity constraint value v and position constraint value p are established by creating prior information about the target vessel. get.

[0064] Specifically, the prior information about the target vessel is as follows:

[0065]

[0066] in, To observe the distance from the satellite to the Earth's center in the scene, The slack is the measurement error of the geocentric distance, which is generally set empirically.

[0067] To simplify the motion model and reduce the complexity of the nonlinear problem, it is assumed that the target ship's position does not move significantly during the observation time.

[0068] To narrow the parameter search range of the positioning model, avoid the algorithm converging to meaningless local optima, and improve the stability and accuracy of positioning, prior information about the target vessel is used. To constrain the model, a constrained maximum likelihood estimation localization model is established, as follows:

[0069]

[0070] in, for The covariance matrix.

[0071] In step S4, the above positioning model is linearized using the weighted least squares method, and then transformed into a convex optimization positioning model using the positive semidefinite relaxation transformation algorithm. The convex optimization positioning model is solved to obtain the predicted position and predicted speed of the target ship.

[0072] Specifically, the above localization model is first linearized using the weighted least squares (WLS) method:

[0073]

[0074] in The variables in the weighted least squares method are, i.e. W is the weighting matrix of the weighted least squares method, i.e. . and It is the parameter matrix of the weighted least squares equation, which respectively correspond to The coefficients of the linear and quadratic terms; The constant term vector is derived from the coordinates of the satellite's geocentric Earth-fixed coordinate system. speed of light It consists of fixed terms such as known parameters and reference signals.

[0075] By linearizing the above positioning model, the nonlinear positioning model can be approximated as a linearized weighted least squares positioning model.

[0076] Furthermore, the linearized positioning model is transformed into a convex optimization positioning model using the semidefinite relaxation transform (SDR) algorithm. The convex optimization positioning model is then solved to obtain the predicted position and velocity of the target vessel. The specific model and solution process are as follows:

[0077]

[0078]

[0079]

[0080] in, Add a new variable to SDR. This is the SDR coefficient matrix.

[0081] In addition, convex optimization transformation methods such as Second-Order Cone Programming (SOCP) can be used to replace the positive semidefinite relaxation transformation algorithm to solve the localization model.

[0082] In step S5, the monostation synthetic aperture radar uses a time-division multiplexing mechanism to periodically transmit radar waves to the predicted position while receiving the radiation pulse signal from the target vessel to obtain the radar echo of the predicted position. The radar echo is then framed within a preset error range of the predicted position to obtain the scene echo of the target vessel. The direction of the transmitted radar beam can be dynamically adjusted according to the real-time predicted position and predicted speed.

[0083] In this embodiment, the direction of the SAR transmitted radar beam can be dynamically adjusted according to the real-time predicted position and predicted velocity.

[0084] Specifically, based on the predicted position of the target vessel With prediction speed Establish a radar wave beam pointing feedback system, including the following steps a-c.

[0085] Step a, in the satellite geocentric geofixed coordinate system (ECEF), the predicted position is... and prediction speed The signal is input to the feedback system, based on the signal processing delay. (Typical value 1~10ms), calculate position correction amount .

[0086] Step b: Calculate the beam pointing angle in the satellite reference frame (satellite body reference frame); first, determine the target position. Switch to satellite reference frame:

[0087]

[0088] in, The predicted position in the satellite reference frame. is the rotation matrix from the ECEF coordinate system to the satellite reference system (determined by the satellite orbital parameters and attitude), and s is the position vector of the satellite's center of mass in the ECEF coordinate system.

[0089] Furthermore, based on the predicted position in the satellite reference frame Calculate the elevation angle in the SAR beam pointing angle and azimuth .

[0090] Step c, based on the pitch angle Beam control is performed based on the elevation and azimuth angles β. If the SAR is a phased array antenna, a phase weight matrix is ​​generated based on the elevation and azimuth angles to perform beam direction control and execution. If the SAR is a mechanical steering radar, a servo motor is driven to make the antenna normal point to the elevation and azimuth angles.

