MPC-based radar radiated power control method, device and equipment

Abstract

The application provides a radar radiation power control method, device and equipment based on MPC. The method comprises the following steps: establishing a target model; the target model comprises a target radiation model and a target state model, wherein the process of the target radiation signal is subject to a Poisson process; under a radar networking architecture, a measurement model is constructed through the target state model; at a preset decision time, the measurement model, an MPC algorithm and a PCRLB are used to measure tracking accuracy, and it is judged whether the center station in the radar networking architecture needs to be tracked actively and a radar radiation control result is obtained. In the application, the tracking accuracy and the anti-interference capability are effectively improved by combining the target radiation model, the target state model and the measurement model, the MPC algorithm and the PCRLB are used to measure the tracking accuracy on this basis, the optimization of the active tracking decision process is realized, and the resource utilization rate and the tracking effect are improved.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and specifically to a radar radiated power control method, apparatus, and equipment based on MPC (Model Predictive Control). Background Technology

[0002] In the field of radar signal processing, target tracking is a crucial technology, widely used in military reconnaissance, civil aviation surveillance, and traffic management. Based on different radar systems, target tracking technologies are mainly divided into two categories: passive tracking and active tracking. Active tracking technology, with its high precision, all-weather operation, and real-time positioning capabilities, performs exceptionally well in many application scenarios. However, its significant radiation signal makes the radar an easy target for enemy attacks, seriously threatening its survivability. In contrast, passive tracking technology, by utilizing only the target's radiation signal for detection, effectively improves the radar's stealth and survivability while reducing costs. However, its insufficient tracking accuracy limits its application scope. Therefore, how to integrate the advantages of active and passive tracking to achieve efficient and covert target tracking has become an urgent technical problem to be solved.

[0003] To overcome the aforementioned technical challenges, researchers have proposed a strategy of constructing passive / active radar networks and implementing radiation control. This strategy aims to balance the accuracy requirements of active tracking and the stealth requirements of passive tracking by optimizing the radar's radiation patterns. For example, existing literature has proposed a radar / electronic support measures (ECM) cooperative tracking method under radar radiation control, which maintains tracking continuity while reducing the risk of enemy detection by controlling the radar to emit intermittent radiation. Another paper models the tracking problem as a partially observable Markov decision process (POMDP), using posterior Cramero bounds and hidden Markov model filters to predict future target tracking accuracy and radar radiation costs, thereby achieving more intelligent resource allocation. The proposal of these methods marks initial progress in the cooperative optimization of radar radiation control and target tracking.

[0004] While the aforementioned methods have improved the overall performance of radar networks to some extent, several challenges remain. First, the decision-making processes of existing methods are relatively simple, making them ill-suited to complex and ever-changing battlefield environments and target behavior patterns. Second, the cost functions of these methods often rely on comparative evaluation of the target's true state, which is frequently unknown in practical applications, limiting the methods' practicality and accuracy. Furthermore, some algorithms attempt to generate virtual states by simulating and extrapolating the target's radiation and motion models, and then calculate tracking accuracy based on these virtual states. While this addresses the issue of unknowable real states to some extent, the algorithm's performance can deteriorate sharply under model mismatch, leading to poor tracking results. Therefore, developing a target tracking and radar radiation control method that can adapt to complex scenarios, does not rely on the target's true state, and is robust to model mismatch has become an important research direction. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a radar radiation power control method, apparatus, and device based on MPC.

[0006] The technical problem to be solved by this invention is achieved through the following technical solution:

[0007] In a first aspect, the present invention provides a radar radiated power control method based on MPC, comprising:

[0008] Establish a target model; the target model includes a target radiation model and a target state model, wherein the process of the target radiation signal follows a Poisson process, and the target state model is described by the target's approximate uniform linear motion;

[0009] In the radar network architecture, a measurement model is constructed through a target state model; the measurement model includes: a target-based measurement model and a central station-based measurement model.

[0010] At the preset decision point, the tracking accuracy is measured by the measurement model, the model predictive control (MPC) algorithm, and the posterior Cramer-Rao bound (PCRLB) to determine whether the central station in the radar network architecture needs to perform active tracking and obtain radar radiation control results.

[0011] Optionally, the target radiation model is expressed as:

[0012] t = [t1, t2, ..., t K ];

[0013] Where t represents the set of times of the target radiation signal, and K represents the total number of times the target radiation signal is emitted. K This indicates the time of the Kth emission signal from the target;

[0014] The target state model is represented as:

[0015]

[0016] in, Indicates the target at t k The state at time t k This indicates the time of the target's k-th radiation signal. Indicates the target at t k-1 The state at any given moment, Indicates the target at t k-1 The state transition matrix at time t k-1 This indicates the time of the (k-1)th radiation signal of the target. Indicates the target at t k-1 Process noise at any given moment.

