Radar communication integrated control method and device, medium and program product

Abstract

The invention provides a radar communication integrated control method and device, a medium and a program product, and the method comprises the steps: firstly estimating the state of a target at a next moment, including the position information and speed information of the target; based on the estimated value of the target state at the next moment, the target estimation covariance and the error rate are used as measurement indexes, the radar end moving trajectory and the integrated signal emission waveform are subjected to joint optimization, and an emission scheme at the next moment is obtained and comprises the trajectory position of the radar end, the frequency modulation rate of the waveform and the symbol duration time. According to the radar communication integrated control method, the problem that in an existing radar communication integrated technology, integrated waveforms cannot be excavated to optimize target tracking performance is solved, the tracking performance is improved, meanwhile, the information transmission requirement of communication is also considered, dynamic balance of radar and communication is achieved, and the reliability of the radar communication integrated control method is improved. The method is suitable for multiple fields of intelligent transportation, Internet of Vehicles, unmanned aerial vehicles and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar communication integration, and specifically relates to a radar communication integration control method, device, medium, and program product. Background Art

[0002] The airborne radar system based on drones can flexibly adjust the position of the radar end, thereby reducing signal blocking between the target and achieving a wider coverage range than fixed radar systems. Therefore, it is widely used in both military and civilian fields. In many modern application scenarios, in addition to using airborne radar to track the target, it is also necessary to communicate with the target. The installation of an additional communication system for tracking targets on drones will increase the flight burden of drones, resulting in a serious decrease in maneuverability and endurance. Therefore, radar communication integrated technology has been developed under this demand background.

[0003] Radar communication integration technology can realize the sharing of RF hardware and wireless infrastructure between radar and communication, and realize the tracking and communication functions of the target by sending radar communication integration signals to the target. However, the high mobility of airborne radars brings new challenges to the radar communication integration system and waveform design while bringing freedom to the radar trajectory. How to combine the integrated waveform and the radar trajectory to improve the target tracking and communication performance is the main control problem of the current radar communication integration system. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a radar communication integrated control method, device, medium, and program product to solve the problem of joint optimization of radar end running trajectory and transmission waveform.

[0005] The present invention achieves the above technical objectives through the following technical means.

[0006] A radar communication integrated control method, comprising:

[0007] Estimate the state of the target at the next moment, including the target's position and speed information;

[0008] Based on the estimated value of the target state at the next moment, the target estimation covariance and bit error rate are used as measurement indicators to jointly optimize the radar end operation trajectory and the integrated signal transmission waveform to obtain the transmission plan at the next moment, including the trajectory position of the radar end, the modulation frequency of the waveform and the symbol duration.

[0009] Furthermore, the objective function of the joint optimization problem is:

[0010]

[0011] Among them, μ k To adjust the frequency, T s，k is the symbol duration, v k is the speed of the radar end, the subscript k indicates the time at k, Φ(·) indicates the normalization operation, L is the total number of targets, l indicates the lth target, P l，k is the target estimated covariance, B l，k Indicates the estimated bit error rate.

[0012] Furthermore, the target estimated covariance P l，k Calculated by the following formula:

[0013]

[0014] Where Tr(·) represents the trace function, P l，k|k-1 represents the target estimated covariance obtained by estimating the target l at time k at time k-1, X l，k|k-1 It represents the target state obtained by estimating the target l at time k at time k-1, H l，k Denotes the measurement function h(X l，k|k-1 , X k ) in predicting the target state X l，k|k-1 The Jacobian matrix at k is the position of the radar end, Γ=diag[c / 2, c / (2f c )] represents the conversion matrix from time delay and Doppler frequency shift to distance and speed, c is the speed of light, f c is the carrier frequency, represents the estimated measurement signal-to-noise ratio, Denotes the Fisher information matrix J l，k The part of the waveform that is affected by the waveform;

[0015] The expression is:

[0016]

[0017] Among them, P t，l，k Indicates the transmit power, T d，l，k represents the signal dwell time, G t，l represents the transmission gain, G r，l represents the receiving gain of the radar, λ is the wavelength of light, σ l represents the radar cross section of the lth target, T r Represents the pulse repetition frequency, N 0 represents the noise power density, is the estimated relative distance of target l from the radar end, which is determined by the measurement function Calculate and obtain, To estimate the relative velocity, Xk-1 represents the radar end position at time k-1;

[0018] The expression is:

[0019]

[0020] in The expression is:

[0021]

[0022]

[0023] The expression is:

[0024]

[0025] The expression is:

[0026]

[0027] Where h is the modulation index, N is the number of communication symbols, and b n Represents a sequence of information being transmitted.

