Radar signal processing method and device based on event driving and parameterized inversion

Abstract

The application provides a radar signal processing method and device based on event driving and parameterized inversion, and the method comprises the following steps: collecting a radar original echo signal at a current time to obtain an initial target set; establishing a parameterized model according to the initial target set, and predicting a prediction parameter at an adjacent time; generating a corresponding prediction echo signal according to the prediction parameter; calculating a residual error according to the original echo signal and the prediction echo signal, and updating, judging and event driving and adaptively deciding the parameterized model according to the residual error. The method can directly perform parameterized modeling at the original signal level, efficiently update by using target dynamic characteristics, and control the calculation amount by using an event triggered adaptive decision mechanism, so that the system calculation complexity is significantly reduced while the detection and tracking accuracy is ensured, and the actual needs of low-altitude platform and low-cost deployment are met.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing, and in particular to a radar signal processing method and apparatus based on event-driven and parameterized inversion. Background Technology

[0002] With the development of the low-altitude economy and unmanned systems, the demand for perception in low-altitude airspace has increased significantly. Typical applications include drone surveillance, low-altitude traffic management, intelligent logistics delivery, and air environment perception for mobile robots and autonomous driving platforms. In these applications, aerial targets (such as drones, low-altitude slow-moving small aircraft, etc.) are typically small in size, highly maneuverable, and fly at low altitudes, easily posing potential safety risks to surrounding equipment, personnel, and other flying objects. Therefore, radar systems not only need to achieve rapid target detection but also continuous and stable tracking to obtain the target's trajectory, speed changes, and behavioral characteristics, thereby supporting advanced applications such as obstacle avoidance decision-making, path planning, and airspace management.

[0003] On the other hand, low-altitude platforms (such as small drones, ground mobile robots, and lightweight monitoring equipment) are typically limited by factors such as size, power consumption, and cost, making it difficult to configure high-performance computing units. Therefore, low-altitude air-to-air radar systems must not only meet detection and tracking performance requirements but also possess low computational complexity and high energy efficiency to adapt to the actual deployment needs of embedded or resource-constrained platforms. Against this backdrop, how to achieve high-precision, continuous target perception under limited computing power conditions has become a critical problem that urgently needs to be solved.

[0004] Currently, existing radar systems generally employ a processing flow based on Fast Fourier Transform (FFT), which sequentially transforms the original echo signal in the range, velocity, and angle directions to construct a range-Doppler or range-Doppler-angle domain image, and then extracts targets using methods such as Constant False Alarm Rate (CFAR). While this approach is mature, it inherently relies on a frame-by-frame global search of the entire observation space, resulting in high computational complexity and difficulty in adaptively adjusting the computational load according to scene changes. In low-altitude scenarios, due to the typically smaller number of targets and their continuous motion, the aforementioned methods still require repeated computation of a large amount of redundant data, leading to low overall efficiency and making it difficult to meet the real-time and power consumption constraints of low-cost platforms.

[0005] To further improve accuracy, existing methods have introduced super-resolution techniques (such as MUSIC, ESPRIT, and atomic norm methods based on sparse representations) to achieve high-resolution estimation through continuous parameter modeling. However, these methods typically rely on covariance matrix estimation or complex optimization processes, which require a large number of snapshots and computational resources, making it difficult to balance accuracy and computational efficiency in low-altitude weak target and rapidly updating scenarios.

[0006] In target tracking, methods such as Extended Kalman Filter (EKF) and Particle Filter are widely used for state estimation. However, these methods typically rely on pre-detection, requiring initial target observations to be obtained through steps like FFT and CFAR before state updates. This "detect first, track later" approach means the system still needs to execute the complete detection process in each frame, failing to fundamentally reduce computational complexity. Furthermore, in low signal-to-noise ratio or weak target echo scenarios, the detection phase itself suffers from missed detections or false alarms, further impacting subsequent tracking performance and leading to a decrease in overall system stability.

[0007] In summary, existing radar signal processing methods still have the following problems in low-altitude air-to-air target detection and tracking: First, they rely on full-space frame-by-frame search, which has high computational overhead and is difficult to adapt to low-cost platforms; second, the resolution is limited, making it difficult to accurately estimate low signal-to-noise ratio targets; third, they fail to make full use of prior information on the sparse number of targets and the continuity of their motion; and fourth, the detection and tracking processes are disconnected, requiring high-overhead detection to be completed before state updates, making it impossible to achieve adaptive allocation of computational resources.

