CN111123214A - High-speed high-mobility target detection method based on polynomial rotation-polynomial Fourier transform - Google Patents

High-speed high-mobility target detection method based on polynomial rotation-polynomial Fourier transform Download PDF

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CN111123214A
CN111123214A CN201911311705.4A CN201911311705A CN111123214A CN 111123214 A CN111123214 A CN 111123214A CN 201911311705 A CN201911311705 A CN 201911311705A CN 111123214 A CN111123214 A CN 111123214A
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CN111123214B (en
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芮义斌
吕宁
谢仁宏
李鹏
郭山红
王丽妍
王欢
孙泽渝
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Nanjing University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/32—Shaping echo pulse signals; Deriving non-pulse signals from echo pulse signals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/04—Systems determining presence of a target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/28—Details of pulse systems
    • G01S7/285—Receivers
    • G01S7/295—Means for transforming co-ordinates or for evaluating data, e.g. using computers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/415—Identification of targets based on measurements of movement associated with the target
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention discloses a high-speed high maneuvering target detection method based on polynomial rotation-polynomial Fourier transform, which comprises the steps of firstly, respectively carrying out digital sampling and pulse compression processing on M radar pulse echoes in accumulation time to obtain a fast time-slow time two-dimensional radar echo data matrix; then, determining the order and the range of a search parameter set according to the motion characteristics of the target to be detected and initializing the search parameter set; then, carrying out coherent accumulation on the data matrix in the whole parameter search space by utilizing polynomial rotation-polynomial Fourier transform to obtain a distance-Doppler distribution map; and finally, judging whether the target exists or not by using constant false alarm detection, and if the target exists, obtaining the distance and motion state information of the target. The polynomial rotation-polynomial Fourier transform can achieve the same theoretical optimal accumulation effect under the condition that the calculation complexity is far smaller than that of the generalized Radon transform, and coherent accumulation of high-speed and high-mobility targets in the adjacent space under the environment with low signal-to-noise ratio is achieved.

Description

High-speed high-mobility target detection method based on polynomial rotation-polynomial Fourier transform
Technical Field
The invention belongs to the technical field of radar signal processing and detection, and particularly relates to a polynomial rotation-polynomial Fourier transform high-speed high-mobility target detection method.
Background
In recent years, with the development of scientific technology, the appearance of high-speed and high-maneuvering targets in the near space brings serious challenges to radar detection, such as hypersonic high-maneuvering fighters and missiles in the air defense field, and orbital targets and space debris which need to be monitored in space. The target flight speed can reach 25 Mach, and the flight trajectory can be changed in various irregular modes such as overturning, jumping and large corners, so that the method has strong maneuverability. In addition, due to the maturity of stealth technology and plasma generated by high-speed motion of an aircraft in the atmosphere, the signal-to-noise ratio of target echo is low, and the detection performance of the radar is reduced.
Pulse coherent accumulation is an effective method for improving target detection probability, and extension of radar target fixation time is an effective means for improving low signal-to-noise ratio target coherent accumulation gain. However, the target ultra-high speed and high mobility impose certain limitations on the accumulation time. On the one hand, the high speed of the target causes a cross-gate phenomenon in the distance dimension of the target within the accumulation time; on the other hand, the high mobility of the target leads to doppler frequency migration in the doppler frequency dimension of the target.
At present, coherent accumulation mainly surrounds Moving Target Detection (MTD) technology, Keystone transformation and Radon Fourier transformation and expansion thereof. Most of the existing methods realize the estimation of target motion parameters by using a time-frequency analysis method after correcting distance walk. However, when the target is subjected to nonlinear range walk and doppler migration due to higher-order motion parameters such as acceleration, most of the existing methods have difficulty in simultaneously performing range walk correction and doppler migration compensation, and thus cannot achieve theoretical detection performance.
Disclosure of Invention
The invention aims to provide a high-speed high-mobility target detection method based on polynomial rotation-polynomial Fourier transform, which is suitable for detecting a high-speed high-mobility target in an adjacent space.