[0028] Furthermore, the estimated bit error rate B l，k Calculated by the following formula:

[0029]

[0030] in, represents the Q function, d min represents the normalized minimum Euclidean distance, SNR l，k It represents the estimated signal-to-noise ratio of the communication channel, and its mathematical expression is:

[0031] SNR l，k =P t，l，k h l，k / N 0

[0032] Among them, h l，k It represents the estimated channel gain, and its mathematical expression is:

[0033]

[0034] Among them, G c，l Indicates the target receiving antenna gain.

[0035] Furthermore, the joint optimization problem is solved as follows:

[0036] S1, the objective function This breaks down into:

[0037]

[0038] in, and Respectively represent μ k and T s，k The optimal solution of

[0039] S2, using RGS algorithm Solve it and get and

[0040] S3, and Substitution Using gradient descent method Solve and get v k The optimization results

[0041] Furthermore, the extended Kalman filter is used for tracking filtering to estimate the target state at the next moment:

[0042] T1, based on the measured value z obtained by echo detection l，k , for the estimated value X l，k|k-1 and P l，k|k-1 To make corrections:

[0043] X l，k|k =X l，k|k-1 +K l，k (z l，k -h(X l，k|k-1 , X k ))

[0044]

[0045] Among them, X l，k|k-1 is the state estimate of the target l at time k-1, P l，k|k-1 Its corresponding estimated covariance, X l，k|k is the state correction value of target l at time k, P l，k|k is the corresponding corrected covariance; K l，k is the Kalman gain, S l，k To add covariance, the mathematical expressions of the two are:

[0046]

[0047]

[0048] Among them, H l，k Denotes the measurement function h(Xl，k|k-1 , X k ) in predicting the target state X l，k|k-1 The Jacobian matrix at l，k =ΓC l，k Γ is the covariance matrix of the measurement error, Γ = diag[c / 2, c / (2f c )] represents the conversion matrix from time delay and Doppler frequency shift to distance and speed, c represents the speed of light, C l，k represents the Cramer-Rao bound for the time delay and Doppler shift estimation of target l between the kth pulses;

[0049] T2, predict the state of the target at the next moment:

[0050] X l，k+1|k =F k+1 X l，k|k

[0051]

[0052] Among them, F k+1 and G k+1 They represent the state transition matrix F and process noise distribution matrix G between the k+1th pulses respectively;

[0053] For the uniform motion model, F and G are defined as:

[0054]

[0055] For the uniform acceleration motion model, F and G are defined as:

[0056]

[0057] For the long turning motion model, F and G are defined as:

[0058]

[0059] Among them, ΔT is the sampling period (i.e., pulse repetition period), I 2 is a 2*2 identity matrix, represents the angular velocity of the target, represents the Kronecker product.

[0060] Furthermore, the state of the target is defined as:

[0061]

[0062] Among them, x l，k and l，k They represent the positions of the target l on the x-axis and y-axis at time k, respectively. and They represent the speed of target l on the x-axis and y-axis at time k, respectively. and They represent the acceleration of target l on the x-axis and y-axis at time k respectively.

[0063] The position of the radar end is defined as:

[0064] X k =[x k ,y k ] T

[0065] The velocity at the radar end is defined as:

[0066]

[0067] where x k and k They represent the position of the radar end on the x-axis and y-axis at time k, respectively. and They represent the speed of the radar end on the x-axis and y-axis at time k respectively.

[0068] A computer device comprising a memory and a processor;

[0069] The memory is used to store computer programs;

[0070] The processor is used to execute the computer program and implement the above-mentioned radar communication integrated control method when executing the computer program.

[0071] A computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to execute the above-mentioned radar communication integrated control method.

[0072] A computer program product includes a computer program, and when the computer program is executed by a processor, the radar communication integrated control method described above is implemented.

[0073] The beneficial effects of the present invention are:

[0074] (1) The present invention provides a radar communication integrated control method. Compared with the traditional method of separately designing radar tracking performance and communication performance, the present invention solves the problem that the existing radar communication integration technology fails to optimize the target tracking performance by utilizing the integrated waveform. While improving the tracking performance, it also takes into account the information transmission requirements of communication, realizes a dynamic balance between radar and communication, and enables the two to achieve optimal performance under the condition of resource sharing. The present invention is suitable for multiple fields such as intelligent transportation, Internet of Vehicles, and drones.

[0075] (2) The present invention uses the integrated signal waveform and system operation trajectory as optimization variables, and takes tracking performance and communication performance as objective functions to establish an optimization model. This objective function covers two key indicators: radar tracking performance and communication performance. It not only achieves the coordinated optimization of radar and communication, but also balances the resource allocation between the two. Through this optimization model, the present invention can achieve the overall optimal configuration of radar and communication performance. Maximize system performance under resource constraints.

