A Method for Optimizing the Design of Wideband Radar Inter-Pulse Phase-Coded Waveforms

Abstract

A method for optimizing the design of a broadband radar inter-pulse phase-coded waveform of the present invention uses chaotic mapping to generate multiple groups of phase-coded sequences from M different initial values, where M is a positive integer; the genetic algorithm is used to optimize the multiple groups of phase-coded sequences to obtain an optimal group of coded sequences. By using chaotic mapping to generate the initial values of large-scale uncorrelated sequences, the search space of the genetic algorithm is reduced, and the global search ability of the genetic algorithm is improved, so as to realize the rapid and robust design of large-scale orthogonal phase-coded waveforms and produce phase-coded sequences with strong large-scale orthogonal performance.

Description

Technical Field

[0001] The present invention relates to the technical field of broadband radar, and particularly relates to an optimized design method for the inter-pulse phase-coded waveform of a broadband radar. Background Art

[0002] Existing radars generally have disadvantages such as low resolution and weak anti-interference ability. Broadband radars have attracted extensive research by scholars due to their high-resolution ability and anti-interference ability. At present, broadband radars are mainly implemented based on the MIMO (multiple input multiple output) system. MIMO radars need to transmit mutually orthogonal signal waveforms between array element antennas, so as to separate the signals of each channel according to the orthogonality of the echoes at the receiving end, providing a basis for subsequent high-precision parameter estimation. There are various methods to achieve waveform orthogonality, such as time diversity, frequency diversity, coding diversity, etc. Different orthogonal waveforms occupy different system resources, and the implementation difficulty is also different.

[0003] Vehicle-mounted broadband MIMO radars are currently a research hotspot. For vehicle-mounted radars, it is necessary to comprehensively consider requirements such as low complexity, low cost, and waveform orthogonality, and select a suitable orthogonal method to design the waveform. Compared with orthogonal waveforms such as time diversity and frequency diversity, the inter-pulse phase-coded orthogonal waveform does not require additional transmission time or transmission bandwidth, and can make full use of system resources; compared with orthogonal waveforms such as polarization diversity and frequency modulation diversity, the phase-coded orthogonal waveform is simpler to generate. Therefore, the phase-coded orthogonal waveform is more suitable for application in vehicle-mounted radars.

[0004] Regarding the optimized design method for the phase-coded orthogonal waveform, common optimization algorithms include heuristic algorithms and exact algorithms. Exact algorithms include linear programming, dynamic programming, etc.; heuristic algorithms mainly include genetic algorithms, particle swarm algorithms, simulated annealing algorithms, etc. The method proposed in this patent is mainly implemented based on the improvement of the genetic algorithm.

[0005] The genetic algorithm mainly includes four parts: fitness calculation, selection, crossover, and mutation. First, generate an appropriate number of population individuals. Then calculate the fitness function of each individual, calculate the probability of each individual being selected for the next iteration according to the fitness, and select the individuals entering the next generation through roulette. After selection, generate the next generation through crossover and mutation, so as to avoid the optimization process falling into a local optimum. Among them, crossover is to exchange a part of the segments between individuals, recombine the individuals and enter the next generation; mutation is to change the segments inside the individuals according to a certain probability, regenerate the individuals and enter the next generation. The above process is the entire process of one iteration of the genetic algorithm. After each iteration, recalculate the optimal fitness value until the current fitness value meets the requirements or the maximum number of iterations has been reached, then stop the optimization and output the optimized result.

[0006] In recent years, due to the continuous improvement of the angular resolution of vehicle-mounted radars, the number of transceiver array elements in the radar has increased, resulting in a multiple increase in the number of phase-coded orthogonal waveforms that need to be designed. Moreover, with more and more vehicles equipped with millimeter-wave radars, vehicle-mounted radars not only need to consider the orthogonality of the transmitted waveforms within their own unit radars but also need to achieve the orthogonality of waveforms between different radars. However, under the demand for large-scale phase-coded orthogonal waveform design, the search space of the traditional genetic algorithm will increase significantly and the search performance will deteriorate sharply. Therefore, there is an urgent need to propose a new method to achieve the design of large-scale phase-coded orthogonal waveforms. Summary of the Invention

[0007] The present invention overcomes one of the deficiencies of the prior art and provides an optimized design method for the inter-pulse phase-coded waveforms of wide-band radars. By using chaotic mapping to generate the initial values of a large number of uncorrelated sequences, the search space of the genetic algorithm is reduced, and the global search ability of the genetic algorithm is improved, thereby achieving the rapid and robust design of large-scale orthogonal phase-coded waveforms.

