Cognitive sar system and method based on snr closed loop control

Abstract

The application provides a cognitive SAR system and method based on a signal-to-noise ratio closed-loop control, comprising: a radar transmitting module: transmitting a linear frequency modulation pulse signal with adjustable pulse width to a target area; a return wave receiving and sampling module: receiving a target return wave, performing down-conversion and analog-to-digital conversion, and outputting a digitized baseband return wave signal; a signal-to-noise ratio sensing module: calculating the signal-to-noise ratio of the return wave signal in real time, and outputting an SNR value to a pulse decision control module; the pulse decision control module: continuously comparing the real-time signal-to-noise ratio measurement value with a target threshold value, making a closed-loop feedback decision, and iteratively adjusting the pulse width; and an adaptive imaging module: based on the current pulse width, performing adaptive pulse compression and energy compensation, and generating a SAR image with stable signal-to-noise ratio.

Description

Technical Field

[0001] This invention relates to the field of spaceborne radar system design and signal processing technology, specifically to a cognitive SAR system and method based on signal-to-noise ratio closed-loop control. Background Technology

[0002] Spaceborne synthetic aperture radar (SAR), as a high-resolution Earth observation technology, transmits broadband signals and receives echoes from ground objects. Through pulse compression and synthetic aperture processing, it achieves two-dimensional high-resolution imaging and has been widely used in surveying, monitoring, and reconnaissance. Traditional SAR systems face significant challenges in complex observation environments: because their transmission parameters typically use preset fixed modes, they lack a mechanism for sensing and feedback on real-time echo quality, making it difficult to adapt to dynamic changes in echo signal-to-noise ratio under different scenarios, leading to image quality fluctuations and insufficient radiation stability. Simultaneously, the inability to dynamically optimize transmission energy based on actual detection conditions results in low system resource utilization and limited imaging performance. To address these issues, this invention introduces a closed-loop control mechanism oriented towards real-time signal-to-noise ratio sensing and an adaptive imaging method. This achieves dynamic control of the transmitted pulse width and intelligent compensation of echo energy, significantly improving the adaptability and image quality stability of the SAR system in complex scenarios.

[0003] The patent "Method and Device for Designing Constant Mode Waveform for Cognitive Radar Based on Maximizing Signal-to-Clutter Noise Ratio" (CN119881805A) proposes a method for synthesizing constant mode signal waveforms for cognitive radar based on signal-to-clutter noise ratio. It calculates the feedback echo signal-to-clutter noise ratio (SCNR) and optimizes the energy spectral density of the transmitted waveform under energy constraints. The method then uses an alternating projection algorithm to synthesize the constant mode signal waveform, thereby improving target detection performance and adaptability in complex clutter environments. This method adopts the cognitive radar concept of dynamically adjusting the waveform through environmental interaction, achieving adaptive waveform optimization and constant mode constraints to reduce amplifier distortion. However, this method is geared towards moving target detection in general radar systems, aiming to maximize the signal-to-clutter noise ratio (SCNR). Since synthetic aperture radar (SAR) aims to acquire high-quality static scene images, this method is not suitable for SAR image acquisition.

[0004] The patent "An Optimization Design Method for a Doppler Filter for Cognitive Radar" (CN118604764A) proposes an optimization method for Doppler filters to suppress clutter in airborne radar. By dynamically adjusting the transmit wave angle and its corresponding Doppler channel center frequency, it achieves precise suppression of main clutter, improving the output signal-to-noise ratio and the target detectable area. This method embodies the environmental perception and adaptive filtering concepts in cognitive radar and possesses certain intelligent feedback characteristics. However, it focuses on clutter suppression and target detection and does not address key aspects of SAR imaging such as pulse control and energy compensation, thus it cannot be directly used to improve SAR image quality.