[0008] Therefore, there is an urgent need for a radar signal processing method and device based on event-driven and parameterized inversion to improve the above problems. Summary of the Invention

[0009] The purpose of this invention is to provide a radar signal processing method and apparatus based on event-driven and parameterized inversion, which can significantly reduce the computational complexity of the system while ensuring detection and tracking accuracy, and meet the practical needs of low-altitude platform deployment at low cost.

[0010] In a first aspect, the present invention provides a radar signal processing method based on event-driven and parameterized inversion, comprising the steps of: acquiring the original radar echo signal at the current moment and obtaining an initial target set; establishing a parameterized model based on the initial target set and making predictions at adjacent moments to obtain prediction parameters; generating corresponding predicted echo signals based on the prediction parameters; calculating residuals based on the original echo signals and predicted echo signals, and performing update processing, decision processing, and event-driven adaptive decision processing on the parameterized model based on the residuals.

[0011] Optionally, acquiring the original radar echo signal at the current moment and obtaining the initial target set includes: acquiring the original radar echo signal at the current moment, and obtaining the initial target set by using range-Doppler processing with fast Fourier transform, constant false alarm rate detection, or super-resolution method; and / or each target parameter in the initial target set includes the target's complex amplitude, range, Doppler, and angle parameters.

[0012] Optionally, establishing a parameterized model based on the initial target set and obtaining prediction parameters by making predictions at adjacent time steps includes: establishing a parameterized model based on the initial target set and obtaining prediction parameters by utilizing the continuity of target motion at adjacent time steps; and / or the parameterized model is:

[0013]

[0014] in, For the reason The echo generation function is determined by the initial target set at time step; for Timing noise and modeling error; for The set of all target parameters at any given time.

[0015] Optionally, updating the parameterized model based on the residuals includes: performing a first-order linear approximation on the parameterized model at the predicted parameters based on the residuals and the Jacobian matrix to obtain processed data; minimizing the sum of squared residuals through first-order linearization based on the processed data to obtain updated parameters; and updating the parameterized model based on the updated parameters to obtain the target parameterized model.

[0016] Optionally, the decision processing includes: using the residual and noise covariance matrices to perform weighted quantification on the matching degree of the target parameter model to obtain an evaluation index.

[0017] Optionally, the event-driven adaptive decision processing includes: setting two thresholds, high and low, and performing hierarchical adaptive control based on the value of the evaluation index; if the evaluation index is lower than the low threshold, the target parameter model is determined to be valid, the currently updated parameters are directly adopted, and the continuous tracking loop begins at the next time step; if the evaluation index is between the low and high thresholds, the target parameter model is determined to have a deviation, and the update process is repeated for iterative correction until the evaluation index falls into the valid range; if the evaluation index is not lower than the high threshold, the target parameter model is determined to be completely invalid, and a re-initialization mechanism is immediately triggered; when the re-initialization mechanism is triggered, the original radar echo signal at the next time step is re-acquired to obtain a new initial target set.

[0018] Secondly, the present invention provides a radar signal processing apparatus based on event-driven and parameterized inversion, the apparatus comprising modules / units for executing any of the possible design methods described in the first aspect above. These modules / units can be implemented in hardware or by hardware executing corresponding software.

[0019] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a program executable on the processor, and when the program is executed by the processor, the electronic device implements a method for performing any of the possible designs described above.

[0020] Fourthly, the present invention provides a readable storage medium storing a program, which, when executed, implements a method of any possible design of any of the above aspects.

[0021] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0022] The beneficial effects of the method of this invention are as follows: An initial target set is obtained by acquiring the original radar echo signal at the current moment; a parameterized model is established based on the initial target set, and prediction parameters are obtained by making predictions at adjacent time points; corresponding predicted echo signals are generated based on the predicted parameters; residuals are calculated based on the original echo signals and predicted echo signals, and the parameterized model is updated, decided, and subjected to event-driven adaptive decision processing based on the residuals. This method enables direct parameterized modeling at the original signal level, efficient updating using the dynamic characteristics of the targets, and control of computational load through an event-triggered adaptive decision mechanism. Therefore, while ensuring detection and tracking accuracy, it significantly reduces the system's computational complexity, meeting the practical needs of low-altitude platform deployment at low cost. Attached Figure Description

[0023] Figure 1 A flowchart illustrating a radar signal processing method based on event-driven and parameterized inversion provided in an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a radar signal processing device based on event-driven and parameterized inversion provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed following the word and its equivalents, but do not exclude other elements or objects.

