A method for enhancing performance of an adaptive fire control system based on a phased array radar

Abstract

The application relates to the field of radar and fire control technology, and particularly provides a performance enhancement method of an adaptive fire control system based on a phased array radar. A closed-loop feedback control system is constructed, multi-channel echo signals of the phased array radar are input into a radar signal adaptive processing module in real time, target motion state estimation and clutter spatial spectrum analysis are combined, and beam forming weights and pulse repetition frequencies are dynamically adjusted; target information processed is cooperatively optimized by a target fusion and prediction module and a fire control instruction generation and feedback module, real-time pointing correction of a fire unit and adaptive matching of firing parameters are realized. The application realizes deep cooperation and closed-loop enhancement between radar detection and fire control, improves tracking stability, hitting probability and response real-time performance of the system in a multi-target and strong interference environment, and provides reliable technical support for accurate attack in a complex environment.

Description

Technical Field

[0001] This invention relates to the field of radar and fire control technology, specifically to a method for enhancing the performance of an adaptive fire control system based on phased array radar. Background Technology

[0002] In the modern environment, airborne and ground targets exhibit complex characteristics such as high speed, high maneuverability, multiple waves, and strong electromagnetic interference, placing extremely high demands on the tracking accuracy, response speed, and environmental adaptability of fire control systems. Phased array radar, due to its advantages such as beam agility and strong multi-target tracking capabilities, has become a core sensor in advanced fire control systems. In existing technologies, phased array radar and fire control systems are mostly loosely coupled architectures of "frontline detection - backend processing," with the radar primarily providing target locations, and the fire control system performing filtering, prediction, and processing based on fixed algorithms. This architecture has significant shortcomings: radar operating modes (such as beam dwell time and pulse repetition frequency) are usually preset or simply adjusted based on the tracking loop, failing to deeply coordinate with the target's real-time threat level, motion characteristics, and the standby status of fire units, resulting in unstable tracking quality for high-value targets under limited resource conditions; fire control command generation relies on a single estimate of the target's state, lacking closed-loop feedback on radar detection quality, environmental interference, and its own control constraints, which can easily lead to command lag or inaccuracy in strong clutter or when the target is maneuvering violently, affecting the hit probability; and the various modules of the system (detection, processing, decision-making, and control) are often optimized independently, lacking a unified adaptive enhancement mechanism based on real-time performance evaluation, making it difficult to achieve optimal overall performance in dynamic combat.

[0003] Therefore, existing technologies lack a performance enhancement method that can deeply integrate radar perception and fire control, and can make online adaptive adjustments based on battlefield situation and combat effects. Summary of the Invention

[0004] This invention overcomes the shortcomings of existing technologies and provides a method for enhancing the performance of an adaptive fire control system based on phased array radar. It deeply couples the phased array radar, fire control computer, and fire units by constructing a closed-loop feedback and collaborative enhancement architecture of "perception-decision-control". The core lies in introducing a closed-loop feedback path from the fire control unit to the radar front end, enabling the radar to adaptively optimize its detection resource allocation and signal processing strategies based on the fire strike effect (such as miss distance prediction and target maneuver response) and current mission priorities. Furthermore, it deeply integrates target tracking quality indicators and environmental interference information provided by the radar during fire control command generation, making the commands more robust. Through this continuous cross-module interaction and collaborative optimization, the overall performance of the system is improved in terms of tracking accuracy, response speed, resource utilization efficiency, and anti-interference capability. This effectively solves the problems existing in existing technologies.

[0005] The technical solution adopted by this invention is as follows: This solution provides an adaptive fire control system based on phased array radar, including phased array radar, fire control computer, fire unit; radar signal adaptive processing module, target fusion and prediction module, and fire control command generation and feedback module.

