Method and device for quickly evaluating accumulation performance of segmented coherent processing algorithm

Abstract

The invention discloses a rapid evaluation method and device for accumulation performance of a segmented coherent processing algorithm, and the method comprises the steps: calculating the maximum segment length of Doppler unambiguity based on the maximum Doppler through determining the maximum Doppler of a target in a detection scene; calculating the maximum loss value of the accumulated gain in the signal processing process based on the requirement of a preset target detection distance and the condition of meeting the maximum segment length given target detection signal-to-noise ratio, and inputting the maximum loss value into a pre-established quantitative evaluation model, according to the method, the accumulated gain loss comparison relation under the conditions of different segments and target Doppler is obtained, the accumulated gain loss is evaluated by adopting the comparison relation, the correctness of the proposed method is verified through simulation and actual measurement data, and according to the quantification method, the accumulated gain loss can be evaluated only according to the target Doppler and the segment time, so that the accuracy of the method is improved. The calculation complexity required by performance evaluation is reduced, and meanwhile, convenience is brought to engineering practical application, especially algorithm parameter selection.

Description

Technical Field

[0001] The present application relates to the technical field of signal processing, and particularly relates to a method and device for quickly evaluating the accumulation performance of a segmented coherent processing algorithm. Background Art

[0002] In Passive Coherent Location, extracting the delay-Doppler information of a target is a key step in target detection. According to detection and parameter estimation theories, the calculation of the two-dimensional cross-correlation function is the optimal method for realizing signal parameter detection. However, in some engineering applications, the number of points in the delay-Doppler two-dimensional detection plane is large, resulting in a significant increase in the computational complexity of the original cross-correlation function. To address this practical problem, relevant scholars have proposed a fast approximation algorithm based on segmented processing of FM waveforms. This method divides the correlation processing process into "fast time" processing within a pulse and "slow time" processing between pulses through signal segmentation. Matching filtering is used within a pulse (fast time) to obtain time accumulation gain, and the fast Fourier transform method is used between pulses (slow time) to achieve phase alignment, obtain the accumulation gain between pulses, and simultaneously extract the target Doppler.

[0003] Compared with directly calculating the cross-correlation function, the computational efficiency of this method has been improved by an order of magnitude. Once proposed, it has attracted the attention of the engineering community and has been widely applied in practice. According to the previous literature research and analysis and engineering application situations, this method is essentially an approximation algorithm, which trades off a certain signal-to-noise ratio accumulation gain to improve computational efficiency. Currently, setting algorithm parameters according to application scenarios often uses simulation and expert experience, resulting in low efficiency and accuracy, which is not conducive to engineering practical applications. Summary of the Invention

[0004] In view of the above problems, the present application provides a method, device, electronic device, and storage medium for quickly evaluating the accumulation performance of a segmented coherent processing algorithm to at least solve the problems existing in the related art.

[0005] In a first aspect, an embodiment of the present application provides a method for quickly evaluating the accumulation performance of a segmented coherent processing algorithm. The method for quickly evaluating the accumulation performance of a segmented coherent processing algorithm includes:

[0006] Determine the maximum Doppler of the target in the detection scenario;

[0007] Calculate the maximum segmented length with unambiguous Doppler based on the maximum Doppler;

[0008] Calculate the maximum loss value of the accumulation gain in the signal processing process based on the requirement of a preset target detection distance and the given target detection signal-to-noise ratio condition that satisfies the maximum segmented length;

[0009] Input the maximum loss value into a pre-established quantitative evaluation model to obtain the relationship between the accumulation gain loss under different segments and target Doppler conditions, and use the relationship to evaluate the accumulation gain loss.

[0010] In some embodiments, the pre-established quantitative evaluation model is established in the following manner:

[0011] Divide the signals within the obtained total accumulation time into multiple segments;

[0012] Establish an equivalent expression according to the accumulation duration of each segment and the time segment corresponding to the accumulation duration of each segment;

[0013] Determine the two-dimensional cross-correlation expression of the reference signal and the echo signal of the target segment;

[0014] Decompose the two-dimensional cross-correlation expression into a weighted sum of two-dimensional cross-correlation expressions of different segments;

[0015] Perform substitution processing on the two-dimensional cross-correlation expression of the target segment to obtain an error estimation expression;

[0016] Based on the error estimation expression, determine the quantitative evaluation model corresponding to when the reference signal envelope is stationary.

