Intra- and inter-pulse joint constrained short code optimization method

Abstract

The application relates to a short code optimization method of intra-pulse-inter-pulse joint constraint, and belongs to the field of radar signal processing. According to current distributed radar transmission system and system requirements, long code and short code mathematical representations of a two-phase code waveform signal model are established; based on the long code and short code mathematical representations, radar transmission waveform target optimization functions of intra-pulse-inter-pulse joint constraint are established from intra-pulse and inter-pulse two-dimensional orthogonality; based on the radar transmission waveform target optimization functions, an immune genetic algorithm is adopted to sequentially perform initialization population, diversity evaluation, immune selection, cross and mutation and population merging on the transmission waveform; according to optimal excitation degree and average fitness as a judgment condition, a set of waveforms of intra-pulse-inter-pulse two-dimensional orthogonality optimization is designed as the transmission waveform. By adding intra-pulse-inter-pulse two-dimensional joint orthogonal constraint orthogonality, the orthogonality of the short code waveform is enhanced, and the purpose of improving the distance resolution and positioning accuracy under the detection mode of multiple radars is achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of radar signal processing, and relates to a radar transmitting waveform intra-pulse-inter-pulse joint optimization method under the simultaneous operation of multiple radars. BACKGROUND

[0002] When working in cooperation, each node of a distributed radar or networked radar generally selects different working frequency bands or time-sharing working modes to ensure complementary interference between each other. However, with the rapid development of wide-band, multi-function, wide-area distribution and other electronic devices, the spectrum environment becomes increasingly congested, and mutual interference between adjacent multiple radar systems easily occurs, resulting in a sharp decline in the performance of the radar system.

[0003] Radar waveform optimization is the core key for multiple radar nodes to realize the same space, same time and same frequency band operation. The existing radar waveform optimization mainly uses code division waveforms, but there are the following two problems: 1) In order to shorten the near distance blind area, a short code signal with a short time width is generally used as a transmitting waveform, and the cross-correlation characteristics of the short code signal are poor, which makes it difficult to effectively separate at the receiving end; 2) The existing waveform optimization method mainly considers intra-pulse optimization, that is, only the optimization performance of multiple transmitting waveforms in a single pulse is considered, so the correlation characteristics between pulses are ignored, resulting in the phenomenon of high sidelobe in the range-Doppler domain when multiple pulses are accumulated, which seriously affects the radar detection performance.

[0004] Since the power amplifier at the radar transmitting end works in a saturated state, the designed orthogonal waveform needs to have a constant modulus characteristic. Due to the consideration of the constant modulus constraint, the waveform design problem is converted into a non-convex optimization problem, and a suitable optimization function needs to be selected according to the cost function and the characteristics of the application scenario and other factors. The existing methods mainly include a cyclic algorithm-new (CAN) and a weighted cyclic algorithm-new (We CAN), which can obtain a unit modulus waveform sequence with low integrated sidelobe level (ISL) performance. However, the calculation speed of the algorithm will significantly increase with the increase of the sequence length, and the calculation complexity is high, which is not suitable for long sequence optimization.

[0005] In order to solve the problem of slow optimization speed of long sequence waveform, the existing method adopts the minimization thought and the projection descent shrinkage method, models the problem on the complex circle flow, and can directly optimize the original optimization problem, but the algorithm cannot handle the scene of a large number of transmitting antennas at present. In order to further improve the orthogonality of the transmitting waveform, a mismatch filter bank can be designed on the basis of the given orthogonal waveform. The existing method optimizes the orthogonal waveform and the mismatch filter bank at the same time, and proposes a main lobe broadening orthogonal waveform design method, which further reduces the low autocorrelation peak sidelobe level (APSL) and the low peak cross correlation level (PCCL) compared with the separate design method.

[0006] In general, due to the slow optimization speed of the traditional waveform optimization method and the optimization result not close to the optimal solution, it is difficult to meet the high-precision detection demand.

[0007] In view of the above problems, the present application provides a short code optimization method of intra-pulse-inter-pulse joint constraint, which converts the traditional one-dimensional waveform optimization method into an intra-pulse-inter-pulse two-dimensional joint orthogonality constraint problem by introducing an intra-pulse-inter-pulse joint constraint term, and combines the existing mainstream immune genetic algorithm (IGA) to accelerate optimization, effectively alleviates the problem of premature convergence of the traditional genetic algorithm (GA), improves the two-dimensional orthogonality of the transmitting waveform in the intra-pulse-inter-pulse, and effectively reduces the mutual interference between radars from the waveform design level, and improves the utilization rate of spectrum resources. SUMMARY

[0008] The technical problem to be solved by the present application is:

[0009] In view of the problem of radar detection performance decline caused by poor orthogonality of the transmitting short code waveform of the focused multi-radar, the present application provides a networked radar short code optimization method based on an immune clone algorithm and intra-pulse-inter-pulse joint constraint. By adding the intra-pulse-inter-pulse two-dimensional joint orthogonality constraint orthogonality, the convergence of the short code waveform nonlinear multi-optimization problem is accelerated, the orthogonality of the short code waveform is enhanced, and the purpose of improving the range resolution and positioning accuracy in the multi-radar detection mode is achieved.