[0005] The paper "Fast Splitting Bregman Iterative ISAR Super-Resolution Imaging under Low SNR" proposes an ISAR super-resolution imaging algorithm for Fast Splitting Bregman Iteration (FSBI). It introduces the low-shift rank feature of the Toplitz matrix and Gohberg-Semencul representation to accelerate matrix inversion. Through a compressed sensing framework and regularization optimization to suppress noise, it achieves efficient sparse reconstruction under low SNR conditions, improving the azimuth resolution of SAR imaging. However, its core is single-pass processing algorithm acceleration and reconstruction optimization, without addressing real-time environmental perception and dynamic adjustment of transmission parameters. Lacking a closed-loop feedback mechanism, it cannot be used for real-time waveform control and energy management in cognitive SAR systems.

[0006] The paper "Adaptive Frequency Allocation in Radar Imaging: Towards Cognitive SAR" (K. Aberman, S. Aviv and YC Eldar, 2017 IEEE RadarConference (RadarConf), Seattle, WA, USA, 2017, pp. 1348-1351) proposes a cognitive SAR method oriented towards spectrum sensing and adaptive transmission. It achieves efficient imaging in spectrum-constrained environments by dynamically selecting available frequency bands for subband transmission and combining compressed sensing technology for image reconstruction. This method demonstrates the ability of cognitive SAR to sense and adapt to the environmental spectrum, improving the signal-to-noise ratio and reducing data volume. However, it relies on the assumption of scene sparsity, focuses on frequency domain resource allocation and subband transmission, and does not involve real-time closed-loop control of the echo signal-to-noise ratio. Its robustness in complex SAR imaging scenarios remains to be verified.

[0007] The paper "A Cognitive Synthetic Aperture Radar Concept for Tracking and Imaging Operation" (F. Stambouli, M. Limbach, T. Rommel and M. Younis, 2019, 20th International Radar Symposium (IRS), Ulm, Germany, 2019, pp. 1-9) proposes a SAR system design based on the cognitive radar concept. It achieves environmental perception and adaptive imaging through a two-stage operation of "mapping mode" and "tracking imaging mode". The system uses prior knowledge and real-time feedback to dynamically adjust beam pointing, power and imaging mode, which significantly improves the signal-to-noise ratio and resolution of the region of interest and realizes multi-functional adaptive imaging and target tracking. However, its hardware architecture relies on multi-channel digital beamforming and fixed multi-beams, and does not achieve fully adaptive control of the transmitted waveform, lacking versatility for SAR imaging.

[0008] In summary, existing spaceborne SAR technologies generally suffer from problems such as fixed transmission parameters, low resource utilization efficiency, and insufficient image radiometric stability when dealing with complex observation environments due to the lack of real-time sensing and feedback capabilities for echo signal-to-noise ratio (SNR). Therefore, there is an urgent need to develop a cognitive SAR imaging method with real-time SNR sensing capabilities, capable of dynamically adjusting transmission parameters and compensating for echo energy through closed-loop feedback, in order to comprehensively improve the adaptability, resource utilization efficiency, and image quality stability of SAR systems in complex environments. Summary of the Invention

[0009] In view of the deficiencies in the prior art, the purpose of this invention is to provide a cognitive SAR system and method based on signal-to-noise ratio closed-loop control.

[0010] According to one aspect of the present invention, a cognitive SAR system based on signal-to-noise ratio closed-loop control includes:

[0011] Radar transmitting module: Transmits linear frequency modulated pulse signals with adjustable pulse width to the target area; Echo reception and sampling module: Receives the target echo, performs down-conversion and analog-to-digital conversion, and outputs a digitized baseband echo signal; Signal-to-noise ratio (SNR) sensing module: calculates the SNR of the echo signal in real time and outputs the SNR value to the pulse decision control module; Pulse decision control module: By continuously comparing the real-time signal-to-noise ratio measurement with the target threshold, it makes decisions based on closed-loop feedback and iteratively adjusts the pulse width; Adaptive Imaging Module: Based on the current pulse width, it performs adaptive pulse compression and energy compensation to generate SAR images with stable signal-to-noise ratio.

[0012] Preferably, the signal-to-noise ratio sensing module specifically includes: M3.1: Measure system noise power during radar silence; M3.2: Calculate signal power by pulse block; M3.3: Calculate the block signal-to-noise ratio.