[0008] According to one aspect of the present disclosure, the present invention provides an optimized design method for the inter-pulse phase-coded waveforms of wide-band radars, and the method includes:

[0009] Using chaotic mapping to generate multiple groups of phase-coded sequences from M different initial values, where M is a positive integer;

[0010] Optimizing the multiple groups of phase-coded sequences by using a genetic algorithm to obtain an optimal group of coded sequences.

[0011] In a possible implementation manner, the step of using chaotic mapping to generate multiple groups of phase-coded sequences from M different initial values includes:

[0012] For each initial value, using chaotic mapping to generate an initial value sequence; for each of the initial value sequences, intercepting M groups of initial value sequences with a length of N;

[0013] Using the M groups of initial value sequences with a length of N to generate S phase-coded sequences with a length of L, and further obtaining an M×S×L phase-coded sequence matrix X, where N, S, and L are positive integers;

[0014] Quantizing the M×S×L phase-coded sequence matrix X into an M×S×L discrete phase-coded sequence matrix Y;

[0015] Constructing a transmitted signal model for the discrete phase-coded sequence matrix Y, and using the transmitted signal model to construct the transmitted signal of each phase-coded sequence in the discrete phase-coded sequence matrix;

[0016] Performing Fourier transform on the transmitted signal of each phase-coded sequence to obtain the frequency-domain signal of each phase-coded sequence;

[0017] Perform integrated sidelobe ratio calculation on the frequency-domain signal of the phase-coded sequence, and select S discrete phase-coded sequences with the smallest integrated sidelobe ratio as the preferred multiple groups of phase-coded sequences.

[0018] In a possible implementation manner, the genetic algorithm is used to optimize the multiple groups of phase-coded sequences to obtain an optimal coded sequence group, including:

[0019] Select N groups of discrete phase-coded sequences from the S groups of discrete phase-coded sequences as N individuals of the genetic algorithm;

[0020] Calculate the fitness value of each individual according to the fitness function;

[0021] Calculate the selection probability of each individual entering the next generation according to the fitness value;

[0022] Perform crossover and mutation genetic operations on the individuals entering the next generation;

[0023] Repeat the above operations until the number of loop iterations reaches the maximum number of iterations, stop the genetic algorithm, and obtain the optimal coded sequence group.

[0024] In a possible implementation manner, the discrete phase-coded sequence matrix Y is:

[0025]

[0026] In the formula, X is the phase-coded sequence matrix X, Y is the quantized discrete phase-coded sequence, and C represents the C-phase coding.

[0027] In a possible implementation manner, performing Fourier transform on the transmitted signal of each phase-coded sequence to obtain the frequency-domain signal of each phase-coded sequence includes:

[0028]

[0029] where k = 0, 1,..., L - 1, representing the k-th value of the coded sequence with length L; represents the transformation factor of the discrete Fourier transform, j is a complex number, and sig s,m Construct a signal for the coded sequence,

[0030] A method for optimizing the design of wideband radar inter-pulse phase-coded waveforms according to the present invention uses chaotic mapping to generate multiple groups of phase-coded sequences from M different initial values, where M is a positive integer; the genetic algorithm is used to optimize the multiple groups of phase-coded sequences to obtain an optimal group of coded sequences. By using chaotic mapping to generate the initial values of large-scale uncorrelated sequences, the search space of the genetic algorithm is reduced, and the global search ability of the genetic algorithm is improved, thereby realizing the rapid and robust design of large-scale orthogonal phase-coded waveforms and generating phase-coded sequences with strong large-scale orthogonality performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings are used to provide a further understanding of the technical solutions of the present application or the prior art, and constitute a part of the specification. Among them, the drawings showing the embodiments of the present application are used together with the embodiments of the present application to explain the technical solutions of the present application, but do not constitute a limitation on the technical solutions of the present application.

