Multi-target hybrid accumulation detection method based on de-correlation clean algorithm

Abstract

This invention discloses a multi-target hybrid accumulation detection method based on a decoherent clean algorithm, comprising: acquiring the beat signal of a sawtooth frequency-modulated continuous wave signal; performing a one-dimensional Fourier transform on the beat signal to obtain the range dimension information of the target echo, and displaying the target echo's range dimension information as a moving target to obtain a preprocessed signal; searching different coherent segments based on the preprocessed signal, performing coherent accumulation within each segment using GRFT transform, and performing non-coherent accumulation between segments using GRT transform to obtain a hybrid accumulation result; acquiring the peak value in the hybrid accumulation result and comparing the peak value with a hybrid accumulation threshold; extracting the target motion parameters corresponding to the peak value and constructing a decoherent clean signal; removing the clean signal from the beat signal to obtain an updated beat signal, until the updated accumulation peak value is less than the hybrid accumulation threshold. This invention can improve the accumulation gain.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, specifically relating to a multi-target hybrid accumulation detection method based on the decoherent clean algorithm. Background Technology

[0002] In actual radar detection, due to changes in the target's material, motion attitude, etc., the radar cross-section of the target is often not a constant, but a fluctuating random variable. This results in the target echo not being completely coherent, causing a significant decrease in the gain of coherent accumulation, while the theoretical gain of non-coherent accumulation is low. Using either coherent or non-coherent accumulation methods may result in insufficient accumulation gain.

[0003] Furthermore, in actual detection, multiple targets are often encountered. In more complex scenarios, the coherence times of multiple fluctuating targets are different, making detection more difficult.

[0004] Therefore, there is an urgent need to improve existing methods and increase cumulative gain. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a multi-target hybrid accumulation detection method based on the decoherent clean algorithm. The technical problem to be solved by this invention is achieved through the following technical solution:

[0006] In a first aspect, the present invention provides a multi-target hybrid accumulation detection method based on the decoherence clean algorithm, comprising:

[0007] Obtain the beat signal of the sawtooth frequency modulated continuous wave signal;

[0008] A one-dimensional Fourier transform is performed on the beat signal to obtain the range dimension information of the target echo, and the range dimension information of the target echo is used to display the moving target, thus obtaining a preprocessed signal.

[0009] Based on the preprocessed signal, different coherent segments are searched. Coherent accumulation is performed within each segment using GRFT transform, and non-coherent accumulation is performed between segments using GRT transform, resulting in a hybrid accumulation of the preprocessed signal.

[0010] The peak value is obtained from the mixed accumulation result of the preprocessed signal. If the peak value is greater than the threshold value of the mixed accumulation, the peak value is the detected target. If the peak value is less than the threshold value of the mixed accumulation, the peak value is not the detected target.

[0011] Extract the target motion parameters corresponding to the peak values ​​and construct a decoherent clean signal;

[0012] Remove the clean signal from the beat signal, obtain the updated beat signal, and continue to update the preprocessed signal until the updated accumulated peak value is less than the threshold value of the mixed accumulation.

[0013] Optionally, the expression for the beat signal S0 is:

[0014]

[0015] Among them, S 0i (n,m) represents the signal component of the i-th target in the echo, k represents the total number of targets, n represents the number of sampling points in the slow time dimension, N represents the number of frequency modulation cycles, m represents the number of sampling points in the fast time dimension, and Noise represents Gaussian white noise.

[0016] Alternatively, the expression for the GRFT transform is:

[0017]

[0018]

[0019]

[0020] Where, round is the rounding function, s is the echo signal, r0 is the initial distance to the target being searched, v0 is the initial velocity of the target being searched, and T... c For the frequency modulation period, ρ r Where B is the distance resolution, C is the signal bandwidth, and D is the speed of light. Let be the phase compensation function, λ be the radar wavelength, exp be an exponential function with base e, and j be the imaginary unit. π is the mathematical constant for a circle.

[0021] Alternatively, the expression for the GRT transform is:

[0022]

[0023]

[0024] Where, round is the rounding function, r0 is the initial distance to the target being searched, v0 is the initial velocity of the target being searched, and T... c For the frequency modulation period, ρ r Where B is the distance resolution, C is the signal bandwidth, and D is the speed of light.

[0025] Optionally, the expression for the mixture accumulation result of the preprocessed signal is:

[0026]

[0027] Among them, G HI(r0,v0,n c ) represents the result of mixed accumulation, n c For the coherent time segmentation of the search, p and q are positive integers and are temporary variables in the calculation process.

[0028] Optionally, the threshold value V for mixed accumulation T The expression is:

[0029]

[0030] Where, σ 2 For noise power, It is the inverse function of the chi-square probability density function. For degrees of freedom, P fa The false alarm rate is constant.

