Broadband flow measurement waveform design method based on genetic algorithm

Abstract

The invention belongs to the technical field of fluid measurement, and provides a broadband flow measurement waveform design method based on a genetic algorithm in order to solve the problem that fine layering and high-precision detection requirements are difficult to consider. A water layer layering model of the acoustic Doppler flow velocity profiler is analyzed based on the broadband coding emission signal model, and an initial coding sequence is obtained; determining a feasible region of the broadband coding emission signal parameters according to a preset broadband coding emission signal constraint condition, and searching a broadband emission signal waveform parameter when the speed measurement standard deviation is minimum in the feasible region; the peak sidelobe ratio of the initial coding sequence is used as an optimization target, the initial coding sequence is optimized by using a genetic algorithm based on broadband emission signal waveform parameters, finally, an optimal emission signal is generated according to an optimization result, and profile flow velocity measurement is performed based on the optimal emission signal. The length is not limited, and waveform parameter selection is facilitated.

Description

Technical Field

[0001] The invention belongs to the technical field of fluid measurement, and in particular relates to a broadband flow measurement waveform design method based on a genetic algorithm. Background Art

[0002] Acoustic Doppler current measurement is a telemetry technique that transmits sound waves underwater to a target area and receives echoes. The technique estimates the target's distance, velocity, composition, and other information based on the echo's amplitude, correlation, and Doppler shift. The Acoustic Doppler Current Profiler (ADCP), a typical example of acoustic Doppler current measurement, is widely used in ocean research, hydrological monitoring, national defense, and other fields due to its high velocity accuracy, multiple measurement parameters, short measurement times, and non-interference with the flow field.

[0003] The flow measuring instrument first transmits an acoustic pulse to the target water body. The receiver then receives the scattered echo of the transmitted sound wave from the scatterers in the water. The signal processing stage divides the echo into multiple small segments according to time. These echo segments correspond to water layers at different depths. The frequency estimation algorithm is used to obtain the radial velocity of each water layer relative to the instrument.

[0004] Commonly used ADCP flow measurement methods include incoherent, coherent, and broadband. Compared to incoherent flow measurement, broadband flow measurement increases the time width by transmitting a repeatedly repeated phase-coded signal, shortening the symbol width and increasing the bandwidth, resulting in higher velocity estimation accuracy and better range resolution. Compared to coherent flow measurement, broadband flow measurement shortens the coherence interval, significantly extending the velocity measurement range. When transmitting coded coherent signals, the selection of encoding format and parameters is crucial for broadband flow measurement. Broadband coded signal waveform parameters primarily include the number of code bits, symbol width, and number of code repetitions. More code bits, a larger symbol width, and a higher number of pulse repetitions improve flow measurement accuracy. However, increasing these three parameters increases signal length, increases layer thickness, and reduces spatial resolution. The codes used by various companies and research institutions are not publicly available, and different broadband ADCPs may use different codes. The encoding method should be a pseudo-random sequence with good autocorrelation. The advantage of using a specific encoding method is that it can enhance signal correlation. Complex hydrological environments place higher demands on the waveform of broadband flow measurement codes. Traditional pseudo-random sequences (such as Barker code, M sequence) are limited in length (Barker code has a maximum length of 13, M sequence has a maximum length of 2 n -1 constraint), it is difficult to take into account both fine stratification and high-precision detection requirements. In order to solve these problems, the present invention proposes a broadband flow measurement waveform design method based on a genetic algorithm. Summary of the Invention

[0005] In order to solve at least one of the above technical problems existing in the prior art, the present invention provides a broadband current measurement waveform design method based on genetic algorithm.

[0006] The present invention is implemented by the following technical solution: a broadband current measurement waveform design method based on genetic algorithm, comprising the following steps:

[0007] S10: constructing a broadband coded transmission signal model, and analyzing a water layer stratification model of an acoustic Doppler current profiler based on the broadband coded transmission signal model to obtain an initial coding sequence of the broadband coded transmission signal;

[0008] S20: determining a feasible domain of broadband coded transmission signal parameters according to preset broadband coded transmission signal constraints, and searching for broadband transmission signal waveform parameters with the minimum speed measurement standard deviation in the feasible domain;

[0009] S30: Taking the peak sidelobe ratio of the initial coding sequence as the optimization target, based on the broadband transmission signal waveform parameters, and using a genetic algorithm to optimize the initial coding sequence, finally generating an optimal transmission signal according to the optimization result, and performing profile flow velocity measurement based on the optimal transmission signal.