[0091] Based on the aforementioned radar beam pointing feedback system, the SAR uses the already calculated elevation angle of the predicted position. and azimuth While receiving the radiation pulse signal from the target vessel, it periodically transmits radar waves toward the predicted location to obtain the radar echo of the predicted location.

[0092] Furthermore, the radar echoes received by SAR at the predicted location are selected within a preset error range to obtain the scene echo of the target ship.

[0093] Specifically, the preset error range is based on the predicted position. The accuracy of the (passive positioning) results and the tolerance for positioning errors in practical application scenarios are set. This preset error range is used to define the spatial area that may contain the target vessel. For example, it can be set to use the predicted position... Centered on, and with a preset error range of Construct a structure with a side length of in three-dimensional space. The cube region, with a preset error range The calculation is as follows:

[0094]

[0095] in, The positioning error margin can be set based on experience; the default is 300m.

[0096] Furthermore, by mapping the spatial cube to the echo domain (physical space to signal domain) using a range-Doppler model, and constructing the correspondence between ship positions and SAR echoes using the range-Doppler model, spatial position information can be converted into the corresponding echo data position in the original echo. The range-Doppler model is determined based on SAR operating parameters, electromagnetic wave propagation characteristics, and the spatial characteristics of the geocentric coordinate system.

[0097] Specifically, the mapping relationship is as follows:

[0098]

[0099] in, For distance direction, To predict the maximum boundary of the location error range in the distance dimension, For small boundaries; If it is a directional time window, then The earliest observation time of the target in the azimuth direction. The latest observation time; The azimuth frequency in the SAR system; The Doppler phase measurement error can be set according to the observation scenario.

[0100] Furthermore, based on the above mapping relationship, the radar echo matrix obtained is used to extract elements that satisfy the following conditions: and The submatrix represents the scene echo of the target ship:

[0101]

[0102] in, The scene echo of the selected target ship, Let be the matrix of the original echo, and Cube be the three-dimensional error cube mapping domain.

[0103] In step S6, a matched filter is designed based on the predicted velocity and Doppler phase measurement. The scene echo is coarsely focused using the matched filter to obtain the focused echo. The peak signal in the focused echo is extracted, and the peak signal is inversely focused to obtain the echo signal of the target ship.

[0104] In this embodiment, the scene echo of the target vessel also includes a mixed signal of the area where the target vessel is located and the areas where other vessels besides the target vessel are located.

[0105] In this embodiment, the scene echo is coarsely focused using a matched filter to obtain a focused echo, and the peak signal in the focused echo is extracted, including:

[0106] By matching the phase characteristics of the scene echo with the matched filter, the scene echo is coarsely focused to obtain a focused echo, so that the actual position of the target ship forms a peak signal in the focused echo.

[0107] The goal of the matched filter design is to compensate for the Doppler frequency shift and phase divergence introduced by the target vessel's motion, thereby focusing the energy of the target vessel's scene echo signal in the azimuth direction. In this embodiment, the design of the matched filter relies on the predicted velocity and Doppler phase measurement matrix obtained during the passive detection phase.

[0108] In this embodiment, the coarse focusing process is as follows:

[0109] First, the scene echo is preprocessed by pulse compression (eliminating range broadening) while preserving the original uncompressed echo characteristics in the azimuth direction, thus preparing for azimuth matched filtering.

[0110] The matched filtering function involves performing a complex convolution operation between the designed matched filter and the pre-processed echo. Phase compensation is used to offset the Doppler phase shift introduced by ship motion, thus concentrating the ship echo energy in the azimuth direction. Specifically:

[0111]

[0112] In the formula, For Fast Fourier Transform, For inverse fast Fourier transform, For coarse focusing results, To select the echo, This is the filtering function in the matched filter.

[0113] In this embodiment, an adaptive peak detection function is employed. For coarse focusing results A scan is performed, and signals whose amplitude exceeds a set threshold are extracted. This threshold is set based on the average noise level of the scene echo (e.g., 3-5 times the noise standard deviation), and the peak point is a local maximum in both the azimuth and range directions.

[0114] Finally, peak value verification is performed, comparing the extracted peak position with the passive localization results. If the difference is less than or equal to the error range Δp, and the deviation exceeds the error range Δp, it is judged as false noise and removed to ensure that the extracted peak value corresponds to the target ship.