[0017] Optionally, the radar network architecture includes: one central station and multiple auxiliary stations;

[0018] The central station is used to radiate signals to the outside world and receive signals radiated by the target, while the auxiliary station is used to receive signals radiated by the target and the central station; the radar network architecture uses extended Kalman filtering to track the target.

[0019] Optionally, the target-based measurement model is represented as:

[0020]

[0021] in, This indicates that the nth auxiliary station is at target t. k Measurements obtained when radiating signals at a given time. This indicates that the nth auxiliary station is located at the target. Actual measurement at the state, This indicates that the nth auxiliary station is at target t. k Zero-mean measurement noise obtained when radiating the signal at time t k This indicates the time corresponding to the k-th radiation signal of the target. Indicates the target at t k The state at any given moment;

[0022] The measurement model based on the central station is represented as follows:

[0023]

[0024] in, This indicates that the nth auxiliary station is located at the central station T. m Measurements obtained when radiating signals at a given time. This indicates that the nth auxiliary station is located at the target. Actual measurement at the state, This indicates that the nth auxiliary station is located at the central station T. m Zero-mean measurement noise obtained when radiating the signal at time 10:00 T represents m The state of the target at any given time, T m This indicates the time of the m-th radiation signal from the central station.

[0025] Optionally, at a preset decision time, the tracking accuracy is measured using a measurement model, the Model Predictive Control (MPC) algorithm, and the posterior Cramer-Rao bound (PCRLB) to determine whether the central station in the radar network architecture needs to perform active tracking and obtain radar radiation control results, including:

[0026] Based on the historical radiation data of the target, a target prediction model is constructed;

[0027] The cost function is constructed based on the measurement model, the target prediction model, and the posterior Cramer-Rao bound PCRLB.

[0028] By using the cost function and model predictive control (MPC) algorithm, it is determined whether the current tracking accuracy meets the preset accuracy threshold, and based on the judgment result, it is determined whether the central station needs to perform active tracking in order to obtain the radar radiation control result.

[0029] Alternatively, the cost function can be expressed as:

[0030]

[0031] in, This represents the value of the cost function. This indicates that the target is at the preset final time t. end Status information at that time G represents prior information. D1 G represents passive measurement information. D2 This represents active measurement information; where G D1 G was constructed using a target prediction model and a target-based measurement model. D2 It was constructed using a target prediction model and a measurement model based on a central station.

[0032] Optionally, using a cost function and a model predictive control (MPC) algorithm, it is determined whether the current tracking accuracy meets a preset accuracy threshold, and based on the determination result, it is determined whether the central station needs to perform active tracking, in order to obtain radar radiation control results, including:

[0033] The decision coefficients in the source measurement information of the control cost function are all 0, and the cost function is pre-processed to obtain the error value, which is used as the current tracking accuracy. The pre-processing is to sequentially invert, find the trace, and take the square root of the cost function.

[0034] Determine whether the error value is less than the preset accuracy threshold;

[0035] When the error value is less than the preset accuracy threshold, it is determined that the central station does not need to perform active tracking, and the absence of active tracking is taken as the result of radar radiation control.

[0036] When the error value is greater than the preset accuracy threshold, it is determined that the central station needs to perform active tracking, and the need for active tracking is taken as the result of radar radiation control.

[0037] Optionally, when the error value exceeds a preset accuracy threshold, it is determined that the central station needs to perform active tracking, and the need for active tracking is used as a radar radiation control result, including:

[0038] When the error value is greater than the preset accuracy threshold, the Euclidean distance between the central station and the estimated target position at the preset decision time is calculated based on the central station's position and the estimated target position to obtain the decision distance value.

[0039] Sort the decision distance values ​​from smallest to largest to obtain the distance sorting results;

[0040] Set the decision coefficients in the active measurement information corresponding to the distance ranking results to 1, and recalculate the error values ​​to obtain the updated error values;

[0041] The preset decision time when the update error value is greater than the preset accuracy threshold is taken as the start time of the active tracking of the central station.

[0042] The activation time of active tracking is used as the result of radar radiation control.

[0043] Secondly, the present invention provides a radar radiation power control device based on MPC, which includes: a model building unit and a decision judgment unit.

[0044] The model building unit is used to: establish a target model; the target model includes a target radiation model and a target state model, wherein the process of the target radiation signal follows a Poisson process, and the target state model is described by the target's approximate uniform linear motion;

[0045] The model building unit is also used to: build measurement models based on target state models in a radar network architecture; the measurement models include: target-based measurement models and central station-based measurement models;

[0046] The decision-making unit is used to: at a preset decision time, measure the tracking accuracy through the measurement model, model predictive control (MPC) algorithm, and posterior Cramer-Rao bound (PCRLB), determine whether the central station in the radar network architecture needs to perform active tracking and obtain radar radiation control results.

[0047] Thirdly, the present invention provides a radar radiation power control device based on MPC, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the MPC-based radar radiation power control device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the radar radiation power control method based on MPC as described in the first aspect above.

