Optimal processing method of orthogonal phase coded signals based on polymorphic operator genetic algorithm

Abstract

The present invention discloses an orthogonal phase coded signal optimization processing method based on a polymorphic operator genetic algorithm, comprising: using a polymorphic operator as a basic unit of information to encode a phase coding unit; constructing a fitness function based on minimizing autocorrelation sidelobes, cross-correlation peaks and their means; using a polymorphic conversion logic gate, through the superposition, entanglement, interference and other characteristics of the operator to achieve iterative updates of the genetic algorithm, and complete orthogonal waveform optimization. The present invention effectively improves the optimization efficiency and the orthogonality of the waveform set.

Description

Technical Field

[0001] The invention relates to the field of radar waveform design. Background Art

[0002] Multiple-input multiple-output (MIMO) radar can search and track a wide range of target airspace by transmitting and receiving multiple beams through a digital array. This requires that the transmitted waveform not only has low autocorrelation sidelobes to reduce sidelobe interference, but also requires low cross-correlation between the transmitted waveforms to reduce mutual interference within the waveform set; excellent orthogonal waveforms can also enable MIMO radar to better extract independent distance, azimuth, speed and other information from the target echo. Therefore, how to effectively reduce the autocorrelation sidelobes and cross-correlation of the transmitted waveform by designing an algorithm is an urgent problem to be solved. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes an orthogonal phase coded signal optimization processing method based on a polymorphic operator genetic algorithm, and the technical solution includes:

[0004] S1: When the population is initialized, the phase-coded waveform set to be optimized is polymorphically encoded and the initial solution of each individual is tested;

[0005] S2: Use the orthogonalization criterion as the fitness function to evaluate the fitness of each individual, and take the best individual as the next generation evolution target;

[0006] S3: Set the iteration number threshold or fitness function value as the termination condition to determine whether the algorithm operation result meets the termination condition;

[0007] S4: Implement algorithm update iteration through polymorphic conversion logic gates.

[0008] Preferably, the information storage unit has two states, and adopts polymorphic bit encoding. The polymorphic unit stores and expresses a gene, and the gene is in a "0" state or a "1" state, or any superposition state thereof; specifically expressed as:

[0009]

[0010] Among them, |0> and |1> represent two different vector states, and α and β are vectors that satisfy |α| 2 +|β| 2 =Amplitude constant of 1.

[0011] Preferably, the solution of the present invention adopts a fitness function under the orthogonal optimization criterion, wherein the optimization function is:

[0012]

[0013] Among them, is the E fitness function, is the autocorrelation function of the signal, is the cross-correlation function of the signal; where k represents the delay of the signal during the autocorrelation operation, The phase coding sequence representing the polymorphic coding of the lth signal is: and A phase-coded sequence representing the polymorphic encoding of the p-th and q-th signals in the signal set.

[0014] Preferably, individuals are iteratively updated through polymorphic conversion logic gates, and the process includes:

[0015]

[0016] Among them, (α i , β i ) T and (α' i , β' i ) T Represents the probability amplitude of the i-th operator of the chromosome before and after being updated by your polymorphic conversion logic gate; θ i is the rotation angle, the size and sign of which are determined by the pre-designed adjustment strategy; U(θ i ) represents the transformation matrix, which is specifically expressed as:

[0017]

[0018] The orthogonal optimization genetic algorithm of the present invention adopts polymorphic bit coding, utilizes the polymorphic characteristics of operators and the conversion of logic gates to realize iterative update of the genetic algorithm, and effectively reduces the autocorrelation side peak lobe and cross-correlation peak of the signal set. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the preferred embodiment of the present invention;

[0020] Figure 2 Change the angle to select the strategy diagram;

[0021] Figure 3 The phase encoding is obtained by optimizing the algorithm of this embodiment;

[0022] Figure 4 It is the autocorrelation function image of the embodiment;

[0023] Figure 5 is a cross-correlation function image of an embodiment;

[0024] Figure 6 It is an iterative evolution diagram of an embodiment. DETAILED DESCRIPTION

[0025] The present invention will be further explained below in conjunction with the accompanying drawings.

[0026] The orthogonal phase coded signal optimization processing method based on the polymorphic operator genetic algorithm proposed in the present invention has a preferred embodiment flow chart as shown in the attached Figure 1 As shown, the specific steps include:

[0027] S1: When the population is initialized, the phase-coded waveform set to be optimized is polymorphically encoded and the initial solution of each individual is tested;

[0028] In this embodiment, the phase coding signal during population initialization is encoded using polymorphic bits. The polymorphic unit stores and expresses a gene, and the gene is in a "0" state or a "1" state, or any superposition state thereof; specifically, it is expressed as:

[0029]

[0030] Among them, |0> and |1> represent two different vector states, and α and β are vectors that satisfy |α| 2 +|β| 2 =1; the polymorphic bit encoding of the phase-coded signal is specifically expressed as follows:

[0031]

[0032] Wherein, N represents the code length of the signal, and in this embodiment, N=40. represents the nth phase code of the lth transmitted signal in the signal set. k is the number of polymorphic bits and g is the polymorphic coding parameter;