[0076] (3) The present invention obtains the optimal integrated waveform and running trajectory based on the rectangular grid search algorithm and the gradient descent method. The rectangular grid search algorithm avoids the solution from falling into the local optimum by performing a global grid search in the solution space. It provides good initial conditions for the subsequent gradient descent method and ensures the accuracy of the final solution. In addition, the gradient descent method uses local derivative information for rapid convergence, which significantly reduces the complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a schematic diagram of the scenario of the radar communication integration system;

[0078] Figure 2 It is the timing diagram of radar communication integration;

[0079] Figure 3 This is a flow chart of the control method of the present invention;

[0080] Figure 4 This is a comparison chart of communication bit error rates under various control schemes for a linear maneuvering target;

[0081] Figure 5 Comparison of target trajectory estimation under various control schemes for a linear maneuvering target;

[0082] Figure 6 Comparison of target estimation performance under various control schemes for linear maneuvering targets;

[0083] Figure 7 This is a comparison chart of communication bit error rates under various control schemes for nonlinear maneuvering targets;

[0084] Figure 8 Comparison of target trajectory estimation under various control schemes for nonlinear maneuvering targets;

[0085] Fig. 9 Comparison of target estimation performance under various control schemes for nonlinear maneuvering targets. DETAILED DESCRIPTION

[0086] Embodiments of the present invention are described in detail below, examples of the illustrated embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0087] 1. Technical solution

[0088] 1. Radar communication integrated system

[0089] Reference Figure 1 As shown in the figure, a typical radar communication integrated system includes a control end (command center), a radar end (mounted on a drone), and several targets that need to be tracked and communicated. Among them, the drone (UVA) uses the airborne radar to transmit an integrated signal to the target under the control of the command center to track the target and transmit command information. In this process, the target state is estimated through the relevant tracking filter algorithm, and based on the estimated position of the target, the operation trajectory of the drone and the transmission waveform of the radar signal (radar communication integrated signal) are adjusted in real time to improve the tracking accuracy and communication accuracy.

[0090] Assume that the above radar communication integrated system includes 1 radar terminal and L targets. The system works by cyclically executing transmission integrated signal → receiving target echo → adjusting transmission plan. In a cycle period t cycle There are three time periods: ①t trans , which represents the time it takes for the integrated signal to travel from the radar end to the target and then reflect back, and is used to detect the target and communicate with it; ②t rece , used to perform matched filtering on the target echo to obtain target measurement information, and then estimate the target state according to the tracking filtering algorithm; ③t move , used to adjust the emission waveform and system trajectory according to the target estimated state. The three time periods meet: t cycle =t trans +t rece +t move , and t cycle Less than or equal to the repetition period of the integrated signal pulse. Assuming that only one cycle occurs within a pulse repetition period, the working timing diagram of the entire integrated system is as follows: Figure 2 shown.

[0091] For the kth radar signal pulse interval:

[0092] 1) The state of the lth target is defined as:

[0093]

[0094] The subscript T indicates transposition, xl，k and l，k Respectively represent the position of the target l on the x-axis and y-axis at this time, and They represent the speed of the target l on the x-axis and y-axis respectively. and They represent the acceleration of the target l on the x-axis and y-axis at this time respectively.

[0095] 2) The position of the radar end is defined as:

[0096] X k =[x k ,y k ] T

[0097] The velocity at the radar end is defined as:

[0098]

[0099] where x k and k They represent the position of the radar end on the x-axis and y-axis respectively. and They represent the speed of the radar end on the x-axis and y-axis at this time respectively.

[0100] Note: In this embodiment, for the sake of simplicity, it is assumed that the height position of the target and the radar end remains unchanged, the z-axis is ignored, and only the xy two-dimensional coordinate system parallel to the horizontal plane is established, and only the position and velocity state of the radar end and the target on the horizontal plane are considered. In actual applications, the motion state of the z-axis can also be considered into the state quantity X defined above as needed. l，k , X k 、v k middle.

[0101] 2. Radar communication integrated signal model

[0102] 2.1, in this embodiment, according to the detection and communication task requirements, the LFM-CPM signal is selected as the transmission signal, and the carrier frequency f of the transmission signal is c =35GHz, modulation index h=1 / 4, transmitted information sequence b i ∈{±1, ±3}, the number of communication symbols N = 100. The single pulse signal sent by the integrated system at the kth pulse is:

[0103]

[0104] Where j represents the imaginary part, y represents time, exp(·) represents the exponential function, and s k (t) represents the normalized complex envelope of the transmission signal, and its mathematical expression is:

[0105]

[0106] Among them, rect(·) represents the rectangular function, μ k represents the LFM modulation frequency between the kth pulses, T s，k represents the kth inter-pulse symbol duration, b n ∈{±1,±3} represents the transmitted information sequence.