[0006] Phased array radar has a built-in antenna array and a connected radar signal processor. The radar signal processor is dedicated to performing real-time processing of radar signals, and runs an adaptive radar signal processing module. The phased array radar scans the monitored airspace and generates multi-channel echo signals, which include target reflection, clutter, and interference spectrum estimation.

[0007] The fire control command generation and feedback module generates initial resource allocation and feedback commands.

[0008] The radar signal adaptive processing module receives multi-channel echo signals and initial resource allocation and feedback commands, and performs joint dynamic optimization of the radar beamforming weights and pulse repetition frequency accordingly, outputting enhanced target trajectory information; among which, the enhanced target trajectory information includes target state estimation, tracking quality assessment, and multi-target trajectory; the target state estimation includes target radial velocity estimation.

[0009] The fire control computer communicates with the radar signal processor and fire units; the fire control computer has a built-in target fusion and prediction module and a fire control command generation and feedback module. The target fusion and prediction module receives enhanced target trajectory information, fuses and predicts the state of multiple target trajectories, and generates target predicted state and covariance information, as well as prediction confidence index; among which, the target predicted state and covariance information includes prediction uncertainty. The fire control command generation and feedback module has a pre-stored historical engagement effectiveness database. It receives target prediction status and covariance information, prediction reliability indicators, and, combined with fire unit constraints and the historical engagement effectiveness database, calculates fire control commands and sends them to the fire unit. Based on the target prediction status and covariance information, prediction reliability indicators, and fire unit constraints, the fire control command generation and feedback module evaluates strike effectiveness, generates new resource allocation and feedback commands, and sends them to the radar signal adaptive processing module. These resource allocation and feedback commands are used to dynamically adjust radar detection resources. These commands include beam dwell time adjustments for specific targets, tracking data rate update commands, and indications of key jamming suppression areas and key targets based on miss distance predictions.

[0010] The fire control unit receives and executes fire control commands.

[0011] The above scheme provides an adaptive fire control system based on phased array radar. The performance enhancement method of the system includes the following steps: Step S1: System initialization. The fire control command generation and feedback module loads default parameters as initial resource allocation and feedback commands, and sends them to the radar signal adaptive processing module; the historical combat effectiveness database completes data loading; Step S2: Radar Signal Adaptive Processing and Tracking. The phased array radar scans the monitored airspace, generating multi-channel echo signals. These multi-channel echo signals include target reflection, clutter, and interference spectrum estimation. Based on the received multi-channel echo signals and initial resource allocation and feedback commands, the radar signal adaptive processing module performs joint dynamic optimization of the radar's beamforming weights and pulse repetition frequency to complete target detection and tracking, generating enhanced target trajectory information. This enhanced target trajectory information includes target state estimation and tracking quality assessment, as well as multi-target trajectories. Target state estimation includes target radial velocity estimation. Step S3: Target Fusion Prediction and Situation Generation. The target fusion and prediction module receives enhanced target trajectory information, fuses multiple target trajectories, and performs state prediction based on an interactive multi-model algorithm to generate target predicted state and covariance information; the target fusion and prediction module calculates the prediction reliability index based on tracking quality assessment. Step S4: Fire Control Command Generation and Closed-Loop Feedback. The fire control command generation and feedback module receives target prediction status and covariance information, prediction reliability index, and combines it with the kinematic constraints of the fire unit, ammunition trajectory model, current prediction reliability index, and pre-stored historical engagement effectiveness database to calculate the fire control command. The fire control command generation and feedback module performs matching and evaluation based on the fire control command and the historical engagement effectiveness database to generate new resource allocation and feedback commands. The new resource allocation and feedback commands are sent to the radar signal adaptive processing module, and the fire control commands are sent to the fire unit for execution. After this, the system proceeds to step S2 and enters the next adaptive enhancement loop.