[0027] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the embodiments of the present invention, the terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to limit the present invention. The singular expressions “a,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of the present invention, “at least one” and “one or more” refer to one or more (including two). The term “and / or” is used to describe the relationship between related objects, indicating that three relationships can exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship.

[0028] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the invention. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," and "in still other embodiments" appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless otherwise specifically emphasized. The term "connection" includes both direct and indirect connections, unless otherwise stated. "First" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0029] In embodiments of the present invention, "exemplarily" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design described as "exemplarily" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or design solutions. Rather, the use of "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0030] like Figure 1 As shown, this invention provides a radar signal processing method based on event-driven and parameterized inversion, including the following steps:

[0031] S101: Collect the original radar echo signal at the current moment to obtain the initial target set.

[0032] In some embodiments, acquiring the original radar echo signal at the current moment and obtaining the initial target set includes: acquiring the original radar echo signal at the current moment, and obtaining the initial target set by using range-Doppler processing with fast Fourier transform, constant false alarm rate detection, or super-resolution method.

[0033] In other embodiments, each target parameter in the initial target set includes the target's complex amplitude, range, Doppler, and angle parameters.

[0034] S102, establish a parameterized model based on the initial target set, and make predictions at adjacent time points to obtain prediction parameters.

[0035] In some embodiments, establishing a parameterized model based on the initial target set and making predictions at adjacent time steps to obtain prediction parameters includes: establishing a parameterized model based on the initial target set and making predictions at adjacent time steps using the continuity of target motion to obtain prediction parameters.

[0036] In other embodiments, the parameterized model is:

[0037]

[0038] in, For the reason The echo generation function is determined by the initial target set at time step; for Timing noise and modeling error; for The set of all target parameters at any given time.

[0039] S103, Generate the corresponding predicted echo signal according to the predicted parameters.

[0040] S104, calculate the residual based on the original echo signal and the predicted echo signal, and perform update processing, decision processing and event-driven adaptive decision processing on the parameterized model based on the residual.

[0041] In some embodiments, updating the parameterized model based on the residuals includes: performing a first-order linear approximation on the parameterized model at the prediction parameters based on the residuals and the Jacobian matrix to obtain processed data; minimizing the sum of squared residuals through first-order linearization based on the processed data to obtain updated parameters; and updating the parameterized model based on the updated parameters to obtain a target parameterized model.

[0042] In some specific embodiments, the decision processing includes: using the residual and noise covariance matrix to perform weighted quantification on the matching degree of the target parameter model to obtain an evaluation index.

[0043] In some more specific embodiments, the event-driven adaptive decision processing includes: setting two thresholds, high and low, and performing hierarchical adaptive control based on the value of the evaluation index; if the evaluation index is lower than the low threshold, the target parameter model is determined to be valid, the currently updated parameters are directly adopted, and the continuous tracking loop begins at the next time step; if the evaluation index is between the low and high thresholds, the target parameter model is determined to have a deviation, and the update process is repeated for iterative correction until the evaluation index falls into the valid range; if the evaluation index is not lower than the high threshold, the target parameter model is determined to be completely invalid, and a re-initialization mechanism is immediately triggered; when the re-initialization mechanism is triggered, the original radar echo signal at the next time step is re-acquired to obtain a new initial target set.

[0044] To facilitate understanding, this embodiment further elaborates on the specific implementation process of the above method in conjunction with a specific application scenario, which includes the following steps:

[0045] (1) Initialization of target parameters

[0046] When the system starts up or triggers reinitialization, the raw radar echo signal at the current moment is acquired. The initial target set is obtained using traditional detection methods (including but not limited to distance-Doppler processing based on fast Fourier transform, constant false alarm rate detection, or super-resolution methods):

[0047]

[0048] Each target parameter is represented as:

[0049]

[0050] These represent the target's complex amplitude, range, Doppler (or radial velocity), and angular parameters, respectively. Simultaneously, the noise or clutter covariance matrix is ​​estimated based on the raw data. This is used for subsequent residual weighting.

