Novel weighted coding-based target detection methods, devices, and equipment

Abstract

This application provides a target detection method, apparatus, and device based on a novel weighted coding method. The method includes: selecting multiple discrete velocities within the predicted relative velocity range between the detection platform and the target; calculating the Doppler shift factor corresponding to each discrete velocity; solving for the locally optimal weighted coding that minimizes the weighted sidelobe level under each Doppler shift factor using an improved solution algorithm, wherein the improved solution algorithm incorporates the Doppler shift factor as a coefficient into the expression for the weighted sidelobe level; and calculating the globally optimal weighted coding based on all locally optimal weighted codings, wherein the globally optimal weighted coding is used to construct the transmitted signal of the detection platform. Through this application, even when the specific relative velocity is unknown, the transmitted signal can ensure that its range sidelobe level is suppressed to a low range at any velocity within the entire prediction range, thereby ensuring a stable signal-to-interference ratio (SIR) after matched filtering.

Description

Technical Field

[0001] This application relates to the field of target detection technology, specifically to a target detection method, apparatus, and equipment based on a novel weighted coding. Background Technology

[0002] Active detection systems (such as sonar and radar) detect targets and estimate their parameters by transmitting specific coded signals and receiving target echoes. In these systems, the design of the transmitted waveform directly determines the system's detection performance. To suppress range sidelobes and avoid strong target echoes from obscuring nearby weak target echoes (i.e., the "near-far obscuring" or "strong-weak obscuring" problem), existing technologies typically employ a method that minimizes the Weighted Integrated Sidelobe Level (WISL) to design the encoded sequence of the transmitted signal. By optimizing the encoded sequence to minimize the WISL, the range sidelobe level of the signal can be effectively reduced, thereby improving the detection capability of weak targets.

[0003] However, existing methods for minimizing WISL waveform design are typically based on the stationary target assumption, meaning they do not consider the impact of Doppler shift during the design process. In real-world applications, there is often relative motion between the detection platform and the target, resulting in a Doppler frequency shift in the target echo signal. If the Doppler factor is not considered in the design phase of the transmitted signal, mismatch will occur during matched filtering at the receiver when the target has relative velocity, leading to a significant increase in the actual range sidelobe level. This increase in sidelobe level can re-initiate the problem of strong targets occluding weak targets, causing a significant decrease in the detection performance of low-sidelobe signals designed based on the stationary assumption in real-world moving scenarios. Summary of the Invention

[0004] This application provides a target detection method, apparatus, and device based on a novel weighted coding, which can solve the technical problem in the prior art where the transmission signal does not take into account the Doppler factor during the design stage, resulting in a decrease in detection performance.

[0005] In a first aspect, embodiments of this application provide a target detection method based on a novel weighted coding, the target detection method based on the novel weighted coding comprising: Multiple discrete velocities are selected within the predicted relative velocity range between the detection platform and the detection target, and the Doppler shift factor corresponding to each discrete velocity is calculated. The local optimal weighted coding that minimizes the weighted sidelobe level under each Doppler offset factor is obtained by solving the improved solution algorithm. The improved solution algorithm introduces the Doppler offset factor as a coefficient into the expression of the weighted sidelobe level. The globally optimal weighted code is calculated based on all locally optimal weighted codes, and the globally optimal weighted code is used to construct the transmission signal of the detection platform.

[0006] Further, in one embodiment, the step of calculating the globally optimal weighted code based on all locally optimal weighted codes includes: For each locally optimal weighted code, the loss factor is calculated based on the weighted sidelobe level of the current locally optimal weighted code under all Doppler offset factors. The locally optimal weighted code with the minimum loss factor is determined as the globally optimal weighted code.

[0007] Furthermore, in one embodiment, the formula for calculating the loss factor is: , in, Let i be the loss factor corresponding to the i-th locally optimal weighted encoding. and For the weight coefficients, satisfying , The number of Doppler shift factors. For the m-th Doppler shift factor, , For the i-th locally optimal weighted encoding, Var represents the weighted sidelobe level of the i-th locally optimal weighted encoding under the m-th Doppler offset factor, where Var denotes the variance calculation.