[0048] This invention provides a radar radiation power control method, apparatus, and device based on MPC. The MPC-based radar radiation power control method includes: establishing a target model; the target model includes a target radiation model and a target state model, wherein the target radiation signal process follows a Poisson process, and the target state model is described by the target's approximate uniform linear motion; under a radar network architecture, constructing a measurement model using the target state model; the measurement model includes a target-based measurement model and a central station-based measurement model; at a preset decision time, measuring the tracking accuracy using the measurement model, the Model Predictive Control (MPC) algorithm, and the posterior Cramer-Rao bound (PCRLB), determining whether the central station in the radar network architecture needs to perform active tracking and obtaining the radar radiation control result. In this invention, by combining the target radiation model, the target state model, and the measurement model, accurate target tracking is achieved, effectively improving tracking accuracy and anti-interference capability. Furthermore, the use of the MPC algorithm and PCRLB to measure tracking accuracy optimizes the active tracking decision-making process, improving resource utilization and tracking performance.

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0050] Figure 1 A flowchart illustrating a radar radiated power control method based on MPC provided in an embodiment of the present invention;

[0051] Figure 2 An illustrative diagram showing the spatial relationship of targets far from the radar network is provided.

[0052] Figure 3 An exemplary spatial relationship diagram of the target's proximity to the radar network is shown;

[0053] Figure 4 An illustrative diagram showing the spatial relationship of targets moving closer to and then further away from a radar network is provided.

[0054] Figure 5 An illustrative comparison of passive tracking and decision tracking results is shown when the target is far from the radar network.

[0055] Figure 6An illustrative comparison of passive tracking and decision tracking results is shown when a target approaches a radar network.

[0056] Figure 7 An illustrative comparison chart shows the results of passive tracking and decision tracking when a target first approaches and then moves away from a radar network.

[0057] Figure 8 A schematic diagram of a radar radiation power control device based on MPC provided in an embodiment of the present invention;

[0058] Figure 9 This is a schematic diagram of a radar radiation power control device based on MPC, provided as an embodiment of the present invention. Detailed Implementation

[0059] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0060] To optimize the active tracking decision-making process and improve resource utilization and tracking performance, this invention provides a radar radiation power control method based on MPC. Figure 1 This is a flowchart illustrating a radar radiated power control method based on MPC, provided as an embodiment of the present invention. Figure 1 As shown, the method includes:

[0061] S101. Establish the target model, which includes the target radiation model and the target state model.

[0062] The process of the target radiation signal follows a Poisson process, and the target state model is described by the target's approximate uniform linear motion.

[0063] Optionally, in this embodiment, the initial state of the target can be represented as: Where x0, y0, and z0 represent the x-coordinate, y-coordinate, and z-coordinate of the target in its initial state, respectively. Let represent the target's lateral velocity, longitudinal velocity, and vertical velocity in the initial state, respectively, and T represent the transpose of the matrix. In this embodiment, an approximate uniform linear motion model is used to describe the target motion, resulting in the target state model. Since the target radiation signal process follows a Poisson process, the time set t of the target radiation signal is represented as:

[0064] t = [t1, t2, ..., t K ];

[0065] Where t represents the set of times of the target radiation signal, and K represents the total number of times the target radiation signal is emitted. K This indicates the time of the Kth emission signal from the target;

[0066] The target state model is represented as:

[0067]

[0068] in, Indicates the target at t k The state at time t k This indicates the time of the target's k-th radiation signal. Indicates the target at t k-1 The state at any given moment, Indicates the target at t k-1 The state transition matrix at time t k-1 This indicates the time of the (k-1)th radiation signal of the target. Indicates the target at t k-1 Process noise at any given moment.

[0069] S102. Under the radar network architecture, a measurement model is constructed through the target state model.

[0070] The measurement models include: target-based measurement models and central station-based measurement models.

[0071] It should be noted that the radar network architecture includes: one central station and multiple auxiliary stations;

[0072] The central station is used to radiate signals to the outside world and receive signals radiated by the target, while the auxiliary station is used to receive signals radiated by the target and the central station; the radar network architecture uses extended Kalman filtering to track the target.

[0073] In this embodiment, the location of the central station can be represented as s = [x, y, z]. T The position of the nth auxiliary station can be represented as s n =[x n ,y n ,z n ] T ,n∈[1,…,N](N is known). Where x, y, z represent the abscissa, ordinate, and vertical coordinate of the central station, respectively. n ,y n ,z n Let x, y, and y represent the x, y, and y coordinates of the nth auxiliary station, respectively, and N represent the total number of auxiliary stations.