[0032] Figure 1 FIG. shows a flowchart of a method for optimizing the design of wideband radar inter-pulse phase-coded waveforms according to an embodiment of the present disclosure;

[0033] Figure 2 FIG. shows a flowchart of a method for optimizing the design of wideband radar inter-pulse phase-coded waveforms according to another embodiment of the present disclosure;

[0034] Figure 3 FIG. shows a schematic diagram of the signal demodulation spectrum in the worst sequence combination of the traditional algorithm;

[0035] Figure 4 FIG. shows a schematic diagram of the signal demodulation spectrum in the worst sequence combination of the improved algorithm according to an embodiment of the present disclosure;

[0036] Figure 5 FIG. shows a schematic diagram of the signal demodulation spectrum in the optimal sequence combination of the traditional algorithm;

[0037] Figure 6 FIG. shows a schematic diagram of the signal demodulation spectrum in the optimal sequence combination of the improved algorithm according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0038] The following will describe in detail the embodiments of the present invention in conjunction with the drawings and embodiments, so as to fully understand how the present invention uses technical means to solve technical problems and the implementation process of achieving corresponding technical effects. The embodiments of the present application and each feature in the embodiments can be combined with each other on the premise of not conflicting, and the formed technical solutions are all within the protection scope of the present invention.

[0039] In addition, the steps shown in the flowchart of the accompanying drawings can be executed in a computer, such as a set of computer-executable instructions. And although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0040] The main idea of an optimization design method for wideband radar inter-pulse phase coding waveforms according to the present disclosure is as follows:

[0041] 1. A deterministic system (Bernoulli map) generates a large-scale continuous coding sequence under different initial values, and then quantifies and preselects the above sequence; that is, a Fourier transform is performed on the time-domain signal formed after quantifying the coding sequence to obtain the signal spectrum, and a sequence with a smaller ISLR is selected.

[0042] 2. Further optimize the selected coding sequence using a genetic algorithm. Generate an initial population from the coding sequence selected by the chaotic sequence, calculate the fitness value of each individual in the population, and then compare the optimal fitness value through selection, crossover, and mutation. Through multiple iterations, when the maximum number of iterations is reached, the algorithm stops iterating. The following introduces the specific method flow:

[0043] Figure 1 The flowchart of an optimization design method for wideband radar inter-pulse phase coding waveforms according to an embodiment of the present disclosure is shown. As Figure 1 shown, the method may include:

[0044] Step S1: Use a chaotic map to generate multiple groups of phase coding sequences from M different initial values, where M is a positive integer.

[0045] In an example, this step may specifically include:

[0046] Step S11: For each initial value, use a chaotic map to generate an initial value sequence; for each initial value sequence, intercept M groups of initial value sequences with a length of N.

[0047] Step S12: Use M groups of initial value sequences with a length of N to generate S phase coding sequences with a length of L, and then obtain an M×S×L phase coding sequence matrix X.

[0048] For example, first select M different initial values, generate an initial value sequence through a Bernoulli map (i.e., Equation (1)), and intercept a section from the sequence generated by each initial value to generate S phase coding sequences with a length of L. The Bernoulli map is:

[0049]

[0050] In the formula, the value range of the initial value x(0) is [-0.5, 0.5], and its mapping space is also [-0.5, 0.5]. u = 2 - ε represents the control parameter of the Bernoulli mapping, where ε approaches 0, n represents the number of mapping iterations, and x(n) represents the value generated after the nth iteration of the chaotic mapping.

[0051] The M groups of S*L phase-coded sequence matrices X are as follows:

[0052]

[0053] Among them, x represents the value generated by the chaotic sequence, M represents the number of phase-coded sequences, L represents the coding length of a sequence, and S represents the number of sequence groups preselected through the chaotic mapping. And there is the following relationship:

[0054] X = [X 1 X 2 … X S Formula (3),

[0055] Among them, X s can represent a group of coding sequences and can be represented by a matrix:

[0056]

[0057] Among them, x represents the value generated by the chaotic sequence, M represents the number of phase-coded sequences, L represents the coding length of a sequence, and s represents the s-th sequence group, and 1 ≤ s ≤ S is satisfied.