[0010] Preferably, the broadband coded transmission signal model is:

[0011]

[0012] Where M is the number of code repetitions; m is the index of the code repetition sequence; x1(t-mLt p ) is the analytical function of the pulse signal, t represents time, L represents the number of code elements in the initial coding sequence, t p Indicates the code element width.

[0013] Preferably, the speed measurement standard deviation calculation formula is:

[0014]

[0015] Where λ is the carrier wavelength; t d is the measurement delay time; t a is the phase difference average time; M a t a The number of code elements in the code; C is a coefficient reflecting the non-ideal degree of coding and signal processing; ρ is the autocorrelation coefficient of the calculation delay, which is approximately ρ = 1-1 / N when the signal-to-noise ratio is large enough. s , N s is the number of pulse repetitions.

[0016] Preferably, taking the peak sidelobe ratio of the initial coding sequence as the optimization target comprises:

[0017] generating a corresponding initial two-phase coded signal based on the initial coding sequence;

[0018] An autocorrelation function is set based on the initial two-phase coded signal, and an objective function with a peak-to-sidelobe ratio as an optimization target is set according to the autocorrelation function.

[0019] Preferably, the complex envelope of the initial two-phase coded signal is expressed as:

[0020]

[0021] Where u(t) is the autocorrelation function of the amplitude modulation function; j is the imaginary unit; represents the phase modulation function.

[0022] Preferably, the autocorrelation function is:

[0023]

[0024] Where P represents the signal energy; t s Indicates the duration of the transmit pulse.

[0025] Preferably, the objective function is:

[0026]

[0027] Where PSLR is the peak-to-sidelobe ratio, R(0) is the mainlobe peak of the autocorrelation function, and max|R(τ)| is the maximum sidelobe peak.

[0028] Preferably, the initial coding sequence is optimized using a genetic algorithm, comprising:

[0029] S31: Divide the initial coding sequence according to preset sequence lengths to obtain an initial random sequence population;

[0030] S32: Setting a fitness function based on the objective function;

[0031] S33: selecting elite individuals from the initial random sequence population based on the fitness function;

[0032] S34: performing crossover using the elite individual as a parent to obtain a new individual;

[0033] S35: performing random mutation on all individuals in the initial random sequence population and the new individual;

[0034] S36: Update the initial random sequence population according to the random mutation result, and determine whether the iterative update stop condition is met. When the iterative update condition is met, stop the iteration; otherwise, repeat steps S32-S36 until the iterative update stop condition is met.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The proposed genetic algorithm-based broadband flow measurement waveform design method offers superior sidelobe suppression compared to conventional pseudo-random coding sequences used in existing acoustic Doppler current profiler systems. This coding sequence is not limited in length, making waveform parameter selection easier. In practical flow measurement scenarios, waveform design using this method can precisely match the spatial resolution of the water layer while ensuring accurate flow measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 The present invention is a schematic diagram of the steps of a broadband flow measurement coding design method based on genetic algorithm;

[0039] Figure 2 Schematic diagram of the water layer stratification model provided for this embodiment;

[0040] Figure 3 A schematic diagram of the process of selecting waveform parameters under multiple constraints;

[0041] Figure 4 A schematic diagram of the genetic algorithm optimization process provided in this embodiment;

[0042] Figure 5 The feasible domain image of the number of pulse code repetitions and code elements;

[0043] Figure 6 A distribution diagram of the speed measurement standard deviation provided in this embodiment;

[0044] Figure 7 is the 9-bit genetic algorithm sequence fuzzy graph. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention are clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other implementations derived by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0046] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Therefore, they have no substantive technical significance. Any structural modification, change in proportional relationship or adjustment of size should fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention. It should be noted that in this specification, relational terms such as first and second are only used to distinguish one entity from several other entities, and do not necessarily require or imply any actual relationship or order between these entities.

[0047] The present invention relates to a broadband flow measurement waveform design method based on a genetic algorithm, which is used to solve the problem that traditional coding sequences (such as Barker codes and M sequences) are difficult to accurately match the spatial resolution of the water layer while ensuring high-precision flow measurement due to their limited length. To this end, the technical solution adopted by the present invention is to establish a broadband coded transmission signal model, solve the feasible domain of waveform parameters based on constraints such as system bandwidth, velocity measurement range, layer thickness range, and broadband flow measurement signal form, conduct a global search to determine the waveform parameters with the smallest standard deviation, construct a fitness function with the goals of sequence autocorrelation sidelobe suppression and mainlobe concentration, use the determined code length as a constraint condition, and obtain the global optimal solution through a global search of the genetic algorithm to complete the coding sequence design of the broadband transmission signal.