[0074] Assume the target is at t k Signals are transmitted continuously, with the central station set as the reference node. The measurement obtained by the nth auxiliary station based on the target's radiated signal, i.e., the target-based measurement model, can be expressed as:

[0075]

[0076] in, This indicates that the nth auxiliary station is at target t. k Measurements obtained when radiating signals at a given time. This indicates that the nth auxiliary station is located at the target. Actual measurement at the state, This indicates that the nth auxiliary station is at target t. k Zero-mean measurement noise obtained when radiating the signal at time t k This indicates the time corresponding to the k-th radiation signal of the target. Indicates the target at t k The state at any given moment. It can be calculated using fuzzy function estimation theory.

[0077] in, This represents the difference in Euclidean distance between the target and the central station and between the target and the nth auxiliary station, divided by the signal propagation speed. This indicates that the target measured by the nth auxiliary station is at t. k The arrival frequency difference of the radiated signal at any given time can be specifically calculated using the Doppler frequency shift.

[0078] The measurement model based on the central station is represented as follows:

[0079]

[0080] in, This indicates that the nth auxiliary station is located at the central station T. m Measurements obtained when radiating signals at a given time. This indicates that the nth auxiliary station is located at the target. Actual measurement at the state, This indicates that the nth auxiliary station is located at the central station T. m Zero-mean measurement noise obtained when radiating the signal at time 10:00 T represents m The state of the target at any given time, T m This indicates the time of the m-th radiation signal from the central station.

[0081] in, This represents the sum of the Euclidean distances from the target to the central station and from the target to the nth auxiliary station, measured by the nth auxiliary station. This represents the elevation angle of the central station relative to the target, measured by the nth auxiliary station. It can be obtained using the maximum likelihood estimation method.

[0082] S103. At the preset decision time, the tracking accuracy is measured by the measurement model, the model predictive control (MPC) algorithm, and the posterior Cramer-Rao bound (PCRLB) to determine whether the central station in the radar network architecture needs to perform active tracking and obtain radar radiation control results.

[0083] This invention provides a radar radiation power control method based on MPC. By combining the target radiation model, target state model and measurement model, it achieves accurate target tracking, effectively improving tracking accuracy and anti-interference capability. On this basis, the MPC algorithm and PCRLB are used to measure tracking accuracy, thereby optimizing the active tracking decision process and improving resource utilization and tracking effect.

[0084] Optionally, S103 may specifically include:

[0085] Based on the historical radiation data of the target, a target prediction model is constructed;

[0086] The cost function is constructed based on the measurement model, the target prediction model, and the posterior Cramer-Rao bound PCRLB.

[0087] By using the cost function and model predictive control (MPC) algorithm, it is determined whether the current tracking accuracy meets the preset accuracy threshold, and based on the judgment result, it is determined whether the central station needs to perform active tracking in order to obtain the radar radiation control result.

[0088] In this embodiment, we assume time T m Let m∈[1,…,M] be the decision time. The central station needs to predict the target's radiation model and state model. The central station will count the number of times the target radiates outwards in the past Δt time period. And use this to estimate the target at time T m The external radiation pattern. Assuming the target radiates a signal according to a Poisson distribution process, and the maximum likelihood estimate of the Poisson distribution parameters is its average value, then at time T... m Regarding the parameters of the target Poisson process The maximum likelihood estimate is:

[0089]

[0090] Based on Poisson process parameters and the final decision moment T ω The target at T can be estimated. ω If the number of radiation events within a time interval is Np, then the estimated set of target radiation times can be expressed as: This indicates the estimated time of the Npth outward radiation signal from the target.

[0091] While estimating the target's radiation Poisson distribution parameters, the central station also estimates the target's radiation parameters at time T. m state of time

[0092]

[0093] in, This indicates that the central station is based on the target T. m The passive tracking results are obtained from the radiation information at any given time.

[0094] according to and the estimated target radiation time set The target at T can be estimated. ω Set of states when radiating signals within a time period and the set of state information at the decision moment Indicates the estimated target at Status information at that time Indicates the estimated target at T M Status information at any given moment.

[0095] In practical applications, since the true state of the target cannot be known, this embodiment of the invention uses the posterior Cramer-Rao bound (PCRLB) to measure tracking accuracy. Therefore, the PCRLB of the target's k-th radiation signal is estimated as follows:

[0096]

[0097] in, This indicates the time of the estimated k-th outward radiation signal from the target. The target state estimated by the time center station based on measurement and The expected value of the difference between the target state at any given time. express The set of target measurement information acquired by the time center station. Indicates radar based on Measurements obtained at time The estimated target is The state at any given moment.

[0098] Indicates the estimated target state of time Bayesian information matrix, It can be iteratively calculated as:

[0099]

[0100] in, and They represent Fisher's information matrix of prior and measurement information about the target from the time-center station.

