CN117688310A - MCPC signal waveform optimization method based on multi-target particle swarm algorithm - Google Patents
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Abstract
The invention discloses an MCPC signal waveform optimization method based on a multi-target particle swarm algorithm, which can solve the problem of MCPC signal waveform optimization of a wide-area random sparse array detection scene. The invention comprises the following steps: constructing a particle swarm, and randomly generating an initial position and a speed of each particle; calculating fitness function values corresponding to each particle in the population, namely PMEPR and PSLL; updating the non-inferior solution set; updating the particle optimally; updating particle velocity and position; judging whether the set iteration times are reached, and outputting a non-inferior solution set after the iteration is completed. The method optimizes the weight of the signal subcarriers under the double constraint of the signals PMEPR and PSLL by utilizing a multi-target particle swarm algorithm, so that the detection performance of the signals can be improved, and the linearity requirement of a system can be met.
Description
Technical Field
The invention relates to the field of wide-area random sparse array signal waveform optimization, and is particularly suitable for multi-carrier phase coded signal waveform optimization of a wide-area random sparse array.
Background
In a wide-area random sparse array detection system, compared with a single-carrier signal, a multi-carrier phase coding signal (MCPC) waveform achieves the purpose of widening the signal bandwidth by transmitting a plurality of carriers modulated with the phase coding signal at the same time, so that peak sidelobe level (PSLL) in a signal waveform fuzzy diagram is reduced, and the detection performance of a radar signal waveform is improved. However, the multi-carrier modulation has a problem that the signal includes large fluctuation and the peak-to-average power ratio (PMEPR) is too high due to non-in-phase superposition of the sub-carrier signals, resulting in degradation of signal detection performance. The MCPC signals of the wide-area random sparse array are optimized, and it is important to ensure that PSLL of the signal blur map meets the system detection requirement while PMEPR is reduced.
Current research on MCPC signal waveform optimization is mainly focused on both the phase coding sequence and the subcarrier complex weight coefficients.
The invention patent of China with application number 202111367175.2 discloses a chaos single hybrid coding-based MCPC signal design method and device, combines the advantages of a Logistic chaos sequence, improves and applies the Logistic chaos sequence to an MCPC signal to improve distance side lobes, and improves multi-target resolution capability of a system.
Document 'SCPSO algorithm-based MCCP radar waveform optimization design' combines chaotic mapping and self-adaptive chaotic particle swarm SCPSO, performs two-stage screening on the SCPSO by utilizing the randomness of a chaotic sequence, and reduces the sidelobe peak value of an autocorrelation function. The scepo algorithm is applied to adjust the PMEPR of the subcarrier weight factor optimized signal.
Most of the existing methods only consider one factor of PSLL or PMEPR, or adopt different methods to jointly optimize the two factors, and the implementation process is complex.
Disclosure of Invention
The invention aims to solve the problems and provide an MCPC signal waveform optimization method based on a multi-target particle swarm algorithm. The method utilizes a multi-target particle swarm algorithm, considers two optimization targets of peak-to-average power ratio and peak side lobe level, optimizes the complex weight of the MCPC signal subcarrier, reduces the peak side lobe level of the signal ambiguity diagram while meeting PMEPR constraint, and improves the detection performance of the system.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
an MCPC signal waveform optimization method based on a multi-target particle swarm algorithm comprises the following steps:
step one, constructing a particle group, and randomly generating an initial position and a speed of each particle;
step two, calculating the fitness function value corresponding to each particle in the particle swarm, namely the peak average envelope power ratio PMEPR and the peak side lobe level PSLL;
step three, updating the non-inferior solution set:
if the step is executed for the first time after the particle swarm is initialized, initially screening a non-inferior solution set; if a certain particle after initialization is not subjected to the control of other particles, namely, the PMEPR and PSLL without other particles are lower than the current particle, putting the particle into a non-inferior solution set;
if the step is executed after the position and the speed of the particles are updated in the iterative process, updating the non-inferior solution set;
step four, updating the particle optimization:
if the step is executed for the first time after the particle swarm is initialized, taking the initialized particle swarm as the individual optimum of each particle; randomly selecting a particle in the non-inferior solution set as the population optimum;
if the particle position and the particle speed are updated in the iterative process, the step is executed, then the dominant relationship between the new particle and the particle history optimal particle is judged in sequence, the dominant particle is selected as the individual optimal, and if the dominant particle cannot be dominant, one of the new particle and the particle history optimal particle is selected as the particle optimal randomly; randomly selecting a particle in the non-inferior solution set as the population optimum;
step five, updating the particle speed and the position:
updating the particle speed at the current moment according to the particle speed at the previous moment, the individual optimum and the group optimum, and updating the position of the particle at the current moment according to the particle position at the previous moment and the particle speed at the current moment;
step six, judging whether the set iteration times are reached, if the iteration is completed, outputting a non-inferior solution set, otherwise, returning to the step two to continue the iteration.