[0115] In addition, for the coarse focusing method mentioned above, the Deramp algorithm (de-slant algorithm) or the CS algorithm (Chirp-Scaling algorithm) can also be used to coarsely focus the scene echo.

[0116] In this embodiment, the peak signal is inversely focused to obtain the echo signal of the target vessel. This includes: inverse focusing is the reverse operation of coarse focusing, and the echo signal of the target vessel is deduced from the peak signal through inverse focusing.

[0117] The specific formula is as follows:

[0118]

[0119] In the formula, for Ship echo obtained by inverse focusing the peak It is the inverse focusing function. This is the peak retrieval function.

[0120] The above-mentioned inverse focusing inversion process includes focusing parameter extraction, inverse phase compensation, and echo signal reconstruction.

[0121] Specifically, focusing parameter extraction is used to record the phase compensation parameters of the matched filter during coarse focusing, such as Doppler phase measurements; then inverse phase compensation is performed, that is, applying phase compensation opposite to that of the matched filter to the extracted peak signal; finally, echo signal reconstruction is performed, and the inverse Fourier transform is performed on the signal after inverse phase compensation to reconstruct the signal of the target ship in the original echo domain, that is, the echo signal of the target ship.

[0122] In addition, the echo signal of the target ship after inverse focusing retains the subtle phase changes caused by the ship's motion (such as phase fluctuations caused by three-dimensional motion and wave disturbance). This information will be further calibrated in subsequent precise imaging through actively detected Doppler phase, ultimately achieving high-precision positioning through active-passive coordination.

[0123] In step S7, the target vessel is imaged using the Doppler phase information contained in the echo signal, and the actual position of the target vessel is determined based on the imaging results.

[0124] Since the echo signal of the target ship is the original echo signal obtained after coarse focusing peak extraction and inverse focusing inversion, the Doppler phase contained in the original echo signal is a direct physical reflection of the ship's motion state.

[0125] Furthermore, by compensating for the ship motion phase error contained in the Doppler phase, the ship echo is focused at high resolution on a two-dimensional plane of distance and azimuth, providing a clear image reference for position determination, thereby achieving accurate images.

[0126] After accurate imaging, the target vessel appears as a high-energy focal point (or a continuous area matching the vessel's size) in the image, and its location can be converted to actual geographic coordinates through the following steps:

[0127] First, the image coordinates are mapped to the geographic coordinates. The SAR imaging system can establish the mapping relationship between image pixel coordinates and the geocentric coordinate system by using ephemeris data (platform position, velocity) and imaging geometric models (such as range-Doppler models).

[0128] Furthermore, the center pixel (or energy centroid) of the ship's focused area is identified, its image coordinates are extracted, and substituted into the above mapping relationship to calculate the corresponding satellite geocentric and geofixed coordinates, which is the actual geographical location of the target ship.

[0129] By combining the passive positioning results, the calculated actual geographical location is verified for consistency. If there are minor deviations (usually caused by residual phase errors), the geometric parameters in the mapping relationship can be further corrected through least-squares iterative optimization to ensure that the positioning error is minimized.

[0130] Through the above steps S1 to S7, the final output of the target ship's actual position not only includes the ship's precise satellite geocentric and geofixed coordinates, but also includes high-resolution imaging results, providing dual data support for subsequent target recognition, trajectory tracking and other applications.

[0131] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

[0132] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of this application, those skilled in the art can make various substitutions and modifications, all of which should fall within the scope of this application.