[0008] Further, in one embodiment, the step of calculating the globally optimal weighted code based on all locally optimal weighted codes includes: The global optimal weighted code is obtained by averaging and fusing all the locally optimal weighted codes.

[0009] Furthermore, in one embodiment, Where T is the pulse width of the carrier frequency signal. denoted as the center frequency of the carrier signal, and N is the number of symbols in the weighted encoding.

[0010] Furthermore, in one embodiment, the expression for the weighted sidelobe level after introducing the Doppler shift factor is: , in, Where is the Doppler offset factor, and N is the number of symbols in the weighted encoding. , For weighted window functions, Make matrix P positive semidefinite. , For weighted encoding, .

[0011] Furthermore, in one embodiment, the target detection method based on novel weighted coding further includes: The intermediate signal is obtained by expanding and modulating the globally optimal weighted code; The intermediate signal is windowed to obtain the transmission signal of the detection platform.

[0012] Furthermore, in one embodiment, the target detection method based on novel weighted coding further includes: The target detection result is obtained by performing segmented sliding window matched filtering on the received signal of the detection platform. The window length is equal to the product of the pulse width of the transmitted signal and the proportion of the weighted components in the weighted window function. The sliding step size is half of the window length. The elements of the weighted window function are only 0 and 1, and the proportion of weighted components with a weight of 1 is the proportion of the total number of weighted components.

[0013] Secondly, embodiments of this application also provide a target detection device based on a novel weighted coding, the target detection device based on the novel weighted coding comprising: The velocity discretization module is used to select multiple discrete velocities within the predicted relative velocity range between the detection platform and the detection target, and calculate the Doppler shift factor corresponding to each discrete velocity. The local solution module is used to solve for the local optimal weighted coding that minimizes the weighted sidelobe level under each Doppler shift factor according to the improved solution algorithm. The improved solution algorithm introduces the Doppler shift factor as a coefficient into the expression of the weighted sidelobe level. The global solution module is used to calculate the global optimal weighted code based on all local optimal weighted codes. The global optimal weighted code is used to construct the transmission signal of the detection platform.

[0014] Thirdly, this application also provides a target detection device based on a novel weighted encoding. The target detection device based on the novel weighted encoding includes a processor, a memory, and a target detection program based on the novel weighted encoding stored in the memory and executable by the processor. When the target detection program based on the novel weighted encoding is executed by the processor, it implements the steps of the target detection method based on the novel weighted encoding described above.

[0015] In this application, for problems where the relative velocity is unknown but the range is known, a discretization method is used to transform the continuous uncertainty problem into a finite number of deterministic problems, covering the main possible motion states of the target. For each deterministic motion state, a Doppler offset factor is introduced as a coefficient in the WISL expression, enabling the optimization objective function to reflect the sidelobe performance of the signal under Doppler distortion. Moreover, the existing iterative solution framework can be used to solve for the locally optimal weighted coding, and the globally optimal weighted coding is calculated based on all locally optimal weighted codings, thereby avoiding the signal being optimized only for a specific velocity while degrading the performance at other velocities. Through this application, even when the specific relative velocity is unknown, the transmitted signal can be guaranteed to have its range sidelobe level suppressed to a low range at any velocity throughout the entire prediction interval, thereby ensuring the stability of the signal-to-interference ratio of the output after matched filtering. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a target detection method based on a novel weighted coding method in one embodiment of this application; Figure 2 This represents the detection results near the target in the simulation experiment of this application.

[0017] Figure 3 This is a schematic diagram of the functional modules of a target detection device based on a novel weighted coding according to an embodiment of this application; Figure 4 This is a schematic diagram of the hardware structure of a target detection device based on novel weighted coding involved in the embodiments of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0020] In a first aspect, embodiments of this application provide a target detection method based on a novel weighted coding.

[0021] Figure 1 A flowchart illustrating a target detection method based on novel weighted coding in one embodiment of this application is shown.

[0022] Reference Figure 1In one embodiment, the target detection method based on novel weighted coding includes the following steps: S1. Select multiple discrete velocities within the predicted relative velocity range between the detection platform and the detection target, and calculate the Doppler shift factor corresponding to each discrete velocity.

