CN111965614B - Maneuvering weak target detection method based on dynamic programming and minimum image entropy - Google Patents
Maneuvering weak target detection method based on dynamic programming and minimum image entropy Download PDFInfo
- Publication number
- CN111965614B CN111965614B CN202010730392.2A CN202010730392A CN111965614B CN 111965614 B CN111965614 B CN 111965614B CN 202010730392 A CN202010730392 A CN 202010730392A CN 111965614 B CN111965614 B CN 111965614B
- Authority
- CN
- China
- Prior art keywords
- target
- value
- state
- image entropy
- iteration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 claims abstract description 49
- 238000001228 spectrum Methods 0.000 claims abstract description 35
- 230000007704 transition Effects 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000021615 conjugation Effects 0.000 claims description 3
- 238000009825 accumulation Methods 0.000 abstract description 20
- 230000005012 migration Effects 0.000 abstract description 14
- 238000013508 migration Methods 0.000 abstract description 14
- 230000001427 coherent effect Effects 0.000 abstract description 12
- 230000001133 acceleration Effects 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 5
- 230000036461 convulsion Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 230000001186 cumulative effect Effects 0.000 description 2
- 108010076504 Protein Sorting Signals Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000739 chaotic effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001627 detrimental effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
Images
Classifications
-
- 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
-
- 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
- G01S7/418—Theoretical aspects
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a maneuvering weak target detection method based on dynamic programming and minimum image entropy, which comprises the steps of firstly, rapidly searching the most probable movement track of a weak target, namely the movement track with the maximum echo energy by using a dynamic programming method; then extracting a target signal, performing FFT to obtain a frequency spectrum of the target signal, taking the image entropy as a cost function, utilizing a Newton method to iterate and quickly search high-order components of the target so as to minimize the image entropy of the frequency spectrum, finally utilizing CA-CFAR to determine a threshold, comparing the maximum value of the compensated frequency spectrum with a detection threshold to judge whether the target exists or not, and providing an effective means for maneuvering weak target detection in radar; therefore, the method uses the DP technology to search the possible track of the target, has higher running speed, can correct the range migration caused by the maneuvering of the target, realizes the coherent accumulation of signals, has high running speed, can compensate the motion component of any order, and has higher detection efficiency and accuracy in the aspect of maneuvering weak target detection in the radar.
Description
Technical Field
The invention belongs to the technical field of radar detection, and particularly relates to a maneuvering weak target detection method based on Dynamic Programming (DP) and Minimum Image Entropy (MIE).
Background
Long-term coherent accumulation methods are often used to detect weak targets. However, in the accumulation process, the target may be mobile, which causes range migration and doppler frequency migration, so that the energy of a weak target is difficult to be effectively accumulated and detected.
In the conventional coherent accumulation method, the Keystone transform is a common method for resolving the range migration of a target. The basic principle of Keystone transformation is to decouple the Doppler frequency of a target echo signal from time delay through slow time scale transformation, so as to eliminate the influence of range migration. However, the Keystone transform is only suitable for eliminating range migration in case of uniform motion. When the target is maneuvering, Keystone transformation can only eliminate partial range migration amount caused by initial velocity, and cannot eliminate range migration caused by high-order motion component of the target. In addition, the Keystone transform has the defects of large calculation amount, fuzzy target speed needing to be estimated in advance and the like.
For Doppler frequency migration caused by maneuvering, a coherent accumulation method for performing high-order phase compensation by using Fractional Fourier transform (FrFT) and generalized Radon-Fourier transform (GRFT) is provided. FrFT is an extension of the conventional fourier transform to compensate for higher order phases by converting the signal to the fractional frequency domain to remove the effects of the second order phase. However, FrFT can only compensate for phase changes caused by acceleration, and cannot do so for jerks and higher order motion components. GRFT is similar to an exhaustive method, which realizes coherent accumulation by jointly searching motion component parameters such as velocity, acceleration, jerk, etc., and it can compensate phase change caused by any high-order component, but it involves joint search of multiple parameters, and often cannot be accepted in terms of computation.
Therefore, for radar maneuvering weak target detection, a coherent accumulation detection algorithm capable of efficiently compensating for high-order motion of a target is urgently needed.
Disclosure of Invention
In order to solve the problems, the invention provides a maneuvering weak target detection method based on dynamic programming and minimum image entropy.