[0031] Optionally, the target motion parameters include the maximum target distance r being searched. 0max The maximum speed v of the target being searched 0max and the maximum coherent time segment n for the search cmax .

[0032] Optionally, the clean signal S clean The expression is:

[0033]

[0034]

[0035]

[0036] Where A0 is the amplitude. The coherent time segment is divided into n segments. cmax The fluctuating random variable, F c Let μ be the radar carrier frequency, μ be the frequency modulation slope, and τ(n) be the time delay.

[0037] The beneficial effects of this invention are:

[0038] This invention provides a multi-target hybrid accumulation detection method based on a decoherent clean algorithm. Within segments, the GRFT (generalized Radon-Fourier transform) algorithm is used for noncoherent accumulation; between segments, the GRT (generalized Radon transform) algorithm is used for noncoherent accumulation. Distance migration is compensated both within and between segments, maximizing the utilization of signal coherent information and solving the distance migration problem caused by high-speed motion. Simultaneously, the coherent accumulation time is increased, resulting in higher accumulation gain. Furthermore, while meeting gain requirements, for multi-target scenarios with fluctuating coherence times, the detection method provided by this invention constructs a decoherent clean signal based on motion parameters obtained from the accumulation algorithm. This eliminates targets with that coherent time from the original echo before performing accumulation detection of other targets. This eliminates strong targets with specific coherent times, followed by detection of other weak targets. This not only improves accumulation gain but also enables the detection and differentiation of multi-targets with fluctuating coherence times, reducing missed detections.

[0039] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0040] Figure 1 This is a flowchart of a multi-target hybrid accumulation detection method based on the decoherence clean algorithm provided in an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of the mixed accumulation results during the simulation experiment provided in this embodiment of the invention;

[0042] Figure 3 This is a schematic diagram of the mixed accumulation results during the simulation experiment provided in the embodiment of the present invention. Detailed Implementation

[0043] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0044] In the prior art, the Shanghai Radio Equipment Research Institute disclosed a method for long-term accumulation detection of multiple targets based on the CLEAN algorithm in its patent application (application number CN201811398217.7, publication number CN109655802B). This method segments the target echo to ensure that the target's motion does not exceed one range Doppler unit within each segment. It then uses segmented Fourier transform to coherently accumulate each echo segment, obtaining multi-segment echo coherent accumulation data. Finally, it utilizes particle swarm optimization to... The subswarm optimization algorithm is used to detect targets and obtain single-target information. The CLEAN algorithm is then used to remove the target's response in each segment of echo coherent accumulation data. The particle swarm optimization algorithm is then used to detect targets again. This process is repeated until all targets have been successfully detected. However, when dealing with fluctuating targets, the above method cannot fully utilize the phase information in the target echo signal, cannot obtain higher accumulation gain, and cannot detect other weak targets, resulting in missed alarms. In addition, the above method requires that the target does not move within the segment, which limits the coherent accumulation time and results in insufficient accumulation gain.

[0045] In view of this, the present invention provides a multi-target hybrid accumulation detection method based on the decoherence clean algorithm, which not only improves the accumulation gain, but also realizes the detection and discrimination of multi-targets with fluctuations in different coherence times.

[0046] Please see Figure 1 As shown, Figure 1 This is a flowchart of a multi-target hybrid accumulation detection method based on the decoherence clean algorithm provided in this invention. The multi-target hybrid accumulation detection method based on the decoherence clean algorithm provided in this invention includes:

[0047] Obtain the beat signal of the sawtooth frequency modulated continuous wave signal;

[0048] A one-dimensional Fourier transform is performed on the beat signal to obtain the range dimension information of the target echo, and the range dimension information of the target echo is used to display the moving target, thus obtaining a preprocessed signal.

[0049] Based on the preprocessed signal, different coherent segments are searched. Coherent accumulation is performed within each segment using GRFT transform, and non-coherent accumulation is performed between segments using GRT transform, resulting in a hybrid accumulation of the preprocessed signal.

[0050] The peak value is obtained from the mixed accumulation result of the preprocessed signal. If the peak value is greater than the threshold value of the mixed accumulation, the peak value is the detected target. If the peak value is less than the threshold value of the mixed accumulation, the peak value is not the detected target.

[0051] Extract the target motion parameters corresponding to the peak values ​​and construct a decoherent clean signal;

[0052] Remove the clean signal from the beat signal, obtain the updated beat signal, and continue to update the preprocessed signal until the updated accumulated peak value is less than the threshold value of the mixed accumulation.