[0107] 2.2, for the relative distance from the radar end is R l，k , relative speed is v l，k The baseband of the target echo emitted by the target l is expressed as:

[0108]

[0109] Among them, ξ l，k represents the radar cross section of the lth target between the kth pulses, τ l，k =2R l，k / c indicates delay, f d_l，k =2v l，k f c / c represents Doppler frequency shift, c is the speed of light, n l，k (t) indicates that the mean is 0 and the real spectrum density is N 0 / 2 complex Gaussian white noise, N 0 is the noise power density. Note: During the period of communication and detection of the target, trans And the time period t for signal reception and processing rece In the process, the radar end maintains a hovering state (no displacement change occurs).

[0110] 3. Target motion and measurement model

[0111] 3.1, the target motion models commonly considered in current tracking mainly include the constant velocity (CV) model, the constant acceleration (CA) motion model and the coordinated turn (CT) motion model.

[0112] The mathematical representation of the target motion model is:

[0113] X l，k+1 =FX l，k +Gω l，k

[0114] in, represents the state transition matrix, represents the process noise distribution matrix, Indicates that the mean is 0 and the covariance matrix is ​​Q kGaussian white noise vector.

[0115] 1) For the CV model, F and G are defined as:

[0116]

[0117] 2) For the CA model, F and G are defined as:

[0118]

[0119] 3) For the CT model, F and G are defined as:

[0120]

[0121] Among them, ΔT is the sampling period (i.e., pulse repetition period), I 2 is the identity matrix of shape 2*2, represents the angular velocity of the target, represents the Kronecker product.

[0122] 3.2, the measured value of target l between the kth pulses is:

[0123] z l，k =h(X l，k , X k )+∈ l，k

[0124] Among them, h(X l，k , X k )=[R l，k , v l，k ] T is the measurement function, ∈ l，r represents the measurement error for target l between the kth pulses. The above measurement function represents the relative distance R l，k , relative speed is v l，k With the target state X l，k The nonlinear relationship between them is expressed as follows:

[0125]

[0126] Note: This embodiment only takes a two-dimensional coordinate system as an example. If it is a three-dimensional coordinate system, the z-axis variable can be added to the calculation formula of the above relative distance and relative speed.

[0127] 3.3, measurement error ∈ l，k Affected by the measurement distance and waveform resolution characteristics, the measurement distance is closely related to the running trajectory, and the waveform resolution characteristics are closely related to the transmission waveform. l，k The covariance matrix of It can better characterize the influence of running trajectory and transmission waveform on measurement error. The covariance matrix U of measurement error l，k The mathematical representation of is:

[0128] U l，k =ΓC l，k Γ

[0129] Where Γ = diag[c / 2, c / (2f c )] represents the conversion matrix from time delay and Doppler frequency shift to distance and speed, c represents the speed of light, C l，k represents the CRLB (Cramé-Rao bound) for estimating the time delay and Doppler shift of target l between the kth pulses, and CRLB can be estimated by the Fisher information matrix J of parameter estimation l，k The inverse of is obtained, and its mathematical expression is:

[0130]

[0131] 3.4, in order to analyze the impact of radar end running trajectory and transmission waveform on performance, J l，k Expressed as where η l，k It represents the signal-to-noise ratio of the lth target channel between the kth pulses, which is only affected by the radar trajectory; Denotes the Fisher information matrix J l，k The part affected by the waveform. l，k and

[0132] 1)η l，k The mathematical representation of is:

[0133]

[0134] Among them, P t，l，k Indicates the transmit power, T d，l，k represents the signal dwell time, G t，l represents the transmission gain, G r，l represents the receiving gain of the radar, λ is the wavelength of light, σ l represents the radar cross section of the lth target, T r Represents the pulse repetition frequency, N 0 represents the noise power density.

[0135] 2) It is only affected by the transmission waveform and is defined as the transmission waveform ambiguity function χ(τ, f d ) at the target delay τ and Doppler frequency shift f d The Hessian matrix at :

[0136]

[0137] 2.1) The mathematical derivation result is:

[0138]

[0139] in,

[0140]

[0141] 2.2) The mathematical derivation result is:

[0142]

[0143] in,

[0144]

[0145] 2.3) The mathematical derivation result is:

[0146]

[0147] in,

[0148]

[0149] 4. Tracking filter model

[0150] Tracking filtering aims to estimate the target state at the next moment based on the target motion model and measurement data. This embodiment uses extended Kalman filtering to achieve target tracking.

[0151] 4.1, it is known that at time k-1 (between k-1 pulses), the state of the target at the next time (time k) is estimated, and the state estimate value X of the corresponding target l is obtained l，k|k-1 and its estimated covariance P l，k|k-1 After the integrated signal is transmitted at time k, the measurement value z is obtained through echo detection l，k , the estimated value is corrected based on the measured value:

[0152] X l，k|k =X l，k|k-1 +K l，k (z l，k -h(X l，k|k-1 , X k ))

[0153]

[0154] Among them, X l，k|k-1is the state estimate of the target l at time k-1, P l，k|k-1 Its corresponding estimated covariance, X l，k|k is the state correction value of target l at time k, P l，k|k is the corresponding corrected covariance; K l，k is the Kalman gain, S l，k To add covariance, the mathematical expressions of the two are:

[0155]

[0156] Among them, H l，k Denotes the measurement function h(X l，k|k-1 , X k ) in predicting the target state X l，k|k-1 The Jacobian matrix at .