The technical solution for realizing the purpose of the invention is as follows: a polynomial rotation-polynomial Fourier transform high-speed high-maneuvering target detection method comprises the following steps:
step 1, performing digital sampling and pulse compression processing on radar pulse echoes in accumulation time to obtain a two-dimensional echo data matrix;
step 2, determining the number and the range of the parameters to be searched;
step 3, starting to perform polynomial rotation transformation on the two-dimensional echo data matrix from the initial search parameter group, and performing non-coherent accumulation on the transformed echo data matrix; if the maximum value of the superposed one-dimensional data is larger than the threshold value, performing polynomial Fourier transform in the step 4 on the transformed echo data matrix; otherwise, changing the searching parameter group and executing the step 3;
step 4, performing phase compensation on the search parameter sets meeting the conditions in the step 3, then accumulating, then changing the search parameter sets, and executing the step 3 until all the search parameter sets are traversed;
step 5, carrying out constant false alarm detection on the obtained distance-Doppler distribution map, and judging whether a target exists or not;
and 6, determining the coordinates of the target position and determining the motion parameters of the target.
Compared with the prior art, the invention has the remarkable advantages that: the polynomial rotation-polynomial Fourier transform can achieve the same theoretical optimal accumulation effect under the condition that the calculation complexity is far smaller than that of the generalized Radon transform, and coherent accumulation of high-speed and high-mobility targets in the adjacent space under the environment with low signal-to-noise ratio is achieved.
Drawings
Fig. 1 is a flow chart of a high-speed and high-mobility target detection method based on polynomial rotation-polynomial fourier transform according to the present invention.
Fig. 2 is a MTD cumulative range-doppler distribution plot.
Fig. 3 is a PRPFT cumulative range-doppler profile.
FIG. 4 is a graph of single PRT non-coherent accumulation results.
Detailed Description
As shown in fig. 1, in the high-speed and high-mobility target detection method of polynomial rotation-polynomial fourier transform (PRPFT), firstly, M radar pulse echoes in accumulation time are respectively subjected to digital sampling and pulse compression processing to obtain a fast-slow two-dimensional radar echo data matrix; then, determining the order and the range of a search parameter set according to the motion characteristics of the target to be detected and initializing the search parameter set; then, carrying out coherent accumulation on the data matrix in the whole parameter search space by utilizing polynomial rotation-polynomial Fourier transform to obtain a distance-Doppler distribution map; and finally, judging whether the target exists or not by using constant false alarm detection, and if the target exists, obtaining the distance and motion state information of the target. The method comprises the following specific steps:
step 1: echo signal sampling and pulse compression
Sampling echo signals of M periods accumulated by coherence and performing pulse compression processing to obtain a two-dimensional echo data matrix s (N, M), wherein N represents a distance dimensional sampling point label, N is 1,2, 1, N is the total number of single-period echo sampling points, M represents an echo signal number label, M is 1,2, M is the total number of coherent accumulated echoes;
step 2: determining a parameter search range
Determining the number and the range of parameters to be searched according to the actual conditions of the radar and the target to be detected;
setting a search speed rangeIs enclosed as [ v ]min,vmax]Wherein is vminIs the lower bound of the velocity search range, vmaxIs the upper bound of the speed search range; setting a speed search interval as delta v, wherein the delta v is lambda/2T, lambda is a carrier wavelength, and T is accumulation time; with NvA speed search point, Nv＝round((vmax-vmin) V), where round (·) is an integer function; setting the search acceleration range to [ a ]min,amax]In which is aminIs the lower bound of the acceleration search range, amaxIs the upper bound of the acceleration search range; set its acceleration search interval to Deltaa, with NaAn acceleration search point, Na＝round((amax-amin) A,/Δ a), if there are higher order motion parameters, the parameter search ranges are set in order, determining a search parameter set (α)1,α2,...,αk) Wherein αiK corresponds to the target ith order motion parameter and αiContaining NiIndividual search points.