A maneuvering weak target detection method based on dynamic programming and minimum image entropy comprises the following steps:
s1: receiving K frames of radar echo signals, and acquiring a track of a weak target with the largest echo energy from the K frames of radar echo signals by adopting a dynamic programming method, wherein each frame of radar echo signals corresponds to one frame of one-dimensional range profile, the one-dimensional range profile comprises a plurality of range units, and the track is formed by the range units where the weak target is located in each frame of one-dimensional range profile;
s2: extracting each of weak targetsEcho data corresponding to each distance unit, and then constructing a track echo signal s corresponding to a weak target according to the extracted echo signals DP (k)：
Wherein A is r For a set target amplitude, λ is the radar wavelength, T is the pulse repetition period of the radar, a 1 ～a H H is a undetermined coefficient, H is 1,2, …, H is a set order, j is an imaginary part, K is 1,2, …, K;
s3: for the track echo signal s DP (k) Performing phase compensation and fourier transform to obtain a compensated spectrum s (m):
wherein m is a frequency point,
is a phase compensation amount, and
s4: obtaining the image entropy E of the spectrum s (m):
wherein C is a set constant;
s5: the following objective function is constructed according to the image entropy E:
wherein a ═ a 2 ,...,a h ,...,a H ]；
S6: let a 1 After being equal to 0, then adoptResolving the target function by using a Newton method to obtain a undetermined coefficient a 1 ～a H To obtain the final frequency spectrum S end (m)；
S7: obtaining a spectrum S end (m) peak value S end (m 0 ) Wherein m is 0 Is the peak value S end (m 0 ) The frequency point of the position;
s8: determine | S end (m 0 )| 2 Whether the threshold value is less than the set CA-CFAR threshold value V T If the target is smaller than the target, the weak target is a false alarm target, and if the target is not smaller than the target, the weak target is a real target.
Further, the method for acquiring the track of the weak target with the maximum echo energy from the K-frame radar echo signals by using the dynamic programming method comprises the following steps:
s11: initial value of the set-point function
Wherein,
the state of the nth range cell in the one-dimensional range profile corresponding to the 1 st frame of radar echo signal is shown, and the state comprises a target and no target,
the square of the amplitude of the radar echo signal of the nth range unit in the one-dimensional range profile corresponding to the 1 st frame of radar echo signal is obtained;
s12: according to the initial value of the value function corresponding to each distance unit in the 1 st stage, iterative calculation is carried out by adopting the following formula to obtain the value function corresponding to each distance unit from the 2 nd stage to the K th stage
Wherein each stage isAcquiring a frame of radar echo signals:
wherein,
and the state of the nth range unit in the one-dimensional range profile corresponding to the kth frame of radar echo signal is defined, the state comprises a target and no target, K is 1,2, …, K,
is the square of the amplitude of the radar echo signal for the nth range bin,
is the most probable transition to the state in all the states of the k-1 stage
A state of (A) and
for all possible transitions to state in the k-1 stage
Max is a function of the maximum value, arg denotes that
Taking the corresponding state at the time of maximum value as
S13: recording the state corresponding to the maximum value of the value function obtained in the K stage as
Wherein n is 0 Is in a state
The serial number of the located distance unit;
s14: based on the state of the K stage
Obtaining the most probable transition to State in stage K-1
State of (1)
Then, the most possible transition state to the K-2 stage is obtained
State of (1)
And repeating the steps until the 1 st stage is reached, and obtaining the track of the weak target with the maximum echo energy.
Further, the setting method of the setting constant C is as follows:
further, the CA-CFAR threshold V T The setting method comprises the following steps:
wherein, P fa In order to set the false alarm rate, R is 1,2, …, R is the number of reference frequency points, S end (m r ) Is a reference frequency point m r Of spectral values of, whereinThe method for acquiring the reference frequency point comprises the following steps:
at frequency point m 0 Taking the frequency points with the set number on the left and right sides as protection frequency points;
the protection frequency points at the outermost peripheries of the left side and the right side are respectively taken as starting points and extend to the left side and the right side, and the frequency points with set quantity on the left side and the right side are taken as reference frequency points.