[0023] Specifically, the relative speed range between the detection platform and the detection target is predicted based on the platform's own motion capabilities (such as underwater vehicles or surface ships) and the typical motion characteristics of the target type within the mission area.

[0024] For example, if the platform's maximum speed is v p The target's maximum speed is v t The relative speed range can then be set as [ (v p +v t ),+(v p +v t )).

[0025] Within the predicted relative velocity range, multiple discrete velocity points are selected. The discretization density can be set according to the velocity resolution requirements. The denser the selection of velocity points, the more refined the coverage of the Doppler effect by the subsequently designed signal, but the computational load increases accordingly. For each selected discrete velocity, the Doppler shift factor is calculated using the following formula: , in denoted as Doppler frequency, v as relative velocity, and c as medium velocity.

[0026] This step transforms a continuous velocity range into a finite number of discrete Doppler scenes, laying the foundation for subsequent scene-by-scene optimization.

[0027] S2. The local optimal weighted coding that minimizes the weighted sidelobe level under each Doppler shift factor is obtained by solving the improved solution algorithm. The improved solution algorithm introduces the Doppler shift factor as a coefficient into the expression of the weighted sidelobe level.

[0028] Specifically, after obtaining the Doppler shift factor corresponding to each discrete velocity, for each Doppler shift factor... Solve for the locally optimal weighted coding that minimizes the weighted sidelobe level (WISL). .

[0029] The improved solution algorithm used in this embodiment is fundamentally based on incorporating the Doppler shift factor as a coefficient into the expression for the weighted sidelobe level. Existing WISL calculations are typically based on the zero Doppler assumption, and their frequency domain expressions only involve the squared magnitude of the signal spectrum. However, in the improved algorithm of this application, considering the Doppler frequency shift, the time domain expression is equivalent to convolving with a Doppler spread / contract factor, which is represented in the frequency domain as multiplying by the Doppler shift factor.

[0030] For example, by deriving using existing techniques, the expression for WISL without considering the Doppler factor can be obtained as follows: , Where N is the number of code elements in the weighted encoding. , For weighted window functions, Make matrix P positive semidefinite. , For weighted encoding, .

[0031] The initial expression after introducing the Doppler shift factor is: , Since each symbol, whether in its original form or after the addition of a carrier signal, has the same frequency, then... It is equivalent to Based on the WISL definition, after taking the average, it can be moved as a coefficient outside the summation, and the initial expression can be further simplified to: .

[0032] Since the Doppler shift factor is introduced as a coefficient into the expression for the weighted sidelobe level, and the form of the expression has not changed fundamentally, the corresponding optimal weighted encoding can be obtained by using the iterative solution framework in the existing technology.

[0033] For example, regarding the solution of the weighted window function, let For matrix And all diagonal elements are 0, let express Find the minimum eigenvalue and solve for it. The solution can be obtained Usually makes .

[0034] For example, the initial weighted encoding symbols can be a sequence of randomly generated symbols of the corresponding number of symbols, or a regular random encoding sequence such as an m-sequence, a golomb sequence, etc.

[0035] For example, the iterative solution framework is as follows: , vector The solution method can be obtained through matrix Solve for F, which is expressed as follows: , , , , make for The DFT matrix, This represents the (k,n)th element of c. ,matrix The A row is a vector The transpose of is such that we can solve for the WISL minimization. .

[0036] , , in, yes The k-th element in yes The k-th element in the middle, where and The formulas above provide the answers.

[0037] Repeat the above iterative steps until the preset termination condition is met, such as the number of iterations reaching the upper limit or the change in weighted encoding falling below the threshold.

[0038] S3. Calculate the global optimal weighted code based on all local optimal weighted codes, where the global optimal weighted code is used to construct the transmission signal of the detection platform.

[0039] Specifically, after obtaining the set of locally optimal weighted codes for all discrete velocities, a globally optimal weighted code needs to be calculated based on this set to ensure optimal overall signal performance across the entire velocity range. The globally optimal weighted code, through operations such as extended coding and modulation, can produce the transmitted signal that maintains a low WISL level at any relative velocity within the predicted relative velocity range, thereby stably detecting nearby weak targets.