[0012] Furthermore, in step S2, the radar signal adaptive processing module performs joint dynamic optimization of the radar beamforming weights and pulse repetition frequency, specifically including: Sub-step S21: Based on the key target indication in the resource allocation and feedback instructions, increase the transmit power weight or receive beam gain of the beam in the azimuth of the target. Sub-step S22: Based on the clutter and interference spectrum estimation in the multi-channel echo signal, and combined with the key interference suppression areas indicated in the resource allocation and feedback instructions, an "deep null" is adaptively formed in beamforming, that is, an area where the gain is significantly suppressed in the specified interference direction is formed. Sub-step S23: Based on the tracking data rate and target radial velocity estimation required in the resource allocation and feedback instructions, adaptively select the optimal value from the system's preset pulse repetition frequency to balance the contradiction between ranging ambiguity and velocity ambiguity.

[0013] Furthermore, in step S4, the fire control command generation and feedback module assesses the expected strike effect and generates resource allocation and feedback commands, specifically including: Sub-step S41: Calculate the target position error ellipse at the future meeting time, i.e. the possible position region, based on the target prediction state and covariance information and prediction confidence index. Sub-step S42: Combining the kinematic constraints of the fire unit and the ballistic model of the ammunition, calculate the hit probability within the target position error ellipse; the system pre-sets a first preset threshold and a second preset threshold; Sub-step S43: If the hit probability is lower than the first preset threshold, the beam dwell time of the target is increased in the resource allocation and feedback instructions to improve its tracking accuracy. Sub-step S44: If the hit probability is lower than the second preset threshold, the radar signal adaptive processing module is simultaneously instructed in the resource allocation and feedback command to strengthen the search and tracking of the target in the possible maneuvering direction.

[0014] Compared with the prior art, the beneficial effects of the present invention are: (1) A real-time feedback path from fire control decision-making to radar perception was established through resource allocation and feedback commands. The radar is no longer an independent sensor that provides data, but can dynamically adjust resource allocation based on the prediction of strike effectiveness, while the fire control commands are robustly optimized based on the tracking quality fed back by the radar. This two-way driven, closed-loop enhancement mechanism significantly improves the overall system's ability to cope with complex situations and sudden threats; (2) By using the radar signal adaptive processing module to dynamically optimize the radar beamforming weights and pulse repetition frequency, interference in a specified direction can be effectively suppressed, and the target motion characteristics can be adaptively matched. The fire control command generation module uses the prediction reliability index to weight and correct the commands. When the prediction uncertainty is large, the feedback mechanism is automatically triggered to require the radar to strengthen tracking. This ensures that even in harsh electromagnetic environments or under severe target maneuvers, the system can still maintain high-precision tracking and a high probability of hit. (3) The method of the present invention can intelligently allocate the spatiotemporal resources (beams, energy, time) of the phased array radar according to the threat level of the target, the standby status of the fire unit, and the expected strike effect. It prioritizes the concentration of valuable radar resources on the most needed high-value or high-uncertainty targets, avoids the waste of resources by averaging them, and thus achieves the ability to track and strike more targets with higher precision under the same resource conditions, thereby improving the overall combat effectiveness of the system. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a method for enhancing the performance of an adaptive fire control system based on phased array radar according to the present invention. Figure 2 This is a block diagram illustrating the collaborative optimization principle of the radar signal adaptive processing module in this embodiment of the invention. The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0017] Example 1:

[0018] Please see Figures 1-2 An adaptive fire control system based on phased array radar in this embodiment includes a phased array radar, a fire control computer, and a fire unit; a radar signal adaptive processing module, a target fusion and prediction module, and a fire control command generation and feedback module.

[0019] Phased array radar has a built-in antenna array and a connected radar signal processor. The radar signal processor is dedicated to performing real-time processing of radar signals, and runs an adaptive radar signal processing module. The phased array radar scans the monitored airspace and generates multi-channel echo signals, which include target reflection, clutter, and interference spectrum estimation.

[0020] The fire control command generation and feedback module generates initial resource allocation and feedback commands.