[0017] In some embodiments, the fast evaluation method for the accumulation performance of the segmented coherent processing algorithm further includes:

[0018] Determine the target segment parameter settings based on the relationship.

[0019] In some embodiments, the calculation of the maximum loss value of the accumulation gain in the signal processing process based on the requirements of the preset target detection distance and the given target detection signal-to-noise ratio condition that satisfies the maximum segment length includes:

[0020] Estimate the condition that satisfies the given target detection signal-to-noise ratio condition of the maximum segment length based on the preset radar equation;

[0021] Calculate the maximum loss value of the accumulation gain in the signal processing process using the loss value expression based on the requirements of the preset target detection distance and the target detection signal-to-noise ratio condition.

[0022] In some embodiments, for the determination of the two-dimensional cross-correlation expression of the reference signal and the echo signal of the target segment, wherein the two-dimensional cross-correlation expression includes:

[0023]

[0024] where τ max is the maximum delay, and χ k(τ,f) is the two-dimensional cross-correlation function of the k-th segment of the reference signal and the echo signal, and the total accumulation time is T int The signals within B T are divided into n B segments, and T is the segment length, T k = kT B .

[0025] In some embodiments, the quantitative evaluation model corresponding to when the reference signal envelope is stationary is determined based on the error estimation expression, where the expression of the quantitative evaluation model includes:

[0026] LF(τ d , f d ) ≈ 20log 10 [sinc(πf d T B )]

[0027] where the error estimation expression is t ∈ [0, T B , |f| ≤ f max , 2fπ·T B ≤ π.

[0028] In some embodiments, the maximum loss value of the accumulation gain in the signal processing process is calculated based on the requirements of the preset target detection distance and the condition of satisfying the given target detection signal-to-noise ratio for the maximum segment length, and the following calculation formula is used for calculation:

[0029]

[0030] where ERP is the equivalent radiated power of the radiation source, G r is the receiving antenna gain, λ is the signal wavelength, σ is the target scattering cross-section area, R t and R r are the distances from the target to the radiation source and the receiving antenna respectively, τ0 is the accumulation time, SNR is the target detection signal-to-noise ratio, F is the system noise factor, and LF0 is the target processing gain loss.

[0031] In a second aspect, an embodiment of the present application provides a fast evaluation device for the accumulation performance of a segmented coherent processing algorithm, including:

[0032] A determination module, configured to determine the maximum Doppler of the target in the detection scenario;

[0033] A first calculation module, configured to calculate the maximum segment length without Doppler ambiguity based on the maximum Doppler;

[0034] A second calculation module, configured to calculate a maximum loss value of the accumulation gain in the signal processing process based on the requirement of a preset target detection distance and the condition of satisfying the given target detection signal-to-noise ratio for the maximum segmentation length;

[0035] An evaluation module, configured to input the maximum loss value into a pre-established quantitative evaluation model to obtain a correspondence relationship between the accumulation gain loss under different segmentation and target Doppler conditions, and use the correspondence relationship to evaluate the accumulation gain loss.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor. A program code that can run on the processor is stored on the memory. When the program code is executed by the processor, a method for quickly evaluating the accumulation performance of a segmented coherent processing algorithm as introduced in any implementation manner of the first aspect is implemented.

[0037] In a fourth aspect, an embodiment of the present application provides a computer storage medium. The computer storage medium stores one or more programs, and the one or more programs can be executed by the electronic device as introduced in the third aspect to implement a method for quickly evaluating the accumulation performance of a segmented coherent processing algorithm as introduced in any implementation manner of the first aspect.

[0038] A method and device for quickly evaluating the accumulation performance of a segmented coherent processing algorithm provided by an embodiment of the present application determine the maximum Doppler of a target in a detection scenario, calculate the maximum segmentation length with unambiguous Doppler based on the maximum Doppler, calculate the maximum loss value of the accumulation gain in the signal processing process based on the requirement of a preset target detection distance and the condition of satisfying the given target detection signal-to-noise ratio for the maximum segmentation length, input the maximum loss value into a pre-established quantitative evaluation model to obtain a correspondence relationship between the accumulation gain loss under different segmentation and target Doppler conditions, and use the correspondence relationship to evaluate the accumulation gain loss. The correctness of the proposed method is verified by simulation and measured data. This quantitative method can evaluate the accumulation gain loss only based on the target Doppler and the segmentation time, which reduces the computational complexity required for performance evaluation and brings convenience to engineering practical applications, especially for the selection of algorithm parameters.