[0051] (2) Parametric signal modeling

[0052] Establish a parameterized model of the radar echo at the raw signal level:

[0053]

[0054] in: For the reason The echo generation function is determined by the initial target set at time step; for Timing noise and modeling error; for The set of all target parameters at any given time.

[0055] This model represents the original signal as a combination of a finite number of target parameters, thus avoiding a discrete search across the entire observation space.

[0056] (3) State prediction

[0057] Between adjacent frames (i.e., adjacent time points), the parameters are predicted by utilizing the continuity of the target motion, resulting in the predicted parameters:

[0058]

[0059] in, The state transition function, in a simplified case, can be represented as a uniform velocity model:

[0060]

[0061] (4) Construction of prediction signal

[0062] Generate the predicted echo signal for the current moment based on the predicted parameters:

[0063]

[0064] This step directly reconstructs the predicted echo in the original signal domain, which forms the basis for subsequent residual calculation and parameter updates.

[0065] (5) Residual calculation

[0066] Calculate the residual between the actual observation (i.e., the original echo signal) and the predicted echo signal:

[0067]

[0068] The residual is used to characterize the deviation between the current model and the actual observations.

[0069] (6) Online parameter update processing (Levenberg-Marquardt optimization)

[0070] The echo model is approximated with a first-order linearization at the prediction parameters:

[0071]

[0072] in, It is a Jacobian matrix; This is the parameter correction amount.

[0073] The parameter update is solved by minimizing the sum of squared residuals using first-order linearization:

[0074]

[0075] The updated parameters are obtained:

[0076]

[0077] in, This is the damping factor, used to improve numerical stability;

[0078] The parameterized model is updated based on the updated parameters to obtain the target parameter model.

[0079] (7) Weighted decision of residuals

[0080] The matching degree of the target parameter model is weighted and quantified using the residual and noise covariance matrices to obtain the evaluation index:

[0081]

[0082] The effectiveness of the current parameter model can be judged based on this indicator.

[0083] (8) Event-driven adaptive mechanism

[0084] Set an adaptive triggering strategy based on the residual size:

[0085] Set two thresholds: high and low. and And the ;

[0086] when When the target parameter model is deemed valid, the updated parameters are directly adopted, and the next continuous tracking loop begins.

[0087] when If the target parameter model is found to have a deviation, the update process is repeated once or multiple times for iterative correction until the evaluation index falls into the effective range.

[0088] when If the target parameter model fails, the re-initialization mechanism is immediately triggered.

[0089] (9) Reinitialization mechanism

[0090] When reinitialization is triggered, step (1) is executed again, that is, the original radar echo signal at the next moment is reacquired to obtain a new initial target set. The new target parameter set is obtained through traditional detection methods to restore the system state and ensure tracking stability.

[0091] This invention addresses the problems of high computational complexity, limited resolution, and disconnect between detection and tracking in low-altitude air-to-air target detection and tracking. It proposes a radar signal processing method based on event-driven and parameterized inversion, the key points of which are as follows:

[0092] (1) Parameterized modeling and direct inversion mechanism based on the original signal

[0093] Unlike traditional full-space discrete search methods based on Fast Fourier Transform, this invention establishes a target parameterization model at the level of the original echo signal, representing the radar echo as a combination of a finite number of target parameters:

[0094]

[0095] By transforming the detection problem into a parameter estimation problem, global scanning of range, Doppler, and angular spaces is avoided, fundamentally reducing computational complexity. Simultaneously, this continuous parameter modeling method overcomes the limitations of discrete frequency grids, achieving high-precision estimation of target parameters and improving the resolution of weak and close-range targets.

[0096] (2) Prediction-driven online parameter update mechanism

[0097] This invention fully utilizes prior information about the continuity of motion of low-altitude air-to-air targets to construct a state prediction model between adjacent frames, transforming the target parameter update problem into a local optimization problem:

[0098]

[0099] Based on this, by linearizing the signal model to the first order, only small-range parameter perturbations are updated, avoiding repeated calculations of the entire observation space, and realizing the transformation from "full-frame detection" to "continuous parameter tracking", the real-time processing burden is significantly reduced.