[0058] Step S13: Quantize the M groups of S*L phase-coded sequence matrices X into M groups of S*L discrete phase-coded sequence matrices Y, where N, S, and L are positive integers.

[0059] Since the sequence generated by the chaotic mapping is continuous, but the radar signal uses a discrete method for phase coding, the generated continuous sequence needs to be quantized. Taking C-phase coding as an example, the phase only exists in {0, 2π / C, 2*2π / C, 3*2π / C, …, (C - 1)*2π / C}, and the quantization of the continuous coding can be obtained by the following formula:

[0060]

[0061] Among them, y represents the result of quantizing the continuous value x generated by the chaotic sequence mapping; C represents C-phase coding; represents the floor symbol.

[0062] The entire group of coding sequences can be represented by the following formula:

[0063]

[0064] Among them, X is a continuous coding matrix generated by a chaotic map, Y is a discrete coding matrix after quantization, and there is

[0065] Y = [Y 1 , Y 2 , … Y S Equation (7),

[0066] Among them, Y s is a discrete phase coding sequence matrix, which can be expressed as:

[0067]

[0068] Among them, y represents the result of quantization of the continuous value x generated by the chaotic sequence map; s represents the s-th sequence group, and satisfies 1 ≤ s ≤ S; L represents the coding length of a segment of sequence.

[0069] Step S14: Construct a transmission signal model for the discrete phase coding sequence matrix Y, and use the transmission signal model to construct the transmission signal of each phase coding sequence of the discrete phase coding sequence matrix.

[0070] Step S15: Perform Fourier transform on the transmission signal of each phase coding sequence to obtain the frequency-domain signal of each phase coding sequence.

[0071] Step S16: Calculate the integrated sidelobe ratio (ISLR) of the frequency-domain signal of the phase coding sequence, and select the S discrete phase coding sequences with the smallest integrated sidelobe ratio as the preferred multiple groups of phase coding sequences.

[0072] For example, construct a transmission signal model for the quantized coding sequence Y:

[0073]

[0074] Among them, Y s represents the s-th coding sequence group, and a signal constructed by a coding sequence can be expressed as follows:

[0075]

[0076] Among them, j represents a complex number, and Y s,m represents the m-th phase coding sequence in the s-th sequence group, and L is the coding sequence length.

[0077] Perform Fourier transform on the signal to obtain the frequency-domain signal, and the transformation formula is as follows:

[0078]

[0079] Among them, k = 0, 1, …, L - 1 represents the k-th value of the coding sequence with length L; Denotes the transformation factor of the discrete Fourier transform, j is a complex number, and sig s,m Constructs a signal for the coded sequence, SIG s,m Is the frequency-domain signal after transformation.

[0080] Calculate the ISLR for the obtained frequency-domain signal, and the calculation formula is as follows:

[0081] ISLR = 10lg[(P total - P main ) / P main Equation (12),

[0082] where, P total Represents the total energy, and P main Represents the main lobe energy.

[0083] Select the coded sequence with a smaller ISLR, so that the spectrum of the coded sequence is relatively stable, thereby obtaining a sequence with better randomness.

[0084] Step S2: Optimize the multiple groups of phase coded sequences by using a genetic algorithm to obtain an optimal group of coded sequences. Among them, this step may include: selecting N groups of discrete phase coded sequences from S discrete phase coded sequence groups as N individuals of the genetic algorithm; calculating the fitness value of each individual according to the fitness function; calculating the selection probability of each individual entering the next generation according to the fitness value; performing crossover and mutation genetic operations on the individuals entering the next generation; repeating the above operations until the number of loop iterations reaches the maximum number of iterations, stop the genetic algorithm, and obtain the optimal group of coded sequences.