[0039] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Description of the Drawings

[0040] Hereinafter, the present application will be described in more detail based on embodiments and with reference to the drawings.

[0041] Figure 1Shows a schematic flow chart of a fast evaluation method for the accumulation performance of a segmented coherent processing algorithm proposed in an embodiment of the present application;

[0042] Figure 2 Shows a comparison chart of the signal-to-noise ratio loss and theoretical estimation of an exemplary fast algorithm proposed in an embodiment of the present application;

[0043] Figure 3 Shows a comparison chart of the signal-to-noise ratio loss and theoretical estimation of another exemplary fast algorithm proposed in an embodiment of the present application;

[0044] Figure 4 Shows a comparison chart of the signal-to-noise ratio loss and theoretical estimation of yet another exemplary fast algorithm proposed in an embodiment of the present application;

[0045] Figure 5 Shows a comparison chart of the signal-to-noise ratio loss and theoretical estimation of still another exemplary fast algorithm proposed in an embodiment of the present application;

[0046] Figure 6 Shows a structural block diagram of a fast evaluation device for the accumulation performance of a segmented coherent processing algorithm proposed in an embodiment of the present application;

[0047] Figure 7 Shows a structural block diagram of an electronic device for executing a fast evaluation method for the accumulation performance of a segmented coherent processing algorithm according to an embodiment of the present application;

[0048] Figure 8 Shows a computer-readable storage medium for storing or carrying an implementation of a fast evaluation method for the accumulation performance of a segmented coherent processing algorithm according to an embodiment of the present application. Detailed implementation manners

[0049] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments and the accompanying drawings. The illustrative embodiments and descriptions thereof of the present invention are only used to explain the present invention and do not limit the present invention.

[0050] In the research on related technologies, in view of the fact that in some engineering applications, the number of points in the delay-Doppler two-dimensional detection plane is large, resulting in a significant increase in the computational complexity of the original cross-correlation function, it is proposed to divide the correlation processing process into "fast time" processing within the pulse and "slow time" processing between pulses by segmenting the signal. Matching filtering is used within the pulse (fast time) to obtain the time accumulation gain, and the fast Fourier transform method is used between pulses (slow time) to achieve phase alignment, obtain the accumulation gain between pulses, and extract the target Doppler at the same time.

[0051] After research by the applicant, it is found that although the above method has an order-of-magnitude improvement in computing efficiency compared with directly calculating the cross-correlation function and is widely used in practice, according to the research analysis and engineering application situation, this method is essentially an approximate algorithm, which sacrifices a certain signal-to-noise ratio accumulation gain in exchange for the improvement of computing efficiency. Currently, setting algorithm parameters according to the application scenario often adopts the methods of simulation and expert experience, resulting in low efficiency and accuracy, which is not conducive to engineering practical applications.

[0052] In response to the above problems, the applicant proposes a fast evaluation method and device for the accumulation performance of a segmented coherent processing algorithm provided in the embodiments of the present application, which brings convenience to engineering practical applications, especially the selection of algorithm parameters, while reducing the computational complexity required for performance evaluation. Among them, a fast evaluation method for the accumulation performance of a segmented coherent processing algorithm will be described in detail in the subsequent embodiments.

[0053] The following introduces the application scenario of a fast evaluation method for the accumulation performance of a segmented coherent processing algorithm provided in the embodiments of the present application:

[0054] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a fast evaluation method for the accumulation performance of a segmented coherent processing algorithm provided in the embodiments of the present application. In this embodiment, a fast evaluation method for the accumulation performance of a segmented coherent processing algorithm can be applied to a fast evaluation device 300 for the accumulation performance of a segmented coherent processing algorithm as shown in Figure 6 and an electronic device 200 as shown in Figure 7 . Among them, the electronic device 200 may include one or more. Exemplarily, the electronic device may include a mobile terminal, a computer, a tablet, etc., and the present application does not limit it.