[0100] (3) Multi-objective joint optimization technique based on Levenberg-Marquardt

[0101] To address the issues of parameter coupling and numerical instability in multi-objective scenarios, this invention employs the Levenberg-Marquardt method for joint parameter optimization:

[0102]

[0103] By introducing a damping term into the Gauss-Newton method, the convergence stability is improved when there is a certain correlation between targets. Furthermore, this method directly applies to the original signal model, achieving unified updates of multi-target parameters, and exhibits higher accuracy and consistency compared to traditional target-by-target or post-processing methods.

[0104] (4) Model validity judgment mechanism based on weighted residuals

[0105] This invention introduces a residual evaluation index based on noise covariance weighting:

[0106]

[0107] This method is used to measure the consistency between the prediction model and actual observations. It can effectively suppress the effects of non-uniform noise and clutter, and has higher discrimination accuracy than the simple energy criterion, thus providing a reliable basis for subsequent adaptive decision-making.

[0108] (5) Event-driven adaptive computation control mechanism

[0109] Based on the residual evaluation results, this invention designs an event-triggered update strategy to dynamically adjust the calculation process according to the model's effectiveness.

[0110] When the model is valid, only perform low-complexity parameter updates;

[0111] When the model deviates, perform a finite number of iterative corrections;

[0112] When the model fails, a re-initialization is triggered.

[0113] This mechanism enables the system to adaptively allocate computing resources according to changes in the scenario, significantly reducing computational overhead when the objective is stable, maintaining robustness in complex scenarios, and achieving a dynamic balance between performance and complexity.

[0114] (6) Signal-level processing framework integrating detection and tracking

[0115] Unlike the traditional separate processing flow of "detect first, then track," this invention unifies the target detection and parameter estimation process at the raw signal level, directly integrating the detection results into continuous parameter updates, thus achieving deep fusion of detection and tracking. This integrated framework avoids redundant calculations and effectively improves system stability and tracking continuity under low signal-to-noise ratio conditions.

[0116] (7) Advantages of low-complexity implementation for low-cost platforms

[0117] This invention avoids frame-by-frame full-space FFT operations, concentrating the main computation on updating a small number of target parameters, thus reducing its complexity compared to the number of targets. Proportional:

[0118]

[0119] In low-altitude target sparse scenarios, compared to traditional methods... This method offers significant computational advantages, potentially reaching or exceeding higher complexity levels. Furthermore, it is suitable for parallel implementation, enabling efficient operation on embedded platforms and meeting the requirements for low power consumption and low-cost deployment.

[0120] The advantage of this invention lies in that, by establishing a parameterized model at the raw signal level and combining it with an event-driven and online optimization mechanism, a significant improvement in the performance of low-altitude air-to-air target detection and tracking is achieved, as detailed below:

[0121] (1) Significantly reduces computational complexity, making it suitable for low-cost platforms

[0122] This invention avoids the frame-by-frame global search process for distance, Doppler, and angle spaces required in traditional methods, focusing the main computation on the online updating of a small number of target parameters. Its computational complexity is reduced from that of traditional methods by:

[0123]

[0124] Reduced to:

[0125]

[0126] in, The target number is typically much smaller than the observation dimension in low-altitude scenarios. When the number of targets is small and the scene changes slowly, this invention only needs to update local parameters to complete target tracking, greatly reducing redundant calculations and thus significantly reducing the system's computing power requirements and power consumption. It is suitable for deployment on embedded and low-cost platforms.

[0127] (2) Break through the limitations of discrete frequency grids and improve the accuracy of parameter estimation.

[0128] Traditional FFT-based methods are limited by discrete frequency division, and their resolution depends on the sampling length and window function setting, resulting in spectral leakage and picket fence effects. This invention directly optimizes and estimates target parameters based on a continuous parameter model, achieving sub-bin resolution through local linearization and iterative updates. This allows for high-precision resolution and stable estimation even when target spacing is close or signal-to-noise ratio is low.

[0129] (3) Improve the robustness of detection and tracking under low signal-to-noise ratio conditions

[0130] This invention constructs a prediction model based on the original signal and uses a weighted residual index for consistency judgment:

[0131]

[0132] It can effectively distinguish between noise disturbances and model biases, and maintain stable tracking even under weak target or low signal-to-noise ratio conditions. At the same time, since the system mainly updates parameters continuously, even if the detection signal in a single frame is weak, robust estimation can be achieved by accumulating information from multiple frames, thereby reducing the probability of missed detections and improving overall detection performance.