[0157] 4.2, at time k, the state of the target at the next moment is predicted based on the motion model:

[0158] X l，k+1|k =F k+1 X l，k|k

[0159] The corresponding state prediction covariance is:

[0160]

[0161] Among them, F k+1 and G k+1 They represent the state transition matrix F and process noise distribution matrix G between the k+1th pulses respectively.

[0162] 5. Adaptive adjustment of the launch plan

[0163] Taking the launch plan of time k adjusted at time k-1 as an example, based on the estimated state X of the target at time k at time k-1 l，k|k-1 , adjust the running trajectory of the radar end at time k and the waveform of the integrated signal emitted.

[0164] 5.1 Radar and Communication Performance Indicators

[0165] 1) This embodiment uses the target estimation covariance as a measure of radar tracking performance. Estimated target estimation covariance The mathematical expression of the trace is:

[0166]

[0167] Where Tr(·) represents the trace function, represents the estimated measurement error covariance (i.e., U l，k The estimated value of is expressed mathematically as:

[0168]

[0169] in, represents the estimated measurement signal-to-noise ratio (i.e., η l，k The estimated value of is expressed mathematically as:

[0170]

[0171] in is the estimated relative distance of target l from the radar end (i.e. R l，k Since the actual distance R l，k is the unknown target state X l，k function, so the target state estimate X is used l，k|k-1 Calculate the estimated relative distance To replace R l，k :

[0172]

[0173] Right now:

[0174]

[0175] Substitute the velocity v of the radar end at time k k After that, the above formula becomes:

[0176]

[0177] Right now:

[0178]

[0179] Among them, t x is the movement time of the radar end between each pulse, v l，k Relative speed The estimated value of .

[0180] 2) In the field of communication, the bit error rate is an important indicator for measuring the effectiveness of communication. Therefore, similar to the tracking indicator, this embodiment uses the estimated bit error rate as a communication performance measurement indicator. The estimated bit error rate of the integrated system is:

[0181]

[0182] in, represents the Q function, d min represents the normalized minimum Euclidean distance, SNR l，k It represents the estimated signal-to-noise ratio of the communication channel, and its mathematical expression is:

[0183] SNR l，k =Pt，l，k h l，k / N 0

[0184] Among them, h l，k It represents the estimated channel gain, and its mathematical expression is:

[0185]

[0186] Among them, G c，l Indicates the target receiving antenna gain.

[0187] 5.2 Optimization Model

[0188] In order to comprehensively improve the target tracking accuracy and communication accuracy, this embodiment adopts the following method to jointly optimize and adjust the radar end operation trajectory and transmission waveform.

[0189] Since the radar performance index and the communication performance index have different dimensions, the radar performance index P is first calculated. l，k And communication performance index B l，k Normalize them and then sum the normalized results. Based on this, the goal of joint optimization is to minimize the above sum:

[0190]

[0191] Among them, Φ(·) represents the normalization operation, μ min and μ max They represent LFM-CPM modulation frequency μ k The minimum and maximum values ​​of T s，min and T s，max Respectively represent the communication symbol duration T s，k The minimum and maximum values ​​of are, and Cons represents the operating limit of the radar end. For the convenience of representation, the above objective function is recorded as

[0192] 5.3, Optimization Problem Solving

[0193] Objective Function There are three related parameters involved, namely, the modulation frequency μ k , symbol duration T s，k , the speed v at the radar end k From a mathematical point of view, the objective function is nonlinear and non-convex, and the modulation frequency, the duration of the coincidence, and the speed of the radar end are coupled. Therefore, the above optimization problem It is difficult to directly obtain the optimal solution. In this regard, this embodiment provides the following solution method:

[0194] 1) Problem decomposition

[0195] According to the expressions of target estimation covariance and communication bit error rate, it can be seen that the former is related to the transmission waveform and the radar end running trajectory, while the latter is only related to the radar end running trajectory. Therefore, the present invention divides the parameters into two parts: those related to the transmission waveform and those related to the running trajectory. It can be decomposed into the transmission waveform optimization sub-problem and radar trajectory optimization subproblem The mathematical representations of these two optimization subproblems are:

[0196]

[0197] in, and For the optimization problem The solved μ k and T s，k Optimal solution.

[0198] 2) Transmitting waveform optimization

[0199] For optimization problems It is still a non-convex nonlinear optimization problem, which is difficult to solve directly. In this regard, this embodiment adopts the RGS (Rectangular grid search) algorithm to solve it. The RGS algorithm is computationally convenient and effective in waveform parameter optimization problems. It iteratively searches the waveform space to find the waveform that minimizes the objective function.