[0101] According to the above formula, PCRLB needs to be updated every time new measurement information is obtained, and each update requires an inversion operation. Meanwhile, at T... ω The target radiates Np signals within a given time period, requiring multiple inversion operations, which significantly impacts the computation speed. Therefore, this embodiment of the invention... The multiple inversion processes are approximated, and the result is directly obtained from T. m Get T at all times ω The PCRLB at time step 1 approximates the multiple inversion processes as a single inversion and multi-term accumulation, significantly reducing the algorithm's complexity. The final cost function after approximate inversion is expressed as:

[0102]

[0103] in, This represents the value of the cost function. This indicates that the target is at the preset final time t. end Status information at that time G represents prior information. D1 G represents passive measurement information. D2 This represents active measurement information; where G D1 G was constructed using a target prediction model and a target-based measurement model. D2 It was constructed using a target prediction model and a measurement model based on a central station.

[0104] In an embodiment of the present invention, The acquisition process can be found in Formula 3-12 on page 30 of the paper "Research on Resource Allocation Algorithm in Cognitive Radar". Further... It can be represented as:

[0105]

[0106] in, Indicates from T m Time to t end The process error matrix at time step 1. Indicates the target is at T m The state transition matrix at time t, T represents m The value of the cost function at time t.

[0107] The second item G D1 For information from passive measurements, it indicates that at the final time T ω The internal target radiated signals Np times. The radar passively tracked the target each time it radiated a signal, as shown in the following description:

[0108]

[0109] express Jacobian matrix, express exist The value at any given moment. for exist The value at time, Indicates the predicted target is in The state information at time j represents the estimated target's j-th outward radiation signal.

[0110] The third term of the cost function is the information from the active measurement, but the system will not be turned on at every decision moment, so it is necessary to determine which decision moment requires the system to be turned on.

[0111] Let u be the decision variable for the m-th decision. m for:

[0112]

[0113] Among them, L m This indicates that the m-th extrapolation simulation contains L. m One startup decision point, u i =1 indicates whether to power on at the i-th decision point in the m-th decision. Therefore, G D2 It can be represented as:

[0114]

[0115] in, express Jacobian matrix, express In T m The value at time, In T m The value at time, Indicates the predicted target at T i State information at time T, where i represents the i-th decision point. i This represents the time corresponding to the i-th decision point.

[0116] Optionally, using a cost function and a model predictive control (MPC) algorithm, it is determined whether the current tracking accuracy meets a preset accuracy threshold, and based on the determination result, it is determined whether the central station needs to perform active tracking, in order to obtain radar radiation control results, including:

[0117] The decision coefficients in the source measurement information of the control cost function are all 0, and the cost function is pre-processed to obtain the error value, which is used as the current tracking accuracy. The pre-processing is to sequentially invert, find the trace, and take the square root of the cost function.

[0118] Determine whether the error value is less than the preset accuracy threshold;

[0119] When the error value is less than the preset accuracy threshold, it is determined that the central station does not need to perform active tracking, and the absence of active tracking is taken as the result of radar radiation control.

[0120] When the error value is greater than the preset accuracy threshold, it is determined that the central station needs to perform active tracking, and the need for active tracking is taken as the result of radar radiation control.

[0121] Optionally, when the error value exceeds a preset accuracy threshold, it is determined that the central station needs to perform active tracking, and the need for active tracking is used as a radar radiation control result, including:

[0122] When the error value is greater than the preset accuracy threshold, the Euclidean distance between the central station and the estimated target position at the preset decision time is calculated based on the central station's position and the estimated target position to obtain the decision distance value.

[0123] Sort the decision distance values ​​from smallest to largest to obtain the distance sorting results;

[0124] Set the decision coefficients in the active measurement information corresponding to the distance ranking results to 1, and recalculate the error values ​​to obtain the updated error values;

[0125] The preset decision time when the update error value is greater than the preset accuracy threshold is taken as the start time of the active tracking of the central station.

[0126] The activation time of active tracking is used as the result of radar radiation control.

[0127] In this embodiment of the invention, the error value is represented as: At the beginning, let all u i =0, set the tracking accuracy requirement η, and determine. If the condition is met, it indicates that no power-on is required, and the tracking accuracy requirement can be achieved solely through passive measurement. If the condition is not met, then the tracking accuracy is determined based on the central station position 's' and the estimated target position. The Euclidean distance between the calculation center station and the estimated target position at the preset decision time. Arrange them in order of distance from nearest to farthest, and let the corresponding u... i =1, and recalculate. judge Whether it is true or not. Until Establishment or all of ui =1, output the decision variable u at this time. m If u1 = 1, it indicates that the device needs to be powered on at the current moment, and correspondingly, power optimization needs to be performed. The power optimization method uses a binary search approach; if u i =0 indicates that the central station does not need to be turned on at the current moment.

[0128] In summary, the radar radiation power control method based on MPC provided by this invention mainly involves: determining the active / active radar network; simulating target radiation and motion behavior; performing passive tracking based on target radiation and passive radar during non-decision times; making decisions for active radar during decision times; extrapolating simulations based on target radiation frequency and passive tracking results to generate virtual positions and virtual measurements; calculating the posterior Cramer-Rao boundary; determining whether to activate the radar based on its distance from the radar network position; optimizing the decision strategy; and outputting the decision result, whereby the decision strategy is radar power radiation control based on MPC.