[0021] The radar signal adaptive processing module receives multi-channel echo signals and initial resource allocation and feedback commands, and performs joint dynamic optimization of the radar beamforming weights and pulse repetition frequency accordingly, outputting enhanced target trajectory information; among which, the enhanced target trajectory information includes target state estimation, tracking quality assessment, and multi-target trajectory; the target state estimation includes target radial velocity estimation.

[0022] The fire control computer is connected to the radar signal processor and the fire unit control interface via a high-speed data bus. The fire control computer runs a target fusion and prediction module and a fire control command generation and feedback module. The target fusion and prediction module receives enhanced target trajectory information, fuses and predicts the states of multiple target trajectories, and generates target predicted state and covariance information, as well as prediction confidence index; among which the target predicted state and covariance information includes prediction uncertainty. The fire control command generation and feedback module has a pre-stored historical engagement effectiveness database. It receives target prediction status and covariance information, prediction reliability indicators, and, combined with fire unit constraints and the historical engagement effectiveness database, calculates fire control commands and sends them to the fire units. Based on target prediction status and covariance information, prediction reliability indicators, and fire unit constraints, the module evaluates strike effectiveness, generates new resource allocation and feedback commands, and sends them to the radar signal adaptive processing module. The resource allocation and feedback commands dynamically adjust radar detection resources. These commands include beam dwell time adjustments for specific targets, tracking data rate update commands, and indications of key jamming suppression areas based on miss distance predictions. The historical engagement effectiveness database, constructed before system deployment through digital and hardware-in-the-loop simulations, stores historical hit probabilities and optimized feedback strategies for different target types, maneuvering modes, jamming environments, and combinations of radar parameters.

[0023] Firepower units, such as artillery and missile launchers, are equipped with control systems that receive and execute fire control commands.

[0024] This embodiment of the method for enhancing the performance of an adaptive fire control system based on a phased array radar includes the following steps: Step S1: System initialization.

[0025] The fire control command generation and feedback module loads a set of default parameters as initial resource allocation and feedback commands. For example, in area air defense mode, the default command configuration is: "Perform a 4Hz search scan of all airspace; automatically initiate 8Hz search-and-track for targets identified as 'fighter jets'; enable broadband interference monitoring and suppression by default." The initial resource allocation and feedback commands are sent to the radar signal adaptive processing module via the system's internal high-speed data bus. The historical engagement effectiveness database is loaded into the fire control computer's memory, completing data preparation. The historical engagement effectiveness database uses a relational data table structure, employing target feature vectors (such as radar cross-section RCS, velocity, and acceleration standard deviation) for rapid indexing and similarity matching.

[0026] Step S2: Adaptive processing and tracking of radar signals.

[0027] The phased array radar operates according to a predetermined scanning mode. The antenna array receives the target's radio frequency signal, which is then down-converted and analog-to-digital converted to form a digitized multi-channel echo signal. This multi-channel echo signal is transmitted in real-time to the radar signal adaptive processing module. In the first processing cycle after system startup, the radar signal adaptive processing module receives and executes initial resource allocation and feedback commands. Specific processing steps: The radar signal adaptive processing module first parses the initial resource allocation and feedback instructions. If the resource allocation and feedback instructions require "increasing the tracking data rate of target T1", the radar signal adaptive processing module immediately and dynamically adjusts the radar's time-energy resource allocation table online, reducing the illumination time for other low-priority tasks, and reallocating the saved time slices to the dedicated tracking beam for target T1 to ensure its data rate requirements; The initial resource allocation and feedback command utilizes the multi-channel echo signal of the current pulse to calculate the spatial spectrum in real time through covariance matrix estimation (such as sampling covariance matrix estimation) to identify the direction of interference. Combining the potentially explicit "key suppression azimuth angle α" in the initial resource allocation and feedback command, a linearly constrained minimum variance beamforming algorithm is employed. This algorithm solves a constrained optimization problem to calculate a set of optimal array weighting coefficients, ensuring the array maintains high gain in the desired target direction while forming sharp gain nulls in the interference directions (including the estimated direction and the direction specified by the command), thus achieving spatial filtering. Based on the currently provided target radial velocity estimate and the data rate required in the resource allocation and feedback instructions, the radar signal adaptive processing module queries a pre-generated "pulse repetition frequency-ambiguity-data rate" relationship lookup table. This table, after offline analysis, ensures that the selected set of pulse repetition frequencies meets both the requirements for unambiguous velocity / range measurement and the data update rate specified in the instructions. For example, for high-speed targets, a high pulse repetition frequency group will be preferentially selected to avoid velocity ambiguity. The data, after the above adaptive processing, ultimately forms a stable and continuous track. Each track, in addition to a state vector (such as position, velocity, and acceleration), also includes a tracking quality assessment value. This assessment value is calculated by a dedicated assessment subroutine and ranges from 0 to 1.