[0013] Preferably, in module M3.2, the formula for calculating the average signal power is as follows:

[0014] in, This refers to the azimuth pulse count. The number of sampling points in the distance direction. The received baseband complex echo signal, For distance to time, This refers to the direction of time.

[0015] Preferably, in module M3.3, the formula for calculating the signal-to-noise ratio in blocks is as follows:

[0016] in, For signal power, This represents noise power.

[0017] Preferably, the closed-loop feedback decision specifically includes: If the current signal-to-noise ratio is lower than the target signal-to-noise ratio tolerance lower limit, then adjust by the factor. Increase pulse width to the upper limit If it exceeds the upper limit of the target signal-to-noise ratio tolerance, then adjust according to the adjustment factor. Reduce pulse width to the lower limit Otherwise, maintain the original pulse width; the adjustment period is every One pulse, Adjustment factor The expression for the closed-loop feedback decision is as follows:

[0018] In the formula, The adjusted pulse width, The current pulse width, This is the pulse width adjustment factor. The maximum pulse width allowed by the system. The minimum pulse width allowed by the system. This is the current signal-to-noise ratio measurement. Set a target signal-to-noise ratio value. This is the signal-to-noise ratio tolerance threshold; Pulse width adjustment factor The absolute value of the signal-to-noise ratio difference in dB, in logarithmic form, is converted into the pulse width adjustment ratio required in the linear domain. This dynamically compensates for echo energy fluctuations, maintaining a stable and consistent signal-to-noise ratio in the SAR image. (Pulse width adjustment factor) The expression is as follows:

[0019] In the formula, This is the current signal-to-noise ratio measurement. Set the target signal-to-noise ratio value.

[0020] Preferably, the adaptive imaging module includes: Module M5.1: Receives the current pulse and updates the modulation frequency based on the current pulse width; Module M5.2: Construct an adaptive matched filter that is conjugate to the transmitted signal; Module M5.3: Applying the energy compensation function; Module M5.4: Performs azimuth focusing operation to generate SAR images with stable signal-to-noise ratio.

[0021] Preferably, the energy compensation function satisfies the following conditions:

[0022] in, Let be the energy compensation function. The signal is after pulse compression. It is a constant.

[0023] According to another aspect of the present invention, a cognitive SAR method based on signal-to-noise ratio closed-loop control is characterized by comprising: Step S1: Transmit a linear frequency modulated pulse signal with adjustable pulse width to the target area; Step S2: Receive the target echo, perform down-conversion and analog-to-digital conversion, and output a digitized baseband echo signal; Step S3: Calculate the signal-to-noise ratio (SNR) of the echo signal in real time and output the SNR value to the pulse decision control module; Step S4: By continuously comparing the real-time signal-to-noise ratio measurement with the target threshold, make decisions based on closed-loop feedback and iteratively adjust the pulse width; Step S5: Based on the current pulse width, perform adaptive pulse compression and energy compensation to generate a SAR image with stable signal-to-noise ratio.

[0024] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention realizes real-time sensing and closed-loop control of echo signal-to-noise ratio, effectively improving the quality and stability of SAR images. By measuring noise during the silent period, calculating the signal-to-noise ratio in blocks, and dynamically adjusting the pulse width according to the set threshold and adjustment factor, a closed-loop mechanism from sensing to control is established, which significantly enhances the system's adaptability to changing scenarios.

[0025] 2. This invention proposes an adaptive collaborative design of pulse width and matched filter. By constructing a conjugate matched filter based on the real-time pulse width and introducing an energy compensation function, the signal-to-noise ratio is effectively improved while ensuring the uniformity of imaging radiation characteristics. This method can maintain the stability of image radiation intensity under different pulse widths, avoiding performance degradation caused by emission energy mismatch, thereby achieving improved resource utilization and imaging quality.