[0055] In passive coherent positioning, coherent accumulation is an important method for detecting targets under low signal-to-noise ratio conditions. It obtains accumulation gain through the two-dimensional cross-correlation of the sample signal and the echo signal, and realizes the extraction of delay and Doppler information. According to the classical signal processing theory, the two-dimensional cross-correlation function of two signals within a finite accumulation time is defined as:

[0056]

[0057] 0≤τ≤τ max , -f max ≤f≤f max

[0058] where χ(τ,f) represents the delay-Doppler two-dimensional cross-correlation function of the reference signal s ref (t) and the echo signal s echo (t), τ represents the bistatic delay of the signal, τmax denotes the maximum delay of the signal of interest, \(f\) represents the Doppler of the signal, \(f\) max represents the maximum Doppler of the signal of interest (which is related to the maximum speed), \(T\) int represents the integration time.

[0059] Since the complexity of directly calculating the cross-correlation function \(\chi(\tau,f)\) is relatively high and it cannot be practically applied in engineering, some scholars, based on the concepts of "fast time" and "slow time" of the signal (i.e., when \(T\) B \(f\) d << 1).

[0060] proposed a fast calculation method for the cross-correlation function \(\chi(\tau,f)\) in the current engineering field. The basic steps are as follows: According to the integration time \(T\) int set a reasonable number of segments \(n\) B (each segment and the number of distance search units where the number of points corresponding to the reference signal in each segment is the number of points corresponding to the signal echo is \(N\) echo = \(N\) B + \(N\) bin ;

[0061] Use FFT to calculate the segment cross-correlation function \(\chi\) k (\(\tau,0\)) in turn.

[0062] \(\chi\) k [\(\tau,0\)] = FFT{FFT(s echo (t)).*FFT(s ref (t - \(\tau\))}, \(k = 0,1,\cdots,n\) B - 1 to obtain the integration gain in the "fast time" dimension within each segment.

[0063] Arrange the calculation results of step \(\chi\) k [\(\tau,0\)] of the segmented two-dimensional cross-correlation function \(\chi\) k (\(\tau,0\)), \(k = 0,1,\cdots,n\) B - 1 in rows, and perform FFT on each delay \(\tau\), that is, on each column:

[0064]

[0065] to obtain the integration gain in the "slow time" dimension between segments.

[0066] In engineering practice, it is found that for the same segmentation method, after the fast algorithm processes different Doppler targets, the accumulation gain is not the same, and the greater the Doppler, the more serious the accumulation gain loss; similarly, for the same Doppler target, different segmentation methods result in different accumulation gains after the fast algorithm processes, and the greater the segmentation length, the greater the accumulation gain loss. For this problem, engineering application personnel often evaluate the accumulation gain loss under different parameter conditions according to experience and simulation methods, but the efficiency is low, which seriously affects the practical application of this fast algorithm in engineering. To address this difficulty, this patent starts from the reference two-dimensional cross-correlation function χ(τ,f), and derives the quantitative relationship between the accumulation gain loss, target Doppler, and segmentation length in the fast algorithm for segmented processing from the perspective of model analysis, so as to guide the performance evaluation of the fast algorithm.

[0067] The following will elaborate in detail on the Figure 1 process shown below. The fast evaluation method for the accumulation performance of a segmented coherent processing algorithm may include S110 to S140.

[0068] S110: Determine the maximum Doppler of the target in the detection scenario.

[0069] From the quantitative evaluation formula LF(τ d , f d ) ≈ 20log 10 [sinc(πf d T B )], it can be seen that the loss of the accumulation gain of the segmented coherent processing algorithm is related to the product of the target maximum Doppler and the segmentation length T B . When the target Doppler f d = 0, there is no loss of the accumulation gain of the fast algorithm; when the segmentation length T B is given, the greater the target Doppler f d , the greater the loss of the accumulation gain of the fast algorithm; when the target Doppler is given, the greater the segmentation length T B , the greater the loss of the SNR of the fast algorithm. According to LF(τ d , f d ), the loss of the accumulation gain can be specifically evaluated.

[0070] S120: Calculate the maximum segmentation length with an unambiguous Doppler based on the maximum Doppler.

[0071] In the embodiments of the present application, according to the detection scenario, the maximum Doppler f max of the target is estimated, and the maximum segmentation length with an unambiguous Doppler is estimated according to T B ≤ 1 / f max .