And step 3: polynomial rotation transformation
From an initial set of search parameters (α)1,α2,...,αk) Starting to perform polynomial rotation transformation on the two-dimensional echo data matrix s (n, m) to obtain a rotated data matrix s (n ', m'), wherein the transformation relation is as follows:
Figure BDA0002324704750000031
where B is the chirp bandwidth, TrFor the pulse repetition interval, c is the speed of light. And performing non-coherent accumulation on the transformed echo data matrix, namely superposing the envelopes of the M echo signals. If the maximum value of the superposed one-dimensional data is larger than the threshold AthThen the polynomial Fourier transform of step 4 is performed on s (n ', m'), threshold AthIs composed of
Figure BDA0002324704750000032
Wherein abs (·) is a modulus function, N is the total number of sampling points of the single-period echo, and M is the total number of coherent accumulation echoes; otherwise, the step 3 is executed by changing the search parameter group.
And 4, step 4: polynomial Fourier transform
And (3) performing phase compensation on the search parameter sets meeting the conditions in the step (3) and then accumulating the search parameter sets
Figure BDA0002324704750000041
Wherein s (n ', m') is the data matrix after rotation transformation, (α)1,α2,...,αk) For searching parameter sets, λ is the carrier wavelength, TrIs the pulse repetition interval. And then changing the search parameter set and executing the step 3 until all the search parameter sets are traversed.
And 5: constant false alarm detection
And carrying out constant false alarm detection on the obtained distance-Doppler distribution diagram, and judging whether the target exists or not.
Step 6: motion parameter estimation
And determining the coordinates of the position where the target exists and determining the motion parameters of the target.
The present invention will be described in detail below with reference to examples and the accompanying drawings.
Examples
A polynomial rotation-polynomial Fourier transform high-speed high-maneuvering target detection method comprises the following steps:
step 1: respectively sampling the radar signal echoes of M periods in the radar coherent accumulation time T at the sampling frequency fsAnd carrying out digital sampling, and then carrying out pulse compression processing on the sampled data to obtain a fast-time-slow-time two-dimensional radar echo data matrix s (n, m). N represents a distance-dimensional sampling point index, N is 1,2, the.
Step 2: setting a search speed range as v for the motion state with higher order above the acceleration of the high-speed high-mobility target to be detectedmin,vmax]Wherein is vminIs the lower bound of the velocity search range, vmaxIs the upper bound of the speed search range. The velocity search interval is set to Δ v, which is λ/2T, where λ is the carrier wavelength and T is the accumulation time. With NvA speed search point, Nv＝round((vmax-vmin) V), where round (·) is an integer function; setting the search acceleration range to [ a ]min,amax]In which is aminIs the lower bound of the acceleration search range, amaxIs the upper bound of the acceleration search range. Setting the acceleration search interval as delta a, lambda/4T2. With NaAn acceleration search point, Na＝round((amax-amin) A,/Δ a), if there are higher order motion parameters, the parameter search ranges are set in order, determining a search parameter set (α)1,α2,...,αk) Wherein αiK corresponds to the target ith order motion parameter and αiContaining NiIndividual search points.
For convenience of explanation, in the following detailed description, the third order and above motion parameters of the radar target are ignored, that is, the target performs uniform acceleration linear motion relative to the radar, and then the search parameter set is determined as (v, a), and the initialization parameter set is determined as (v, a)min,amin)。
And step 3: polynomial rotation transformation relation can be obtained by utilizing search parameter group
Figure BDA0002324704750000051
Wherein (v)i,aj) For the current set of search parameters, B is the chirp bandwidth, TrFor the pulse repetition interval, c is the speed of light. Transforming s (n, m) into s (n ', m') by utilizing a polynomial rotation transformation relation to obtain a rotated fast time-slow time two-dimensional echo data matrix, and performing non-coherent accumulation on radar echoes in a slow time dimension by the following method
Figure BDA0002324704750000052
Wherein abs (·) is a modulo function. Setting a threshold AthIs composed of
Figure BDA0002324704750000053
When max (R (n')) > AthAnd if so, executing polynomial Fourier transform in the step 3, otherwise, updating the search parameter group and performing polynomial rotation transform again until all the search parameter groups are traversed.