[0133] (4) Achieve integrated detection and tracking to improve the overall efficiency of the system.

[0134] This invention unifies target detection and parameter estimation within the same signal model framework, performing joint processing directly at the raw signal level, thus avoiding the separate "detect first, track later" process in traditional methods. By replacing frame-by-frame repeated detection with continuous parameter updates, not only is computational redundancy reduced, but the consistency and continuity of target state estimation are also improved, thereby enhancing the overall processing efficiency and stability of the system.

[0135] (5) Supports event-driven processing to achieve adaptive allocation of computing resources.

[0136] This invention designs an event-triggered mechanism based on residual evaluation results. It performs only low-complexity updates when the model is valid, and triggers re-initialization only when the model deviates or fails. This mechanism enables the system to dynamically adjust the computational load according to changes in the actual scene, significantly reducing computational overhead when the target motion is stable or the scene changes little, while maintaining accuracy under complex conditions, achieving an adaptive balance between performance and complexity.

[0137] (6) Enhance stability and continuity in multi-objective scenarios

[0138] By jointly optimizing multi-objective parameters using the Levenberg-Marquardt method and combining stability constraints with an adaptive reinitialization mechanism, this invention maintains good numerical stability and tracking continuity even in the presence of multiple targets. Compared to traditional methods where targets are prone to loss or abrupt changes, this invention achieves smoother and more reliable target trajectory estimation.

[0139] (7) Practical application requirements for low-altitude air-to-air scenarios

[0140] This invention fully utilizes the characteristics of sparse target numbers and continuous movement in low-altitude scenarios, reducing computational complexity while ensuring detection and tracking accuracy. It is particularly suitable for applications such as UAV monitoring, low-altitude traffic management, and robot aerial perception. By providing continuous and accurate target state information, it can effectively support high-level tasks such as obstacle avoidance decision-making, path planning, and airspace scheduling, demonstrating significant engineering application value.

[0141] like Figure 2 As shown, based on the above method, the present invention provides a radar signal processing device based on event-driven and parameterized inversion, comprising: an acquisition unit 201, used to acquire the original radar echo signal at the current time and obtain an initial target set; a prediction unit 202, used to establish a parameterized model based on the initial target set and perform prediction at adjacent times to obtain prediction parameters; a processing unit 203, used to generate corresponding predicted echo signals based on the prediction parameters; and a calculation unit 204, used to calculate residuals based on the original echo signals and predicted echo signals, and perform update processing, decision processing, and event-driven adaptive decision processing on the parameterized model based on the residuals.

[0142] It should be understood that all relevant content of each step involved in the above method embodiments can be referenced to the functional description of the corresponding functional module, and will not be repeated here. Furthermore, the use of suffixes such as "module," "component," or "unit" to represent elements is merely for the purpose of illustrative purposes and has no specific meaning in itself. Therefore, "module," "component," or "unit" can be used interchangeably. Terminals can be implemented in various forms. For example, the terminals described in this invention may include mobile terminals such as mobile phones, tablets, laptops, handheld computers, personal digital assistants (PDAs), portable media players (PMPs), navigation devices, wearable devices, smart bracelets, pedometers, etc., as well as fixed terminals such as digital TVs and desktop computers. The following description will use mobile terminals as examples; those skilled in the art will understand that, in addition to elements specifically designed for mobile purposes, the construction according to embodiments of the present invention can also be applied to fixed-type terminals.

[0143] In other embodiments of the present invention, an electronic device 300 is disclosed, such as... Figure 3 As shown, the device may include: one or more processors 301; memory 302; display 303; one or more application programs (not shown); and one or more computer programs 304. These devices can be connected via one or more communication buses 305. The one or more computer programs 304 are stored in the memory 302 and configured to be executed by the one or more processors 301. The one or more computer programs 304 include instructions that can be used to perform actions such as... Figure 1 Each step in the corresponding embodiment.

[0144] Processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0145] The memory 302 can be an internal storage unit of the electronic device 300, such as a hard disk or RAM of the electronic device 300. The memory 302 can also be an external storage device of the electronic device 300, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or FlashCard equipped on the electronic device 300. Furthermore, the memory 302 can include both internal and external storage units of the electronic device 300. The memory 302 is used to store computer programs and other programs and data required by the electronic device. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0146] The computer program 304 can be divided into one or more modules / units. The one or more modules / units can be a series of computer program instruction segments that can perform a specific function. The instruction segments are used to describe the execution process of the computer program 304 in the electronic device 300.

