Parameter selection method and device for linear frequency modulation signal

Abstract

The invention discloses a parameter selection method and device for a linear frequency modulation signal, and relates to the field of active sonar detection, and the method comprises the steps: carrying out the parameter expansion operation of the linear frequency modulation signal in a reverberation environment; echo data of the linear frequency modulation signals after parameter expansion are obtained; and based on a particle swarm optimization algorithm, finding a target echo position in echo data through signal processing, calculating a fitness function value near the target echo position through a sliding window, and updating an extended parameter variable of the linear frequency modulation signal after each calculation is completed until a termination condition is reached. And the parameter of the linear frequency modulation signal corresponding to the fitness function value is the optimal parameter. On the basis of not changing the chirp signal, selectable parameters are added, a larger selection space is provided for parameter determination of the chirp signal in the reverberation environment, and the flexibility of parameter selection of the chirp signal is improved.

Description

Technical Field

[0001] The present application relates to the field of active sonar detection, and in particular to a method and device for selecting parameters of a linear frequency modulation signal. Background Art

[0002] The detection problem of active sonar in a reverberation background has always been a hot and difficult issue in the field of underwater acoustics. Due to the strong correlation between reverberation and signals, simply increasing the transmission power cannot effectively improve the detection performance.

[0003] Active sonar usually transmits linear frequency modulation signals as the transmitting waveform. The linear frequency modulation signal parameter selection space is small, the flexibility is low, and the determination of parameters is often based on empirical formulas. In addition, the detection performance of linear frequency modulation signals in the reverberation background is poor. It is generally believed that the gain obtained after the linear frequency modulation signal is matched and filtered is 10lgBT, where B is the signal bandwidth and T is the signal pulse width. However, in the reverberation background, due to the strong correlation between the reverberation and the transmitted signal, the single change of parameters such as bandwidth, pulse width or signal amplitude cannot improve the detection gain in the reverberation background. At the same time, the increase in bandwidth and pulse width increases the complexity of the transmitting system, which makes the parameter selection of linear frequency modulation signals in the reverberation background a difficult problem. Summary of the invention

[0004] The present application provides a method and device for selecting parameters of a linear frequency modulation signal, which can solve the problems of small selection space and low flexibility of linear frequency modulation signal parameters in the prior art.

[0005] In a first aspect, an embodiment of the present application provides a method for selecting parameters of a linear frequency modulation signal, the parameter selection method comprising:

[0006] In a reverberation environment, perform parameter expansion operations on linear frequency modulation signals;

[0007] Obtain echo data of the linear frequency modulation signal after parameter expansion;

[0008] Based on the particle swarm optimization algorithm, the target echo position is found in the echo data through signal processing, and the fitness function value near the target echo position is calculated through a sliding window. After each calculation, the expanded parameter variables of the linear frequency modulation signal are updated until the termination condition is reached. At this time, the parameters of the linear frequency modulation signal corresponding to the fitness function value are the optimal parameters.

[0009] In combination with the first aspect, in one implementation, before the parameter expansion operation, the reverberation environment is judged:

[0010] After preliminary judgment in the reverberation environment through the sonar equation,

[0011] The echo data obtained for the first time is preliminarily segmented, and the length of each segment is not less than the signal pulse width. When the normalized Itakura distance between two adjacent segments of data is greater than 0.3, it is data in a reverberation environment.

[0012] In combination with the first aspect, in one implementation, performing a parameter expansion operation on the linear frequency modulation signal includes:

[0013] dividing the linear frequency modulation signal into a plurality of sub-segments;

[0014] Determine the extended parameter variables of the linear frequency modulation signal, the parameter range of each extended parameter variable, and the accuracy of the extended parameter variables, wherein the extended parameter variables include three types: sub-segment signal center frequency, sub-segment signal center frequency and sub-segment signal bandwidth, and sub-segment signal center frequency, sub-segment signal bandwidth and sub-segment signal pulse width.