[0033] The specific process of testing the initial solution selection of each individual is as follows: randomly generate a number in the interval [0,1]. If it is greater than the square of the probability amplitude, the measurement result is 1, otherwise it is 0. The measurement can obtain a set of definite solutions. in, is the jth solution (the measurement value of the jth individual) in the tth generation population, expressed as a binary string of length m, where each bit is 0 or 1, according to the probability of the polymorphic operator ( or )Select the obtained;

[0034] S2: Use the orthogonalization criterion as the fitness function to evaluate the fitness of each individual, and take the best individual as the next generation evolution target;

[0035] In this embodiment, the expression E using the orthogonalization criterion as the fitness function is as follows:

[0036]

[0037] in, is the autocorrelation function of the signal, is the cross-correlation function of the signal; where k represents the time delay of the signal during the autocorrelation operation, L represents the number of waveforms, and in this embodiment, L=4, The phase coding sequence representing the polymorphic coding of the lth signal is: and a phase-coded sequence representing the polymorphic encoding of the p-th and q-th signals in the signal set; and The relationship is: M is the number of phases, in this embodiment, M=4;

[0038] S3: Set the iteration number threshold or fitness function value as the termination condition to determine whether the algorithm operation result meets the termination condition;

[0039] S4: Implement algorithm update iteration through polymorphic conversion logic gates.

[0040] Individuals are iteratively updated through polymorphic conversion logic gates. The process includes:

[0041]

[0042] Among them, (α i , β i ) T and (α' i , β' i ) T Represents the probability amplitude of the i-th operator of the chromosome before and after being updated by your polymorphic conversion logic gate; θ i is the rotation angle, the size and sign of which are determined by the pre-designed adjustment strategy; U(θ i ) represents the transformation matrix, which is specifically expressed as:

[0043]

[0044] The polymorphic conversion gate is the execution mechanism that ultimately implements the evolution operation. In this embodiment, the specific adjustment strategy is Figure 2 As shown, where x i is the i-th position of the current chromosome; best i is the i-th position of the current optimal chromosome; f(x) is the fitness function; s(α i ,β i ) is the rotation angle direction; Δθ i is the rotation angle, and its value is based on Figure 2 The adjustment strategy is to select the individual The fitness of the current measured value f(x) is equal to the fitness value of the best individual in the population f(best i) Compare. If f(x) > f(best), then adjust the corresponding polymorphic bits in i , so that the probability amplitude pair (α i ) evolves in the direction that is beneficial to the appearance of x; conversely, if f(x) < f(best), then adjust the corresponding polymorphic bits in i , so that the probability amplitude pair (α i ) evolves in the direction that is beneficial to the appearance of best.

[0045] In this embodiment, the evolutionary algebra is used as the condition for judging whether the algorithm continues or not, and the termination algebra is set to 400; Figure 3 is a set of phase encodings obtained by optimizing the algorithm. The autocorrelation function images and cross-correlation function images of the corresponding signal sets are respectively as Figure 4 and Figure 5 shown. The sidelobes of the autocorrelation function are 0.1275, 0.125, 0.15, 0.1521, and the cross-correlation peaks are 0.216, 0.1953, 0.2016, 0.2, 0.2062, 0.2016 respectively, having good orthogonality; Figure 6 is the iterative evolution diagram of the algorithm in this embodiment.

Claims

1. A method for optimizing orthogonal phase coded signal processing based on a polymorphic operator genetic algorithm, characterized in that: S1: When the population is initialized, the phase-coded waveform set to be optimized is polymorphically encoded and the initial solution of each individual is tested; S2: Use the orthogonalization criterion as the fitness function to evaluate the fitness of each individual, and take the best individual as the next generation evolution target; S3: Set the iteration number threshold or fitness function value as the termination condition to determine whether the algorithm operation result meets the termination condition; S4: Implement algorithm update iteration through polymorphic conversion logic gates.

2. The orthogonal phase coded signal optimization processing method based on a polymorphic operator genetic algorithm according to claim 1, characterized in that: The information storage unit has two states, using polymorphic bit encoding. The polymorphic unit stores and expresses a gene, which is in the "0" state or the "1" state, or any superposition state thereof, expressed as: in, represents the gene of the polymorphic operator genetic algorithm, |0> and |1> represent two different vector states, α and β are the vectors satisfying |α| 2 +|β| 2 =Amplitude constant of 1.

3. The orthogonal phase coded signal optimization processing method based on a polymorphic operator genetic algorithm according to claim 1, characterized in that: The fitness function under the orthogonal optimization criterion is adopted, and the fitness function includes: Among them, is the E fitness function, is the autocorrelation function of the signal, is the cross-correlation function of the signal; where k represents the delay of the signal during the autocorrelation operation, The phase coding sequence representing the polymorphic coding of the lth signal is: and A phase-coded sequence representing the polymorphic encoding of the p-th and q-th signals in the signal set.

4. The orthogonal phase coded signal optimization processing method based on a polymorphic operator genetic algorithm according to claim 1, characterized in that: Individuals are iteratively updated through polymorphic conversion logic gates. The process includes: Among them, (α i , β i ) T and (α' i , β' i ) T Represents the probability amplitude before and after the update of the i-th operator logic gate of the chromosome; θ i is the rotation angle, the size and sign of which are determined by the pre-designed adjustment strategy; U(θ i ) represents the transformation matrix, expressed as:

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