Further, the specific mode of the first step is as follows:
setting the number of subcarriers of MCPC signals as N, the number of particles as P, generating a random complex number with the length of P groups as N, the modulus value being more than 0 and less than 1 and the phase being more than 0 and less than 360 degrees as a particle initial position X 0 The method comprises the steps of carrying out a first treatment on the surface of the Generating a random complex number with the length of P groups of N, the modulus value of more than 0 and less than 1, and the phase of more than 0 and less than 360 degrees as a particle velocity initial value V 0 。
Further, in the third step, the specific way of updating the non-inferior solution set is:
step 301, non-bad solution set and: combining the updated new particle swarm with the current non-inferior solution set;
step 302, non-bad solution set update: and judging each particle in the combined non-inferior solution set in sequence, and if the particle is not subjected to other particles, putting the particle into the updated non-inferior solution set.
Compared with the background technology, the invention has the following beneficial effects:
1. the multi-objective particle swarm optimization method has the advantages that the peak-to-average power ratio is low, the peak side lobe level is low, the PSLL is optimized while the constraint of the peak-to-average power ratio is met, and the low side lobe advantage of the MCPC broadband signal is reserved.
2. The traditional sub-band complex weighting approach based on the hamming window and Schroeder can drop PMEPR from 4 to 2.187, but results in a PSLL rise from-48 dB to-44.8 dB, resulting in degraded detection performance. Under the same conditions, the method can reduce PMEPR and PSLL to 1.9 dB and-49.5 dB respectively.
Drawings
FIG. 1 is a flow chart of multi-objective particle swarm optimization in an embodiment of the present invention.
FIG. 2 is an optimized non-bad solution set in an embodiment of the present invention.
Detailed Description
The following detailed description of specific embodiments of the invention refers to the accompanying drawings.
An MCPC signal waveform optimization method based on a multi-target particle swarm algorithm comprises the following steps:
step one, constructing a particle group, and randomly generating an initial position and a speed of each particle; the specific method is as follows:
setting the number of subcarriers of MCPC signals as N, the number of particles as P, generating a random complex number with the length of P groups as N, the modulus value being more than 0 and less than 1 and the phase being more than 0 and less than 360 degrees as a particle initial position X 0 The method comprises the steps of carrying out a first treatment on the surface of the Generating a random complex number with the length of P groups of N, the modulus value of more than 0 and less than 1, and the phase of more than 0 and less than 360 degrees as a particle velocity initial value V 0 ；
And step two, calculating fitness function values corresponding to each particle in the population, namely PMEPR and PSLL.
And step three, updating the non-inferior solution set.
If the step is performed for the first time after population initialization, the non-inferior solution sets are initially screened. If a particle after initialization is not subject to other particles (i.e., the PMEPR and PSLL where no other particles are present are both lower than the current particle), the particle is placed in a non-bad solution set.
If the step is performed after the particle position and velocity are updated in the iterative process, the non-inferior solution set is updated, and the updating of the non-inferior solution set can be further divided into two steps of non-inferior solution set combination and updating.
And step 301, merging the non-inferior solution sets, and merging the updated new particle swarm with the current non-inferior solution set.