[0085] For example, select N groups of sequences from S discrete sequence groups as N individuals of the genetic algorithm. Each individual contains M sequences, and the code length of each sequence is L. It can be represented by a matrix as follows:

[0086]

[0087] where, the nth individual of the genetic algorithm can be represented by the following matrix Ψ n Indicates:

[0088]

[0089] where, L is the length of the coded sequence, n represents the nth individual in the population, and satisfies 1 ≤ n ≤ N, then there is

[0090] Ψ = [Ψ 1 Ψ 2 … Ψ N Equation (15),

[0091] However, if an individual contains M coding sequences, it can be expressed as:

[0092] Ψ n =[C n1 ,C n2 ,…,C nm ,…,C nM T Equation (16),

[0093] where T represents the transpose of the matrix, and C nm represents the m-th coding sequence in the n-th individual, and satisfies 1 ≤ m ≤ M, and C nm can be expressed as:

[0094] C nm =[y m ((n - 1)*L + 1),y m ((n - 1)*L + 2),…,y m (n*L)] Equation (17),

[0095] Calculate the fitness function of M phase coding sequences in each individual in the genetic algorithm. The demodulated first transmitted signal received by the Rm2-th receiving channel can be expressed as:

[0096] SA Tm1,Rm2 =[y Tm1 ((n - 1)*L + 1),…,y Tm1 (n*L)]. / [y Rm2 ((n - 1)*L + 1),…,y Rm2 (n*L)] Equation (18),

[0097] where Tm1 and Rm2 represent the m1-th transmitting channel and the m2-th receiving channel, and m1 and m2 satisfy m1 ≠ m2, 1 ≤ m1 ≤ M, 1 ≤ m2 ≤ M, and M represents the total number of transmitting or receiving channels, that is, the M phase coding sequences transmitted.

[0098] Perform Fourier transform on this signal to obtain the frequency-domain signal, and calculate the corresponding ISLR Tm1,Rm2 . There are a total of A M 2 combinations for pairwise demodulation between M sequences. Calculate the average value of the ISLR of all receiving channels, and add the product of the minimum ISLR and the weight coefficient w.

[0099] Find the fitness values of N individuals in the population according to the fitness function:

[0100]

[0101] In the formula, n = 1, 2, …, N represents the individual serial number, A​M 2 = M*(M - 1) / 2, where M represents the total number of transmitting or receiving channels, and w represents the weight coefficient.

[0102] Calculate the probability of an individual entering the next generation iteration according to the fitness value. From the fitness expression, it can be seen that the larger the fitness value, the greater the probability of being selected. The selection probability can be calculated in the following way:

[0103]

[0104] In the formula, αn represents the fitness value of the nth individual, and max[·] represents taking the maximum value.

[0105] After obtaining the probability of each individual, use the roulette wheel method to select the individuals entering the next generation. Among them, the roulette wheel method is also called the proportional selection method, and its basic idea is that the probability of each individual being selected is proportional to its fitness value.

[0106] After selecting individuals, perform population individual crossover and mutation genetic operations. Among them, crossover means that at a certain crossover probability, one sequence of an individual is exchanged with the corresponding sequence of another individual, specifically manifested as C n1 m and C n2 m are exchanged. Where n1 and n2 represent the n1th and n2th individuals. Mutation means that one of the coding sequences of an individual changes into another sequence at a certain mutation probability. Among them, the fitness calculation, selection, crossover, and mutation of each individual are one full process of iteration. After looping and iterating to the maximum number of iterations, the algorithm stops iterating and obtains the optimal coding sequence group.

[0107] Application Example

[0108] Figure 2 Shows a flowchart of a wideband radar inter-pulse phase coding waveform optimization design method according to another embodiment of the present disclosure.

[0109] As Figure 2 shown, first, a large number of phase coding sequences are generated through chaotic mapping, then the Fourier transform is performed on the coding sequences and the spectral ISLR is calculated, so as to screen out the sequences with relatively stable spectral fluctuations and form the initial individuals of the genetic algorithm. Based on this, further use the genetic algorithm to calculate the fitness function between each individual (evaluating the orthogonality performance between the coding sequences within the individual), and perform multiple iterations through selection, crossover, and mutation, so as to obtain a large-scale coding sequence set with the optimal fitness value.