[0040] Therefore, in this embodiment, for problems where the relative velocity is unknown but the range is known, a discretization method is used to transform the continuous uncertainty problem into a finite number of deterministic problems, covering the main motion states that the target may exhibit. For each deterministic motion state, a Doppler offset factor is introduced as a coefficient in the WISL expression, enabling the optimization objective function to reflect the sidelobe performance of the signal under Doppler distortion. Moreover, the existing iterative solution framework can be used to solve for the locally optimal weighted coding, and the globally optimal weighted coding is calculated based on all locally optimal weighted codings, thereby avoiding the signal being optimized only for a specific velocity while degrading the performance at other velocities. Through this embodiment, even when the specific relative velocity is unknown, the transmitted signal can ensure that its range sidelobe level is suppressed to a low range at any velocity throughout the entire prediction interval, thereby ensuring the stability of the signal-to-interference ratio of the output after matched filtering.

[0041] Further, in one embodiment, the step of calculating the globally optimal weighted code based on all locally optimal weighted codes includes: For each locally optimal weighted code, the loss factor is calculated based on the weighted sidelobe level of the current locally optimal weighted code under all Doppler offset factors. The locally optimal weighted code with the minimum loss factor is determined as the globally optimal weighted code.

[0042] In this embodiment, each locally optimal weighted code is used as a candidate code, and one of them is selected as the globally optimal weighted code. By introducing the loss factor as an evaluation index, the candidate code must be tested throughout the entire velocity range. This involves considering not only the WISL of the candidate code under its corresponding Doppler shift factor (the minimized WISL obtained during the solution process), but also the WISL of the candidate code under other Doppler shift factors (calculated by substituting into the WISL expression).

[0043] By selecting the code with the minimum loss factor, we are essentially choosing the code with the weakest bottleneck effect. While this globally optimal weighted code may not be the theoretical absolute minimum at a single velocity point, it exhibits the best consistency and stability across the entire unknown velocity range. This results in a transmission signal with stronger Doppler tolerance, ensuring that the system maintains stable low sidelobe performance even when the target's specific velocity is unknown and varies, thus reliably detecting strong and weak neighboring targets.

[0044] Optionally, the formula for calculating the loss factor is: , in, Let i be the loss factor corresponding to the i-th locally optimal weighted encoding. and For the weight coefficients, satisfying , The number of Doppler shift factors. For the m-th Doppler shift factor, , For the i-th locally optimal weighted encoding, Var represents the weighted sidelobe level of the i-th locally optimal weighted encoding under the m-th Doppler offset factor, where Var denotes the variance calculation.

[0045] Further, in one embodiment, the step of calculating the globally optimal weighted code based on all locally optimal weighted codes includes: The global optimal weighted code is obtained by averaging and fusing all the locally optimal weighted codes.

[0046] This embodiment uses average fusion rather than selection to determine the globally optimal code, based on the idea of ​​enhancing robustness through multi-solution fusion. While locally optimal codes obtained under different Doppler shift factors may differ in phase or weight distribution, their core objective is to suppress sidelobes. These locally optimal solutions are often distributed within the neighborhood of the globally optimal solution in the solution space. Average fusion is equivalent to finding a centroid in the solution space, which is typically located at a position where the sum of distances to each locally optimal solution is small. This means that while the fused code may not be the theoretical minimum at a single velocity point, its performance deviation across velocity points is small, avoiding the risk of drastic performance degradation at other velocities due to selecting a single locally optimal solution.

[0047] Furthermore, in one embodiment, Where T is the pulse width of the carrier frequency signal. denoted as the center frequency of the carrier signal, and N is the number of symbols in the weighted encoding.

[0048] In this embodiment, by limiting the quantitative relationship between the pulse width, center frequency, and number of weighted coding symbols of the carrier signal, the effectiveness of the proposed solution can be guaranteed. If this condition is not met, the autocorrelation performance of the transmitted signal will decrease.