And 302, updating the non-inferior solution set, sequentially judging in the combined non-inferior solution set, and if the particles are not subjected to the control of other particles, putting the particles into the updated non-inferior solution set.
And step four, optimally updating the particles.
If the step is executed for the first time after the population is initialized, the initialized particle group is regarded as the individual optimization of each particle. A particle is randomly selected as population optimization in the non-inferior solution set.
If the particle position and the particle velocity are updated in the iterative process and then the step is executed, the dominant relationship between the new particle and the particle history optimal particle is judged in sequence, the dominant particle is selected as the individual optimal, and if the dominant particle cannot be dominant, one particle is selected as the particle optimal randomly from the new particle and the particle history optimal particle. A particle is randomly selected as population optimization in the non-inferior solution set.
And fifthly, updating the particle speed and the position. And updating the particle speed at the current moment according to the particle speed at the previous moment, the individual optimum and the population optimum, and updating the position of the particle at the current moment according to the particle position at the previous moment and the particle speed at the current moment.
And step six, judging whether the set iteration times are reached, if the iteration is completed, outputting a non-inferior solution set, otherwise, returning to the step two to continue to execute.
Thus, the MCPC signal waveform optimization method based on the multi-target particle swarm algorithm is completed.
The following is a more specific example:
an MCPC signal waveform optimization method based on a multi-target particle swarm algorithm, referring to FIG. 1, comprises the following steps:
step one, constructing a particle group, and randomly generating an initial position and a speed of each particle; the specific method is as follows:
setting the number of subcarriers of MCPC signals as N, the number of particles as P, generating a random complex number with the length of P groups as N, the modulus value being more than 0 and less than 1 and the phase being more than 0 and less than 360 degrees as a particle initial position X 0 The method comprises the steps of carrying out a first treatment on the surface of the Generating a random complex number with the length of P groups of N, the modulus value of more than 0 and less than 1, and the phase of more than 0 and less than 360 degrees as a particle velocity initial value V 0 ；
Step two, calculating fitness function values corresponding to each particle in the population, namely PMEPR and PSLL, and further dividing the steps of PMEPR calculation and PSLL calculation.
Step 201, calculate PMEPR. Each particle comprises N complex weights, and firstly, the N complex weights are multiplied with N paths of phase coding signals respectively to obtain frequency domain weighted sub-band data; then, performing N-point IFFT operation on the N sub-band data, performing parallel-serial conversion, obtaining a time domain signal S of multi-carrier phase coding, and obtaining a modulus value of the time domain signal S; and finally, obtaining the PMEPR by comparing the maximum time module value data with the average value of all time module values.
Step 202, calculate PSLL. After obtaining a time domain signal S corresponding to the particles, calculating a corresponding fuzzy graph; and finding out a main lobe peak value and a maximum side lobe peak value of the fuzzy graph, and converting the main lobe peak value on the maximum side lobe peak value ratio into a dB form to obtain the PSLL.
And step three, updating the non-inferior solution set.
If the step is performed for the first time after population initialization, the non-inferior solution sets are initially screened. If a particle after initialization is not subject to other particles (i.e., the PMEPR and PSLL where no other particles are present are both lower than the current particle), the particle is placed in a non-bad solution set.
If the step is performed after the particle position and velocity are updated in the iterative process, the non-inferior solution set is updated, and the updating of the non-inferior solution set can be further divided into two steps of non-inferior solution set combination and updating.
And step 301, merging the non-inferior solution sets, and merging the updated new particle swarm with the current non-inferior solution set.
And 302, updating the non-inferior solution set, sequentially judging in the combined non-inferior solution set, and if the particles are not subjected to the control of other particles, putting the particles into the updated non-inferior solution set.
And step four, optimally updating the particles.
If the step is executed for the first time after the population is initialized, the initialized particle group is regarded as the individual optimization of each particle. A particle is randomly selected as population optimization in the non-inferior solution set.