Further, the target function is solved by adopting a Newton method to obtain an undetermined coefficient a 1 ～a H The value of (b) specifically comprises the following steps:
s61: let a be [ a ] 2 ,...,a h ,...,a H ]Is 0, the initial value of the image entropy E is obtained, then the coefficient updating operation is executed on the initial value of the image entropy E, and the updated undetermined coefficient a is obtained 2 ～a H (ii) a Wherein the coefficient updating operation specifically comprises:
s61 a: respectively with a 2 ～a H Obtaining the entropy E of the image to be determined as a variable 2 ～a H First partial derivative of
Wherein, Imag represents an imaginary part, and represents conjugation;
s61 b: treating a fixed coefficient a based on image entropy E 2 ～a H First partial derivative of
Then obtaining the image entropy E to-be-determined coefficient a 2 ～a H Second partial derivative of
Wherein,
and l represents the amount of phase compensation in the second partial derivative
The serial number of (2);
s61 c: calculating the cross term in the second partial derivative according to whether k and l are equal
Wherein when k is l:
wherein, Re represents a real part, and IDFT represents inverse discrete Fourier transform;
when k ≠ l:
s61 d: the first partial derivative
And second partial derivative
Are respectively marked as
And
then according to
And
updating the undetermined coefficient a 2 ～a H ：
Wherein,
is a coefficient to be determined h The iteration value obtained in the (i + 1) th iteration,
is a coefficient to be determined h The iteration value obtained in the ith iteration, I is the iteration number, and I is 0,1,2, …, I and I are the set upper limit value of the iteration number,
is a ═ a 2 ,...,a h ,...,a H ]0;
s62: judging the updated undetermined coefficient a 2 ～a H Whether the iteration termination condition is reached or not, if any iteration termination condition is met, the undetermined coefficient a obtained by the iteration is obtained 2 ～a H If the values are not satisfied, the step S63 is entered, where the iteration termination condition is:
each undetermined coefficient a 2 ～a H Corresponding first derivative
The absolute values of the difference values with 0 are all smaller than a set threshold value;
the iteration times reach an iteration time upper limit value I;
s63: adopting the updated undetermined coefficient a 2 ～a H The entropy E of the image is updated, thenRe-executing the coefficient updating operation on the updated image entropy E to obtain a secondary updated undetermined coefficient a 2 ～a H (ii) a And so on until the updated undetermined coefficient a 2 ～a H An iteration end condition is reached.
Has the beneficial effects that:
the invention provides a maneuvering weak target detection method based on dynamic programming and minimum image entropy, which comprises the steps of firstly, quickly searching a weak target most likely, namely a movement track with maximum echo energy by using a dynamic programming method; then extracting a target signal, performing FFT to obtain a frequency spectrum of the target signal, taking the image entropy as a cost function, utilizing a Newton method to iterate and quickly search high-order components of the target so as to minimize the image entropy of the frequency spectrum, finally utilizing CA-CFAR to determine a threshold, comparing the maximum value of the compensated frequency spectrum with a detection threshold to judge whether the target exists or not, and providing an effective means for maneuvering weak target detection in radar; compared with the existing distance migration correction methods such as Keystone conversion and the like, the method has the advantages that the possible track of the target is searched by using the DP technology, the running speed is higher, and the distance migration caused by target maneuvering can be corrected; compared with the existing phase compensation methods such as FrFT and GRFT, the method disclosed by the invention has the advantages that the target high-order motion component is quickly searched in an iterative manner by minimizing the image entropy of the signal frequency spectrum, the coherent accumulation of the signal is realized, the running speed is high, and the motion component of any order can be compensated; in conclusion, the invention has higher detection efficiency and accuracy in the aspect of maneuvering weak target detection in the radar.
Drawings
FIG. 1 is a flow chart of a method for detecting a maneuvering weak target based on dynamic programming and minimum image entropy according to the present invention;
FIG. 2 is a schematic distance-time plane of a radar echo provided by the present invention;
FIG. 3 is a diagram of an initial spectrum provided by the present invention;
FIG. 4 is a diagram illustrating iteration of high-order motion components provided by the present invention using Newton's method;
FIG. 5 is a diagram of a DP-MIE processed spectrum according to the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
As shown in FIG. 1, the invention provides a coherent accumulation detection algorithm combining dynamic programming and minimum image entropy, which first searches a possible target track and corrects range migration by using a dynamic programming algorithm; then extracting a track echo and performing FFT (fast Fourier transform) to obtain a frequency spectrum of the track echo, and carrying out fast iterative search on high-order motion components such as acceleration and jerk by a Newton method to minimize the image entropy of the frequency spectrum by taking the image entropy as a cost function; and finally, compensating a high-order phase of the target signal by using the searched high-order motion parameters to finish coherent accumulation, and setting a threshold by using a Cell-average constant false alarm (CA-CFAR) detection method to finish the detection of the weak target. The overall process comprises the following steps:
s1: receiving K frames of radar echo signals, and acquiring a track of a weak target with the largest echo energy from the K frames of radar echo signals by adopting a dynamic programming method, wherein each frame of radar echo signal corresponds to one frame of one-dimensional range profile, the one-dimensional range profile comprises a plurality of range units, and the track is formed by the range units of the weak target in each frame of one-dimensional range profile.