[0072] S130: Calculate the maximum loss value of the accumulation gain in the signal processing based on the requirements of the preset target detection distance and the given target detection signal-to-noise ratio condition that satisfies the maximum segment length.

[0073] In some embodiments, S130 includes: S131 to S132.

[0074] S131: Estimate the condition that satisfies the given target detection signal-to-noise ratio for the maximum segment length based on the preset radar equation.

[0075] S132: Calculate the maximum loss value of the accumulation gain in the signal processing using the loss value expression based on the requirements of the preset target detection distance and the target detection signal-to-noise ratio condition.

[0076] In the embodiments of the present application, the following calculation formula is used for calculation:

[0077]

[0078] Among them, ERP is the equivalent radiated power of the radiation source, G r is the receiving antenna gain, λ is the signal wavelength, σ is the target scattering cross-section area, R t and R r are the distances from the target to the radiation source and the receiving antenna respectively, τ0 is the accumulation time, SNR is the target detection signal-to-noise ratio, F is the system noise factor, and LF0 is the target processing gain loss.

[0079] Calculate the maximum loss value of the accumulation gain LF0 according to the above formula, and substitute this value into the quantitative evaluation formula LF(τ d , f d ) to obtain the accumulation gain loss comparison table under different segmentations and target Doppler conditions (as shown in Table 1), and select the appropriate segmentation parameter settings according to this table.

[0080] Table 1 Accumulation gain loss table under different segmentations and Doppler conditions (dB)

[0081]

[0082] The above steps can quickly evaluate the accumulation gain loss of the segmented coherent processing, and can dynamically adjust the segment length according to the target Doppler, calculation complexity, etc., and make an effective evaluation of the rationality of the algorithm parameter settings.

[0083] S140: Input the maximum loss value into the pre-established quantitative evaluation model to obtain the accumulation gain loss comparison relationship under different segmentations and target Doppler conditions, and use the comparison relationship to evaluate the accumulation gain loss.

[0084] In some embodiments, the quantitative evaluation model established in advance in S140 is established by steps S141 to S146.

[0085] S141: Divide the signals within the total accumulation time obtained into multiple segments.

[0086] S142: Establish an equivalent expression according to the accumulation duration of each segment and the time segment corresponding to the accumulation duration of each segment.

[0087] In the above embodiment, the signals within the total accumulation time T int are divided into n B segments, and the accumulation duration of each segment corresponds to the time segment T k = kT B .

[0088] S143: Determine the two-dimensional cross-correlation expression of the reference signal and the echo signal of the target segment.

[0089] In this embodiment, the two-dimensional cross-correlation expression includes:

[0090]

[0091] where τ max is the maximum delay, X k (τ,f) is the two-dimensional cross-correlation function of the reference signal and the echo signal of the k-th segment, and the signals within the total accumulation time T int are divided into n B segments, T B is the segment length, corresponds to the time segment T k = kT B .

[0092] S144: Decompose the two-dimensional cross-correlation expression into a weighted sum of different segment two-dimensional cross-correlation expressions.

[0093] In this embodiment, where τ max is the maximum delay. Denote χ k (τ,f) as the two-dimensional cross-correlation function of the reference signal and the echo signal of the k-th segment

[0094]

[0095] Therefore, the optimal two-dimensional cross-correlation function χ(τ,f) of the echo signal s echo (t) and the reference signal s ref (t) can be decomposed into a weighted sum of a series of segment two-dimensional cross-correlation functions χ k (τ,f).

[0096] S145: Replace the two-dimensional cross-correlation expression of the target segment to obtain an error estimation expression.

[0097] In this embodiment, for the two-dimensional cross-correlation function χ of the k-th segment k (τ,f), the exponential term e inside the integral sign -j2πft is replaced with . After simplification and substitution into the two-dimensional cross-correlation expression, we can obtain

[0098]

[0099] The first term is the approximation term, denoted as χ approx (τ,f),

[0100]

[0101] where the second integral term is the error term and satisfies the following error estimation

[0102]

[0103] Finally, based on the above two expressions, the following error estimation can be obtained

[0104]

[0105] Therefore, when f max T B << 1, that is, when the product of T B and the maximum Doppler f of the signal max is much less than 1, the estimation formula χ approx (τ,f) can be used as an approximation of the two-dimensional cross-correlation expression of the optimal two-dimensional cross-correlation function. The "fast time" in engineering refers to the time sampling at the sampling frequency f B within each segment [0,T s . The "slow time" refers to the phase remaining unchanged within each segment.