[0049] Furthermore, in one embodiment, the target detection method based on novel weighted coding further includes: The intermediate signal is obtained by expanding and modulating the globally optimal weighted code; The intermediate signal is windowed to obtain the transmission signal of the detection platform.

[0050] Specifically, for the globally optimal weighted encoded signal The expansion encoding operation is performed, and each symbol is expanded a number of times. , That is, each symbol repeats Then, the expanded weighted coded signal is represented as: .

[0051] The extended encoded signal Convert to phase angle The signal is modulated to obtain a preliminary transmission signal. , , where n represents the discrete time series.

[0052] To further ensure the detection performance of the transmitted signal, while suppressing spectral leakage and facilitating actual engineering transmission, the initial transmitted signal (referred to as the intermediate signal in this embodiment) is windowed. , This represents a window function, and different types of window functions, such as Hanning windows, can be added. Each window has a length of... Repeat N times to obtain The length of the window function vector, then .

[0053] Furthermore, in one embodiment, the target detection method based on novel weighted coding further includes: The target detection result is obtained by performing segmented sliding window matched filtering on the received signal of the detection platform. The window length is equal to the product of the pulse width of the transmitted signal and the proportion of the weighted components in the weighted window function. The sliding step size is half of the window length. The elements of the weighted window function are only 0 and 1, and the proportion of weighted components with a weight of 1 is the proportion of the total number of weighted components.

[0054] In this embodiment, the segmented sliding window matched filtering detection can effectively reduce the amount of computation per calculation, reduce the requirements for computing resources, and improve detection efficiency by using parallel computing methods, which is closer to engineering practice.

[0055] Of course, in other embodiments, matched filtering detection can also be performed on the entire received signal.

[0056] To further illustrate the effectiveness of this method, a linear frequency modulated signal is used as a comparison reference. The simulation parameters are set as follows: For the weighted coded signal, the number of symbols is set to 100, and the weighting window function is... The setting is as shown in the following formula: .

[0057] The center frequency of the carrier signal is The speed range is considered to be 0-20kΩ, and the speed discretization step size is set to 1kΩ. Based on the parameter selection mentioned earlier in this method, the signal pulse width is set to... Sampling rate The parameter settings meet the requirements. For a linear frequency modulated signal, the center frequency of the signal is set to... bandwidth is The pulse width and sampling rate are consistent with the signal proposed in this method.

[0058] Echo data simulation parameters are set to construct the echo signal, and the speed of sound is set to... The data length is Assume there are two targets in the echo data, located at... and The target echo amplitude was set to 1 and 0.025. To illustrate the effectiveness of this method, a typical example is given, in which Gaussian white noise is added to the echo, and the signal-to-noise ratio is set to 10dB.

[0059] Figure 2 The simulation results of this application show the detection results near the target.

[0060] Reference Figure 2 By performing the same matched filtering process on the waveform mentioned in this method and the linear frequency modulated signal respectively, it can be clearly seen that the linear frequency modulated signal cannot detect the nearby weak target, while the method of this invention can clearly distinguish the two targets.

[0061] Secondly, embodiments of this application also provide a target detection device based on a novel weighted coding.

[0062] Figure 3 A schematic diagram of the functional modules of a target detection device based on a novel weighted coding according to an embodiment of this application is shown.

[0063] Reference Figure 3 In one embodiment, the target detection device based on novel weighted coding includes: The velocity discretization module 10 is used to select multiple discrete velocities within the predicted relative velocity range between the detection platform and the detection target, and calculate the Doppler shift factor corresponding to each discrete velocity. The local solution module 20 is used to solve the local optimal weighted coding that minimizes the weighted sidelobe level under each Doppler offset factor according to the improved solution algorithm. The improved solution algorithm introduces the Doppler offset factor as a coefficient into the expression of the weighted sidelobe level. The global solution module 30 is used to calculate the global optimal weighted code based on all local optimal weighted codes, wherein the global optimal weighted code is used to construct the transmission signal of the detection platform.