[0010] To solve the above technical problems, the technical scheme adopted by the present application is:

[0011] A short code optimization method of intra-pulse-inter-pulse joint constraint, characterized in that it comprises:

[0012] According to current distributed radar launch system and the requirement of system, long code and short code mathematical representation of two-phase code waveform signal model is established;

[0013] Based on long code and short code mathematical representation, radar launch waveform target optimization function of joint constraint of intra-pulse and inter-pulse is established from intra-pulse and inter-pulse two-dimensional orthogonality;

[0014] Based on radar launch waveform target optimization function, immune genetic algorithm is adopted to successively carry out initialization population, diversity evaluation, immune selection, cross and variation and combined population of launch waveform;

[0015] According to optimal excitation degree and average fitness as a decision condition, a group of waveforms of intra-pulse and inter-pulse two-dimensional orthogonality optimum is designed as launch waveform.

[0016] The further technical scheme of the present application is that according to current distributed radar launch system and the requirement of system, long code and short code mathematical representation of two-phase code waveform signal model is established, and specifically:

[0017] For a signal number L, sub-pulse number P and sub-pulse length N of orthogonal polyphase code set S and its phase sequence are represented as:

[0018] S=[s1,s2,...,s l ,...,s L ] T

[0019] Wherein, the dimension of phase matrix S is L*N*P, is the lth long code signal, is the launch phase of the lth launch long code signal, and the long code form phase matrix of S is further represented as:

[0020]

[0021] Wherein, is the phase of the nth element of the pth sub-pulse of the lth signal, and the short code form signal phase matrix of S is further represented as:

[0022]

[0023] The further technical scheme of the present application is that the radar launch waveform target optimization function of joint optimization of intra-pulse and inter-pulse is established from intra-pulse and inter-pulse two-dimensional orthogonality, and specifically:

[0024] According to long code and short code mathematical representation, the orthogonality constraint is obtained as:

[0025]

[0026] Wherein, autocorrelation peak side lobe level of a short code sequence representing all orthogonal transmitted signals, cross-correlation peak level of all transmitted signals, w1 represents the proportion of the two optimization functions;

[0027] Assuming the pulse repetition frequency is PRF, the fast time sampling frequency is F s , the number of fast time sampling points is N, the number of pulse accumulations in a coherent processing period is P, the MTD matrix of size N×P is Y(k, f d , the loss function with the minimum peak side lobe level as the constraint is represented as:

[0028]

[0029] Wherein, Δf=PRF / P is the Doppler frequency interval.

[0030] A computer system, characterized in comprising: one or more processors, a computer readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0031] A computer readable storage medium, characterized in storing computer executable instructions, the instructions when executed are used to implement the above method.

[0032] A computer program product, characterized in comprising computer executable instructions, the instructions when executed are used to implement the above method.

[0033] The beneficial effects of the present application are:

[0034] The present application aims at the problem of poor orthogonality and weak adaptability of short code waveform of networked radar, and carries out the design research of two-phase code waveform of intra-pulse-inter-pulse joint optimization. Firstly, the long code form and short code form of two-phase code waveform are modeled and analyzed, and the waveform optimization criteria are determined as autocorrelation peak sidelobe level (APSL), peak cross correlation level (PCCL) and peak sidelobe ratio of MTD result. Then, combined with the immune idea, the non-linear multi-objective optimization problem is modeled and analyzed. Finally, combined with simulation experiment, the waveform optimization criteria and immune genetic algorithm are verified to accelerate the optimization speed of algorithm and improve the effectiveness of generated code type performance. Compared with the waveform optimized by genetic algorithm, the present application can relieve the premature problem of genetic algorithm, accelerate the convergence of optimization process, and the obtained result is closer to the optimal solution; compared with the waveform without using intra-pulse-inter-pulse two-dimensional constraint, the present application effectively reduces the sidelobe peak of moving target detection result, and improves the probability of correct detection of target. BRIEF DESCRIPTION OF DRAWINGS

[0035] The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:

[0036] Figure 1 (a) is the model of two-phase code waveform, assuming that the phase encoding set is composed of L signals, each signal is called long code; each signal is composed of P sub-pulses, each sub-pulse is called short code, which is composed of N complex arrays with a modulus of 1.