[0106]

[0107] The signal Doppler can be estimated by piecewise constant phase. From the approximate expression χ approx (τ,f), it can be seen that in order to ensure that the exponential term is non-ambiguous, the phase should ensure that 2fπ·T B ≤ π, that is, it satisfies

[0108] f max ≤ 1 / (2T B ).

[0109] S146: Determine the corresponding quantitative evaluation model when the reference signal envelope is stationary based on the error estimation expression.

[0110] In this embodiment, under the assumption of a stationary reference signal envelope, the accumulation gain loss of the segmented coherent processing fast algorithm satisfies the following relationship

[0111] LF(τ d , f d ) ≈ 20log 10 [sinc(πf d T B )]

[0112] The formula LF(τ d , f d ) gives the quantitative relationship between the accumulation gain loss LF of the segmented coherent processing algorithm and the target maximum Doppler f d , the segmented length T B . This can quickly evaluate the accumulation gain loss according to the parameter settings, thereby guiding the parameter settings of the fast algorithm in different scenarios.

[0113] In some embodiments, a fast evaluation method for the accumulation performance of a segmented coherent processing algorithm further includes S210:

[0114] S210: Determine the target segmented parameter settings based on the control relationship.

[0115] The present invention brings convenience to engineering practical applications, especially the selection of algorithm parameters, while reducing the computational complexity required for performance evaluation. To verify the effectiveness of the invention, we recorded the reference signal in the actual environment and modulated targets with different Dopplers in the reference signal as the echo signal. After segmented coherent processing, the correctness and effectiveness of the quantitative evaluation model were verified. For details, see the implementation examples. Specific implementation manner:

[0117] To verify the effectiveness of the fast evaluation method for the accumulation performance loss of the segmented coherent processing proposed by the present invention, using the radio signal in a certain recording environment as the reference signal, target echo signals with Doppler fd = 0Hz, 100Hz, 200Hz,..., 900Hz were constructed through velocity modulation. In the segmented coherent processing algorithm, when verifying the segmented accumulation lengths TB = 218us, 330us, 470us, 940us, directly calculate the error between the accumulation gain loss and the quantitative evaluation model. From Figure 2 it can be seen that when the segmented accumulation length is 218us, the error between the theoretically calculated and the actually calculated accumulation gain loss does not exceed 1.5dB; from Figure 3 it can be seen that when the segmented accumulation length is 330us, the error between the theoretically calculated and the actually calculated accumulation gain loss does not exceed 1dB; from Figure 4It can be seen that when the accumulated length of the segmented length is 470 μs, the accumulated gain error between the theoretical calculation and the actual calculation does not exceed 0.7 dB; from Figure 5 It can be seen that when the segmented accumulation length is 940 μs, the accumulated gain error between the theoretical calculation and the actual calculation does not exceed 0.5 dB. The above comparison results fully verify the correctness and effectiveness of the quantitative evaluation model and the parameter setting in Table 1.

[0118] In summary, when the segmented length T B is given, the greater the target Doppler f d , the greater the loss of the accumulated gain of the fast algorithm; when the target Doppler f d is given, the greater the segmented length T B , the greater the loss of the SNR of the fast algorithm. According to the quantitative evaluation model and Table 1, the loss of the accumulated gain can be specifically evaluated. The fast evaluation method of the present invention can be applied to engineering practice.

[0119] Please refer to Figure 6 , Figure 6 , which is a structural block diagram of a fast evaluation device for the accumulation performance of a segmented coherent processing algorithm provided by the present application. The fast evaluation device 300 for the accumulation performance of the segmented coherent processing algorithm includes: a determination module 310, a first calculation module 320, a second calculation module 330, and an evaluation module 340, where:

[0120] The determination module 310 is configured to determine the maximum Doppler of the target in the detection scenario.

[0121] The first calculation module 320 is configured to calculate the maximum segmented length with an unambiguous Doppler based on the maximum Doppler.

[0122] The second calculation module 330 is configured to calculate the maximum loss value of the accumulated gain in the signal processing process based on the requirement of the preset target detection distance and the given target detection signal-to-noise ratio condition that meets the maximum segmented length.