[0064] Furthermore, in one embodiment, the global solver module 30 is used for: For each locally optimal weighted code, the loss factor is calculated based on the weighted sidelobe level of the current locally optimal weighted code under all Doppler offset factors. The locally optimal weighted code with the minimum loss factor is determined as the globally optimal weighted code.

[0065] Furthermore, in one embodiment, the formula for calculating the loss factor is: , in, Let i be the loss factor corresponding to the i-th locally optimal weighted encoding. and For the weight coefficients, satisfying , The number of Doppler shift factors. For the m-th Doppler shift factor, , For the i-th locally optimal weighted encoding, Var represents the weighted sidelobe level of the i-th locally optimal weighted encoding under the m-th Doppler offset factor, where Var denotes the variance calculation.

[0066] Furthermore, in one embodiment, the global solver module 30 is used for: The global optimal weighted code is obtained by averaging and fusing all the locally optimal weighted codes.

[0067] Furthermore, in one embodiment, Where T is the pulse width of the carrier frequency signal. denoted as the center frequency of the carrier signal, and N is the number of symbols in the weighted encoding.

[0068] Furthermore, in one embodiment, the expression for the weighted sidelobe level after introducing the Doppler shift factor is: , in, Where is the Doppler offset factor, and N is the number of symbols in the weighted encoding. , For weighted window functions, Make matrix P positive semidefinite. , For weighted encoding, .

[0069] Furthermore, in one embodiment, the target detection device based on novel weighted coding further includes a signal construction module, used for: The intermediate signal is obtained by expanding and modulating the globally optimal weighted code; The intermediate signal is windowed to obtain the transmission signal of the detection platform.

[0070] Furthermore, in one embodiment, the target detection device based on novel weighted coding further includes a segmented detection module, used for: The target detection result is obtained by performing segmented sliding window matched filtering on the received signal of the detection platform. The window length is equal to the product of the pulse width of the transmitted signal and the proportion of the weighted components in the weighted window function. The sliding step size is half of the window length. The elements of the weighted window function are only 0 and 1, and the proportion of weighted components with a weight of 1 is the proportion of the total number of weighted components.

[0071] Secondly, embodiments of this application also provide a target detection device based on a novel weighted coding, the target detection device based on the novel weighted coding comprising: The velocity discretization module is used to select multiple discrete velocities within the predicted relative velocity range between the detection platform and the detection target, and calculate the Doppler shift factor corresponding to each discrete velocity. The local solution module is used to solve for the local optimal weighted coding that minimizes the weighted sidelobe level under each Doppler shift factor according to the improved solution algorithm. The improved solution algorithm introduces the Doppler shift factor as a coefficient into the expression of the weighted sidelobe level. The global solution module is used to calculate the global optimal weighted code based on all local optimal weighted codes. The global optimal weighted code is used to construct the transmission signal of the detection platform.

[0072] The functions of each module in the target detection device based on the novel weighted coding described above correspond to the steps in the target detection method embodiment based on the novel weighted coding described above, and their functions and implementation processes will not be described in detail here.

[0073] Thirdly, embodiments of this application provide a target detection device based on a novel weighted coding. The target detection device based on the novel weighted coding can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0074] Figure 4 A schematic diagram of the hardware structure of a target detection device based on novel weighted coding, as shown in an embodiment of this application, is illustrated.

[0075] Reference Figure 4 In this embodiment of the application, the target detection device based on the novel weighted coding may include a processor, a memory, a communication interface, and a communication bus.

[0076] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0077] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces for interconnecting internal components of the target detection device based on the novel weighted coding, as well as interfaces for interconnecting the target detection device based on the novel weighted coding with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0078] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0079] The processor can be a general-purpose processor, which can call a target detection program based on novel weighted encoding stored in memory and execute the target detection method based on novel weighted encoding provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the target detection program based on novel weighted encoding is called can be referred to in the various embodiments of the target detection method based on novel weighted encoding of this application, and will not be repeated here.

[0080] Those skilled in the art will understand that Figure 4 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0081] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0082] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0083] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0084] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0085] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish 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 sequentially or in parallel, and these operations or steps may be combined.