[0110] Next, in combination with simulation test examples, the application effect of the present invention will be described. The parameter settings in this simulation are shown in the following table:

[0111] Table 1 Simulation Experiment Parameter Table

[0112]

[0113]

[0114] Among them, in the improved algorithm of the present invention, a single coding sequence is used as an individual for optimization processing, while the traditional genetic algorithm needs to optimize each coding value in the sequence. Therefore, the present invention can effectively reduce the optimization variables, narrow the search space, and quickly iterate to generate the required orthogonal coding sequence.

[0115] After generating a large-scale coding sequence group by using the traditional genetic algorithm and the method of the present invention respectively, the orthogonality of the coding sequences is analyzed. For example, in the simulation experiment, 2, 3, and 4 sequences are respectively extracted from the generated 30 coding sequences for permutation and combination, and the signals of all sequence combinations are demodulated, and then the mean values, minimum values, and fluctuation conditions of the spectral peak sidelobe ratio and the integrated sidelobe ratio are compared. The results are shown in Table 2 and Table 3 below.

[0116] Table 2 Peak Sidelobe Ratio of Traditional Algorithm and Improved Algorithm

[0117]

[0118]

[0119] As can be seen from Table 2, when 2, 3, and 4 sequences are respectively extracted from 30 sequences, the minimum values of the peak sidelobe ratio of the improved algorithm of the present invention are increased by 0.39 dB, 0.79 dB, and 0.80 dB respectively, and the fluctuation differences between the maximum value and the minimum value are reduced by 0.87 dB, 0.82 dB, and 0.71 dB.

[0120] Table 3 Integrated Sidelobe Ratio of Traditional Algorithm and Improved Algorithm

[0121]

[0122] As can be seen from Table 3, when 2, 3, and 4 sequences are respectively extracted from 30 sequences, the minimum values of the integrated sidelobe ratio of the improved algorithm are increased by 0.04 dB, 0.41 dB, and 0.31 dB respectively, and the fluctuation differences between the maximum value and the minimum value are reduced by 0.03 dB, 0.03 dB, and 0.16 dB.

[0123] Figure 3 Shows the signal demodulation spectrum schematic diagram of the traditional algorithm in the worst sequence combination; Figure 4 Shows the signal demodulation spectrum schematic diagram of the improved algorithm according to an embodiment of the present disclosure in the worst sequence combination.

[0124] Taking the extraction of 3 sequences from 30 sequences as an example, the orthogonality of the sequences generated by the traditional algorithm and the improved algorithm of the present invention is further compared. From Figure 3 it can be seen that the peak sidelobe ratio of the traditional genetic algorithm is 8.11 dB, and the integrated sidelobe ratio is 16.77 dB; from Figure 4 it can be seen that the peak sidelobe ratio of the improved algorithm is 8.90 dB, and the integrated sidelobe ratio is 16.90 dB. That is, by using this method, the peak sidelobe ratio can be increased by 0.79 dB, and the integrated sidelobe ratio can be increased by 0.13 dB. That is, the orthogonality performance of this method is significantly better than that of the traditional algorithm under the worst sequence combination conditions.

[0125] Figure 5 shows the signal demodulation spectrum schematic diagram of the traditional algorithm in the optimal sequence combination; Figure 6 shows the signal demodulation spectrum schematic diagram of the improved algorithm according to an embodiment of the present disclosure in the optimal sequence combination.

[0126] From Figure 5 it can be seen that the peak sidelobe ratio of the traditional genetic algorithm is 16.31 dB, and the integrated sidelobe ratio is 19.51 dB; from Figure 6 it can be seen that the peak sidelobe ratio of the improved algorithm is 16.28 dB, and the integrated sidelobe ratio is 20.27 dB. That is, by using this method, the peak sidelobe ratio remains the same, and the integrated sidelobe ratio can be increased by 0.76 dB. The orthogonality performance is also better than that of the traditional algorithm under the optimal sequence combination conditions.