[0200] Specifically, in the mth round of iterative search, the RGS algorithm sets μ of the rectangular area to be searched k and T s，k The intervals of the two dimensions are and In each rectangular search area, the parameter μ k and T s，k In terms of dimensions, there are N μ and N T grid points. That is, for the parameter μ k The dimensions of the grid are For parameter T s，k The dimensions of the grid are The above grid points are evenly distributed on and The RGS algorithm searches for the optimal parameters and

[0201]

[0202] In the subsequent iterative search of the m+1th round, and as the center of the new search area, and based on the points around them and Form a new rectangular area to be searched and

[0203] Note: The superscripts (m) and (m+1) are used to indicate the number of iterations.

[0204] The above process is repeated until the preset number of iterations is reached, and the final optimization result is output. and

[0205] 3) Trajectory optimization

[0206] In getting and Then, substitute it into the radar trajectory optimization sub-problem

[0207] Among them, Cons x Cons y They respectively represent the limits of the radar's running distance at each moment in the x-axis and y-axis directions.

[0208] Objective Function It is non-convex, so this embodiment uses the gradient descent method to solve it.

[0209] Specifically, suppose that in the mth iteration, to fit the objective function, At a given point and Implement a first-order Taylor expansion:

[0210]

[0211] in and Respectively represent functions about and The gradient of . The last two terms on the right side of the equation are written as The optimization problem Translates to:

[0212]

[0213] Use the search method to solve the above objective function and get and

[0214] Since the above optimization problem is a minimization problem, and Representation parameters and The objective function can be obtained by searching The step length ω * ∈[0, 1]. The given point of the mth iteration is obtained by the following formula for the next iteration:

[0215]

[0216] The above process is repeated until the preset number of iterations is reached, and the final optimization result is output. and Then according to X k-1 Calculate the position X of the radar end at time k k .

[0217] In summary, refer to Figure 3 As shown in the figure, the cyclic control process of the radar end is:

[0218] Step 1: At the current time (time k), based on the launch plan determined at the previous time (time k-1), the radar end k The radar communication integrated signal is sent to the target at the position, where the frequency modulation is μ k , symbol duration is T s，k ;

[0219] Step 2: After receiving the echo, use the maximum likelihood method to obtain the target measurement value z l，k , and use the Kalman filter algorithm to estimate the target state value X l，k|k-1 and P l，k|k-1 Make corrections and get the correction value X l，k|k and P l，k|k ; Then estimate the target state X at the next moment (k+1 moment) based on the target motion model l，k+1|k and P l，k+1|k ;

[0220] Step 3: Based on the estimated state value X of the target at the next moment (k+1 moment) l，k+1|k , for the optimization problem P 0 Solve and get the emission plan for the next moment (k+1 moment), that is, μ k+1 , T s，k+1 , X k+1 .

[0221] 2. Simulation Test

[0222] This embodiment uses Matlab simulation software for simulation testing, where the integrated system includes 1 radar end and 2 targets, and the value range of the LFM-CPM modulation frequency of the integrated signal waveform is μ min=0.01MHz / s,μ max =1MHz / s, the symbol duration range is T s，min =1μs, T s，max =100μs, sampling period ΔT=1s.

[0223] Initial position X of the radar end 1 =[x 1 ,y 1 ] T =[100m, 100m] T , the initial positions of both targets are [800m, 1000m] T . Target radar cross section ξ l，k The value range is (0, 1], the noise power N 0 =-110dBm, transmit power P t，l，k =10dBm, carrier frequency f c =35GHz, transmission gain G t，l =35dB, radar receiving gain G r，l =36dB, target receiving antenna gain G c，l =36dB, covariance matrix Q k =diag([0.01, 0.01]), radar speed limit Cons x Cons y All simulation results are obtained by Monte Carlo method.

[0224] In order to verify the performance of the technical solution of the present invention, this embodiment selects five representative existing technical means and conducts comparative tests with the present invention in terms of target tracking accuracy and bit error rate. The selected existing technical means are:

[0225] ① Fixed radar transmits a fixed waveform (Fixed Location and Fixed Waveform, FLFW) scheme, in which the frequency modulation rate is 0.1MHz and the symbol duration is 80μs;

[0226] ② Fixed Location and Optimized Waveform (FLOW) scheme for transmitting adaptive optimal waveform for fixed radar, in which RGS algorithm is used to optimize the integrated transmitting waveform;

[0227] ③ The dynamic radar maintains a constant speed and transmits a fixed waveform (Fixed Velocity and Fixed Waveform, FVFW) scheme, in which the radar runs at a speed of -2m / s on the x-axis and 3m / s on the y-axis, the frequency modulation rate is 0.1MHz, and the symbol duration is 80μs.