[0048] like Figure 1 As shown, an embodiment of the present invention provides a broadband current measurement waveform design method based on a genetic algorithm, comprising the following steps:

[0049] S10: constructing a broadband coded transmission signal model, and analyzing a water layer stratification model of an acoustic Doppler current profiler based on the broadband coded transmission signal model to obtain an initial coding sequence of the broadband coded transmission signal.

[0050] In this embodiment, the acoustic Doppler current profiler uses a transceiver that transmits a broadband coded signal into the water. This signal propagates through the water and is reflected by scatterers moving with the water flow, forming an echo. After receiving the echo signal, the ADCP divides the water body into multiple time windows based on the round-trip time of the sound wave, dividing the water body into multiple vertical water layers. By analyzing the Doppler frequency shift of the echo signal in each time window, the flow velocity of each layer is calculated, thereby obtaining the flow velocity profile of the water body. The specific flow velocity profile is as follows: Figure 2 shown.

[0051] The broadband coded transmission signal is a two-phase coded signal. The broadband current measurement method transmits a pulse signal containing two or more pseudo-random sequence codes at a time. Its analytical form is:

[0052]

[0053] Where, is the phase modulation function, which can only be 0 or π for a repetitive two-phase coded signal; f0 represents the carrier signal frequency; j is the imaginary unit; u(t) is the autocorrelation function of the amplitude modulation function, and the envelope of the two-phase coded signal is set to be rectangular, that is:

[0054]

[0055] Where P is the signal energy; t s is the duration of the transmitted pulse. When the number of code repetitions is M, the number of transmitted two-phase pseudo-random code signal symbols is L, and the symbol width is t p , the form of the transmitted signal is:

[0056]

[0057] Where M is the number of code repetitions; m is the index of the code repetition sequence; x1(t-mLt p ) is the analytical function of the pulse signal, t represents time, L represents the number of code elements in the initial coding sequence, t p Indicates the code element width.

[0058] In this embodiment, Figure 2 In the water layer stratification model shown, Δz is the layer thickness, and 0~z0 is the detection blind area. The two pulses are (0,t a ) and (t d ,T0), where t d represents the coherence delay time, t a It indicates the pulse width, and T0 indicates the total length of the transmitted signal. It can be seen that the overlapping part of the two pulses is (t d ,t a ), and the delay between them is t d Less than the pulse width t a The transducer transmits a sound wave signal into the water at a certain angle at time 0. After being reflected by scatterers in different layers of water, it receives echo signals from different depths starting from time t1. Since the propagation speed of sound waves in water is fixed, the time it takes for the scatterers to reach the transducer is directly related to the distance it has traveled. The layer thickness Δz is related to the pulse width t of the transmitted signal. a Correspondingly, this ensures that the signal received in each time window T is the reflected echo in the corresponding water layer.

[0059] S20: Determine a feasible domain of broadband coded transmission signal parameters according to preset broadband coded transmission signal constraints, and search for broadband transmission signal waveform parameters with the minimum speed measurement standard deviation in the feasible domain.

[0060] In this embodiment, the feasible domain of parameters such as the symbol width, coherence delay time, phase difference averaging time, number of coded symbols, and number of code repetitions of the broadband transmission signal is determined, taking into account the constraints of the speed measurement range, layer thickness range, system bandwidth, etc. According to the broadband flow measurement performance formula, a global search is performed within the feasible domain to obtain the pulse repetition number and code length with the minimum speed measurement standard deviation, and finally the waveform parameters of the broadband transmission signal are determined. The selection process is as follows: Figure 3 shown.

[0061] In this embodiment, when transmitting coded coherent signals in a broadband flow measurement mode, the principle of selecting the coding format and parameters is to select based on the system working index. p It cannot exceed the reciprocal of the bandwidth B of the ADCP system and generally will not change once determined. d The choice of should consider the actual measurement situation, according to the maximum measurable flow velocity ν max Determine; by t p and t d The number of code elements L in the coding sequence can be further determined; the depth unit layer thickness Δz determines the phase difference average time t a Note d Cannot exceed t a , otherwise you need to redetermine t d The value of t d and t a The number of times M that the code of the pseudo-random sequence is repeated can be further determined.