[0127] A wideband radar inter-pulse phase coding waveform optimization design method of the present invention can generate a large-scale phase coding waveform, with smaller fluctuations in the signal peak sidelobe ratio and integrated sidelobe ratio under different sequence combinations, and the orthogonality performance is also significantly better than that of the traditional genetic algorithm, which can improve the orthogonality of the radar channel signals. Compared with the existing technologies, the present invention also has the following advantages:

[0128] 1. Obtain a large-scale phase coding orthogonal sequence. When the demand for the number of orthogonal waveforms increases, the search space of the genetic algorithm will be very large, and the computational complexity of the search process will increase sharply, making it difficult to effectively obtain individuals with excellent orthogonality. The chaotic sequences are uncorrelated after multiple iterations with different initial values. This feature can be used to generate a large number of uncorrelated sequences.

[0129] 2. Shorten the time for generating sequences. When generating a large-scale coded sequence, using the genetic algorithm alone for global search will consume a lot of time, while the improved genetic algorithm after chaotic mapping reduces the search space and shortens the time for generating sequences. Therefore, the proposal and engineering implementation of the present invention have high popularization and application value in the field of radar waveform design.

[0130] Although the embodiments disclosed in the present invention are as described above, the content described is only an embodiment adopted for the convenience of understanding the present invention and is not intended to limit the present invention. Any person skilled in the art within the technical field to which the present invention pertains may make any modifications and changes in the form of implementation and details without departing from the spirit and scope disclosed by the present invention. However, the scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A method for optimizing the design of a wideband radar inter-pulse phase-coded waveform, characterized in that, the method includes: using a chaotic map to generate multiple groups of phase-coded sequences from M different initial values, where M is a positive integer; using a genetic algorithm to optimize the multiple groups of phase-coded sequences to obtain an optimal group of coded sequences; the step of using a chaotic map to generate multiple groups of phase-coded sequences from M different initial values includes: for each initial value, using a chaotic map to generate an initial value sequence; for each of the initial value sequences, intercepting M groups of initial value sequences with a length of N; using the M groups of initial value sequences with a length of N to generate S phase-coded sequences with a length of L, and further obtaining an M×S×L phase-coded sequence matrix X; quantizing the M×S×L phase-coded sequence matrix X into an M×S×L discrete phase-coded sequence matrix Y, where N, S, and L are positive integers; constructing a transmitted signal model for the discrete phase-coded sequence matrix Y, and using the transmitted signal model to construct the transmitted signal of each phase-coded sequence in the discrete phase-coded sequence matrix; performing a Fourier transform on the transmitted signal of each phase-coded sequence to obtain the frequency-domain signal of each phase-coded sequence; calculating the integrated sidelobe ratio of the frequency-domain signal of the phase-coded sequence, and selecting the S discrete phase-coded sequences with the smallest integrated sidelobe ratio as the preferred multiple groups of phase-coded sequences.

2. The method according to claim 1, characterized in that, the step of using a genetic algorithm to optimize the multiple groups of phase-coded sequences to obtain an optimal group of coded sequences includes: selecting N discrete phase-coded sequences from the S discrete phase-coded sequence groups as N individuals of the genetic algorithm; calculating the fitness value of each individual according to a fitness function; calculating the selection probability of each individual entering the next generation according to the fitness value; performing crossover and mutation genetic operations on the individuals entering the next generation; repeating the above operations until the number of loop iterations reaches the maximum number of iterations, stopping the genetic algorithm, and obtaining an optimal group of coded sequences.

3. The method according to claim 1, characterized in that, the discrete phase-coded sequence matrix Y is: where X is the phase-coded sequence matrix X, Y is the quantized discrete phase-coded sequence, and C represents the C-phase code.

4. The method according to claim 3, characterized in that, the step of performing a Fourier transform on the transmitted signal of each phase-coded sequence to obtain the frequency-domain signal of each phase-coded sequence includes: where k = 0, 1, …, L-1, representing the k-th value of the coding sequence of length L; represents the transformation factor of the discrete Fourier transform, j is a complex number, sig s,m constructs a signal for the coding sequence,

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