[0228] ④ The radar maintains a constant speed and transmits an adaptive optimal waveform (Fixed Velocity and Optimized Waveform, FVOW) solution, where the radar runs at a speed of -2m / s on the x-axis and 3m / s on the y-axis, and the RGS algorithm is used to optimize the integrated waveform.

[0229] ⑤The radar runs at the optimal speed and transmits a fixed waveform (Optimized Velocity and Fixed Waveform, OVFW) scheme, in which the trajectory optimization algorithm is used to obtain the optimal trajectory, the frequency modulation rate is 0.1MHz, and the symbol duration is 80μs.

[0230] Comparative test results such as Figures 4 to 9 As shown, Figures 4 to 6 The target is shown as performing a linear maneuver. Figure 7 to Figure 9 The target is shown performing nonlinear maneuvers.

[0231] Figure 4 The curve of the average bit error rate changing with the sampling time when tracking a linear maneuvering target is shown in the figure. As shown in the figure: the method proposed in the present invention can adaptively adjust the running trajectory of the radar end according to the change of the target's motion state, thereby maintaining a closer distance with the tracked target and ultimately achieving a lower bit error rate.

[0232] Figure 5 The comparison diagram of the target running trajectory estimated by each scheme and the real target running trajectory is shown. From the comparison results, it can be seen that the scheme proposed by the present invention performs best, that is, the estimated trajectory result is closest to the real running trajectory.

[0233] Figure 6 A curve showing the variation of the mean square error of the target's position and velocity estimation with the sampling time is given. The figure shows that the present invention achieves the lowest mean square error of the position and velocity estimation.

[0234] Figure 7 The curves of the average bit error rate of each scheme changing with the sampling time when tracking a nonlinear maneuvering target are shown. It can be seen from the figure that the present invention adaptively adjusts the radar end running trajectory to maintain a closer distance to the tracking target. A closer communication distance can achieve a lower bit error rate.

[0235] Figure 8The comparison between the target running trajectory estimated by each scheme and the target real running trajectory is given. It can be seen that the trajectory result estimated by the present invention is closest to the target real running trajectory.

[0236] Fig. 9 The curve of the average mean square error of the target position and speed estimation versus the sampling time is given. It can be seen that the present invention achieves the lowest average mean square error of the position and speed estimation.

[0237] III. Devices, Storage Media, and Program Products

[0238] 1. Based on the same inventive concept as the above-mentioned radar communication integrated control method, the present application also provides an electronic device, which includes a processor and a memory, in which a computer-readable code is stored, wherein when the computer-readable code is executed by the processor, the radar communication integrated control method of the present invention is implemented.

[0239] Among them, the memory includes a non-volatile storage medium and an internal memory; the non-volatile storage medium can store an operating system and a computer-readable code. The computer-readable code includes program instructions, which, when executed, can enable the processor to execute the radar communication integrated control method. The processor is used to provide computing and control capabilities to support the operation of the entire electronic device. The memory provides an environment for the operation of the computer-readable code in the non-volatile storage medium, and when the computer-readable code is executed by the processor, the processor can execute the radar communication integrated control method.

[0240] It should be understood that the processor may be a central processing unit, other general-purpose processors, digital signal processors, application-specific integrated circuits, field programmable gate arrays or other programmable logic devices, transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or any conventional processor.

[0241] 2. The present application also provides a readable storage medium, which may be an internal storage unit of the electronic device described in the aforementioned embodiment, such as a hard disk or memory of the computer device. The readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, smart memory card, secure digital card, etc. equipped on the electronic device.

[0242] 3. The present application also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements the radar communication integrated control method of the present invention.

[0243] In the description of the present invention, it is necessary to understand that the terms "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0244] The present invention is not limited to the above-mentioned embodiments. Any obvious improvement, substitution or deformation that can be made by those skilled in the art without departing from the essential content of the present invention belongs to the protection scope of the present invention.

Claims

1. A radar communication integrated control method, characterized in that: include: Estimate the state of the target at the next moment, including the target's position and speed information; Based on the estimated value of the target state at the next moment, the target estimation covariance and bit error rate are used as measurement indicators to jointly optimize the radar end operation trajectory and the integrated signal transmission waveform to obtain the transmission plan at the next moment, including the trajectory position of the radar end, the modulation frequency of the waveform and the symbol duration.

2. The radar communication integrated control method according to claim 1, characterized in that: The objective function of the joint optimization problem is: Among them, μ k To adjust the frequency, T s,k is the symbol duration, v k is the speed of the radar end, the subscript k indicates the time k, Φ(·) indicates the normalization operation, L is the total number of targets, l indicates the lth target, P l,k is the target estimated covariance, B l,k Indicates the estimated bit error rate.