It should be noted that the dynamic programming method searches for a track by energy recursion of the echo signal on the distance-time plane, and can quickly screen out a track with higher energy, which is used as a possible track of a target. The specific implementation steps of the DP technology for quickly searching the possible target track are as follows:
s11: initial value of the set-point function
Wherein,
the state of the nth range unit in the one-dimensional range profile corresponding to the 1 st frame of radar echo signal is obtained, the state comprises a target and no target,
the square of the amplitude of the radar echo signal of the nth range unit in the one-dimensional range profile corresponding to the 1 st frame of radar echo signal is obtained;
s12: according to the initial value of the value function corresponding to each distance unit in the 1 st stage, iterative calculation is carried out by adopting the following formula to obtain the value function corresponding to each distance unit from the 2 nd stage to the K th stage
To achieve accumulation of a value function, wherein each stage acquires a frame of radar echo signals:
wherein,
also known as a cost function or cumulative observation, which records the state of the kth stage along a certain trajectory (this trajectory is
) A non-coherent accumulation of the observed values of (a);
the state of the nth range unit in the one-dimensional range profile corresponding to the kth frame radar echo signal is shown, the state comprises a target and no target, and K is 1,2, …, K;
the square of the amplitude of the radar echo signal for the nth range bin also represents the pair state
The observed value of (a);
is the most probable transition to the state in all the states of the k-1 stage
And is set to 0 at initialization, i.e.
For representing the starting point of the track;
for all possible transitions to state in the k-1 stage
Max is a function of the maximum value, arg denotes that
Taking the corresponding state at the maximum value as
S13: recording the state corresponding to the maximum value of the value function obtained in the K stage as
Wherein n is 0 Is in a state
The number of the located distance unit.
It should be noted that this step is to screen the value function, and in the K stageFinding the maximum value function and obtaining the corresponding state of the value function
As shown in the following formula:
s14: based on the state of the K stage
Obtaining the most probable transition to State in stage K-1
State of (1)
Then, the most possible transition to the state in the K-2 stage is obtained
State of (1)
And repeating the steps until the 1 st stage is reached, and obtaining the track of the weak target with the maximum echo energy, namely the maximum sum of the value functions.
Further, the process of backtracking is formulated as follows:
wherein track (k) indicates that the final state is
State of the trace in the k-th stage.
S2: extracting echo data corresponding to each distance unit where the weak target is located, and then constructing the weak target according to the extracted echo signalsCorresponding track echo signal s DP (k)：
Wherein, A r For a set target amplitude, λ is the radar wavelength, T is the pulse repetition period of the radar, a 1 ～a H To be a coefficient of undetermination, a h And a motion component representing the H-th order of the target, wherein the first order is velocity, the second order is acceleration, the third order is jerk, and the like, H is 1,2, …, H is a set order, j is an imaginary part, and K is 1,2, …, K.
S3: for the track echo signal s DP (k) Performing phase compensation and fourier transform to obtain compensated spectrum s (m):
wherein, m is a frequency point,
is a phase compensation quantity, and
that is, after the dynamic programming process, range migration of weak targets has been corrected and the target signal s extracted DP (k) If phase compensation is needed, the compensated spectrum s (m) can be obtained by FFT.
S4: obtaining the image entropy E of the spectrum s (m):
wherein C is a set constant equal to the total energy of the signal sequence, and
it should be noted that the image entropy may represent the confusion and complexity of the image. The larger the entropy, the more chaotic (defocused), random, complex the image; conversely, the smaller the image entropy, the more ordered (focused), regular, and simple the image. When the entropy value reaches the theoretical minimum value of 0, the image energy is completely focused at one point. In radar data processing, if target signal Doppler migration compensation is not performed, when a target has a high-order motion component, MTD processing is directly performed on a target echo signal, the energy of the target is divergent on a frequency spectrum (the entropy value is large), and the target cannot be ideally focused on a certain frequency. This results in a reduction in pulse accumulation gain, which is detrimental to subsequent target detection. If MTD processing is performed after the compensation of the higher-order motion component is completed, a good focused spectrum (with a small entropy value) can be obtained. Therefore, the image entropy is used as a cost function, and then high-order motion components such as acceleration and jerk are quickly and iteratively searched through a Newton method so as to minimize the image entropy of the frequency spectrum; then, compensating the high-order phase of the target signal by using the searched high-order motion parameters to complete phase-coherent accumulation, and setting a threshold by using a CA-CFAR detection method to complete the detection of the weak target, specifically referring to steps S5-S8:
s5: the following objective function is constructed according to the image entropy E:
wherein a ═ a 2 ,...,a h ,...,a H ]。
It should be noted that when
Satisfy the requirements of
The image entropy E of the spectrum is minimal, so that the higher order motion component a is a cost function of the image entropy E 2 ,...,a h ,...,a H Optimizing the entropy E of the frequency spectrum image for the variable needing to be optimizedAnd obtaining the target function.