[0086] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0087] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A target detection method based on a novel weighted coding method, characterized in that, The target detection method based on novel weighted coding includes: Multiple discrete velocities are selected within the predicted relative velocity range between the detection platform and the detection target, and the Doppler shift factor corresponding to each discrete velocity is calculated. The local optimal weighted coding that minimizes the weighted sidelobe level under each Doppler offset factor is obtained by solving the improved solution algorithm. The improved solution algorithm introduces the Doppler offset factor as a coefficient into the expression of the weighted sidelobe level. The globally optimal weighted code is calculated based on all locally optimal weighted codes, and the globally optimal weighted code is used to construct the transmission signal of the detection platform.

2. The target detection method based on novel weighted coding as described in claim 1, characterized in that, The step of calculating the globally optimal weighted code based on all locally optimal weighted codes includes: For each locally optimal weighted code, the loss factor is calculated based on the weighted sidelobe level of the current locally optimal weighted code under all Doppler offset factors. The locally optimal weighted code with the minimum loss factor is determined as the globally optimal weighted code.

3. The target detection method based on novel weighted coding as described in claim 2, characterized in that, The formula for calculating the loss factor is: ， in, Let i be the loss factor corresponding to the i-th locally optimal weighted encoding. and For the weight coefficients, satisfying , The number of Doppler shift factors. For the m-th Doppler shift factor, , For the i-th locally optimal weighted encoding, Var represents the weighted sidelobe level of the i-th locally optimal weighted encoding under the m-th Doppler offset factor, where Var denotes the variance calculation.

4. The target detection method based on novel weighted coding as described in claim 1, characterized in that, The step of calculating the globally optimal weighted code based on all locally optimal weighted codes includes: The global optimal weighted code is obtained by averaging and fusing all the locally optimal weighted codes.

5. The target detection method based on novel weighted coding as described in claim 1, characterized in that, Where T is the pulse width of the carrier frequency signal. denoted as the center frequency of the carrier signal, and N is the number of symbols in the weighted encoding.

6. The target detection method based on novel weighted coding as described in claim 1, characterized in that, The expression for the weighted sidelobe level after introducing the Doppler shift factor is: ， in, Where is the Doppler offset factor, and N is the number of symbols in the weighted encoding. ， For weighted window functions, Make matrix P positive semidefinite. ， For weighted encoding, .

7. The target detection method based on novel weighted coding as described in claim 1, characterized in that, The target detection method based on novel weighted coding also includes: The intermediate signal is obtained by expanding and modulating the globally optimal weighted code; The intermediate signal is windowed to obtain the transmission signal of the detection platform.

8. The target detection method based on novel weighted coding as described in claim 1, characterized in that, The target detection method based on novel weighted coding also includes: The target detection result is obtained by performing segmented sliding window matched filtering on the received signal of the detection platform. The window length is equal to the product of the pulse width of the transmitted signal and the proportion of the weighted components in the weighted window function. The sliding step size is half of the window length. The elements of the weighted window function are only 0 and 1, and the proportion of weighted components with a weight of 1 is the proportion of the total number of weighted components.

9. A target detection device based on a novel weighted coding method, characterized in that, The target detection device based on novel weighted coding includes: The velocity discretization module is used to select multiple discrete velocities within the predicted relative velocity range between the detection platform and the detection target, and calculate the Doppler shift factor corresponding to each discrete velocity. The local solution module is used to solve for the local optimal weighted coding that minimizes the weighted sidelobe level under each Doppler shift factor according to the improved solution algorithm. The improved solution algorithm introduces the Doppler shift factor as a coefficient into the expression of the weighted sidelobe level. The global solution module is used to calculate the global optimal weighted code based on all local optimal weighted codes. The global optimal weighted code is used to construct the transmission signal of the detection platform.

10. A target detection device based on a novel weighted coding method, characterized in that, The target detection device based on novel weighted coding includes a processor, a memory, and a target detection program based on novel weighted coding stored in the memory and executable by the processor, wherein when the target detection program based on novel weighted coding is executed by the processor, it implements the steps of the target detection method based on novel weighted coding as described in any one of claims 1 to 8.

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