[0028] Step S3: Target fusion prediction and situation generation.

[0029] Enhanced target trajectory information is sent to the target fusion and prediction module via a data bus; The target fusion and prediction module associates tracks. If the system is equipped with multiple radars, it performs spatiotemporal alignment and statistical weighted fusion on the tracks of the same target reported by different sources to obtain a unified track with higher accuracy. The target fusion and prediction module initiates an interactive multi-model algorithm predictor for each target. This predictor runs multiple motion model filters in parallel (such as uniform velocity models, uniform acceleration models, and "current" statistical models), interacting between models through Markov transition probabilities and updating the probabilities of each model in real time based on observation data. During prediction, the model with the highest probability is selected, or the prediction results of multiple models are fused according to their probabilities to obtain the target's predicted state and covariance information at the future moment of engagement, generating a state vector and a covariance matrix. This covariance matrix characterizes the uncertainty of the prediction. The target fusion and prediction module calculates a comprehensive prediction reliability index based on the tracking quality assessment value of the trajectory, the size of the prediction covariance matrix (such as the determinant value), and the probability of the highest-probability model in the multi-model prediction. The prediction reliability index is also normalized to 0 to 1, with a higher value indicating a more reliable prediction of the target's future state.

[0030] Step S4: Fire control command generation and closed-loop feedback.

[0031] The target prediction status and covariance information, along with the prediction confidence index, are transmitted to the fire control command generation and feedback module. The fire control command generation and feedback module performs the following specific operations: Fire control calculation: Combining the physical constraints of the fire unit (such as maximum azimuth / elevation turning rate, acceleration limit) and the ballistic model of the selected ammunition (such as considering gravity, air resistance, wind speed), the firing parameters required to hit the predicted point are calculated, and fire control commands are formed. The fire control commands include target designation, aiming point coordinates, and firing timing. Effectiveness assessment and database matching: This is the core of generating intelligent feedback. The fire control command generation and feedback module first uses the predictive covariance matrix to calculate the target's position error ellipse at the expected hit time. Combined with the inherent dispersion of this fire unit (given by the firing table), the theoretical hit probability is calculated. The fire control command generation and feedback module uses the current target's feature vector (such as type, speed, maneuverability, and the interference environment) as the search key to perform a fast similarity search in the historical engagement effectiveness database in memory, finding the several historical engagement records most similar to the current situation; New resource allocation and feedback instructions are generated by combining the current hit probability calculation with optimization experience from similar historical cases. For example, if the current hit probability is lower than the first threshold (e.g., 80%), and the most similar case record in the database shows that "increasing the tracking data rate from 10Hz to 20Hz can increase the hit probability by 15%", then a new instruction is generated: "Increase the tracking data rate to 20Hz for target T1 and increase the beam dwell time accordingly". If the hit probability is lower than the second threshold (e.g., 60%), the generated instruction will include: "Allocate radar resources for forward blind spot search within the fan-shaped area of ​​the target's current direction of movement" to detect possible sudden maneuvers in advance. Command Issuance and Loop Closure: The generated fire control commands are sent to the fire unit's control system. New resource allocation and feedback commands are issued in real time via the data bus. The radar signal adaptive processing module receives and parses the new resource allocation and feedback commands at the beginning of the next processing cycle and immediately applies them to the radar signal processing of that cycle, thereby adjusting its sensing behavior and forming a complete closed loop. The system then continuously operates in a closed-loop adaptive enhancement loop of S2→S3→S4→S2.