[0015] In combination with the first aspect, in one implementation, determining the extended parameter variable of the linear frequency modulation signal includes:

[0016] Under normal sea conditions, the sub-segment signal center frequency is selected as the target variable; under complex sea conditions, the sub-segment signal center frequency and sub-segment signal bandwidth are selected as target variables; under extreme conditions, the sub-segment signal center frequency, sub-segment signal bandwidth, and sub-segment signal pulse width are selected as target variables.

[0017] In combination with the first aspect, in one implementation, the parameter range of each extended parameter variable includes:

[0018] Sub-segment signal center frequency range f c_seg ∈[f c -0.5*B+B seg , f c +0.5*BB seg ],

[0019] Sub-segment signal bandwidth setting range B seg ∈(0,B),

[0020] Sub-segment signal pulse width setting range T seg ∈(0,T),

[0021] Among them, f c is the center frequency of the linear frequency modulation signal, B is the bandwidth of the linear frequency modulation signal, and T is the duration of the linear frequency modulation signal;

[0022] When the extended parameter variable only selects the sub-segment signal center frequency, the sub-segment signal bandwidth B seg =B / N, sub-segment signal pulse width T seg =T / N, where N is the number of sub-segments.

[0023] In combination with the first aspect, in one implementation, obtaining echo data of the linear frequency modulation signal after parameter expansion includes:

[0024] Transmit the linear frequency modulation signal after parameter expansion to obtain echo data;

[0025] Alternatively, the echo data of the linear frequency modulation signal after parameter expansion is obtained through reverberation model simulation.

[0026] In combination with the first aspect, in one implementation, finding the target echo position in the echo data by signal processing includes:

[0027] The target echo position is found by performing matched filtering, unit average constant false alarm and data smoothing operations on the echo data.

[0028] In combination with the first aspect, in one embodiment, the termination condition is: the number of iterations reaches a first preset threshold; or, after the number of consecutive iterations reaches a second preset threshold, the change in the fitness function value does not exceed a preset percentage.

[0029] In combination with the first aspect, in one implementation, if the change in the fitness function value exceeds a preset percentage after the number of iterations reaches a first preset threshold, the weight coefficient of the optimization algorithm is modified, and the extended parameter variable and the parameter range of the extended parameter variable are increased, and the method is re-executed;

[0030] The weight coefficients include pre-set speed limit, inertia weight, self-learning factor and group learning factor.

[0031] In a second aspect, an embodiment of the present application provides a parameter selection device based on any one of the linear frequency modulation signal parameter selection methods, the device comprising:

[0032] An expansion module, which is used to perform expansion operations on linear frequency modulation signals in a reverberation environment;

[0033] A data acquisition module, which is used to obtain echo data of the linear frequency modulation signal after parameter expansion;

[0034] The execution module, based on the particle swarm optimization algorithm, finds the target echo position in the echo data through signal processing, calculates the fitness function value near the target echo position through a sliding window, and updates the expanded parameter variables of the linear frequency modulation signal after each calculation until the termination condition is reached. At this time, the parameters of the linear frequency modulation signal corresponding to the fitness function value are the optimal parameters.

[0035] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0036] By performing parameter expansion operation on the linear frequency modulation signal in a reverberation environment, optional parameters are added without changing the linear frequency modulation signal, which provides a larger selection space for parameter determination of the linear frequency modulation signal in a reverberation environment and improves the flexibility of linear frequency modulation signal parameter selection.

[0037] On the basis of the linear frequency modulation signal parameter expansion, the particle swarm optimization algorithm is used to iterate the fitness function value near the target echo position to achieve the selection of the optimal parameters, further improving the flexibility of the linear frequency modulation signal parameter selection. This application can improve the detection performance of active sonar in a reverberant environment through the parameter selection of the linear frequency modulation signal, and improve its detection ability in a reverberant background. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for selecting parameters of a linear frequency modulation signal of the present application;

[0039] Figure 2 A schematic diagram of time domain comparison between a linear frequency modulation signal and a linear frequency modulation extended parameter signal of the present application;