[0026] 3. This invention possesses system-level intelligent feedback and resource optimization capabilities, overcoming the limitations of existing methods in terms of versatility and practicality. With fully adaptive transmission control as its core, it has closed-loop decision-making, real-time response, and hardware-software synergy capabilities, providing a reliable technical path for high-quality and high-stability SAR radar imaging in complex observation environments. Attached Figure Description

[0027] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart of a cognitive SAR method for signal-to-noise ratio closed-loop control.

[0028] Figure 2 This is a structural diagram of a cognitive SAR system oriented towards signal-to-noise ratio closed-loop control. Detailed Implementation

[0029] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0030] This invention provides a cognitive SAR system for signal-to-noise ratio (SNR) closed-loop control. The cognitive SAR system for SNR closed-loop control can be implemented by executing the process steps of the cognitive SAR method for SNR closed-loop control. That is, those skilled in the art can understand the cognitive SAR method for SNR closed-loop control as a preferred embodiment of the cognitive SAR system for SNR closed-loop control.

[0031] The present invention provides a cognitive SAR system for signal-to-noise ratio closed-loop control, comprising: Module M1: Radar Transmission Module; Module M2: Echo reception and sampling module; Module M3: Signal-to-noise ratio sensing module; Module M4: Pulse Decision Control Module; Module M5: Adaptive Imaging Module.

[0032] In more preferred embodiments, the M1 radar transmitting module is used to transmit a linear frequency modulated pulse signal with adjustable pulse width to the target area, and the time-domain expression of the transmitted linear frequency modulated pulse signal is:

[0033] in, For fast time variables, For the amplitude of the transmitted signal, For adjustable pulse width, For carrier frequency, It is a range-modulated frequency and satisfies , For signal bandwidth, It is the imaginary unit.

[0034] In more preferred embodiments, the M2 echo receiving and sampling module is used to receive the target echo and output a digitized baseband echo signal after down-conversion and analog-to-digital conversion.

[0035] In more preferred embodiments, the M3 signal-to-noise ratio sensing module is connected to the echo receiving and sampling module for real-time calculation of the signal-to-noise ratio (SNR) of the echo signal and outputting the SNR value to the pulse decision control module, including: M3.1: Measure system noise power during radar silence; M3.2: Calculate signal power by pulse block; M3.3: Calculate the block signal-to-noise ratio.

[0036] In more preferred embodiments, module M3.2 processes the echo signal according to... Divide the signal into pulse blocks and calculate the average signal power of the echo signal:

[0037] in, This refers to the azimuth pulse count. The number of sampling points in the distance direction. The received baseband complex echo signal, For distance to time, This refers to the direction of time.

[0038] In more preferred embodiments, module M3.3 calculates the signal-to-noise ratio in blocks.

[0039]

[0040] in, For signal power, This represents noise power.

[0041] In more preferred embodiments, the M4 pulse decision control module is connected to the signal-to-noise ratio sensing module and the radar transmission module, and is used to make decisions based on closed-loop feedback and iteratively adjust the pulse width by continuously comparing the real-time signal-to-noise ratio measurement value with the target threshold.

[0042] In more preferred embodiments, the M4 pulse decision control module achieves pulse width optimization based on closed-loop feedback decision through the following adjustment strategy: if the current signal-to-noise ratio is lower than the target signal-to-noise ratio tolerance lower limit, then adjust the pulse width by the adjustment factor. Increase pulse width to the upper limit If it exceeds the upper limit of the target signal-to-noise ratio tolerance, then adjust according to the adjustment factor. Reduce pulse width to the lower limit Otherwise, maintain the original pulse width. The adjustment period is every [number missing]. pulses, of which Adjustment factor satisfy .

[0043]

[0044] in, The adjusted pulse width, The current pulse width, This is the pulse width adjustment factor. The maximum pulse width allowed by the system. The minimum pulse width allowed by the system. This is the current signal-to-noise ratio measurement. Set a target signal-to-noise ratio value. This is the signal-to-noise ratio tolerance threshold.