[0032] Example 2:

[0033] This embodiment, based on Embodiment 1, further details the internal collaborative optimization principle and workflow of the radar signal adaptive processing module in step S2.

[0034] The radar signal adaptive processing module can be physically divided into three closely coordinated, real-time data-interacting software sub-units, all deployed on the radar signal processor: The beam scheduling and resource management subunit receives and parses resource allocation and feedback instructions. When it receives an instruction such as "increase the data rate of target T1 to 20Hz", the beam scheduling and resource management subunit performs online rescheduling based on a preset priority strategy (such as "ensuring the instruction target takes precedence over the search target"). This ensures that the computational and timing resources required to generate a tracking beam with a higher data rate for target T1 are available.

[0035] The adaptive beamforming subunit receives multi-channel echo signals in real time. Within each coherent processing interval, it rapidly estimates the direction and intensity of spatial interference based on the received data. Combining this with the current beam pointing information from the beam scheduling and resource management subunit, and the "prior" interference suppression azimuth (from historical experience or the previous cycle's evaluation) included in the resource allocation and feedback instructions, it constructs an optimization problem for adaptive beamforming. A linearly constrained minimum variance criterion is employed: minimizing the total power of the array output (i.e., suppressing all interference and noise from non-target directions) while ensuring a gain of 1 for the desired target direction. The optimal weighting vector is solved in real time using a recursive algorithm and applied to the received data of the current pulse to achieve spatial filtering. The suppression azimuth specified in the instructions is given a stronger constraint weight to ensure the formation of a deep null in that direction.

[0036] The pulse repetition frequency adaptive selection subunit receives the latest estimate of the target's radial velocity and is aware of the data rate required in the resource allocation and feedback commands. Internally, this subunit maintains a pulse repetition frequency pattern library. This library associates different pulse repetition frequency groups (high, medium, and low) with their maximum unambiguous measurement velocity / distance range and maximum supported data rate. Based on the current target velocity, the subunit quickly filters out pulse repetition frequency groups that meet the unambiguous velocity measurement requirements, and then selects a specific pulse repetition frequency value that meets the command's data rate requirements. For example, for high-speed targets, even if the command requires a high data rate, a suitable value from the high pulse repetition frequency group is preferentially selected to avoid velocity ambiguity leading to tracking failure.

[0037] These three sub-units form an internal micro-loop: the beam scheduling and resource management sub-unit provides the time reference and spatial orientation for the entire processing; the pulse repetition frequency adaptive selection sub-unit directly affects the Doppler spectrum of the multi-channel echo signal, thus affecting the adaptive beamforming sub-unit's ability to distinguish moving interference; the quality of the adaptive beamforming sub-unit, in turn, affects the tracking accuracy and the accuracy of target velocity estimation, providing a more reliable input for the pulse repetition frequency adaptive selection sub-unit. This deep internal coordination is the foundation for the radar signal adaptive processing module to respond quickly and accurately to external resource allocation and feedback commands, achieving dynamic performance optimization.

[0038] Example 3:

[0039] This embodiment, based on Embodiment 1, uses a specific tactical scenario simulation to explain in detail how closed-loop feedback drives the entire process of iterative enhancement of system performance, and elucidates the online learning mechanism of the historical combat effectiveness database.