[0062] (1) Code element width t p

[0063] t p It cannot be less than the inverse of the system bandwidth B, which gives the lower limit of the symbol width:

[0064]

[0065] (2) Delay time t d

[0066] Since the inverse tangent operation restricts the phase function to the range [-π,π], and the actual phase value is within this range without velocity ambiguity, it can be concluded that the period of the maximum measured Doppler shift cannot exceed half of the coherence delay time. Therefore, the delay time should meet the following conditions:

[0067]

[0068] (3) Phase difference average time t a

[0069] The depth unit layer thickness Δz determines the phase difference averaging time t a, assuming the layer thickness range is Δz min ~Δz max , from which we can determine t a The value range of is:

[0070]

[0071] (4) Sub-pulse t s The number of code elements L and the number of coding repetitions M

[0072] Taking into account the autocorrelation of the initial coding sequence, the lower limit of L in this embodiment is set to 5, and the delay time is set to the width of the sub-pulse t s , L should meet the following conditions:

[0073]

[0074] Broadband flow measurement requires that the number of encoding repetitions is not less than 2, which is determined by t a It can be further determined that the values ​​of M and L should meet the following conditions:

[0075]

[0076] The constraints above allow us to calculate the feasible region for the number of pulse repetitions and the number of code elements. By plotting all the integer points that meet the requirements within the feasible region and searching for the index with the minimum standard deviation, we can determine the optimal waveform parameters.

[0077] The calculation expression of speed standard deviation is as follows:

[0078]

[0079] Where λ is the carrier wavelength; t d is the measurement delay time; t a is the phase difference average time; M a t a The number of code elements in the code; C is a coefficient reflecting the non-ideal degree of coding and signal processing; ρ is the autocorrelation coefficient of the calculation delay, which is approximately ρ = 1-1 / N when the signal-to-noise ratio is large enough. s , N s is the number of pulse repetitions.

[0080] S30: Taking the peak sidelobe ratio of the initial coding sequence as the optimization target, based on the broadband transmission signal waveform parameters, and using a genetic algorithm to optimize the initial coding sequence, finally generating an optimal transmission signal according to the optimization result, and performing profile flow velocity measurement based on the optimal transmission signal.

[0081] Optionally, taking the peak sidelobe ratio of the initial coding sequence as the optimization target includes: generating a corresponding initial two-phase coded signal based on the initial coding sequence; setting an autocorrelation function based on the initial two-phase coded signal, and setting an objective function with the peak sidelobe ratio as the optimization target according to the autocorrelation function.

[0082] In this embodiment, the peak sidelobe ratio of the coding sequence after autocorrelation processing is used as the optimization target, and according to the number of coding symbols determined in step S20, a genetic algorithm is used to optimize the initial coding sequence and search to obtain a global optimal solution.

[0083] The first step is to determine the optimization target, obtain a coding sequence with a sharp autocorrelation main peak and minimum autocorrelation sidelobes, and generate a corresponding two-phase coded signal based on the coding sequence.

[0084] The complex envelope of the two-phase coded signal can be expressed as:

[0085]

[0086] Where u(t) is the autocorrelation function of the amplitude modulation function; j is the imaginary unit; represents the phase modulation function.

[0087] In this embodiment, R(τ) is the inner product between the signal and its delayed version. For a continuous signal x(t), the autocorrelation function is defined as:

[0088]

[0089] For the case of a two-phase coded signal, there are two possible phases in each symbol period (e.g. or ). Therefore, assuming there are two adjacent symbols x(t) and x(t+τ), the autocorrelation function is adjusted to:

[0090]

[0091] When τ=0, therefore:

[0092]

[0093] because In the case of binary coding, it is always 1, meaning that R(0) reaches its maximum value, indicating strong signal correlation when there is no delay. When τ > 0 and is greater than the symbol duration, due to the characteristics of binary-coded pseudo-random sequences, the phase changes between symbols cause the exponential terms to cancel each other out, causing the integral to approach zero. A good pseudo-random sequence should have a prominent main peak and low sidelobes to reduce interference and improve measurement accuracy.

[0094] The Peak Sidelobe Lobe Ratio (PSLR) of the wideband coded transmit signal after autocorrelation processing is used as the objective function, that is:

[0095]

[0096] Where PSLR is the peak-to-sidelobe ratio, R(0) is the mainlobe peak of the autocorrelation function, and max|R(τ)| is the maximum sidelobe peak.