If the particle position and the particle velocity are updated in the iterative process and then the step is executed, the dominant relationship between the new particle and the particle history optimal particle is judged in sequence, the dominant particle is selected as the individual optimal, and if the dominant particle cannot be dominant, one particle is selected as the particle optimal randomly from the new particle and the particle history optimal particle. A particle is randomly selected as population optimization in the non-inferior solution set.
And fifthly, updating the particle speed and the position. And updating the particle speed at the current moment according to the particle speed at the previous moment, the individual optimum and the population optimum, and updating the position of the particle at the current moment according to the particle position at the previous moment and the particle speed at the current moment.
And step six, judging whether the set iteration times are reached, if the iteration is completed, outputting a non-inferior solution set, otherwise, returning to the step two to continue to execute.
The optimized non-inferior solution set is shown in fig. 2, the abscissa represents the peak-to-average power ratio of each group of solutions, the ordinate represents the maximum sidelobes of each group of solutions, and solutions with PMEPR and PSLL of 1.9-49.5 dB can be found from the maximum sidelobes, which proves the optimization effect of the optimization method.
In a word, the invention optimizes the weight of the signal subcarrier under the double constraint of the signals PMEPR and PSLL by utilizing the multi-target particle swarm algorithm, thereby not only improving the detection performance of the signals, but also meeting the linear requirement of the system.
Claims (3)
1. The MCPC signal waveform optimization method based on the multi-target particle swarm algorithm is characterized by comprising the following steps of:
step one, constructing a particle group, and randomly generating an initial position and a speed of each particle;
step two, calculating the fitness function value corresponding to each particle in the particle swarm, namely the peak-to-average power ratio PMEPR and the peak side lobe level PSLL;
step three, updating the non-inferior solution set:
if the step is executed for the first time after the particle swarm is initialized, initially screening a non-inferior solution set; if a certain particle after initialization is not subjected to the control of other particles, namely, the PMEPR and PSLL without other particles are lower than the current particle, putting the particle into a non-inferior solution set;
if the step is executed after the position and the speed of the particles are updated in the iterative process, updating the non-inferior solution set;
step four, updating the particle optimization:
if the step is executed for the first time after the particle swarm is initialized, taking the initialized particle swarm as the individual optimum of each particle; randomly selecting a particle in the non-inferior solution set as the population optimum;
if the particle position and the particle speed are updated in the iterative process, the step is executed, then the dominant relationship between the new particle and the particle history optimal particle is judged in sequence, the dominant particle is selected as the individual optimal, and if the dominant particle cannot be dominant, one of the new particle and the particle history optimal particle is selected as the particle optimal randomly; randomly selecting a particle in the non-inferior solution set as the population optimum;
step five, updating the particle speed and the position:
updating the particle speed at the current moment according to the particle speed at the previous moment, the individual optimum and the group optimum, and updating the position of the particle at the current moment according to the particle position at the previous moment and the particle speed at the current moment;
step six, judging whether the set iteration times are reached, if the iteration is completed, outputting a non-inferior solution set, otherwise, returning to the step two to continue the iteration.
2. The method for optimizing the MCPC signal waveform based on the multi-objective particle swarm algorithm according to claim 1, wherein the specific manner of the step one is as follows:
setting the number of subcarriers of MCPC signals as N, the number of particles as P, generating a random complex number with the length of P groups as N, the modulus value being more than 0 and less than 1 and the phase being more than 0 and less than 360 degrees as a particle initial position X 0 The method comprises the steps of carrying out a first treatment on the surface of the Generating a random complex number with the length of P groups of N, the modulus value of more than 0 and less than 1, and the phase of more than 0 and less than 360 degrees as a particle velocity initial value V 0 。
3. The method for optimizing the MCPC signal waveform based on the multi-objective particle swarm algorithm according to claim 1, wherein in the third step, the specific way of updating the non-inferior solution set is as follows:
step 301, non-bad solution set and: combining the updated new particle swarm with the current non-inferior solution set;
step 302, non-bad solution set update: and judging each particle in the combined non-inferior solution set in sequence, and if the particle is not subjected to other particles, putting the particle into the updated non-inferior solution set.
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