[0129] Combining the present invention with the prior art, the main effects of the method of the present invention are as follows:

[0130] 1. Adapt to complex scenarios and dynamic target behaviors:

[0131] The background section mentions that existing methods have simple decision-making processes, making them difficult to adapt to complex and ever-changing battlefield environments and target behavior patterns. This invention, by simulating target radiation and motion behavior, can more accurately predict the future state of a target, thereby formulating a more reasonable radar radiation power control strategy. This predictive capability enables the radar system to better adapt to dynamically changing scenarios and target behavior, improving tracking accuracy and robustness.

[0132] 2. Does not depend on the target's actual state:

[0133] Existing methods rely on the target's true state when calculating the cost function, which is impractical in real-world applications. This invention generates virtual positions and measurements through simulation extrapolation and uses this virtual data to calculate the posterior Cramer-Rao bound (PCRLB), which serves as the basis for evaluating tracking accuracy and making decisions. This method avoids dependence on the target's true state, improving the algorithm's practicality and accuracy.

[0134] 3. Optimize radar activation frequency and radiated power:

[0135] This invention utilizes passive radar for passive tracking during non-decision-making periods, reducing reliance on active radar and lowering the frequency of radar activation and radiated power. During decision-making periods, an optimal radar radiated power control strategy is formulated by comprehensively considering factors such as target radiation frequency, passive tracking results, and radar network location. This strategy ensures tracking accuracy while minimizing radar activation frequency and radiated power, thereby improving radar survivability and stealth.

[0136] 4. The advantages of balancing passive and active tracking:

[0137] This invention achieves complementary advantages of passive and active tracking by constructing a passive / active radar network and combining it with the MPC method for radiated power control. In the passive tracking phase, target radiated signals are used for sensing, reducing the radar's radiated power and the risk of detection. In the active tracking phase, high-precision tracking is achieved by optimizing the radar's radiated power and activation strategy. This balanced strategy enables the radar system to achieve optimal performance in different scenarios.

[0138] 5. Optimize decision-making strategies:

[0139] The MPC method employed in this invention is an optimization control algorithm that can formulate the optimal decision-making strategy based on consideration of the target state, radar resources, and environmental constraints over a future period. This strategy not only considers current tracking requirements but also future possibilities, thereby achieving globally optimal control performance.

[0140] To verify the effectiveness of the radar radiated power control method based on MPC proposed in this embodiment of the invention, simulation experiments were also conducted, as follows:

[0141] 1. Experimental Scenario:

[0142] Set the simulation scenario and simulation parameters. Refer to Table 1 for the simulation scenario and parameters.

[0143] Table 1 Radar Networking and Target Simulation Parameter Settings

[0144]

[0145] 2. Simulation content:

[0146] In a given simulation scenario, the tracking errors of using the tracking method of this invention and using only the passive tracking method are analyzed. In the following description, the tracking method of this invention will be referred to as decision tracking.

[0147] Simulation scenarios can be specifically divided into three types:

[0148] 1. When the target is far from the radar network. Correspondingly, Figure 2An illustrative spatial relationship diagram of the target's location away from the radar network is shown. For example... Figure 2 As shown, the radar network includes: a central station, auxiliary stations, and targets. Figure 2 The pentagram in the image represents the target's initial position, and the dashed line represents the target's actual trajectory, which shows the target gradually moving away from the radar network.

[0149] 2. When the target approaches the radar network. Correspondingly, Figure 3 An illustrative spatial relationship diagram of the target's proximity to the radar network is shown. For example... Figure 3 As shown, the radar network includes: a central station, auxiliary stations, and targets. Figure 3 The pentagram in the diagram represents the initial position of the target, and the dashed line represents the actual trajectory of the target, which gradually approaches the radar network.

[0150] 3. When the target first approaches and then moves away from the radar network. Correspondingly, Figure 4 An illustrative diagram shows the spatial relationship of targets moving closer to and then further away from a radar network. For example... Figure 4 As shown, the radar network includes: a central station, auxiliary stations, and targets. Figure 4 The pentagram in the diagram represents the target's initial position, and the dashed line represents the target's actual trajectory. The target's actual trajectory shows that it first approaches the radar network and then gradually moves away from the radar network.

[0151] 3. Simulation Result Analysis:

[0152] Figure 5 An illustrative comparison of passive tracking and decision tracking results is shown when the target is far from the radar network. From... Figure 5 The results show that when the target is far from the radar network and the active radar (central radar) is not powered on, the decision tracking result is exactly the same as the passive tracking result. When the active radar is powered on, the tracking error decreases significantly. Although the tracking error gradually increases afterward, it is still lower than the error of pure passive tracking. The power-on points in the figure are represented by circles, and the power-on power is represented by the size of the circles. Four power-on decision points were set at 10s, 20s, 30s, and 40s. Since the target was constantly moving away from the radar network, power-on was selected at all four decision points. At 40s, since power-on had already been performed three times, the power-on power decreased, reducing the probability of being detected by the target.