[0040] Figure 3 This is a schematic diagram of the time domain difference between the linear frequency modulation signal and the linear frequency modulation extended parameter signal of this application;

[0041] Figure 4 This is a schematic diagram of the time-frequency comparison between the linear frequency modulation signal and the linear frequency modulation extended parameter signal of this application;

[0042] Figure 5 This is a schematic diagram of the overall comparison between the linear frequency modulation extended parameter signal and the traditional frequency modulation signal of this application;

[0043] Figure 6 for Figure 5 A partial enlarged schematic diagram;

[0044] Figure 7 This is a schematic diagram of echo data in a reverberation background in an embodiment of the present application.

[0045] Figure 8 Schematic diagram comparing the target detection results of the embodiment of the present application and the conventional method. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0047] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0048] In a first aspect, an embodiment of the present application provides a method for selecting parameters of a linear frequency modulation signal.

[0049] In one embodiment, referring to Figure 1 , Figure 1 FIG. 1 is a flow chart of an embodiment of a method for selecting parameters of a linear frequency modulation signal of the present application. Figure 1 As shown, the parameter selection method includes the steps of:

[0050] S1. Perform parameter expansion operation on the linear frequency modulation signal in a reverberation environment.

[0051] S2. Obtain echo data of the linear frequency modulation expanded parameter signal (i.e., the linear frequency modulation signal after parameter expansion).

[0052] S3. Based on the particle swarm optimization algorithm, the target echo position is found in the echo data through signal processing, and the fitness function value near the target echo position is calculated through a sliding window. After each calculation, the expanded parameter variable of the linear frequency modulation signal is updated until the termination condition is reached. At this time, the parameters of the linear frequency modulation signal corresponding to the fitness function value are the optimal parameters.

[0053] In this embodiment, by performing parameter expansion operation on the linear frequency modulation signal in a reverberation environment, optional parameters are added without changing the linear frequency modulation signal, providing a larger selection space for parameter determination of the linear frequency modulation signal in a reverberation environment, and improving the flexibility of linear frequency modulation signal parameter selection. Based on the linear frequency modulation signal parameter expansion, the particle swarm optimization algorithm is used to select the optimal parameters, further improving the flexibility of linear frequency modulation signal parameter selection.

[0054] This embodiment is applicable to a reverberation environment. If it is in a noisy environment, the above method is not applicable.

[0055] Furthermore, in one embodiment, before step S1, the reverberation environment can be judged in advance. First, the sonar equation is used to preliminarily judge whether the working environment is a reverberation environment or a noise environment, combining prior information such as the transducer opening angle, bandwidth, sound source level, and environmental bottom quality parameters used in active sonar detection.

[0056] After the initial judgment of the reverberation environment, the echo data obtained for the first time is preliminarily segmented. After the preliminary segmentation, the length of each segment is not less than (that is, greater than or equal to) the length of the signal pulse width. The Itakura distance between two adjacent segments of data is used to determine whether it is a reverberation environment. Set the two segments of echo data to be d i (t) and d i+1(t), where the subscript i represents the i-th segment of data. i The power spectrum of (t) i (ω), and for data d i+1 The power spectrum of (t) i (ω) The asymmetric Itakura distance is expressed by the following formula:

[0057]

[0058] in, Yes i (t) is the linear estimation asymptotic residual. When the normalized Itakura distance between two adjacent data segments is greater than 0.3, it is the data in the reverberation environment.

[0059] Further, in one embodiment, the step of performing parameter expansion operation on the linear frequency modulation signal in S1 above includes the following steps:

[0060] S11. Divide the linear frequency modulation signal into a plurality of sub-segments.

[0061] Initialize the extended parameter signal, the expression of the linear frequency modulation signal is as follows:

[0062] s(t)=Aw(t)exp[j(2πf c t+πkt 2 )], t∈[0,T],

[0063] Where A is the signal amplitude, w(t) is the window function, usually a rectangular window, and f c is the center frequency of the linear frequency modulation signal, k is the modulation index, and k=B / T, B is the bandwidth of the linear frequency modulation signal, T is the duration of the linear frequency modulation signal, and t is the sampling time.