[0123] The evaluation module 340 is configured to input the maximum loss value into a pre-established quantitative evaluation model to obtain the comparison relationship of the accumulated gain loss under different segmented and target Doppler conditions, and to evaluate the accumulated gain loss by using the comparison relationship.

[0124] The device embodiment in the present application may further include other modules, which specifically correspond to the content in the above method part.

[0125] It should be noted that the device embodiment in the present application corresponds to the foregoing method embodiment. The specific principle in the device embodiment can be referred to the content in the foregoing method embodiment, and will not be elaborated herein.

[0126] In several embodiments provided in this embodiment, the coupling between modules may be electrical, mechanical, or other forms of coupling.

[0127] In addition, in each embodiment of the present invention, each functional module may be integrated in a processing module, may exist physically separately for each module, or two or more modules may be integrated in one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0128] Please refer to Figure 7 , Figure 7 FIG. is a structural block diagram of an electronic device 200 that can execute the fast evaluation method for the accumulation performance of the above-mentioned one-stage coherent processing algorithm provided in the embodiment of the present application. The electronic device 200 may be a smart phone, a tablet computer, a computer, or a portable computer and other devices.

[0129] The electronic device 200 further includes a processor 202 and a memory 204. Among them, the memory 204 stores a program that can execute the content in the foregoing embodiments, and the processor 202 can execute the program stored in the memory 204.

[0130] Among them, the processor 202 may include one or more cores for processing data and a message matrix unit. The processor 202 connects various parts within the entire electronic device 200 through various interfaces and lines, and executes various functions of the electronic device 200 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 204, and by calling data stored in the memory 204. Optionally, the processor 202 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 202 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem decoder. Among them, the CPU mainly processes the operating system, user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the displayed content; the modem is used to process wireless communication. It can be understood that the above-mentioned modem decoder may also not be integrated into the processor and be implemented separately through a communication chip.

[0131] The memory 204 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. The memory 204 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 204 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as instructions for a user to obtain a random number), instructions for implementing the following various method embodiments, etc. The data storage area may also store data created during the use of the terminal (such as random numbers), etc.

[0132] The electronic device 200 may further include a network module and a screen. The network module is used to receive and send electromagnetic waves, implement the mutual conversion between electromagnetic waves and electrical signals, so as to communicate with a communication network or other devices, for example, communicate with an audio playback device. The network module may include various existing circuit elements for performing these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, subscriber identity module (SIM) cards, memories, and so on. The network module can communicate with various networks such as the Internet, enterprise intranets, wireless networks, or communicate with other devices through a wireless network. The above-mentioned wireless network may include a cellular phone network, a wireless local area network, or a metropolitan area network. The screen can display interface content and perform data interaction.

[0133] Please refer to Figure 8 , Figure 8 FIG. shows a structural block diagram of a computer-readable storage medium provided by an embodiment of the present application. Program code 410 is stored in the computer-readable storage medium 400, and the program code 410 can be called by a processor to execute the method described in the above method embodiments.

[0134] The computer-readable storage medium 400 can be an electronic memory such as a flash memory, an Electrically Erasable Programmable Read-Only Memory (EEPROM), an EPROM, a hard disk, or a ROM. Optionally, the computer-readable storage medium includes a non-transitory computer-readable storage medium. The computer-readable storage medium 400 has a storage space for the program code 410 for executing any method step in the above method. These program code 410 can be read out from one or more computer program products or written into these one or more computer program products. The program code 410 can be compressed in an appropriate form, for example.

[0135] The embodiment of the present application also provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes a method for quickly evaluating the accumulation performance of a segmented coherent processing algorithm described in the above various optional implementation manners.

[0136] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for rapidly evaluating the cumulative performance of a piecewise coherent processing algorithm, characterized in that: The method comprises: Determining a maximum Doppler of a target in the detection scene; Calculate a maximum segment length of Doppler unambiguity based on the maximum Doppler; Calculate the maximum loss value of the accumulated gain in the signal processing process based on the requirement of the preset target detection distance and the condition of the target detection signal-to-noise ratio meeting the maximum segment length; The maximum loss value is input into a pre-established quantitative evaluation model to obtain a cumulative gain loss comparison relationship under different segmentation and target Doppler conditions, so as to evaluate the cumulative gain loss using the comparison relationship.