And 4, step 4: for the current search parameter set (v)i,aj) And performing polynomial Fourier transform to realize coherent accumulation. The polynomial rotation transformation screens out possible search parameter groups to carry out polynomial Fourier transformation, and the algorithm computation complexity is reduced. Fourier transform is as follows
Figure BDA0002324704750000054
Wherein s (n ', m') is a data matrix after rotation transformation, (v)i,aj) For searching parameter sets, λ is the carrier wavelength, TrIs the pulse repetition interval. And then updating the search parameter set and executing the step 3 until all the search parameter sets are traversed.
The method (v, a) for traversing all the search parameter sets is as follows: total number of cycles is NvNaLet k be the current cycle number, k 1,2vNaAssume that the kth set of search parameters is (v)i,aj) Wherein i ═ floor (k/N)v) Floor (·) is a floor rounding function, j ═ mod (k, N)v) Mod (·) is a remainder function. k is NvNaThe search parameter space representing the whole is traversed to the end. At this time, a coherent-accumulation range-doppler distribution map can be obtained.
And 5: the constant false alarm detection is carried out on the distance-Doppler distribution diagram to judge whether a target exists or not, and the judgment method is as follows
Figure BDA0002324704750000061
And (vi, aj) is a search parameter group, η is a detection threshold, and N is the total number of sampling points of the single-cycle echo.
Step 6: if the target exists, extracting target position information as n' and speed information as viAcceleration information is aj。
The effect of the invention is verified by Matlab as follows:
the simulation parameters are set as follows: carrier frequency f01GHz, 60us pulse width T, 2MHz bandwidth B, pulse repetition frequency Tr600us, number of accumulated pulses Npulse64, sampling frequency fs4MHz, target initial distance R04000km, radial initial velocity V04000m/s and the acceleration a 100m/s2。
Simulation results and analysis: as can be seen from fig. 2, the conventional MTD algorithm has poor accumulation performance in the face of high-speed and high-maneuvering targets. The target distance walk causes large deviation of a target estimated distance unit, and Doppler migration causes that target energy cannot be well gathered through FFT. So that the final accumulation performance cannot meet the requirement of constant false alarm detection. As can be seen from FIG. 3, the energy accumulated by the target after the polynomial rotation-polynomial Fourier transform is well accumulated, which is obviously better than the MTD algorithm. Fig. 4 shows the result after PRT non-coherent accumulation, and it can be seen that the target energy peak is obvious but the noise floor is too high, which is not suitable for target detection, but only for reference of whether a target exists.

Claims (7)

1. A polynomial rotation-polynomial Fourier transform high-speed and high-mobility target detection method is characterized by comprising the following steps:
step 1, performing digital sampling and pulse compression processing on radar pulse echoes in accumulation time to obtain a two-dimensional echo data matrix;
step 2, determining the number and the range of the parameters to be searched;
step 3, starting to perform polynomial rotation transformation on the two-dimensional echo data matrix from the initial search parameter group, and performing non-coherent accumulation on the transformed echo data matrix; if the maximum value of the superposed one-dimensional data is larger than the threshold value, performing polynomial Fourier transform in the step 4 on the transformed echo data matrix; otherwise, changing the searching parameter group and executing the step 3;
step 4, performing phase compensation on the search parameter sets meeting the conditions in the step 3, then accumulating, then changing the search parameter sets, and executing the step 3 until all the search parameter sets are traversed;
step 5, carrying out constant false alarm detection on the obtained distance-Doppler distribution map, and judging whether a target exists or not;
and 6, determining the coordinates of the target position and determining the motion parameters of the target.