[0064] The linear frequency modulation signal s(t) is expanded. First, s(t) is segmented into N segments, each segment is a sub-segment, T seg Represents the pulse width of each sub-segment signal. The pulse widths of the sub-segment signals can be the same or different. The constraint condition that needs to be satisfied is that the sum of the pulse widths of the sub-segment signals is equal to the pulse width of the linear frequency modulation signal. In order to simplify the representation, in this embodiment, the pulse widths of the sub-segment signals are the same. Let the signal after expansion be x(t), then the expression of x(t) is as shown in the formula:

[0065]

[0066] Theoretically, the number of segments N can be set to infinity. In actual situations, the application scenario, transducer performance in active sonar detection, and power amplifier capabilities should be considered. Here, a general recommended range is given, and the duration of each sub-segment signal is recommended to be no less than 1ms. It can also be changed as appropriate according to the transducer and power amplifier capabilities.

[0067] S12, determining the extended parameter variables of the linear frequency modulation signal, the parameter range of each extended parameter variable, and the accuracy of the extended parameter variables.

[0068] The above-mentioned extended parameter variables include three types: sub-segment signal center frequency, sub-segment signal center frequency and sub-segment signal bandwidth, and sub-segment signal center frequency, sub-segment signal bandwidth and sub-segment signal pulse width.

[0069] Usually, the sea conditions are classified in advance and can be divided into general sea conditions, complex sea conditions and extreme conditions according to the actual situation. The classification conditions can be set by yourself. For general sea conditions, the sub-segment signal center frequency is selected as the target variable; for complex sea conditions, the sub-segment signal center frequency and sub-segment signal bandwidth are selected as the target variables; for extreme conditions, the sub-segment signal center frequency, sub-segment signal bandwidth, and sub-segment signal pulse width are selected as the target variables. Generally speaking, the more variables there are, the stronger the ability to find the optimal parameters and the better the effect, but the greater the computational complexity, so it needs to be considered in combination with operability.

[0070] In the above S12, determining the parameter range of each extended parameter variable of the linear frequency modulation signal includes:

[0071] Sub-segment signal center frequency range f c_seg ∈[f c -0.5*B+B seg , f c +0.5*BB seg ],

[0072] Sub-segment signal bandwidth setting range B seg ∈(0,B),

[0073] Sub-segment signal pulse width setting range T seg ∈(0,T),

[0074] Among them, f c is the center frequency of the linear FM signal, B is the bandwidth of the linear FM signal, and T is the duration of the linear FM signal.

[0075] Moreover, when the extended parameter variable only selects the sub-segment signal center frequency, the sub-segment signal bandwidth B seg =B / N, sub-segment signal pulse width T seg =T / N, where N is the number of sub-segments. With the increase of variables, it can be adjusted flexibly. In this embodiment, only recommended reference values ​​that can achieve better results are given. The parameter range of each expanded parameter variable does not exceed the range of the linear frequency modulation signal.

[0076] Determining the accuracy of the extended parameter variable in the above S12 includes: after determining the parameter range of the extended parameter variable and the parameter variable, determining the accuracy of the individual variable according to the transducer frequency response curve and the calculation accuracy requirement. The higher the accuracy, the longer the calculation time. Usually, it is necessary to consider the calculation time of the converter capability and the task requirements. In this embodiment, the accuracy of the sub-segment signal center frequency is 1 Hz, the accuracy of the sub-segment signal bandwidth is 1 Hz, and the accuracy of the sub-segment signal pulse width is 0.1 ms.

[0077] Furthermore, in one embodiment, in the above S2, there are two ways to obtain the echo data of the linear frequency modulation signal after parameter expansion. One is to transmit the linear frequency modulation signal after parameter expansion to obtain the echo data; the other is to perform reverberation model simulation through a unit scattering reverberation model to obtain the echo data of the linear frequency modulation signal after parameter expansion.