[0153] Figure 6 An illustrative comparison of passive tracking and decision tracking results is shown when a target approaches a radar network. From... Figure 6The results show that when the target approaches the radar network and the active radar is not powered on, the decision-making tracking results are exactly the same as those of the passive radar. When the active radar is powered on, the tracking error decreases significantly. Although the tracking error gradually increases afterward, it is still lower than that of the purely passive tracking error. The power-on points in the figure are represented by circles, and the power-on power is represented by the size of the circles. Four power-on decision points were set at 10s, 20s, 30s, and 40s. At the 10s decision point, the target is far from the radar network, so the decision is to power on. Since the radar is powered on at the 10s, and the target continues to approach the radar network, the decision is not to power on at the 20s and 30s. Since the radar is not powered on at 20s and 30s, and the prediction accuracy is higher at the 40s, the decision is to power on.

[0154] Figure 7 An illustrative comparison chart shows the results of passive tracking and decision tracking when a target first approaches and then moves away from a radar network. From... Figure 7 The results show that when the target first approaches the radar network and then moves away, and the active radar is not powered on, the decision-making tracking results are exactly the same as those of the passive tracking results. When the active radar is powered on, the tracking error decreases significantly. Although the tracking error gradually increases afterward, it is still lower than that of the purely passive tracking error. The power-on points in the figure are represented by circles, and the power-on power is represented by the size of the circles. Four power-on decision points were set at 10s, 20s, 30s, and 40s. At the 10s decision point, since the target is closer to the radar network at 20s and 30s, it is determined that powering on at 20s and 30s would meet the accuracy requirements; therefore, the decision is not to power on at 10s. Since the target is not powered on at 10s, the decision is to power on at 20s and 30s. At 40s, the target moves away from the radar network, so the decision is to power on. At 40s, since the target has already been powered on twice, the power-on power decreases, reducing the probability of being detected by the target.

[0155] The method provided in this embodiment of the invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc., and this embodiment of the invention does not limit the application to such devices.

[0156] Based on the same inventive concept, embodiments of the present invention also provide a radar radiation power control device based on MPC. For example... Figure 8 This is a schematic diagram of a radar radiation power control device based on MPC, provided as an embodiment of the present invention. Figure 8 As shown, it includes: a model building unit 801 and a decision-making unit 802;

[0157] The model building unit 801 is used to: establish a target model; the target model includes a target radiation model and a target state model, wherein the process of the target radiation signal follows a Poisson process, and the target state model is described by the target's approximate uniform linear motion;

[0158] The model building unit 801 is also used to: build a measurement model through a target state model under a radar networking architecture; the measurement model includes: a target-based measurement model and a central station-based measurement model;

[0159] The decision-making unit 802 is used to: at a preset decision time, measure the tracking accuracy through the measurement model, model predictive control (MPC) algorithm and posterior Cramer-Rao bound (PCRLB), determine whether the central station in the radar network architecture needs to perform active tracking and obtain radar radiation control results.

[0160] Figure 9 This invention provides a schematic diagram of a radar radiation power control device based on MPC, comprising: a processor 910, a storage medium 920, and a bus 930. The storage medium 920 stores machine-readable instructions executable by the processor 910. When the MPC-based radar radiation power control device is running, the processor 910 communicates with the storage medium 920 via the bus 930, and the processor 910 executes the machine-readable instructions to perform the steps of the above-described method embodiment. Specific implementation methods and technical effects are similar and will not be described in detail here.

[0161] The storage medium may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the storage medium may also be at least one storage device located remotely from the aforementioned processor.

[0162] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be 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, or discrete hardware components.