2. The method for detecting the high-speed and high-mobility target by the polynomial rotation-polynomial Fourier transform as claimed in claim 1, wherein the step 1 is specifically as follows:
sampling echo signals of M periods accumulated by coherence and carrying out pulse compression processing to obtain a two-dimensional echo data matrix s (N, M), wherein N represents a distance dimension sampling point label, N is 1,2, 1.
3. The method for detecting the high-speed and high-mobility target by the polynomial rotation-polynomial fourier transform as claimed in claim 1, wherein the step 2 is specifically: setting the search speed range to [ v ]min,vmax]Wherein v isminIs the lower bound of the velocity search range, vmaxIs the upper bound of the speed search range; setting a speed search interval as delta v, wherein the delta v is lambda/2T, lambda is a carrier wavelength, and T is accumulation time; with NvA speed search point, Nv＝round((vmax-vmin) V), where round (·) is an integer function; setting the search acceleration range to [ a ]min,amax]Wherein a isminIs below the acceleration search rangeBoundary, amaxIs the upper bound of the acceleration search range; set its acceleration search interval to Deltaa, with NaAn acceleration search point, Na＝round((amax-amin) A/delta a), if there are higher order motion parameters, the parameter search ranges are set in turn, determining the search parameter set (α)1,α2,...,αk) Wherein αiCorresponding to the ith order motion parameter of the target and αiContaining NiThe number of search points, i ═ 1, 2.
4. The method for detecting the high-speed and high-mobility target by the polynomial rotation-polynomial fourier transform as claimed in claim 1, wherein the step 3 is specifically:
from an initial set of search parameters (α)1,α2,...,αk) Starting to perform polynomial rotation transformation on the two-dimensional echo data matrix s (n, m) to obtain a rotated data matrix s (n ', m'), wherein the transformation relation is as follows:
Figure FDA0002324704740000021
where k is the polynomial order, B is the chirp bandwidth, TrIs the pulse repetition interval, c is the speed of light; performing non-coherent accumulation on the transformed echo data matrix, namely superposing M echo signal envelopes; if the maximum value of the superposed one-dimensional data is larger than the threshold AthThen the polynomial Fourier transform of step 4 is performed on s (n ', m'), threshold AthIs composed of
Figure FDA0002324704740000022
Wherein abs (·) is a modulus function, and N is the total number of sampling points of the single-period echo;
otherwise, the step 3 is executed by changing the search parameter group.
5. The method for detecting the high-speed and high-mobility target by the polynomial rotation-polynomial fourier transform as claimed in claim 1, wherein the step 4 is specifically:
and (3) performing phase compensation on the search parameter sets meeting the conditions in the step (3) and then accumulating the search parameter sets
Figure FDA0002324704740000023
Wherein s (n ', m') is the data matrix after rotation transformation, (α)1,α2,...,αk) For searching parameter sets, λ is the carrier wavelength, TrIs a pulse repetition interval; and then changing the search parameter set and executing the step 3 until all the search parameter sets are traversed.
6. The method for detecting the high-speed and high-mobility target by the polynomial rotation-polynomial fourier transform as claimed in claim 1, wherein the step 5 is specifically as follows:
the constant false alarm detection is carried out on the distance-Doppler distribution diagram, and whether a target exists or not is judged, wherein the judgment method comprises the following steps:
Figure FDA0002324704740000024
in the formula (v)i,aj) And for searching the parameter set, η is a detection threshold, N is the total number of sampling points of the single-cycle echo, if the amplitude value of the detection unit is higher than the threshold, the detection unit is judged to have a target, otherwise, the detection unit is judged to have no target, and the detection processing of the subsequent unit is continued.
7. The method for detecting the high-speed and high-mobility target through the polynomial rotation-polynomial Fourier transform as claimed in claim 1, wherein the target motion parameters in the step 6 comprise target speed and acceleration information.
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