Claims

1. A method for active and passive cooperative localization of a single-station moving vessel based on relaxation transform, characterized in that, Includes the following steps: S1, a single-station synthetic aperture radar receives multiple frames of radiated pulse signals from a target vessel along the azimuth direction, processes the multiple frames of radiated pulse signals to obtain the total pulse repetition interval, time difference of arrival measurement, and Doppler phase measurement of the target vessel, wherein each frame of the radiated pulse signal contains multiple pulses; S2, determine the passive measurement matrix of the target vessel based on the arrival time difference measurement and the Doppler phase measurement; S3. Based on the sum of the pulse repetition intervals and the relationship between the velocity constraint value and the position constraint value of the target vessel, determine the theoretical measurement matrix of the target vessel; based on the theoretical measurement matrix and the passive measurement matrix, construct the positioning model of the target vessel. S4. After linearizing the positioning model using the weighted least squares method, it is transformed into a convex optimization positioning model through a positive semidefinite relaxation transformation algorithm. The convex optimization positioning model is then solved to obtain the predicted position and predicted speed of the target vessel. S5, the monostation synthetic aperture radar, through a time-division multiplexing mechanism, periodically transmits radar waves to the predicted position while receiving the radiation pulse signal of the target vessel to obtain the radar echo of the predicted position, and selects the radar echo within a preset error range of the predicted position to obtain the scene echo of the target vessel; wherein the direction of the transmitted radar wave beam can be dynamically adjusted according to the real-time predicted position and the predicted speed. S6. Based on the predicted speed and the Doppler phase measurement, a matched filter is designed. The scene echo is coarsely focused using the matched filter to obtain a focused echo. The peak signal in the focused echo is extracted. The peak signal is inversely focused and inverted to obtain the echo signal of the target ship. S7. The target vessel is imaged using the Doppler phase information contained in the echo signal, and the actual position of the target vessel is determined based on the imaging results.

2. The method according to claim 1, characterized in that, Processing the multi-frame radiation pulse signal includes compressing the bandwidth of the multi-frame radiation pulse signal.

3. The method according to claim 1, characterized in that, The sum of the pulse repetition intervals of the target vessel is the sum of the time intervals between each frame of the multi-frame radiation pulse signal and the sum of the time intervals between multiple pulses in each frame of the radiation pulse signal.

4. The method according to claim 1, characterized in that, The calculation method for the arrival time difference measurement includes: The first pulse in the first frame of the multi-frame radiation pulse signal is taken as the reference time; the arrival time is the time when the first pulse in each frame of the multi-frame radiation pulse signal arrives at the monostation synthetic aperture radar; the arrival time is obtained by subtracting the sum of the pulse repetition intervals and then subtracting the reference time from the arrival time.

5. The method according to claim 1, characterized in that, The calculation method for the Doppler phase measurement includes: The peak phase of the first pulse in each frame of the multi-frame radiation pulse signal is obtained by applying a non-uniform Fourier transform.

6. The method according to claim 1, characterized in that, The positioning model of the target vessel includes: This is achieved by constructing a maximum likelihood estimate with the error between the theoretical measurement matrix and the passive measurement matrix as the objective function; The maximum likelihood estimate of the objective function is constrained by the speed constraint value and the position constraint value of the target vessel; Both the velocity constraint value and the position constraint value are determined by the physical relationship between the monostation synthetic aperture radar and the target vessel.

7. The method according to claim 6, characterized in that, The theoretical measurement matrix consists of the theoretical time difference of arrival measurement and the theoretical Doppler phase measurement of the target vessel; The theoretical Doppler phase measurement is related to the carrier frequency of the radiation pulse signal, the speed of light, the coordinates of the satellite in the geocentric-geocentric coordinate system, and the velocity and position constraints of the target vessel; the theoretical time difference of arrival measurement is related to the coordinates of the satellite in the geocentric-geocentric coordinate system, the speed of light, and the velocity and position constraints of the target vessel.

8. The method according to claim 1, characterized in that, In step S6, the scene echo is coarsely focused using the matched filter to obtain a focused echo, and the peak signal in the focused echo is extracted, including: The matched filter is matched with the phase characteristics in the scene echo to perform coarse focusing processing on the scene echo to obtain a focused echo, so that the actual position of the target ship forms the peak signal in the focused echo.

9. The method according to claim 1, characterized in that, Inverse focusing and inversion of the peak signal yields the echo signal of the target vessel, including: The echo signal of the target vessel is derived from the peak signal through the inverse focusing inversion. The inverse focusing inversion is the reverse operation of the coarse focusing process.

10. The method according to claim 1, characterized in that, The scene echo of the target vessel includes a mixture of signals from the area where the target vessel is located and signals from other vessels besides the target vessel.

Images