[0097] Optionally, the initial coding sequence is optimized using a genetic algorithm, comprising:

[0098] S31: Divide the initial coding sequence according to preset sequence lengths to obtain an initial random sequence population;

[0099] S32: Setting a fitness function based on the objective function;

[0100] S33: selecting elite individuals from the initial random sequence population based on the fitness function;

[0101] S34: performing crossover using the elite individual as a parent to obtain a new individual;

[0102] S35: performing random mutation on all individuals in the initial random sequence population and the new individual;

[0103] S36: Update the initial random sequence population according to the random mutation result, and determine whether the iterative update stop condition is met. When the iterative update condition is met, stop the iteration; otherwise, repeat steps S32-S36 until the iterative update stop condition is met.

[0104] In this embodiment, Figure 4 As shown in Figure 1, Step 1: Initialize the population. Set the required sequence length and generate multiple initial random sequences to form a population. Each sequence is called an individual.

[0105] Step 2: Configure the genetic algorithm's operation mode, convergence accuracy, convergence speed and other options.

[0106] Step 3: Select the fitness function. Since the optimization target of the genetic algorithm tool converges to the minimum value, the inverse of the peak-to-sidelobe ratio is used as the fitness function f:

[0107]

[0108] Step 4: Selection: Select elite individuals from the current population based on their fitness values.

[0109] Step 5: Crossover. Randomly select multiple crossover points with probability P cThe selected individuals are crossed and the parent individual of each gene is determined by binary mask to generate new individuals.

[0110] Step 6: Mutation. All individuals in the population change the values ​​of some individuals with a pre-set probability. The mutation probability of the genetic algorithm is P m , generate a random number r in [0,1] for each bit of the population, if r<P m , then perform a binary mutation operation on this bit, that is, invert the corresponding value. Mutation operation can prevent the algorithm from converging to a local optimum too early and enhance population diversity.

[0111] Step 7: Update the population. After the selection, crossover, and mutation process, a new next generation population is obtained.

[0112] Step 8: Set the stopping condition. If the stopping condition is met, stop the iteration; otherwise, repeat steps 3 to 7 until the stopping condition is met.

[0113] In this embodiment, the parameters of the genetic algorithm are set as follows: population size is 10,000; crossover probability is 0.9; mutation probability is 0.1; the number of elite individuals retained in each generation is 2; and the maximum iterative update condition is 60 times.

[0114] To overcome the shortcomings of the existing technology, the present invention aims to apply genetic algorithms to broadband flow measurement coding design, selecting waveform parameters with optimal flow measurement accuracy under the constraints of environmental factors such as velocity measurement range, system bandwidth, and fine stratification. To this end, the technical solution adopted by the present invention is to establish a broadband flow measurement layered model based on the layered flow measurement principle, determine the feasible domain of broadband coding transmission signal parameters that meet the index conditions based on measurement requirements and actual hardware limitations, and use the broadband flow measurement performance formula to globally search within the feasible domain to obtain the number of pulse repetitions and code length with the minimum velocity measurement standard deviation. The peak sidelobe ratio of the initial coding sequence autocorrelation function is used as the optimization object of the genetic algorithm. After multiple generations of selection, crossover, and mutation search, the global optimal solution is obtained, and finally a new coding sequence is determined. The designed broadband coding signal is used for hydrological measurement. Compared with the traditional pseudo-random coding sequence used in existing acoustic Doppler current profiler systems, the coding sequence designed by this method has good sidelobe suppression characteristics and is not limited in length, which facilitates waveform parameter selection. In actual flow measurement scenarios, the waveform designed using this method can accurately match the spatial resolution of the water layer while ensuring flow measurement accuracy.

[0115] In order to further verify the effectiveness of the method of the present invention, an experiment was carried out using an acoustic Doppler flow profiler as an example.

[0116] The working indicators of an acoustic Doppler current profiler are as follows:

[0117] (1) The operating frequency is 10MHz and the system bandwidth is 500kHz.

[0118] (2) The speed measurement range is -0.5m / s to 0.5m / s.

[0119] (3) The layer thickness range is 0.17 to 0.23 m.

[0120] According to the preset wideband coded transmission signal constraints, the feasible domain of the pulse repetition times and the number of code elements that meet the above constraints can be obtained as follows: Figure 5 As shown. Since the code length and the number of repetitions must be integers, the feasible domain of the code length and the number of repetitions is determined according to the speed measurement range and the layering constraints. All the integer points that meet the conditions in the feasible domain can be plotted, and the optimal waveform parameters can be determined by searching for the index of the minimum standard deviation. Figure 6 The figure shows the distribution of speed standard deviation. When the waveform parameters (L=9, M=18) are set, the speed standard deviation of the GA sequence is the smallest (σ ν =1.96cm / s), (L=7, M=22) can get the minimum speed standard deviation of Baker code (σ ν =2cm / s).