S6: let a 1 When the value is equal to 0, then the objective function is solved by adopting Newton method to obtain undetermined coefficient a 1 ～a H To obtain the final frequency spectrum S end (m), comprising the steps of:
s61: let a be [ a ] 2 ,...,a h ,...,a H ]Is 0, the initial value of the image entropy E is obtained, then the coefficient updating operation is executed on the initial value of the image entropy E, and the updated undetermined coefficient a is obtained 2 ～a H (ii) a Wherein the coefficient updating operation specifically comprises:
s61 a: respectively with a 2 ～a H Obtaining the entropy E of the image to be determined as a variable 2 ～a H First partial derivative of
Wherein, Imag represents an imaginary part, and one represents conjugation;
s61 b: treating a fixed coefficient a based on image entropy E 2 ～a H First partial derivative of
Then obtaining the image entropy E to-be-determined coefficient a 2 ～a H Second partial derivative of
Wherein,
and l represents the amount of phase compensation in the second partial derivative
The serial number of (2);
s61 c: calculating the cross term in the second partial derivative according to whether k and l are equal
Wherein, when k ═ l:
wherein Re represents a real part, and IDFT represents inverse discrete Fourier transform;
when k ≠ l:
s61 d: first partial derivative of
And second partial derivative
Are respectively marked as
And with
Then according to
And
updating the undetermined coefficient a 2 ～a H ：
Wherein,
is a coefficient to be determined h The iteration value obtained in the (i + 1) th iteration,
is a coefficient to be determined h The iteration value obtained in the ith iteration, I is the iteration number, and I is 0,1,2, …, I is the set upper limit value of the iteration number,
is a ═ a 2 ,...,a h ,...,a H ]0;
s62: judging the updated undetermined coefficient a 2 ～a H Whether the iteration termination condition is met or not, if any iteration termination condition is met, the undetermined coefficient a obtained by the iteration is obtained 2 ～a H If the values are not satisfied, the step S63 is entered, where the iteration termination condition is:
each coefficient to be determined a 2 ～a H Corresponding first derivative
The absolute values of the difference values with 0 are all smaller than a set threshold value;
the iteration times reach an iteration time upper limit value I;
s63: adopting the updated undetermined coefficient a 2 ～a H Updating the image entropy E, and then re-executing coefficient updating operation on the updated image entropy E to obtain a secondary updated undetermined coefficient a 2 ～a H (ii) a And so on until the updated undetermined coefficient a 2 ～a H An iteration end condition is reached.
S7: obtaining a spectrum S end (m) peak value S end (m 0 ) Wherein m is 0 Is the peak value S end (m 0 ) The frequency point of the position;
s8: judging | S end (m 0 )| 2 Whether is less than the set CA-CFAR threshold V T If the weak target is smaller than the target, the weak target is a false alarm target, and if the weak target is not smaller than the target, the weak target is a real target.
The CA-CFAR threshold V T The setting method comprises the following steps:
wherein, P fa In order to set the false alarm rate, R is 1,2, …, R is the number of reference frequency points, S end (m r ) Is a reference frequency point m r The reference frequency point is obtained by the following steps:
at frequency point m 0 Taking the frequency points with the set number on the left and right sides as protection frequency points;
the protection frequency points at the outermost peripheries of the left side and the right side are respectively taken as starting points and extend to the left side and the right side, and the frequency points with set quantity on the left side and the right side are taken as reference frequency points.
To verify the validity of the accumulation detection method described previously. The method is based on simulation experiment data, and the maneuvering weak target detection algorithm combining dynamic programming and minimum image entropy is adopted to complete the detection of the maneuvering weak target. The parameters of the simulation are shown in table 1:
TABLE 1 simulation parameters
| Parameter(s) | Value of |
| Radar wavelength | 0.02m |
| Center frequency of radar | 16GHz |
| Bandwidth of radar | 800MHz |
| Pulse repetition frequency | 500Hz |
| Width of distance unit | 0.12m |
| Target motion component [ a 1 ,a 2 ,a 3 ] | [0.8,0.3,0.1] |
| Target signal-to-noise ratio | 3dB |
The range-time plane of the radar echo is shown in fig. 2, which has 500 pulses of radar echo data, the target begins to appear at 540m, and significant maneuvers occur during the movement.
Step one, DP finds the target track:
and performing modular calculation on the multi-frame one-dimensional range profile data, and performing recursive accumulation by taking signal energy as a value function to obtain a target track. And setting the state transition number of the dynamic programming to be 3, accumulating 500 frames in a recursion manner, and selecting the state of the highest value function as a possible track of the target.