[0078] Furthermore, in one embodiment, in the above S3, the particle swarm parameters are selected based on the particle swarm optimization algorithm. In order to reach the steady-state condition faster and avoid the local optimal solution, the weight coefficients are set to include the preset speed limit, inertia weight, self-learning factor and group learning factor. In this embodiment, the inertia weight value range is recommended to be [0.2 1.2], the speed setting is based on the accuracy of the aforementioned sub-segment signal, and the positive and negative values ​​of the speed do not exceed 20 times the accuracy; the self-learning factor and the group learning factor are between 0.5 and 1.5. The transmission waveform parameters are adjusted by the weight coefficients to obtain echo data.

[0079] In addition, a termination condition needs to be set. In this embodiment, two termination conditions are set. When one of the termination conditions is met, the calculation can be terminated. The first termination condition is: the number of iterations reaches the first preset threshold. The first threshold can be set according to the actual situation and is usually greater than the second threshold. The second termination condition is: after the number of consecutive iterations reaches the second preset threshold, the change in the fitness function value does not exceed the preset percentage; in this embodiment, the second preset threshold is 5, and the preset percentage is 1%, that is, after the number of consecutive iterations reaches 5 times, the change in the fitness function value does not exceed 1%.

[0080] Furthermore, in one embodiment, in the above S3, the target echo position is found in the echo data through signal processing, including: finding the target echo position by performing signal processing operations such as matched filtering, unit average constant false alarm rate and data smoothing on the echo data.

[0081] In the above S3, it is assumed that the target echo position is x l , let x rec =[x l-M ,x l-M+1 ,…x l ,…x l+M ] represents the sampling data near the target, then M represents the signal interception range. Generally, 2M is set as the length corresponding to the signal pulse width. Then the fitness function SIR is expressed as:

[0082]

[0083] In the above S3, the fitness function value near the target echo position is calculated by sliding window. After each calculation, the extended parameter variable of the linear frequency modulation signal is updated until the termination condition is reached. At this time, the parameter of the linear frequency modulation signal corresponding to the fitness function SIR value is the optimal parameter.

[0084] Furthermore, in one embodiment, if the number of iterations reaches a first preset threshold and the calculation stops, if the change in the fitness function value exceeds a preset percentage, the weight coefficient of the optimization algorithm is modified, and the expanded parameter variables and the parameter range of the expanded parameter variables are increased, and the parameter selection method is re-executed.

[0085] In a second aspect, an embodiment of the present application further provides a parameter selection device based on any of the above-mentioned linear frequency modulation signal parameter selection method embodiments, the device comprising a parameter expansion module, a data acquisition module and an execution module.

[0086] The parameter expansion module is used to perform parameter expansion operations on linear frequency modulation signals in a reverberation environment.

[0087] The data acquisition module is used to obtain the echo data of the linear frequency modulation signal after parameter expansion.

[0088] The execution module, based on the particle swarm optimization algorithm, finds the target echo position in the echo data through signal processing, calculates the fitness function value near the target echo position through a sliding window, and updates the expanded parameter variables of the linear frequency modulation signal after each calculation until the termination condition is reached. At this time, the parameters of the linear frequency modulation signal corresponding to the fitness function value are the optimal parameters.

[0089] In this embodiment, by performing parameter expansion operation on the linear frequency modulation signal, optional parameters are increased, thereby solving the problem of small parameter selection space and low flexibility of the linear frequency modulation signal in the related art.

[0090] Next, the difference between the linear frequency modulation extended parameter signal and the linear frequency modulation signal is analyzed by simulation modeling, and the simulation results are given as follows Figure 2 , Figure 3 and Figure 4 As shown in the figures, it is not difficult to find that when the center frequency, pulse width, and bandwidth are set the same, there is no difference between the linear frequency modulation signal and the linear frequency modulation extended parameter signal in the time domain and frequency domain. The invariance of the signal after the expansion is further verified through simulation modeling.