[0040] Scenario setting: The system is responsible for the air defense of key areas and has detected a high-speed, highly maneuverable suspected cruise missile target (number T1) approaching.

[0041] First processing cycle (baseline performance): S2: The radar uses initial resource allocation and feedback commands (conventional search and track mode) to track T1. Due to the target's high maneuverability, the standard tracking filter (such as the α-β filter) and default data rate used by the radar cannot perfectly match its motion, resulting in a tracking quality assessment value of only 0.6; S3: The target fusion and prediction module makes predictions based on low-quality tracks, resulting in a prediction confidence index of 0.65 and a large prediction error ellipse. S4: The fire control command generation and feedback module calculates the fire control command to instruct firing, but the calculated theoretical hit probability is only 70%. The fire control command generation and feedback module then queries the historical engagement effectiveness database, searching based on T1's current characteristics (high speed, high maneuverability). The historical engagement effectiveness database returns similar cases, showing: "For this type of target, increasing the tracking data rate to 30Hz and enabling the interactive multi-model algorithm for filtering can increase the average hit probability to 85%." Therefore, the fire control command generation and feedback module generates a new resource allocation and feedback command: "For target T1, switch to 30Hz high-precision tracking mode, enable the filter using the interactive multi-model algorithm, and perform sector-enhanced search in its velocity vector direction."

[0042] Second processing cycle (initial optimization): S2: The radar signal adaptive processing module executes new resource allocation and feedback instructions. The radar signal adaptive processing module allocates more dedicated time slices to T1; the pulse repetition frequency adaptive selection subunit switches to high pulse repetition frequency mode; the radar signal adaptive processing module employs a more complex interactive multi-model algorithm. Due to resource concentration and algorithm optimization, the tracking quality assessment value output in this cycle is significantly improved to 0.8; S3: Based on higher quality tracks, the predictor of the interactive multi-model algorithm can better match target maneuvers, improve the prediction confidence index to 0.82, and reduce the prediction error ellipse. S4: Based on more reliable predictions, the recalculated hit probability has risen to 85%, reaching an acceptable threshold. The new resource allocation and feedback instructions may be adjusted to: "Maintain the current optimized tracking mode for T1".

[0043] The third processing cycle and beyond (steady state and learning): The system maintains optimized high-performance tracking until the battle ends; Engagement Result Learning: After this interception concludes (regardless of success or failure), the system will automatically initiate a background learning process. Scene recording: Extract T1's complete motion trajectory, interference environment characteristics, resource allocation and feedback instructions, final miss distance, and damage assessment; Effectiveness assessment: The actual effectiveness score of this engagement is calculated by back-calculating the number of misses. Database Update: This new record is compared with similar older records in the historical engagement effectiveness database. If the new strategy (such as the 30Hz+ interactive multi-model algorithm) outperforms the average strategy in the older records, the system will increase the weight of the new strategy. If this is a completely new scenario feature, the record will be added to the historical engagement effectiveness database as a new case. Through this mechanism, systems deployed in different battlefield environments can gradually accumulate localized optimal strategies in the historical engagement effectiveness database.

[0044] The above complete process demonstrates that the present invention, through a closed loop of "evaluation-feedback-optimization-re-evaluation," can not only improve system performance in real time within a single engagement, but also continuously increase the overall intelligence and adaptability of the system over time through continuous learning from the historical engagement effectiveness database.