[0045] Pulse width adjustment factor The absolute value of the signal-to-noise ratio difference in dB, in logarithmic form, is converted into the pulse width adjustment ratio required in the linear domain. By dynamically compensating for echo energy fluctuations, the signal-to-noise ratio of the SAR image is kept stable and consistent.

[0046] in, This is the current signal-to-noise ratio measurement. Set the target signal-to-noise ratio value.

[0047] In more preferred embodiments, the M5 adaptive imaging module is connected to the echo reception and sampling module and the pulse decision control module, and is used to perform adaptive pulse compression and energy compensation for the current pulse width to generate a SAR image with stable signal-to-noise ratio, including: Module M5.1: Receives the current pulse and updates the modulation frequency based on the current pulse width; Module M5.2: Construct an adaptive matched filter that is conjugate to the transmitted signal; Module M5.3: Applying the energy compensation function; Module M5.4: Performs azimuth focusing operation to generate SAR images with stable signal-to-noise ratio.

[0048] In more preferred embodiments, module M5.1 receives the current pulse and updates the distance directional modulation frequency according to the current pulse width.

[0049]

[0050] in, For signal bandwidth, This represents the current pulse width.

[0051] In more preferred embodiments, the frequency domain response of the adaptive matched filter in module M5.2 is conjugate matched with the transmitted signal spectrum, and its time domain expression is as follows:

[0052] in, For distance to time, The current pulse width, For range-directed frequency modulation, It is the imaginary unit.

[0053] In more preferred embodiments, the energy compensation function of module M5.3 balances the echo energy fluctuations under different pulse widths. The energy compensation function ensures energy normalization and is expressed as follows:

[0054] in, For reference pulse width, This represents the current pulse width.

[0055] The energy compensation function satisfies the following conditions to maintain image radiance consistency.

[0056]

[0057] in, Let be the energy compensation function. This is the signal after pulse compression. It is a constant.

[0058] A cognitive SAR method for signal-to-noise ratio closed-loop control according to the present invention includes: Step S1: SAR linear frequency modulated pulse transmission; Step S2: Digital echo signal acquisition; Step S3: Calculate the signal-to-noise ratio; Step S4: Pulse width update based on closed-loop strategy; Step S5: Adaptive imaging.

[0059] Specifically, step S1, SAR linear frequency modulated pulse transmission, transmits an adjustable pulse width linear frequency modulated pulse through module M1 radar transmission module.

[0060] Specifically, step S2, digital echo signal acquisition, involves acquiring digital echo signals through module M2, the echo receiving and sampling module.

[0061] Specifically, step S3, signal-to-noise ratio calculation, is performed by the signal-to-noise ratio sensing module M3 to calculate the current signal-to-noise ratio.

[0062] Specifically, step S4, pulse width update based on closed-loop strategy, updates subsequent pulse widths according to closed-loop strategy through module M4 pulse decision control module.

[0063] Specifically, step S5 adaptive imaging performs pulse compression and energy compensation on the real-time pulse width through module M5 adaptive imaging module, and outputs a SAR image with a stable signal-to-noise ratio.

[0064] The present invention also provides a cognitive SAR system based on signal-to-noise ratio closed-loop control. The cognitive SAR system based on signal-to-noise ratio closed-loop control can be implemented by executing the process steps of the cognitive SAR method based on signal-to-noise ratio closed-loop control. That is, those skilled in the art can understand the cognitive SAR method based on signal-to-noise ratio closed-loop control as a preferred embodiment of the cognitive SAR system based on signal-to-noise ratio closed-loop control.

[0065] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0066] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A cognitive SAR system based on signal-to-noise ratio closed-loop control, characterized in that, include: Radar transmitting module: Transmits linear frequency modulated pulse signals with adjustable pulse width to the target area; Echo reception and sampling module: Receives the target echo, performs down-conversion and analog-to-digital conversion, and outputs a digitized baseband echo signal; Signal-to-noise ratio (SNR) sensing module: calculates the SNR of the echo signal in real time and outputs the SNR value to the pulse decision control module; Pulse decision control module: By continuously comparing the real-time signal-to-noise ratio measurement with the target threshold, it makes decisions based on closed-loop feedback and iteratively adjusts the pulse width; Adaptive Imaging Module: Based on the current pulse width, it performs adaptive pulse compression and energy compensation to generate SAR images with stable signal-to-noise ratio.