[0147] In addition to the above-described structure, those skilled in the art will understand that Figure 3This is merely an example of electronic device 300 and does not constitute a limitation on electronic device 300. Electronic device 300 may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0148] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0149] Based on the above embodiments, the present invention also discloses a computer-readable storage medium having at least one computer program stored thereon, wherein the computer program, when executed by a processor, implements the methods described in the foregoing embodiments.

[0150] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. This available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0151] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0152] Although the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. The above descriptions are merely embodiments of the present invention and do not limit the patent scope of the present invention. However, it should be understood that such modifications and variations fall within the scope and spirit of the present invention. Moreover, the present invention described herein may have other embodiments and can be implemented or realized in various ways. All equivalent transformations made based on the description and drawings of the present invention, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A radar signal processing method based on event-driven and parameterized inversion, characterized in that, Including the following steps: Collect the raw radar echo signal at the current moment to obtain the initial target set; A parameterized model is established based on the initial target set, and prediction parameters are obtained by making predictions at adjacent time points; Generate the corresponding predicted echo signal based on the predicted parameters; The residual is calculated based on the original echo signal and the predicted echo signal, and the parameterized model is updated, decided, and subjected to event-driven adaptive decision processing based on the residual. The parameterization model is as follows: in, For the reason The echo generation function is determined by the initial target set at time step; for Timing noise and modeling error; for The set of all target parameters at any given time; This is the original echo signal; The judgment process includes: The matching degree of the target parameter model is weighted and quantified using the residual and noise covariance matrices to obtain the evaluation index; The event-driven adaptive decision processing includes: Two thresholds, high and low, are set, and hierarchical adaptive control is performed based on the numerical value of the evaluation index. If the evaluation index is lower than the low threshold, the target parameter model is deemed valid, the currently updated parameters are directly adopted, and the next continuous tracking loop begins. If the evaluation index is between the low threshold and the high threshold, it is determined that the target parameter model has a deviation, and the update process is repeated for iterative correction until the evaluation index falls into the effective range. If the evaluation index is not lower than the high threshold, the target parameter model is determined to be completely invalid, and the re-initialization mechanism is immediately triggered. When the reinitialization mechanism is triggered, the original radar echo signal of the next moment is reacquired to obtain a new initial target set.

2. The method according to claim 1, characterized in that, Acquire the raw radar echo signal at the current moment to obtain the initial target set, including: The original radar echo signal at the current moment is acquired, and the initial target set is obtained by using range-Doppler processing of fast Fourier transform, constant false alarm rate detection, or super-resolution method. And / or each target parameter in the initial target set includes the target's complex amplitude, range, Doppler, and angle parameters.

3. The method according to claim 2, characterized in that, A parameterized model is established based on the initial target set, and predictions are made at adjacent time steps to obtain the prediction parameters, including: A parameterized model is established based on the initial target set, and prediction parameters are obtained by using the continuity of target motion at adjacent time points.

4. The method according to any one of claims 1-3, characterized in that, Updating the parameterized model based on the residuals includes: Based on the residuals and Jacobian matrix, the parameterized model is approximated by a first-order linear approximation at the prediction parameters to obtain the processed data. Based on the processed data, the updated parameters are obtained by minimizing the sum of squared residuals using first-order linearization. The parameterized model is updated based on the updated parameters to obtain the target parameter model.

5. A radar signal processing device based on event-driven and parameterized inversion, used in the method according to any one of claims 1-4, characterized in that, include: The acquisition unit is used to acquire the original radar echo signal at the current moment and obtain the initial target set; The prediction unit is used to establish a parameterized model based on the initial target set and to make predictions at adjacent time points to obtain prediction parameters. The processing unit is used to generate a corresponding predicted echo signal based on the predicted parameters; The calculation unit is used to calculate the residual based on the original echo signal and the predicted echo signal, and to perform update processing, decision processing and event-driven adaptive decision processing on the parameterized model based on the residual.

6. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a program that can run on the processor, and when the program is executed by the processor, causes the electronic device to perform the method of any one of claims 1-4.

7. A readable storage medium storing a program, characterized in that, When the program is executed, it implements the method of any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1-4.

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