[0091] In addition, the parameter selection method of the linear frequency modulation signal of the present application can be analyzed by simulation modeling, and the local signal-to-interference ratio of the linear frequency modulation extended parameter signal after active sonar transmission optimization is calculated, which is about 3dB higher than that of the traditional linear frequency modulation signal. The simulation results are as follows Figure 5 and Figure 6 As shown, calculation Figure 5 Local signal-to-interference ratio SIR, the range window length is 500 sampling points. It can be calculated that the parameter selection method used in this application improves the local signal-to-interference ratio of the linear frequency modulation signal by about 3dB.

[0092] The following is a specific example to further illustrate the method for selecting parameters of a linear frequency modulation signal of the present application.

[0093] First, determine the basic parameters. In this example, for the convenience of simulation calculation, assume that the linear frequency modulation signal s(t) has a center frequency f c is 5kHz, the linear FM signal bandwidth B is 6kHz, and the linear FM signal sampling rate f s Set to 30kHz, the linear frequency modulation signal pulse width T is 30ms, and the ambient sound speed c is assumed to be a constant 1500m / s. To facilitate understanding and simplify the steps, the number of linear frequency modulation extended parameter signal segments N=3 is set here, and the time of each segment is equal; the sub-segment signal bandwidth B n Equal, both 2kHz; sub-segment signal pulse width T seg =10ms, sub-segment signal center frequency f n .

[0094] Then determine the parameters to be optimized. To better illustrate the effect of this method, only the sub-segment signal center frequency f is used. n As the parameter to be optimized. And the optimization problem description satisfies:

[0095]

[0096] A1. Initialize signal parameters, divide the linear frequency modulation expansion parameter signal into 3 segments, keep the total pulse width and bandwidth unchanged, and use x(t) to represent the initialized linear frequency modulation expansion parameter signal.

[0097] A2. Based on the parameters of each updated linear frequency modulation expansion signal, a signal reverberation model is established. In this method, the reverberation model adopts the common unit scattering reverberation model (statistical model is also acceptable), and the echo data time length is T = 1s. For ease of understanding, the target distance of the simulated echo is set to 600m (target echo position). The reverberation data of the echo is as follows: Figure 7 shown.

[0098] A3. The particle swarm algorithm is used to optimize the sub-segment signal center frequency of the initialized linear frequency modulation extended parameter signal, and the local signal-to-interference ratio (SIR) of the echo data is used as the fitness function. To ensure the rigor of the simulation effect, the calculation is performed 100 times each time, and the average value of the local signal-to-interference ratio (SIR) of the echo data is taken as the final fitness function to solve the optimal parameter solution of the extended parameter signal.

[0099] The echo data corresponding to the optimal parameters of the linear frequency modulation extended parameter signal obtained in step A3 are analyzed with the original echo data, and the echo data are analyzed using the unit average constant false alarm and data smoothing algorithm. The center frequencies of the optimized sub-segment parameters are 5kHz, 4kHz, and 6.5kHz, respectively, and the bandwidths are all 2kHz. Figure 8 It can be seen that compared with conventional methods, the method of the present application has lower overall reverberation background clutter interference, and can obtain obvious signal-to-interference ratio gain at the target echo, thereby improving the target detection capability.

[0100] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0101] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0102] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0103] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0104] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0105] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for selecting parameters of a linear frequency modulation signal, characterized in that: The parameter selection method includes: In a reverberation environment, perform parameter expansion operations on linear frequency modulation signals; Obtain echo data of the linear frequency modulation signal after parameter expansion; Based on the particle swarm optimization algorithm, the target echo position is found in the echo data through signal processing, and the fitness function value near the target echo position is calculated through a sliding window. After each calculation, the expanded parameter variables of the linear frequency modulation signal are updated until the termination condition is reached. At this time, the parameters of the linear frequency modulation signal corresponding to the fitness function value are the optimal parameters.

2. The method for selecting parameters of a linear frequency modulation signal according to claim 1, wherein: Before expanding the parameters, determine the reverberation environment: After preliminary judgment in the reverberation environment through the sonar equation, The echo data obtained for the first time is preliminarily segmented, and the length of each segment is not less than the signal pulse width. When the normalized Itakura distance between two adjacent segments of data is greater than 0.3, it is data in a reverberation environment.