[0045] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An adaptive fire control system based on phased array radar, characterized in that, It includes a phased array radar, a fire control computer, and fire units. The phased array radar has a built-in radar signal adaptive processing module; the fire control computer has a built-in target fusion and prediction module and a fire control command generation and feedback module. Phased array radar generates multi-channel echo signals; The fire control command generation and feedback module generates initial resource allocation and feedback commands. The radar signal adaptive processing module receives multi-channel echo signals and initial resource allocation and feedback commands from the fire control command generation and feedback module. After optimization, it outputs enhanced target trajectory information, which includes multi-target trajectories and target state estimation. The target fusion and prediction module receives enhanced target trajectory information and generates target prediction status and covariance information, as well as prediction confidence index. The fire control command generation and feedback module has a pre-stored historical engagement effectiveness database. The fire control command generation and feedback module receives target prediction status and covariance information, prediction reliability index, and combines the kinematic constraints of the fire unit, the ammunition trajectory model and the historical engagement effectiveness database to calculate the fire control command. The fire control command generation and feedback module generates new resource allocation and feedback commands and sends them to the radar signal adaptive processing module. The fire control unit receives and executes fire control commands.

2. The adaptive fire control system based on phased array radar according to claim 1, characterized in that: The fire control command generation and feedback module calculates the target position error ellipse at the future encounter time based on the target prediction state and covariance information, as well as the prediction reliability index; and calculates the hit probability within the target position error ellipse by combining the kinematic constraints of the fire unit and the ammunition trajectory model.

3. The adaptive fire control system based on phased array radar according to claim 1, characterized in that: The historical engagement effectiveness database stores historical hit probabilities and optimized feedback strategies for different target types, maneuvering modes, jamming environments, and combinations of radar parameters used.

4. An adaptive fire control system based on phased array radar according to claim 2, characterized in that: The fire control command generation and feedback module generates new resource allocation and feedback commands, specifically including: using the feature vector of the current target as the search key, performing a similarity search in the historical combat effectiveness database, finding several combat records that are similar to the current situation in history, and generating new resource allocation and feedback commands by combining the currently calculated hit probability with the optimization experience of the combat records.

5. A performance enhancement method applied to the adaptive fire control system according to any one of claims 1-4, characterized in that: The method includes the following steps: Step S1: System initialization: The fire control command generation and feedback module loads default parameters as initial resource allocation and feedback commands, and sends them to the radar signal adaptive processing module; the historical combat effectiveness database completes data loading; Step S2: Radar signal adaptive processing and tracking: The phased array radar generates multi-channel echo signals; the radar signal adaptive processing module performs joint dynamic optimization of the radar beamforming weight and pulse repetition frequency based on the received multi-channel echo signals and initial resource allocation and feedback commands, completes target detection and tracking, and generates enhanced target trajectory information; Step S3: Target Fusion Prediction and Situation Generation: The target fusion and prediction module receives enhanced target trajectory information, fuses multiple target trajectories, and performs state prediction based on an interactive multi-model algorithm to generate target predicted state and covariance information; the target fusion and prediction module calculates prediction reliability index based on the tracking quality assessment in the enhanced target trajectory information. Step S4: Fire control command generation and closed-loop feedback: The fire control command generation and feedback module receives the target prediction status and covariance information, prediction reliability index, and combines the kinematic constraints of the fire unit, the ammunition trajectory model, and the historical combat effectiveness database to calculate the fire control command and send it to the fire unit; the fire control command generation and feedback module generates new resource allocation and feedback commands and sends them to the radar signal adaptive processing module; thereafter, the system proceeds to step S2 and enters the next adaptive enhancement loop.

6. The method for enhancing the performance of an adaptive fire control system based on phased array radar according to claim 5, characterized in that: The radar signal adaptive processing module performs joint dynamic optimization of the radar beamforming weights and pulse repetition frequency, specifically including: Sub-step S21: Based on the key target indication in the resource allocation and feedback instructions, increase the transmit power weight or receive beam gain of the beam in the azimuth of the target. Sub-step S22: Based on the estimation of clutter and interference spectrum in the multi-channel echo signal, and combined with the key interference suppression area indicated in the resource allocation and feedback instructions, a deep null is adaptively formed in beamforming; Sub-step S23: Based on the tracking data rate in the resource allocation and feedback instructions and the target radial velocity estimate in the target state estimation, adaptively select the optimal value from the system's preset pulse repetition frequencies.

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