3. The radar communication integrated control method according to claim 2, characterized in that: Target estimated covariance P l,k Calculated by the following formula: Where Tr(·) represents the trace function, P l,k|k-1 represents the target estimated covariance obtained by estimating the target l at time k at time k-1, X l,k|k-1 It represents the target state obtained by estimating the target l at time k at time k-1, H l,k Denotes the measurement function h(X l,k|k-1 ,X k ) in predicting the target state X l,k|k-1 The Jacobian matrix at k is the position of the radar end, Γ=diag[c / 2,c / (2f c )] represents the conversion matrix from time delay and Doppler frequency shift to distance and speed, c is the speed of light, f c is the carrier frequency, represents the estimated measurement signal-to-noise ratio, Denotes the Fisher information matrix J l,k The part of the waveform that is affected by the waveform; The expression is: Among them, P t,l,k Indicates the transmit power, T d,l,k represents the signal dwell time, G t,l represents the transmission gain, G r,l represents the receiving gain of the radar, λ is the wavelength of light, σ l represents the radar cross section of the lth target, T r represents the pulse repetition frequency, N0 represents the noise power density, is the estimated relative distance of target l from the radar end, which is determined by the measurement function Calculate and obtain, To estimate the relative velocity, X k-1 represents the radar end position at time k-1; The expression is: in The expression is: The expression is: The expression is: Where h is the modulation index, N is the number of communication symbols, and b n Represents a sequence of information being transmitted.

4. The radar communication integrated control method according to claim 3, characterized in that: Estimated bit error rate B l,k Calculated by the following formula: in, represents the Q function, d min represents the normalized minimum Euclidean distance, SNR l,k It represents the estimated signal-to-noise ratio of the communication channel, and its mathematical expression is: SNR l,k =P t,l,k h l,k / N0 Among them, h l,k It represents the estimated channel gain, and its mathematical expression is: Among them, G c,l Indicates the target receiving antenna gain.

5. The radar communication integrated control method according to claim 4, characterized in that: The joint optimization problem is solved as follows: S1, the objective function This breaks down into: in, and Respectively represent μ k and T s,k The optimal solution of S2, using RGS algorithm Solve it and get and S3, and Substitution Using gradient descent method Solve and get v k The optimization results 6. The radar communication integrated control method according to claim 1, characterized in that: The extended Kalman filter is used for tracking filtering to estimate the target state at the next moment: T1, based on the measured value z obtained by echo detection l,k , for the estimated value X l,k|k-1 and P l,k|k-1 To make corrections: X l,k|k =X l,k|k-1 +K l,k (z l,k -h(X l,k|k-1 ,X k )) Among them, X l,k|k-1 is the state estimate of the target l at time k-1, P l,k|k-1 Its corresponding estimated covariance, X l,k|k is the state correction value of target l at time k, P l,k|k is the corresponding corrected covariance; K l,k is the Kalman gain, S l,k To add covariance, the mathematical expressions of the two are: Among them, H l,k Denotes the measurement function h(X l,k|k-1 ,X k ) in predicting the target state X l,k|k-1 The Jacobian matrix at l,k =ΓC l,k Γ is the covariance matrix of the measurement error, Γ=diag[c / 2,c / (2f c )] represents the conversion matrix from time delay and Doppler frequency shift to distance and speed, c represents the speed of light, C l,k represents the Cramer-Rao bound for the time delay and Doppler shift estimation of target l between the kth pulses; T2, predict the state of the target at the next moment: X l,k+1|k =F k+1 X l,k|k Among them, F k+1 and G k+1 They represent the state transition matrix F and process noise distribution matrix G between the k+1th pulses respectively; For the uniform motion model, f and G are defined as: For the uniform acceleration motion model, F and G are defined as: For the long turning motion model, F and G are defined as: Among them, ΔT is the sampling period (i.e., the pulse repetition period), I2 is the unit matrix of 2*2 shape, represents the angular velocity of the target, represents the Kronecker product.

7. The radar communication integrated control method according to claim 2, characterized in that: The state of a target is defined as: Among them, x l,k and l,k They represent the positions of the target l on the x-axis and y-axis at time k, respectively. and They represent the speed of target l on the x-axis and y-axis at time k, respectively. and They represent the acceleration of target l on the x-axis and y-axis at time k respectively. The position of the radar end is defined as: X k =[x k ,y k ] T The velocity at the radar end is defined as: where x k and k They represent the position of the radar end on the x-axis and y-axis at time k, respectively. and They represent the speed of the radar end on the x-axis and y-axis at time k respectively.

8. A computer device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the radar communication integrated control method according to any one of claims 1 to 7 when executing the computer program.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor executes the radar communication integrated control method according to any one of claims 1 to 7.

10. A computer program product, characterized in that: It comprises a computer program, which, when executed by a processor, implements the radar communication integrated control method as claimed in any one of claims 1 to 7.

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