[0037] Figure 1 (b) is the correspondence between the waveform and the short code pulse.

[0038] Figure 1 (c) is the correspondence between each individual in the population and the waveform.

[0039] Figure 2 is the main steps of immune genetic algorithm combined with the characteristics of genetic algorithm and immune system.

[0040] Figure 3 (a) is the fitness function of experimental group 3 (GA algorithm) and experimental group 4 (IGA algorithm) with the change of training round number, which is used for comparative experiment;

[0041] Figure 3 (b) and (c) are the autocorrelation local of the optimized code type results of experimental group 3 (GA algorithm) and experimental group 4 (IGA algorithm) respectively, which are used for comparative observation of the orthogonality performance of generated two-phase code sequence;

[0042] Figure 3(d) and (e) are the autocorrelation peak side lobe ratio and cross-correlation peak level of the optimized code pattern of experiment group 3 (GA algorithm) and experiment group 4 (IGA algorithm) respectively;

[0043] Figure 4 (a) is the fitness curve of experiment groups 4 and 6 (group 6 contains MTD constraint) ;

[0044] Figure 4 (b) is the MTD result of the optimized waveform of experiment group 4, containing 5dB Gaussian noise;

[0045] Figure 4 (c) is the MTD result of the optimized waveform of experiment group 6, containing 5dB Gaussian noise;

[0046] Figure 5 Flow chart of the method of the present application. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0048] The present application provides a short code optimization method with intra-pulse-inter-pulse joint constraint, as shown in the following figure: Figure 5 First, according to the current distributed radar transmission system and system requirements, the long code and short code mathematical representation of the bi-phase code waveform signal model is established; secondly, starting from the intra-pulse and inter-pulse two-dimensional orthogonality, the radar transmission waveform target optimization function of intra-pulse-inter-pulse joint optimization is established; then, combining the immune genetic algorithm, the initialization population of the transmission waveform, diversity evaluation, immune selection, crossover and mutation, and the merging of the population are completed; finally, according to the optimal excitation degree and average fitness as the judgment condition, a group of waveforms with optimal intra-pulse-inter-pulse two-dimensional orthogonality is designed as the transmission waveform. The specific steps are as follows:

[0049] Step 1: Establish the long code model and short code model of the bi-phase code signal. For an orthogonal polyphase code set S with a signal number L, a sub-pulse number P, and a sub-pulse length N, and its phase sequence can be represented as:

[0050] S = [s1, s2,..., s l ,...,s L ] T (1)

[0051] wherein the dimension of the phase matrix S is L x N x P, is the lth long code signal, is the transmit phase of the first transmitted long code signal. The long code form phase matrix of S can be further expressed as:

[0052]

[0053] wherein, is the phase of the nth element of the pth sub-pulse of the lth signal. The signal phase matrix of the short code form of S can be further expressed as:

[0054]

[0055] Step 2: According to the model established in step 1, the orthogonality constraint can be obtained as:

[0056]

[0057] wherein, represents the autocorrelation peak side lobe level of the long code sequence of all the orthogonal transmitted signals, represents the cross-correlation peak level of all the transmitted signals, and w1 represents the proportion of the two optimization functions.

[0058] Suppose the pulse repetition frequency is PRF, the fast time sampling frequency is F s , the fast time sampling point number is N, and the pulse accumulation number of one coherent processing period is P. The MTD matrix Y(k, f d ) of size N×P can be obtained. The loss function with the constraint of minimizing the peak side lobe level can be expressed as:

[0059]

[0060] wherein, Δf=PRF / P is the Doppler frequency interval.

[0061] Step 3: Using the genetic algorithm and the immune genetic algorithm, comparative experiments are conducted on the immune algorithm and the intra-pulse and inter-pulse two-dimensional constraint under different constraint combinations in step 2. The input parameters of the genetic algorithm and the immune genetic algorithm are one population. One population is composed of a plurality of individuals, and each individual is a one-dimensional vector composed of all code elements in one repetition period of all types of waveforms, as shown in Figure 1 . The output parameters are the population modified through operations such as crossover, mutation and immunity, and the individual with the optimal fitness is taken out according to the requirement.

[0062] In order for those skilled in the art to better understand the present application, the present application will be described in detail below in conjunction with specific embodiments.

[0063] The parameters of the orthogonal two-phase code waveform of the embodiments of the present application and the parameters of the genetic algorithm and the parameters of the immune genetic algorithm are shown in Tables 1-3.