[0053] For details, please continue to see Figure 1 As shown in the figure, this embodiment provides a multi-target hybrid accumulation detection method based on a decoherent clean algorithm. Within segments, the GRFT (generalized Radon-Fourier transform) algorithm is used for non-coherent accumulation, and between segments, the GRT (generalized Radon transform) algorithm is used for non-coherent accumulation. Distance migration is compensated both within and between segments, maximizing the utilization of signal coherent information and solving the distance migration problem caused by high-speed motion. Simultaneously, the coherent accumulation time is increased, resulting in higher accumulation gain. Furthermore, under the premise of satisfying the gain, facing multi-target scenarios with fluctuating coherence times, the detection method provided in this embodiment constructs a decoherent clean signal based on the motion parameters obtained from the accumulation algorithm. This eliminates targets with that coherent time from the original echo before performing accumulation detection of other targets. This can eliminate strong targets with specific coherent times, followed by the detection of other weak targets. This not only improves the accumulation gain but also enables the detection and differentiation of multi-targets with fluctuating coherence times, reducing missed alarms.

[0054] It should be noted that displaying moving targets based on the range dimension information of the target echo can eliminate stationary clutter.

[0055] In an optional embodiment of the present invention, the expression for the beat signal S0 is:

[0056]

[0057] Among them, S 0i (n,m) represents the signal component of the i-th target in the echo, k represents the total number of targets, n represents the number of sampling points in the slow time dimension, N represents the number of frequency modulation cycles, m represents the number of sampling points in the fast time dimension, and Noise represents Gaussian white noise.

[0058] In an optional embodiment of the present invention, the expression for the GRFT transform is:

[0059]

[0060]

[0061]

[0062] Where, round is the rounding function, s is the echo signal, r0 is the initial distance to the target being searched, v0 is the initial velocity of the target being searched, and T... c For the frequency modulation period, ρ r Where B is the distance resolution, C is the signal bandwidth, and D is the speed of light. Let be the phase compensation function, λ be the radar wavelength, exp be an exponential function with base e, and j be the imaginary unit. π is the mathematical constant for a circle.

[0063] In an optional embodiment of the present invention, the expression for the GRT transform is:

[0064]

[0065]

[0066] Where, round is the rounding function, r0 is the initial distance to the target being searched, v0 is the initial velocity of the target being searched, and T... c For the frequency modulation period, ρ r Where B is the distance resolution, C is the signal bandwidth, and D is the speed of light.

[0067] It should be noted that the noncoherent accumulation method used in this embodiment is a square-law detector.

[0068] In an optional embodiment of the present invention, the expression for the hybrid accumulation result of the preprocessed signal is:

[0069]

[0070] Among them, G HI (r0,v0,n c ) represents the result of mixed accumulation, n c For the coherent time segmentation of the search, p and q are positive integers and are temporary variables in the calculation process.

[0071] In an optional embodiment of the invention, the threshold value V for hybrid accumulation is... T The expression is:

[0072]

[0073] Where, σ 2 For noise power, It is the inverse function of the chi-square probability density function. For degrees of freedom, P fa The false alarm rate is constant.

[0074] In an optional embodiment of the invention, the target motion parameters include the maximum target distance r to be searched.0max The maximum speed v of the target being searched 0max and the maximum coherent time segment n for the search cmax .

[0075] In an optional embodiment of the invention, the clean signal S clean The expression is:

[0076]

[0077]

[0078]

[0079] Where A0 is the amplitude. The coherent time segment is divided into n segments. cmax The fluctuating random variable, F c Let μ be the radar carrier frequency, μ be the frequency modulation slope, and τ(n) be the time delay.

[0080] In an optional embodiment of the present invention, the following simulation experiment is used for illustration.

[0081] Please refer to Table 1, which shows the radar simulation parameters during the simulation experiment.

[0082] Table 1 Radar Simulation Parameters

[0083] parameter Parameter value Radar carrier frequency (GHz) 77 Signal bandwidth (MHz) 2400 Frequency modulation period (µs) 160 Frequency modulation cycle number 15360 Distance dimension sampling rate (MHz) 10 Radar constant false alarm rate 1e-6

[0084] Please refer to Table 2, which shows the target motion parameters during the simulation experiment.