2. The system according to claim 1, characterized in that, The signal-to-noise ratio sensing module specifically includes: M3.1: Measure system noise power during radar silence; M3.2: Calculate signal power by pulse block; M3.3: Calculate the block signal-to-noise ratio.

3. The system according to claim 2, characterized in that, In module M3.2, the formula for calculating the average signal power is as follows: in, This refers to the azimuth pulse count. The number of sampling points in the distance direction. The received baseband complex echo signal, For distance to time, This refers to the direction of time.

4. The system according to claim 2, characterized in that, In module M3.3, the formula for calculating the signal-to-noise ratio in blocks is as follows: in, For signal power, This represents noise power.

5. The system according to claim 1, characterized in that, The closed-loop feedback decision specifically includes: If the current signal-to-noise ratio is lower than the target signal-to-noise ratio tolerance lower limit, then adjust by the factor. Increase pulse width to the upper limit If it exceeds the upper limit of the target signal-to-noise ratio tolerance, then adjust according to the adjustment factor. Reduce pulse width to the lower limit Otherwise, maintain the original pulse width; the adjustment period is every One pulse, Adjustment factor The expression for the closed-loop feedback decision is as follows: In the formula, The adjusted pulse width, The current pulse width, This is the pulse width adjustment factor. The maximum pulse width allowed by the system. The minimum pulse width allowed by the system. This is the current signal-to-noise ratio measurement. Set a target signal-to-noise ratio value. This is the signal-to-noise ratio tolerance threshold; Pulse width adjustment factor The absolute value of the signal-to-noise ratio difference in dB, in logarithmic form, is converted into the pulse width adjustment ratio required in the linear domain. This dynamically compensates for echo energy fluctuations, maintaining a stable and consistent signal-to-noise ratio in the SAR image. (Pulse width adjustment factor) The expression is as follows: In the formula, This is the current signal-to-noise ratio measurement. Set the target signal-to-noise ratio value.

6. The system according to claim 1, characterized in that, The adaptive imaging module includes: Module M5.1: Receives the current pulse and updates the modulation frequency based on the current pulse width; Module M5.2: Construct an adaptive matched filter that is conjugate to the transmitted signal; Module M5.3: Applying the energy compensation function; Module M5.4: Performs azimuth focusing operation to generate SAR images with stable signal-to-noise ratio.

7. The system according to claim 6, characterized in that, The energy compensation function satisfies the following conditions: in, Let be the energy compensation function. The signal is after pulse compression. It is a constant.

8. A cognitive SAR method based on signal-to-noise ratio closed-loop control, characterized in that, include: Step S1: Transmit a linear frequency modulated pulse signal with adjustable pulse width to the target area; Step S2: Receive the target echo, perform down-conversion and analog-to-digital conversion, and output a digitized baseband echo signal; Step S3: Calculate the signal-to-noise ratio (SNR) of the echo signal in real time and output the SNR value to the pulse decision control module; Step S4: By continuously comparing the real-time signal-to-noise ratio measurement with the target threshold, make decisions based on closed-loop feedback and iteratively adjust the pulse width; Step S5: Based on the current pulse width, perform adaptive pulse compression and energy compensation to generate a SAR image with stable signal-to-noise ratio.

9. The method according to claim 1, characterized in that, The signal-to-noise ratio sensing module specifically includes: S3.1: Measure system noise power during radar silence; S3.2: Calculate signal power by pulse block; S3.3: Calculate the block signal-to-noise ratio.

10. The method according to claim 9, characterized in that, In module M3.2, the formula for calculating the average signal power is as follows: in, This refers to the azimuth pulse count. The number of sampling points in the distance direction. The received baseband complex echo signal, For distance to time, This refers to the direction of time.

Images