2. The method for rapidly evaluating the accumulated performance of a piecewise coherent processing algorithm according to claim 1, characterized in that: The pre-established quantitative evaluation model is established in the following manner: Divide the acquired signal within the total accumulation time into multiple segments; Establishing an equivalent expression according to the accumulated duration of each segment and the time segment corresponding to the accumulated duration of each segment; Determine a two-dimensional cross-correlation expression of the reference signal and the echo signal of the target segment; Decomposing the two-dimensional cross-correlation expression into a weighted sum of different segmented two-dimensional cross-correlation expressions; Performing a replacement process on the two-dimensional cross-correlation expression of the target segment to obtain an error estimation expression; A quantitative evaluation model corresponding to when the reference signal envelope is stable is determined based on the error estimation expression.

3. The method for rapidly evaluating the accumulated performance of a piecewise coherent processing algorithm according to claim 1, characterized in that: The method further comprises: A target segment parameter setting is determined based on the comparison relationship.

4. The method for rapidly evaluating the accumulated performance of a piecewise coherent processing algorithm according to claim 1, characterized in that: The maximum loss value of the accumulated gain in the signal processing process is calculated based on the requirement of the preset target detection distance and the condition of the signal-to-noise ratio of the target detection given by the maximum segment length, including: Based on a preset radar equation, a signal-to-noise ratio condition for target detection that satisfies the maximum segment length is estimated; Based on the requirement of preset target detection distance and the target detection signal-to-noise ratio condition, a loss value expression is used to calculate the maximum loss value of the accumulated gain in the signal processing process.

5. The method for rapidly evaluating the accumulated performance of a piecewise coherent processing algorithm according to claim 2, characterized in that: The two-dimensional cross-correlation expression of the reference signal and the echo signal of the target segment is determined, wherein the two-dimensional cross-correlation expression includes: Among them, τ max is the maximum delay, χ k (τ,f) is the two-dimensional cross-correlation function of the k-th reference signal and the echo signal, and the total accumulation time is T int The signal in is divided into n B Duan, T B is the segment length, The corresponding time segment is T k =kT B .

6. A method for rapidly evaluating the accumulated performance of a piecewise coherent processing algorithm according to claim 5, characterized in that: The quantitative evaluation model corresponding to when the reference signal envelope is stable is determined based on the error estimation expression, wherein the expression of the quantitative evaluation model includes: LF(τ d ,f d )≈20log 10 [sinc(πf d T B )] The error estimation expression is: t∈[0,T B ],|f|≤f max , 2fπ·T B ≤π.

7. The method for rapidly evaluating the accumulated performance of a piecewise coherent processing algorithm according to claim 1, characterized in that: The maximum loss value of the accumulated gain in the signal processing process is calculated based on the requirement of the preset target detection distance and the condition of the target detection signal-to-noise ratio of the maximum segment length, and is calculated using the following calculation formula: Where ERP is the equivalent radiated power of the radiation source, G r is the receiving antenna gain, λ is the signal wavelength, σ is the target scattering cross-sectional area, R t and R r are the distances from the target to the radiation source and the receiving antenna respectively, τ0 is the integration time, SNR is the target detection signal-to-noise ratio, F is the system noise factor, and LF0 is the target processing gain loss.

8. A rapid evaluation device for the cumulative performance of a piecewise coherent processing algorithm, characterized in that: The device comprises: A determination module, used to determine the maximum Doppler of the target in the detection scene; A first calculation module, configured to calculate a maximum segment length of Doppler unambiguity based on the maximum Doppler; A second calculation module is used to calculate the maximum loss value of the accumulated gain in the signal processing process based on the requirement of the preset target detection distance and the condition of the target detection signal-to-noise ratio meeting the maximum segment length; The evaluation module is used to input the maximum loss value into a pre-established quantitative evaluation model to obtain a cumulative gain loss comparison relationship under different segmentation and target Doppler conditions, so as to evaluate the cumulative gain loss using the comparison relationship.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores program codes that can be executed on the processor, and when the program codes are executed by the processor, a method for rapidly evaluating the accumulated performance of a segmented coherent processing algorithm as described in any one of claims 1-7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, and the program codes can be called by one or more processors to execute a method for quickly evaluating the accumulated performance of a segmented coherent processing algorithm as described in any one of claims 1-7.

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