Step two, extracting the track echo, and iteratively optimizing the frequency spectrum image entropy by using a Newton method:
extracting the echo signal of the target, and setting the motion compensation amount a of each step 1 ,a 2 ,a 3 The initial values are all 0, the spectrum is obtained by performing FFT after the compensation by the initial values, and the initial spectrum distribution is shown in FIG. 3. It can be seen that the spectrum of the target is spread and the signal to noise ratio is very low, about 9dB, and difficult to detect. Using Newton's method, for higher order components a 2 ,a 3 The iteration is carried out for 1000 times, as shown in figure 4, finally a 2 ,a 3 Gradually converge to the true value [0.3,0.1 ]]. And finally, detecting after compensating the frequency spectrum by using the motion components obtained by iteration, as shown in fig. 5, greatly enhancing the signal-to-noise ratio of the target frequency spectrum, reaching 27.2dB, and consuming 9 s.
And we utilize a theoretically optimal coherent accumulation algorithm: GRFT, performing third-order motion search compensation, performing accumulation detection under the same hardware condition, wherein the weak target signal-to-noise ratio after accumulation is 28.3dB, but the time is 4300 s. The results of the specific comparisons of the two methods are shown in table 2:
TABLE 2 DP-MIE vs GRFT examples
| Evaluation index | DP-MIE | GRFT |
| Cumulative signal-to-noise ratio | 27.2dB | 28.3dB |
| Operation time consumption | 9s | 4300s |
It can be seen that the method of the present invention loses a very small accumulated gain, but greatly improves the operation efficiency, and verifies the real-time performance and the effectiveness.
The method can be applied to long-time coherent accumulation of radars and realizes efficient detection of maneuvering weak targets.
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it will be understood by those skilled in the art that various changes and modifications may be made herein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (4)
1. A maneuvering weak target detection method based on dynamic programming and minimum image entropy is characterized by comprising the following steps:
s1: receiving K frames of radar echo signals, and acquiring a track of a weak target with the maximum echo energy from the K frames of radar echo signals by adopting a dynamic programming method, wherein each frame of radar echo signal corresponds to one frame of one-dimensional range profile, the one-dimensional range profile comprises a plurality of range units, and the track is formed by the range units of the weak target in each frame of one-dimensional range profile; the method for acquiring the track of the weak target with the maximum echo energy from the K frames of radar echo signals by adopting the dynamic programming method comprises the following steps:
s11: initial value of the set-point function
Wherein,
the state of the nth range cell in the one-dimensional range profile corresponding to the 1 st frame of radar echo signal is shown, and the state comprises a target and no target,
the square of the amplitude of the radar echo signal of the nth range unit in the one-dimensional range profile corresponding to the 1 st frame of radar echo signal is obtained;
s12: according to the initial value of the value function corresponding to each distance unit in the 1 st stage, iterative calculation is carried out by adopting the following formula to obtain the value function corresponding to each distance unit from the 2 nd stage to the K th stage
Wherein, every stage all acquires a frame radar echo signal:
wherein,
the state of the nth range cell in the one-dimensional range profile corresponding to the kth frame radar echo signal is shown, and the state includes a target and no target, K is 1,2, …, K,
is the square of the amplitude of the radar echo signal of the nth range unit,
is the most probable transition to the state in all the states of the k-1 stage
A state of (1), and
all possibilities in the k-1 stageTransition to a State
Max is a function taking the maximum value, arg denotes the number of states that will be processed
Taking the corresponding state at the time of maximum value as
Representation collection
The value function corresponding to all the states;
s13: recording the state corresponding to the maximum value of the value function obtained in the K stage as
Wherein n is 0 Is in a state
The serial number of the located distance unit;
s14: based on the state of the K stage
Obtaining the most probable transition to State in stage K-1
State of (1)
Then, the most possible transition to the state in the K-2 stage is obtained
State of (1)
In this way, until the stage 1 is traced back, the track of the weak target with the maximum echo energy is obtained;
s2: extracting echo data corresponding to each distance unit where the weak target is located, and then constructing a track echo signal s corresponding to the weak target according to the extracted echo signals DP (k)：
Wherein A is r For a set target amplitude, λ is the radar wavelength, T is the pulse repetition period of the radar, a 1 ～a H H is 1,2, …, H is a set order, j is an imaginary part, K is 1,2, …, K;
s3: for the track echo signal s DP (k) Performing phase compensation and fourier transform to obtain a compensated spectrum s (m):
wherein m is a frequency point,
is a phase compensation amount, and
s4: obtaining the image entropy E of the spectrum s (m):
wherein C is a set constant;
s5: the following objective function is constructed according to the image entropy E:
wherein a ═ a 2 ,...,a h ,...,a H ]；
S6: let a be 1 When the value is equal to 0, then the objective function is solved by adopting Newton method to obtain undetermined coefficient a 1 ～a H To obtain the final spectrum S end (m)；
S7: obtaining a spectrum S end (m) peak value S end (m 0 ) Wherein m is 0 Is a peak value S end (m 0 ) The frequency point of the position;
s8: determine | S end (m 0 )| 2 Whether the threshold value is less than the set CA-CFAR threshold value V T If the weak target is smaller than the target, the weak target is a false alarm target, and if the weak target is not smaller than the target, the weak target is a real target.