3. The method for selecting parameters of a linear frequency modulation signal according to claim 1, wherein: The step of performing parameter expansion operation on the linear frequency modulation signal includes: dividing the linear frequency modulation signal into a plurality of sub-segments; Determine the extended parameter variables of the linear frequency modulation signal, the parameter range of each extended parameter variable, and the accuracy of the extended parameter variables, wherein the extended parameter variables include three types: sub-segment signal center frequency, sub-segment signal center frequency and sub-segment signal bandwidth, and sub-segment signal center frequency, sub-segment signal bandwidth and sub-segment signal pulse width.

4. The method for selecting parameters of a linear frequency modulation signal as claimed in claim 3, characterized in that: The step of determining the extended parameter variable of the linear frequency modulation signal includes: Under normal sea conditions, the sub-segment signal center frequency is selected as the target variable; under complex sea conditions, the sub-segment signal center frequency and sub-segment signal bandwidth are selected as target variables; under extreme conditions, the sub-segment signal center frequency, sub-segment signal bandwidth, and sub-segment signal pulse width are selected as target variables.

5. The method for selecting parameters of a linear frequency modulation signal as claimed in claim 3, characterized in that: The parameter range of each extended parameter variable includes: Sub-segment signal center frequency range f c_seg ∈[f c -0.5*B+B seg , f c +0.5*BB seg ], Sub-segment signal bandwidth setting range B seg ∈(0,B), Sub-segment signal pulse width setting range T seg ∈(0,T), Among them, f c is the center frequency of the linear frequency modulation signal, B is the bandwidth of the linear frequency modulation signal, and T is the duration of the linear frequency modulation signal; When the extended parameter variable only selects the sub-segment signal center frequency, the sub-segment signal bandwidth B seg =B / N, sub-segment signal pulse width T seg =T / N, where N is the number of sub-segments.

6. The method for selecting parameters of a linear frequency modulation signal according to claim 1, wherein: The obtaining of echo data of the linear frequency modulation signal after parameter expansion includes: Transmit the linear frequency modulation signal after parameter expansion to obtain echo data; Alternatively, the echo data of the linear frequency modulation signal after parameter expansion is obtained through reverberation model simulation.

7. The method for selecting parameters of a linear frequency modulation signal as claimed in claim 3, characterized in that: The step of finding the target echo position in the echo data by signal processing comprises: The target echo position is found by performing matched filtering, unit average constant false alarm and data smoothing operations on the echo data.

8. The method for selecting parameters of a linear frequency modulation signal as claimed in claim 3, characterized in that: The termination condition is: the number of iterations reaches a first preset threshold; or, after the number of consecutive iterations reaches a second preset threshold, the change in the fitness function value does not exceed a preset percentage.

9. The method for selecting parameters of a linear frequency modulation signal as claimed in claim 8, characterized in that: If the change in the fitness function value exceeds a preset percentage after the number of iterations reaches the first preset threshold, the weight coefficient of the optimization algorithm is modified, and the expanded parameter variable and the parameter range of the expanded parameter variable are increased, and the method is executed again; The weight coefficients include pre-set speed limit, inertia weight, self-learning factor and group learning factor.

10. A parameter selection device based on the linear frequency modulation signal parameter selection method according to any one of claims 1 to 9, characterized in that: The device comprises: An expansion module, which is used to perform expansion operations on linear frequency modulation signals in a reverberation environment; A data acquisition module, which is used to obtain echo data of the linear frequency modulation signal after parameter expansion; The execution module, based on the particle swarm optimization algorithm, finds the target echo position in the echo data through signal processing, calculates the fitness function value near the target echo position through a sliding window, and updates the expanded parameter variables of the linear frequency modulation signal after each calculation until the termination condition is reached. At this time, the parameters of the linear frequency modulation signal corresponding to the fitness function value are the optimal parameters.

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