[0163] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0164] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In the description of the invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0165] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the inventive concept, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A radar radiated power control method based on MPC, characterized in that, include: Establish a target model; The target model includes a target radiation model and a target state model, wherein the process of the target radiation signal follows a Poisson process, and the target state model is described by the target's approximate uniform linear motion. Under the radar network architecture, a measurement model is constructed using the target state model; The measurement models include: a target-based measurement model and a central station-based measurement model; At a preset decision time, the tracking accuracy is measured using the measurement model, the Model Predictive Control (MPC) algorithm, and the posterior Cramer-Rao bound (PCRLB) to determine whether the central station in the radar network architecture needs to perform active tracking and obtain radar radiation control results, including: Based on the historical radiation data of the target, a target prediction model is constructed; A cost function is constructed based on the measurement model, the target prediction model, and the posterior Cramer-Rao bound PCRLB; the cost function is expressed as: ； in, This represents the value of the cost function. Indicates the target at the preset final time. Status information at that time Represents prior information, Indicates passive measurement information. This represents active measurement information; among which, It was constructed using a target prediction model and a target-based measurement model. It was constructed using a target prediction model and a measurement model based on a central station; Using the cost function and the model predictive control (MPC) algorithm, it is determined whether the current tracking accuracy meets the preset accuracy threshold, and based on the determination result, it is determined whether the central station needs to perform active tracking in order to obtain the radar radiation control result. The process of using the cost function and the model predictive control (MPC) algorithm to determine whether the current tracking accuracy meets a preset accuracy threshold, and determining whether the central station needs to perform active tracking based on the determination result, to obtain the radar radiation control result, includes: The decision coefficients in the source measurement information of the cost function are all controlled to be 0, and the cost function is subjected to preset processing to obtain an error value, which is used as the current tracking accuracy; the preset processing is to sequentially perform inversion, trace finding and square root taking on the cost function; Determine whether the error value is less than the preset accuracy threshold; When the error value is less than the preset accuracy threshold, it is determined that the central station does not need to perform active tracking, and the absence of active tracking is taken as the radar radiation control result. When the error value is greater than the preset accuracy threshold, it is determined that the central station needs to perform active tracking, and the need to perform active tracking is taken as the radar radiation control result. When the error value is greater than the preset accuracy threshold, it is determined that the central station needs to perform active tracking, and the need for active tracking is taken as the radar radiation control result, including: When the error value is greater than the preset accuracy threshold, the Euclidean distance between the central station and the estimated target position at the preset decision time is calculated based on the position of the central station and the estimated target position to obtain the decision distance value. The decision distance values ​​are sorted from smallest to largest to obtain the distance sorting result; Set the decision coefficients in the active measurement information corresponding to the distance sorting results to 1 in sequence, and recalculate the error value to obtain the updated error value; The preset decision time corresponding to when the update error value is greater than the preset accuracy threshold is taken as the activation time of the active tracking of the central station; the power-on power of the active tracking of the central station is optimized by using a binary search method. The activation time of the active tracking is taken as the result of the radar radiation control.

2. The radar radiated power control method based on MPC according to claim 1, characterized in that, The target radiation model is expressed as follows: ； in, Represents the set of times of the target's radiated signal. This indicates the total number of times the target radiated signal is received. Indicates the target number The timing of the secondary radiation signal; The target state model is represented as follows: ； in, Indicates the target is The state at any given moment, Indicates the target number The timing of the secondary radiation signal Indicates the target is The state at any given moment, Indicates the target is The state transition matrix at time t, Indicates the target is in the first place. The timing of the secondary radiation signal Indicates the target is Process noise at any given moment.

3. The radar radiated power control method based on MPC according to claim 1, characterized in that, The radar network architecture includes: one central station and multiple auxiliary stations; The central station is used to radiate signals to the outside and receive signals radiated by the target, and the auxiliary station is used to receive signals radiated by the target and the central station; the radar network architecture uses extended Kalman filtering to track the target.

4. The radar radiated power control method based on MPC according to claim 1, characterized in that, The target-based measurement model is expressed as: ； in, Indicates the first Individual support station target Measurements obtained when radiating signals at a given time. Indicates the first Individual auxiliary station at the target location Actual measurement at the state, Indicates the first Individual support station target Zero-mean measurement noise obtained when radiating the signal at time 10:

00. Indicates the target is in the first place. The time corresponding to the secondary radiation signal. Indicates the target is The state at any given moment; The measurement model based on the central station is represented as follows: ； in, Indicates the first Each auxiliary station is located at the central station. Measurements obtained when radiating signals at a given time. Indicates the first Individual auxiliary station at the target location Actual measurement at the state, Indicates the first Each auxiliary station is located at the central station. Zero-mean measurement noise obtained when radiating the signal at time 10:

00. express The status of the target at all times. Indicates the central station number The moment of the next radiation signal.

5. A radar radiation power control device based on MPC, characterized in that, The radar radiation power control device based on MPC, used to implement the radar radiation power control method according to any one of claims 1-4, comprises: a model building unit and a decision judgment unit; The model building unit is used to: establish a target model; the target model includes a target radiation model and a target state model, wherein the process of the target radiation signal follows a Poisson process, and the target state model is described by the target's approximate uniform linear motion; The model building unit is also used to: construct a measurement model through the target state model under the radar network architecture; the measurement model includes: a target-based measurement model and a central station-based measurement model; The decision-making unit is used to: at a preset decision time, measure the tracking accuracy using the measurement model, model predictive control (MPC) algorithm, and posterior Cramer-Rao bound (PCRLB), and determine whether the central station in the radar network architecture needs to perform active tracking and obtain radar radiation control results.

6. A radar radiation power control device based on MPC, characterized in that, include: The device includes a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the MPC-based radar radiation power control device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the MPC-based radar radiation power control method as described in any one of claims 1-4.

Images