2. The method for detecting the maneuvering weak target based on the dynamic programming and the minimum image entropy as claimed in claim 1, characterized in that the setting method of the setting constant C is as follows:
3. the method for detecting maneuvering weak targets based on dynamic programming and minimum image entropy as claimed in claim 1, characterized in that the CA-CFAR threshold V is T The setting method comprises the following steps:
wherein, P fa In order to set the false alarm rate, R is 1,2, …, R is the number of reference frequency points, S end (m r ) Is a reference frequency point m r The reference frequency point obtaining method comprises the following steps:
at frequency point m 0 Taking a set number of frequency points on the left side and the right side of the center as protection frequency points;
the protection frequency points at the outermost peripheries of the left side and the right side are respectively taken as starting points and extend to the left side and the right side, and the frequency points with the set number on the left side and the right side are taken as reference frequency points.
4. The maneuvering weak target detection method based on dynamic programming and minimum image entropy as claimed in claim 1, characterized in that the objective function is solved by Newton method to obtain undetermined coefficient a 1 ～a H The value of (b) specifically comprises the following steps:
s61: let a be [ a ] 2 ,...,a h ,...,a H ]Is 0, the initial value of the image entropy E is obtained, then the coefficient updating operation is executed on the initial value of the image entropy E, and the updated undetermined coefficient a is obtained 2 ～a H (ii) a Wherein the coefficient updating operation specifically comprises:
s61 a: respectively with a 2 ～a H For variable, obtaining image entropy E to-be-determined coefficient a 2 ～a H First partial derivative of
Wherein, Imag represents an imaginary part, and one represents conjugation;
s61 b: treating a fixed coefficient a based on image entropy E 2 ～a H First partial derivative of
Obtaining the entropy E of the image to be determinedCoefficient a 2 ～a H Second partial derivative of
Wherein,
and l represents the amount of phase compensation in the second partial derivative
The serial number of (2);
s61 c: calculating the cross term in the second partial derivative according to whether k and l are equal
Wherein when k is l:
wherein Re represents a real part, and IDFT represents inverse discrete Fourier transform;
when k ≠ l:
s61 d: the first partial derivative
And second partial derivative
Are respectively marked as
Then according to
And
updating the undetermined coefficient a 2 ～a H ：
Wherein,
is a coefficient to be determined h The iteration value obtained in the (i + 1) th iteration,
is a coefficient to be determined h The iteration value obtained in the ith iteration, I is the iteration number, and I is 0,1,2, …, I is the set upper limit value of the iteration number,
is a ═ a 2 ,...,a h ,...,a H ]0;
s62: judging the updated undetermined coefficient a 2 ～a H Whether the iteration termination condition is met or not, if any iteration termination condition is met, the undetermined coefficient a obtained by the iteration is obtained 2 ～a H If the values are not satisfied, the step S63 is entered, where the iteration termination condition is:
each coefficient to be determined a 2 ～a H Corresponding first derivative
The absolute values of the difference values with 0 are all smaller than a set threshold value;
the iteration times reach an iteration time upper limit value I;
s63: adopting the updated undetermined coefficient a 2 ～a H Updating the image entropy E, and then re-executing the coefficient updating operation on the updated image entropy E to obtain a secondary updated undetermined coefficient a 2 ～a H (ii) a And so on until the updated undetermined coefficient a 2 ～a H An iteration end condition is reached.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010730392.2A CN111965614B (en) | 2020-07-27 | 2020-07-27 | Maneuvering weak target detection method based on dynamic programming and minimum image entropy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010730392.2A CN111965614B (en) | 2020-07-27 | 2020-07-27 | Maneuvering weak target detection method based on dynamic programming and minimum image entropy |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111965614A CN111965614A (en) | 2020-11-20 |
| CN111965614B true CN111965614B (en) | 2022-08-19 |
Family
ID=73362953
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010730392.2A Active CN111965614B (en) | 2020-07-27 | 2020-07-27 | Maneuvering weak target detection method based on dynamic programming and minimum image entropy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111965614B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102608590A (en) * | 2012-03-21 | 2012-07-25 | 电子科技大学 | Coherent integration method based on dynamic programming and back image projection algorithm |