[0085] Table 2 Target Motion Parameters

[0086]

[0087] Please see Figure 2 and Figure 3 As shown, Figure 2 This is a schematic diagram of the mixed accumulation results during the simulation experiment provided in this embodiment of the invention. Figure 3 This is a schematic diagram of the mixed accumulation results during the simulation experiment provided in this embodiment of the invention. Figure 2 The display shows that when the maximum coherent time segment n is found... cmax When the cumulative result is 128, only the cumulative result of objective 2 is greater than the threshold value V of mixed accumulation. T Target 2 can be detected, and the accumulated result of Target 1 is less than the threshold value V of the mixed accumulation. T Target 1 could not be detected, resulting in a false alarm; the threshold value V for mixed accumulation. T =1.45e12; Figure 3 The display shows that after performing the decoherence clean algorithm to eliminate the echo of target 2, the maximum coherent time segment n found in the search... cmax When V = 32, the threshold value for mixed accumulation is V T =1.2e12, target 1 can be detected. Thus, it is shown that the detection method provided in this embodiment can effectively reduce missed alarms.

[0088] In an optional embodiment of the present invention, the detection method provided in the above embodiment can be applied to vehicle-mounted millimeter-wave radar. One of the tasks of vehicle-mounted radar is to detect other vehicles and pedestrians near the vehicle, obtain distance and speed information from other vehicles and pedestrians, and provide early warning to the driver. When facing complex road environments, the movement postures of motor vehicles, non-motor vehicles and pedestrians are usually different, resulting in different target characteristics. This embodiment can effectively distinguish targets with different coherence times, reduce missed alarms, and protect the safety of drivers and pedestrians.

[0089] Furthermore, the detection method provided in the above embodiments is also applicable to pulse radar systems using linear frequency modulated signals and can be applied in the military field. Currently, the surface material technology of high-speed aircraft is developing rapidly, and the radar cross-section of a target is usually a random variable. In practical applications, when facing multiple enemy flying targets, different surface materials and flight attitudes will cause differences in target characteristics, that is, the fluctuation characteristics of these targets are different. The detection method provided in this embodiment can effectively detect and distinguish targets, reduce missed detections, and has good application prospects in improving radar inspection capabilities and enhancing our defense system.

[0090] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that an article or device comprising a list of elements includes not only those elements but also other elements not expressly listed. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device comprising said element. Terms such as "connected" or "linked" are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect. The orientations or positional relationships indicated by terms such as "upper," "lower," "left," and "right" are based on the orientations or positional relationships shown in the accompanying drawings and are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0091] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0092] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A multi-target hybrid accumulation detection method based on the decoherent clean algorithm, characterized in that, include: Obtain the beat signal of the sawtooth frequency modulated continuous wave signal; A one-dimensional Fourier transform is performed on the beat signal to obtain the range dimension information of the target echo, and the range dimension information of the target echo is used to display the moving target to obtain a preprocessed signal. Based on the preprocessed signal, different coherent segments are searched, and coherent accumulation is performed within the segments using GRFT transform, while non-coherent accumulation is performed between the segments using GRT transform, to obtain a mixed accumulation result of the preprocessed signal. The peak value is obtained from the mixed accumulation result of the preprocessed signal. If the peak value is greater than the threshold value of the mixed accumulation, the peak value is the detected target. If the peak value is less than the threshold value of the mixed accumulation, the peak value is not the detected target. Extract the target motion parameters corresponding to the peak value and construct a decoherent clean signal; The clean signal is removed from the beat signal to obtain an updated beat signal, and the preprocessed signal is updated continuously until the updated accumulated peak value is less than the threshold value of the mixed accumulation. The beat signal The expression is: ； in, For the first echo The signal components of each target The total number of targets, The number of sampling points in the slow time dimension. The number of frequency modulation cycles. The number of sampling points in the fast time dimension. It is Gaussian white noise; The expression for the GRFT transform is: ； ； ； in, This is the rounding function. For echo signal, The initial distance to the target being searched. The initial velocity of the target being searched. For frequency modulation period, For distance resolution, For signal bandwidth, At the speed of light, For phase compensation function, For radar wavelength, For An exponential function with base 0. The symbol for the imaginary unit. , Pi; The expression for the GRT transform is: ； ； in, This is the rounding function. The initial distance to the target being searched. The initial velocity of the target being searched. For frequency modulation period, For distance resolution, For signal bandwidth, The speed of light; The expression for the hybrid accumulation result of the preprocessed signal is: ； in, For the results of mixed accumulation, For coherent time segmentation of the search, and These are positive integers, and are all temporary variables during the calculation process; The target motion parameters include the maximum distance of the target being searched. The maximum speed for performing the search target and the maximum coherent time segment for the search ; The clean signal The expression is: ； ； ； in, For amplitude, For coherent time segmentation Fluctuating random variables, For radar carrier frequency, For frequency modulation slope, For time delay.

2. The multi-target hybrid accumulation detection method based on the decoherent clean algorithm according to claim 1, characterized in that, The threshold value of the hybrid accumulation The expression is: ； in, For noise power, It is the inverse function of the chi-square probability density function. For degrees of freedom, The false alarm rate is constant.

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