[0121] According to the waveform parameters selected above, the optimal coding pattern with peak sidelobe ratio when L=9 is obtained through global optimization of genetic algorithm. The ambiguity function can reflect the delay and frequency shift resolution of the signal. Figure 7 The ambiguity map of the optimal transmitted signal is shown in Figure 2. The phase-coded signal has higher delay resolution and frequency resolution, as well as a larger Doppler tolerance. In summary, the effectiveness of the waveform parameter selection method in this paper can be verified.

[0122] The foregoing description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be readily conceived by a person skilled in the art within the technical scope disclosed herein should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A broadband current measurement waveform design method based on genetic algorithm, characterized in that: The steps include: S10: constructing a broadband coded transmission signal model, and analyzing a water layer stratification model of an acoustic Doppler current profiler based on the broadband coded transmission signal model to obtain an initial coding sequence of the broadband coded transmission signal; S20: determining a feasible domain of broadband coded transmission signal parameters according to preset broadband coded transmission signal constraints, and searching for broadband transmission signal waveform parameters with the minimum speed measurement standard deviation in the feasible domain; S30: Taking the peak sidelobe ratio of the initial coding sequence as the optimization target, based on the broadband transmission signal waveform parameters, and using a genetic algorithm to optimize the initial coding sequence, finally generating an optimal transmission signal according to the optimization result, and performing profile flow velocity measurement based on the optimal transmission signal.

2. A broadband current measurement waveform design method based on genetic algorithm according to claim 1, characterized in that: The broadband coded transmission signal model is: Where M is the number of code repetitions; m is the index of the code repetition sequence; x1(t-mLt p ) is the analytical function of the pulse signal, t represents time, L represents the number of code elements in the initial coding sequence, t p Indicates the code element width.

3. The broadband current measurement waveform design method based on genetic algorithm according to claim 1 is characterized in that: The formula for calculating the speed standard deviation is: Where λ is the carrier wavelength; t d is the measurement delay time; t a is the phase difference average time; M a t a The number of code elements in; C is a coefficient reflecting the non-ideal degree of coding and signal processing; ρ is the autocorrelation coefficient of the calculation delay, which is approximately ρ = 1-1 / N when the signal-to-noise ratio is large enough. s , N s is the number of pulse repetitions.

4. The method for designing a broadband current measurement waveform based on a genetic algorithm according to claim 1, characterized in that: Taking the peak sidelobe ratio of the initial coding sequence as an optimization target, the method includes: generating a corresponding initial two-phase coded signal based on the initial coding sequence; An autocorrelation function is set based on the initial two-phase coded signal, and an objective function with a peak-to-sidelobe ratio as an optimization target is set according to the autocorrelation function.

5. The method for designing a broadband current measurement waveform based on a genetic algorithm according to claim 4, characterized in that: The complex envelope of the initial two-phase coded signal is expressed as: Where u(t) is the autocorrelation function of the amplitude modulation function; j is the imaginary unit; represents the phase modulation function.

6. A broadband current measurement waveform design method based on genetic algorithm according to claim 4, characterized in that: The autocorrelation function is: Where P represents the signal energy; t s Indicates the duration of the transmit pulse.

7. The method for designing a broadband current measurement waveform based on a genetic algorithm according to claim 4, characterized in that: The objective function is: Where PSLR is the peak-to-sidelobe ratio, R(0) is the mainlobe peak of the autocorrelation function, and max|R(τ)| is the maximum sidelobe peak.

8. The method for designing a broadband current measurement waveform based on a genetic algorithm according to claim 7, characterized in that: Optimizing the initial coding sequence using a genetic algorithm comprises: S31: Divide the initial coding sequence according to preset sequence lengths to obtain an initial random sequence population; S32: Setting a fitness function based on the objective function; S33: selecting elite individuals from the initial random sequence population based on the fitness function; S34: performing crossover using the elite individual as a parent to obtain a new individual; S35: performing random mutation on all individuals in the initial random sequence population and the new individual; S36: Update the initial random sequence population according to the random mutation result, and determine whether the iterative update stop condition is met. When the iterative update condition is met, stop the iteration; otherwise, repeat steps S32-S36 until the iterative update stop condition is met.

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