[0064] Table 1 Experimental group arrangement

[0065]

[0066] Table 2 Transmit waveform requirements

[0067]

[0068] Table 3 GA algorithm and IGA algorithm parameters

[0069]

[0070] The autocorrelation function of the first transmit waveform of the radar and the cross-correlation function between different transmit waveforms can be expressed as:

[0071]

[0072]

[0073] Wherein, A1(s l ,k) represents the autocorrelation function of the first code signal, C1(φ p ,φ q ,k) represents the cross-correlation function of the pth and qth signals, and k represents the discrete time index.

[0074] The autocorrelation function of the pth short code signal of the first transmit waveform of the radar and the cross-correlation function of the pth and qth short code signals can be expressed as:

[0075]

[0076]

[0077] Wherein, A2(s lp ,k) represents the autocorrelation function of the pth short code signal of the first transmit waveform, C2(s l ,φ p ,φ q ,k) represents the cross-correlation function of the pth and qth short code signals of the first transmit waveform, and k represents the discrete time index.

[0078] From the perspectives of IGA algorithm and intra-pulse inter-pulse joint constraint:

[0079] (1) Compared with the GA algorithm, the immune genetic algorithm converges at about 1000 rounds under the condition of the same fitness function, and the convergence speed is faster than that of the genetic algorithm. By comparing the performance of the binary code sequence generated by the GA algorithm and the IGA algorithm, the autocorrelation and cross-correlation characteristics of the experimental group are better. From the perspective of the autocorrelation and cross-correlation characteristics of the experimental group, the autocorrelation and cross-correlation characteristics of the experimental group are better. Figure 3It can be seen that the code pattern generated by the IGA algorithm has a higher PCCL, which is 2-3 dB higher than the GA algorithm on average; and has a lower APSL, which is 2-4.5 dB lower than the GA algorithm. It is proved that the IGA algorithm alleviates the premature problem of the GA algorithm, and the obtained result is closer to the optimal solution.

[0080] (2) After adding the MTD constraint, the code pattern is Figure 4 (a) It can be seen that under the premise of using the same IGA algorithm, the convergence speed of the fitness function of the code pattern training is greatly accelerated; and Figure 4 (b) and (c) It can be seen that the addition of the MTD constraint effectively reduces the sidelobe peak value of the MTD result, and improves the probability of correctly detecting the target.

[0081] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A method for intra- and inter-pulse joint constrained short code optimization, characterized in that, The method comprises the following steps: According to the current distributed radar launch system and the requirements of the system, long code and short code mathematical representations of the two-phase code waveform signal model are established; Based on the long code and short code mathematical representations, a radar launch waveform target optimization function with intra-pulse and inter-pulse joint constraints is established from the intra-pulse and inter-pulse two-dimensional orthogonality, and the function is specifically: According to the long code and short code mathematical representations, the orthogonality constraint is obtained as: wherein represents the autocorrelation peak side lobe level of the long code sequence of all orthogonal transmitted signals, represents the cross-correlation peak level of the short code of all transmitted signals, represents the proportion of these two optimization functions; Assuming the pulse repetition frequency is , the fast time sampling frequency is , the fast time sampling point number is , the pulse accumulation number of a coherent processing period is , the MTD matrix with a size of is obtained as , and the loss function expression with the constraint of minimizing the peak side lobe level is obtained as wherein is the Doppler frequency spacing; Based on the radar launch waveform target optimization function, an immune genetic algorithm is used to sequentially perform launch waveform initialization population, diversity evaluation, immune selection, crossover and mutation, and population merging; According to the optimal excitation degree and the average fitness as the judgment condition, a set of waveforms with optimal intra-pulse and inter-pulse two-dimensional orthogonality is designed as the launch waveform.

2. The method of claim 1, wherein the method is a method of intra- and inter- pulse joint constrained short code optimization. The long code and short code mathematical representations of the two-phase code waveform signal model are established according to the current distributed radar launch system and the requirements of the system, and the method comprises the following steps: For a signal number of , a sub-pulse number of , and a sub-pulse length of , the orthogonal polyphase code set and its phase sequence are represented as: where the phase matrix has dimension , is the th long code signal, is the th transmit phase for transmitting the long code signal, the long code form phase matrix is further expressed as: wherein, is the phase of the first element of the first sub-pulse of the first signal; is the phase of the first element of the first sub-pulse of the first signal; is the phase of the first element of the first sub-pulse of the first signal; is the phase of the first element of the first sub-pulse of the The signal phase matrix in short code form of Equation (1) is further represented as: 。 3. A computer system, characterized by The method comprises the following steps: One or more processors, a computer readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of claim 1.

4. A computer-readable storage medium, characterized in that Computer executable instructions are stored, and the instructions are executed to implement the method of claim 1.

5. A computer program product, characterised in that Computer executable instructions are stored, and the instructions are executed to implement the method of claim 1.

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