| CN103344949A (en) * | 2013-06-18 | 2013-10-09 | 中国人民解放军海军航空工程学院 | Radar slightly-moving target detection method based on Radon-linear canonical ambiguity function |
| CN104076355A (en) * | 2014-07-04 | 2014-10-01 | 西安电子科技大学 | Method for conducting before-detection tracking on weak and small target in strong-clutter environment based on dynamic planning |
| CN104898116A (en) * | 2015-06-25 | 2015-09-09 | 苏州途视电子科技有限公司 | Phase-coherent accumulation tracking-before-detection method based on minimum entropy |
| CN105809718A (en) * | 2016-03-14 | 2016-07-27 | 西南交通大学 | Object tracking method with minimum trajectory entropy |
| CN111352104A (en) * | 2020-03-18 | 2020-06-30 | 清华大学 | Weak target tracking-before-detection method based on information accumulation |
-
2020
- 2020-07-27 CN CN202010730392.2A patent/CN111965614B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102608590A (en) * | 2012-03-21 | 2012-07-25 | 电子科技大学 | Coherent integration method based on dynamic programming and back image projection algorithm |
| CN103344949A (en) * | 2013-06-18 | 2013-10-09 | 中国人民解放军海军航空工程学院 | Radar slightly-moving target detection method based on Radon-linear canonical ambiguity function |
| CN104076355A (en) * | 2014-07-04 | 2014-10-01 | 西安电子科技大学 | Method for conducting before-detection tracking on weak and small target in strong-clutter environment based on dynamic planning |
| CN104898116A (en) * | 2015-06-25 | 2015-09-09 | 苏州途视电子科技有限公司 | Phase-coherent accumulation tracking-before-detection method based on minimum entropy |
| CN105809718A (en) * | 2016-03-14 | 2016-07-27 | 西南交通大学 | Object tracking method with minimum trajectory entropy |
| CN111352104A (en) * | 2020-03-18 | 2020-06-30 | 清华大学 | Weak target tracking-before-detection method based on information accumulation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111965614A (en) | 2020-11-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111352102A (en) | Multi-target number detection method and device based on frequency modulation continuous wave radar | |
| CN110095766B (en) | Maneuvering target coherent accumulation detection method based on non-uniform resampling technology | |
| CN108089171B (en) | A kind of radar rapid detection method for unmanned plane target | |
| CN110161477B (en) | Maneuvering target detection method based on multi-variable resampling correlation function | |
| CN107356922B (en) | Multi-frequency radar target tracking method for ambiguity resolution by spread-table-aided method | |
| CN103197297B (en) | Radar moving target detection method based on cognitive framework | |
| CN111505599B (en) | Coherent accumulation detection method based on accurate distance evolution model | |
| CN107356908B (en) | Frequency agile signal coherent accumulation method | |
| CN111965613B (en) | Weak target detection method based on dynamic programming and fractional Fourier transform | |
| CN111007473B (en) | High-speed weak target detection method based on distance frequency domain autocorrelation function | |
| CN115577207A (en) | Radiation source target time difference and frequency difference estimation method | |
| CN116449320A (en) | Long-time accumulation and parameter estimation method under frequency agile radar system | |
| CN113267756B (en) | Space-based radar space moving target detection and parameter estimation method and system | |
| CN113885006A (en) | Method for detecting coherent accumulation of maneuvering turning target | |
| CN111965614B (en) | Maneuvering weak target detection method based on dynamic programming and minimum image entropy | |
| CN111796266B (en) | Tracking method before plane detection of uniformly accelerated moving target RD | |
| Zhou et al. | Coherent integration for targets with constant Cartesian velocities based on accurate range model | |
| CN111983579A (en) | Method for eliminating radar speed ambiguity by using pulse repetition time of difference | |
| CN116413715A (en) | Maneuvering target detection and parameter estimation method for broadband radar | |
| CN107015210B (en) | Radar slow time domain constant modulus signal design method based on similarity constraint | |
| CN116165619A (en) | High maneuvering target motion parameter estimation and coherent accumulation detection method | |
| CN107967395A (en) | A kind of nonlinear time_varying system Fast Identification Method based on the expansion of beta wavelet basis functions | |
| CN111796267B (en) | Maneuvering turning target tracking-before-detection method based on pseudo-spectrum matched filtering | |
| US6683820B1 (en) | Method and apparatus for tracking sonar targets | |
| CN113050131A (en) | Capturing method based on preprocessing FFT and barrier effect correction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |