CN114594425A - Clutter interference resistant short-time pulse train waveform design method - Google Patents

Clutter interference resistant short-time pulse train waveform design method Download PDF

Info

Publication number
CN114594425A
CN114594425A CN202210246873.5A CN202210246873A CN114594425A CN 114594425 A CN114594425 A CN 114594425A CN 202210246873 A CN202210246873 A CN 202210246873A CN 114594425 A CN114594425 A CN 114594425A
Authority
CN
China
Prior art keywords
waveform
short
pulse train
time pulse
expressed
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.)
Granted
Application number
CN202210246873.5A
Other languages
Chinese (zh)
Other versions
CN114594425B (en
Inventor
易青颖
张翠
黄钰林
张寅�
裴季方
霍伟博
杨建宇
张永超
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Zhentai Technology Co.,Ltd.
Original Assignee
University of Electronic Science and Technology of China
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN202210246873.5A priority Critical patent/CN114594425B/en
Publication of CN114594425A publication Critical patent/CN114594425A/en
Application granted granted Critical
Publication of CN114594425B publication Critical patent/CN114594425B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • G01S7/023—Interference mitigation, e.g. reducing or avoiding non-intentional interference with other HF-transmitters, base station transmitters for mobile communication or other radar systems, e.g. using electro-magnetic interference [EMI] reduction techniques
    • 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
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4052—Means for monitoring or calibrating by simulation of echoes
    • 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
    • G01S7/414—Discriminating targets with respect to background clutter
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention discloses a short-time pulse train waveform design method for clutter interference resistance, which utilizes target prior information to construct a maximum signal-to-noise ratio target function and solves an optimal transmitting waveform and an optimal matching filter based on a Lagrange multiplier method; constructing a combined target function of waveform power spectrum approximation and autocorrelation integral sidelobe level suppression by combining a short-time pulse string waveform emission rule; and iterating in a mode of pattern search to obtain the optimal short-time pulse train waveform. The short-time pulse train waveform designed by the invention has the capability of being intercepted by an anti-jamming machine, not only can inhibit the interference of environmental clutter on target detection, but also can avoid the interference forwarded after being intercepted by an enemy jamming machine, thereby realizing the simultaneous inhibition of the environmental clutter and the jamming machine interference and improving the target detection capability in a clutter environment.

Description

Clutter interference resistant short-time pulse train waveform design method
Technical Field
The invention belongs to the technical field of radar detection, and particularly relates to target detection suitable for a radar in a clutter environment.
Background
The radar emission waveform controls the performance of the system such as range resolution, Doppler resolution, peak-to-side lobe ratio and energy distribution, and the like, which determines that the emission waveform plays a critical role in the radar system. Therefore, radar transmit waveform design is an effective way to improve performance. The short-time pulse train signal has the characteristics of narrow pulse width and high peak power, has the capability of resisting interception, can effectively inhibit relevant interference forwarded by an enemy, and has great potential for high-probability target detection of the airborne radar. Therefore, under a complex electromagnetic environment, in order to reduce the influence of the environmental clutter on target detection, the target detection probability can be improved by designing a short-time pulse train waveform transmitted by the radar.
The signal related clutter in different detection environments always exists, and the signal related clutter has strong correlation with a transmitted waveform and is easy to influence the detection of a weak target. In order to improve the performance of Gaussian Point target Detection under Signal-related Clutter, an optimal waveform frequency domain water injection method expression under energy constraint is deduced in the literature (Kay, S.Optial Signal Design for Detection of Gaussian Point Targets in statistical Gaussian mixture Cluter/conversion [ J ]. IEEE Journal of Selected characteristics in Signal Processing,2007,1(1):31-41) according to NP criterion, and energy spectrum density is concentrated at a frequency band with small interference and noise energy, so that the interference of Clutter on target Detection is inhibited. In order to ensure that a waveform has good fuzzy function characteristics and spectrum coexistence capability at the same time, in the literature (Signal-to-Signal-plus-Noise Ratio (SCNR) target function), Augusto autory et al propose a combined design method for transmission and reception under the constraints of similarity, energy and spectrum, and a sequence iteration optimization algorithm to solve the above problem, wherein the obtained transmitted waveform has no interception capability and is not easily interfered by related signals forwarded by an jammer, although the transmitted waveform can inhibit corresponding Clutter and Noise interference.
Disclosure of Invention
Aiming at the problem that the conventional waveform design in the prior art cannot be realized and simultaneously inhibits the environmental clutter and the interference of an interference machine, the invention provides a short-time pulse train waveform design method for resisting clutter interference.
For the convenience of describing the contents of the present invention, the following terms are explained.
The term 1: autocorrelation Integrated side lobe level (ISL)
The autocorrelation ISL is the sum of squares of all autocorrelation sidelobe levels of the signal, and since the autocorrelation function of the signal is the same as the matched filtering result obtained by the pulse compression technique, the autocorrelation ISL is used to represent the integrated sidelobe level of the signal matched filtering output result.
The specific technical scheme of the invention is as follows: a method for designing short-time pulse train waveform for resisting clutter interference is characterized in that the short-time pulse train waveform is a signal model formed by a plurality of sub-pulses with the same duty ratio, each sub-pulse is formed by M chips, and T is set1For the duration of the pulse train, τ2Is the pulse repetition interval of the sub-pulses, τ3Is the sub-pulse width. Let T be1The burst emission waveform during the time period is s (t), and s (t) can be expressed as:
Figure BDA0003545111370000021
wherein N is the number of sub-pulses, xij(t) is the ith chip in the jth sub-pulse, with a chip width of τ1. Let M be τ3/τ1＝Bτ3Then the transmit waveform dispersion vector can be expressed as:
Figure BDA0003545111370000022
wherein ,
Figure BDA0003545111370000023
representing a vector of complex field dimension K x 1, 01×PAn all-zero matrix with a dimension of 1 × P is shown, the size of P is determined by the duty cycle of the burst sequence, and K ═ N (M + P) indicates the total number of chips of the burst sequence.
The short-time pulse train waveform adopts a more complex modulation mode, has a special waveform structure system with sub-pulses as pulse trains, and has two advantages different from other transmitted waveforms: 1) the pulse duration is short, and the pulse is difficult to intercept by radar; 2) the peak power is high, and the radar action distance is long.
The method specifically comprises the following steps:
the method comprises the following steps: the optimal waveform frequency spectrum is solved,
under the strong clutter interference environment, clutter and noise information in the environment are firstly acquired through an environment sensing technology before the radar works, and a transmitting waveform can be designed by utilizing prior information on the assumption that the clutter in the detection environment is a stable clutter.
Let echo signal y (t) be:
y(t)＝s(t)*g(t)+s(t)*c(t)+n(t) (3)
wherein g (t) is the target parameter, c (t) is the clutter impulse response, and n (t) is the noise.
By definition, t0The SCNR at time is:
Figure BDA0003545111370000024
wherein ,Pc(f) Is the power spectral density of the clutter, Pn(f) Is the power spectral density of the noise, s (f) is the transmit signal spectrum, g (f) is the target parameter spectrum, and h (f) is the receive filter spectrum.
In order to make the energy of the optimized waveform constant, it is necessary to limit the energy of the optimized waveform to be constant. According to the Pasteval theorem, the energy constraint can be carried out on the frequency spectrum of the optimized waveform, and the energy E of the optimized waveformsCan be expressed as
Figure BDA0003545111370000031
In conclusion, a problem model for maximizing the Signal-to-Clutter-plus-Noise Ratio (SCNR) criterion is constructed:
Figure BDA0003545111370000032
order to
Figure BDA0003545111370000033
The echo signal to noise ratio can be expressed as:
Figure BDA0003545111370000039
wherein ,L-1(f) Is the reciprocal of L (f);
contracting the above formula by using the Schwarz inequality to obtain
Figure BDA0003545111370000034
If and only if
Figure BDA0003545111370000035
If the equal sign is true, the receiving filter expression is:
Figure BDA0003545111370000036
wherein ,α1Is a constant. Constant α here1Is a constant coefficient derived from a formula, and since the solution is not affected, the constant coefficient is represented by a symbol.
At this time, the SCNR can be expressed as:
Figure BDA0003545111370000037
the problem model of equation (6) can be converted to:
Figure BDA0003545111370000038
and constructing a function by using a Lagrange multiplier method, and converting the constrained optimization problem into an unconstrained optimization problem, wherein the constructed function is as follows:
Figure BDA0003545111370000041
wherein ,α2Are constants in the lagrange multiplier method. (iv) ventilation holes of J [ | S (f)2]To routing electricity through | S (f)2Is a general function of (a). According to the principle of the variational method, when the extreme value of the generic function is taken, the optimization parameter is in the optimal state. Therefore, the frequency domain amplitude satisfying the extreme value of the above formula is the square of the frequency domain amplitude of the optimal transmission waveform. The above formula pair | S (f) routing light2And (4) obtaining a derivative, wherein the derivative is 0, and solving the optimal frequency spectrum S of the transmitting waveform based on the maximum signal-to-noise-ratio criteriono(f)：
Figure BDA0003545111370000042
wherein ,α2The value of (c) is determined by the energy of the transmitted signal if:
Figure BDA0003545111370000043
the maximum signal-to-noise-plus-noise ratio is:
Figure BDA0003545111370000044
step two: short-time pulse train waveform design
According to the time domain form of the short-time pulse string which cannot be obtained by the optimal waveform spectrum obtained by solving based on the maximized SCNR rule, the square error of the optimal waveform spectrum and the short-time pulse string waveform spectrum is minimized by combining a short-time pulse string signal model, and the short-time pulse string signal waveform similar to the optimal emission waveform spectrum can be obtained.
The short-time pulse train signal model is shown as the formula (1), and the frequency spectrum thereof is St(f) And (4) showing. The optimal emission waveform frequency spectrum obtained based on the maximum signal-to-noise-and-noise ratio criterion is So(f) Then the objective function can be expressed as the Squared Error of the two (SE)
SE＝|St(f)-So(f)|2 (16)
However, the autocorrelation sidelobes of the burst waveform obtained by considering only the spectral similarity also affect the detection probability of the target. Therefore, a cost function is constructed, and meanwhile, the frequency spectrum and the integral sidelobe level of the short-time pulse string are optimized, and the cost function can be expressed as follows:
C＝λSE+(1-λ)ISL (17)
where λ represents the weight of the squared error function, ISL is the integral sidelobe of the short-time burst, and by definition, ISL can be written as:
Figure BDA0003545111370000051
where R is the autocorrelation function of the burst s, and R (k), the kth side lobe of the autocorrelation function, is represented as:
Figure BDA0003545111370000052
wherein ,snThe nth element of the sequence representing the burst,
Figure BDA0003545111370000053
is the conjugate of the (n-k) th element of the burst sequence.
The discrete phase parameter corresponding to the burst sequence of equation (2) can be expressed as:
Figure BDA0003545111370000054
by changing the phase parameter of the transmitted waveform to make the square error between the spectrum of the transmitted waveform and the spectrum of the optimal transmitted waveform as small as possible, and the short-time burst integral sidelobe as low as possible, the problem model based on the criterion of the minimum square error can be expressed as:
Figure BDA0003545111370000055
because the objective function is a nonlinear function, such optimization problems cannot be solved directly by using a convex optimization tool, a Pattern Search (PS) algorithm is selected to solve the problem, that is, traversal Search is performed on the phase parameters of the waveform, the multidimensional optimization Search problem is converted into a plurality of one-dimensional optimization Search problems in each iteration process by means of an iteration mode, and the optimal phase parameter Φ is obtained through a plurality of iterations.
Further, the specific steps of the pattern search algorithm are as follows:
(1) the initialization phase parameter Φ may be a random phase, or may be an initial phase of a burst waveform composed of phases of a common phase-coded waveform such as a P3 code or a P4 code according to a burst signal model.
(2) Phase parameter phi for the nth phase of the transmit waveformnThe objective function SE can be expressed as the phaseA univariate function SE [ phi ] of the parametern]. At this time, the multidimensional optimization problem of the formula (21) is converted into a one-dimensional optimization problem:
Figure BDA0003545111370000056
the phase parameter Φ is updated using the one-dimensional optimization search result of equation (22) until all phase parameters of all waveforms are updated once.
(3) And (3) repeating the step (2) until a set stop condition (such as iteration times, vector variation of phase parameters of two iterations of the vector, variation of the cost function and the like) is reached.
The PS algorithm converts the multi-dimensional optimization problem into a plurality of one-dimensional optimization problems, and the phase parameter which minimizes the objective function is searched when one-dimensional optimization is carried out each time, so that the objective function can be further reduced, and a lower objective function value can be obtained after each iteration, thereby approaching the minimum value.
The invention has the beneficial effects that: the method utilizes target prior information to construct a maximum signal-to-noise-and-noise ratio target function, and solves an optimal transmitting waveform and an optimal matching filter based on a Lagrange multiplier method; constructing a combined target function of waveform power spectrum approximation and autocorrelation integral sidelobe level suppression by combining a short-time pulse string waveform emission rule; and iterating in a mode of pattern search to obtain the optimal short-time pulse train waveform. The short-time pulse train waveform designed by the invention has the capability of being intercepted by an anti-jamming machine, not only can inhibit the interference of environmental clutter on target detection, but also can avoid the interference forwarded after being intercepted by an enemy jamming machine, thereby realizing the simultaneous inhibition of the environmental clutter and the jamming machine interference and improving the target detection capability in a clutter environment.
Drawings
FIG. 1 is a schematic diagram of a short burst signal model according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for designing a short-time burst waveform for clutter interference rejection according to an embodiment of the present invention;
FIG. 3 is a comparison of a clutter power spectrum and an optimal waveform spectrum provided by an embodiment of the present invention;
FIG. 4 is a graph comparing an optimized short-time burst spectrum with an optimized waveform spectrum according to an embodiment of the present invention;
FIG. 5 is a graph of the output of the point target matched filter for an un-optimized waveform provided by an embodiment of the present invention;
fig. 6 is a graph of a point target matched filter output result of optimizing a short-time burst waveform according to an embodiment of the present invention;
fig. 7 is a diagram illustrating comparison results of SCNR enhancement before and after optimization of signals with different lengths according to an embodiment of the present invention.
Detailed Description
The invention mainly adopts a simulation experiment method to verify the effectiveness of the short-time pulse train waveform design method for resisting clutter interference. All steps and conclusions are verified to be correct on the Windows10 operating system through the MATLAB 2018a platform. In order to facilitate understanding of the technical contents of the present invention, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of the short burst transmission signal with anti-capture capability according to the present invention, wherein the signal transmission rule can be summarized as a pulse repetition interval T3A pulse train consisting of a plurality of sub-pulses is internally transmitted, and each sub-pulse is a phase-coded signal consisting of a plurality of sub-chips.
The short-time burst waveform design flow of the present invention is shown in fig. 2. Firstly, obtaining an optimal spectrum of a transmitted waveform and an optimal receiver based on a maximized SCNR criterion; secondly, constructing a cost function for minimizing the square error of the weighted pulse train frequency spectrum and the optimal waveform frequency spectrum and the waveform autocorrelation sidelobe by combining the characteristics of short-time pulse train signal transmission; and finally, obtaining an optimized time domain form of the short-time pulse string by using a mode search algorithm, and completing the design of the anti-interference waveform of the short-time pulse string. The method comprises the following specific steps:
the method comprises the following steps: solving for optimal transmit waveform spectrum
Clutter and noise information in the environment can be rapidly acquired by using an environment sensing technology, and a transmitting waveform can be designed by using priori information on the assumption that the clutter in the detection environment is stable clutter. Let the echo signal y (t) be
y(t)＝s(t)*g(t)+s(t)*c(t)+n(t) (23)
Wherein "") represents convolution operation, s (t) is a transmitting signal, g (t) is a target parameter, c (t) is a clutter pulse response, and n (t) is noise.
Defined by the echo signal-to-noise ratio, we can obtain:
Figure BDA0003545111370000071
wherein ,Pc(f) Is the power spectral density of the clutter, Pn(f) Is the power spectral density of the noise, s (f) is the transmit signal spectrum, g (f) is the target parameter spectrum, and h (f) is the receive filter spectrum.
The transmit waveform is often limited by a constant energy, E, of the transmit waveform according to the pascal's theoremsCan be expressed as
Figure BDA0003545111370000072
In conclusion, a problem model for maximizing the signal-to-noise-and-noise ratio criterion is constructed
Figure BDA0003545111370000073
Order to
Figure BDA0003545111370000074
The echo signal to noise ratio can be expressed as:
Figure BDA0003545111370000075
contracting the above formula by using the Schwarz inequality to obtain
Figure BDA0003545111370000076
If and only if
Figure BDA0003545111370000077
When the number is equal, the equal sign is established. Wherein, (. cndot.)*Representing the conjugate, the receive filter expression is:
Figure BDA0003545111370000081
the signal-to-noise-and-noise ratio at this time can be expressed as:
Figure BDA0003545111370000082
the optimization problem model is converted into:
Figure BDA0003545111370000083
and constructing a function by using a Lagrange multiplier method, and converting the constrained optimization problem into an unconstrained optimization problem, wherein the constructed function is as follows:
Figure BDA0003545111370000084
wherein ,α2Are constants in the lagrange multiplier method. (iv) ventilation holes of J [ | S (f)2]To routing electricity through | S (f)2Is a general function of (a). According to the variational principle, when the extreme value of the general function is taken, the optimization parameter is in the optimal state. Therefore, the frequency domain amplitude satisfying the extreme value of the above formula is the square of the frequency domain amplitude of the optimal transmission waveform. (ii) conducting light through the above formula2And (4) obtaining a derivative, wherein the derivative is 0, and solving the optimal frequency spectrum S of the transmitting waveform based on the maximum signal-to-noise-ratio criteriono(f)：
Figure BDA0003545111370000085
wherein ,α2Is determined by the energy of the transmitted signal.
If the following conditions are met:
Figure BDA0003545111370000086
the maximum signal-to-noise-plus-noise ratio is:
Figure BDA0003545111370000087
step two: short-time pulse train waveform design
The time domain form of the short-time pulse train cannot be obtained according to the optimal waveform spectrum obtained by solving based on the maximization SCNR criterion, and the short-time pulse train signal waveform meeting the spectrum requirement is obtained by minimizing the square error of the optimal waveform spectrum and the waveform spectrum of the short-time pulse train.
The short-time pulse train signal model is shown as the formula (1), and the frequency spectrum thereof is St(f) And (4) showing. The optimal emission waveform frequency spectrum obtained based on the maximum signal-to-noise-and-noise ratio criterion is So(f) Then the objective function can be expressed as the Squared Error of the two (SE)
SE＝|St(f)-So(f)|2 (36)
However, the autocorrelation sidelobes of the burst waveforms, which are obtained by considering only the spectral similarity, also affect the detection probability of the target. Therefore, a cost function is constructed, and the frequency spectrum and the integral side lobe level of the short-time pulse string are optimized. The cost function can be expressed as
C＝λSE+(1-λ)ISL (37)
Where λ represents the weight of the squared error function, ISL is the integral side lobe of the short-time burst, and by definition, ISL is expressed as:
Figure BDA0003545111370000091
where R (k) is the autocorrelation function of the burst s, expressed as:
Figure BDA0003545111370000092
the phase parameter of the discrete vector of the transmitted waveform can be expressed as:
Figure BDA0003545111370000093
by changing the phase parameter of the transmitted waveform to make the square error between the spectrum of the transmitted waveform and the spectrum of the optimal transmitted waveform as small as possible, and the short-time burst integral sidelobe as low as possible, the problem model based on the criterion of the minimum square error can be expressed as:
Figure BDA0003545111370000094
because the objective function is a nonlinear function, such optimization problems cannot be solved directly by a convex optimization tool, a Pattern Search (PS) algorithm is selected to solve the problem, that is, traversal Search is performed on the phase parameters of the waveform, the multidimensional optimization Search problem is converted into a plurality of one-dimensional optimization Search problems in each iteration process by means of an iteration mode, and the optimal phase parameter Φ is obtained through multiple iterations.
The specific steps of the pattern search algorithm are as follows:
(1) the initialization phase parameter Φ may be a random phase, or may be an initial phase of a burst waveform composed of phases of a common phase-coded waveform such as a P3 code or a P4 code according to a burst signal model.
(2) Phase parameter phi for the nth phase of the transmit waveformnThe objective function SE can be expressed as a univariate function SE [ phi ] of the phase parametern]. At this time, the multidimensional optimization problem of equation (41) is converted into a one-dimensional optimization problem:
Figure BDA0003545111370000101
the phase parameter Φ is updated using the one-dimensional optimization search result of equation (42) until all phase parameters of all waveforms are updated once.
(3) And (3) repeating the step (2) until a set stop condition (such as iteration times, vector variation of phase parameters of two iterations of the vector, variation of the cost function and the like) is reached.
The PS algorithm converts the multi-dimensional optimization problem into a plurality of one-dimensional optimization problems, and the phase parameter which minimizes the objective function is searched when one-dimensional optimization is carried out each time, so that the objective function can be further reduced, and a lower objective function value can be obtained after each iteration, thereby approaching the minimum value.
When the short-time pulse train waveform is designed, the short-time pulse train is composed of 5 pulses, the pulse width of one pulse train is 5 mu s, the signal length N is 500, the pulse duty ratio is 80%, and the initial phase of the pulse train is a random sequence.
FIG. 3 is a comparison of a clutter power spectrum with an optimal spectrum obtained according to step two of the present invention. It can be seen that the energy of the optimal waveform spectrum is concentrated at the position where the clutter power spectrum is extremely low, and lower signal energy is distributed at the position where the clutter power spectrum energy is higher, so that the optimal transmit waveform can suppress the interference of the clutter in the frequency domain. However, the above steps can only obtain the transmitted waveform spectrum, and in order to obtain the burst time domain transmitted waveform, the invention combines the burst transmission form to obtain the optimal burst transmitted waveform according to the third step in the specific implementation of the invention.
FIG. 4 is a comparison of the optimized short-time burst spectrum with the optimized waveform spectrum. It can be seen that the designed short-time pulse train frequency spectrum is consistent with the optimal waveform frequency spectrum energy distribution rule. In order to verify that the designed short-time pulse train waveform has the capability of resisting clutter, the invention respectively provides the point target matched filtering output of the unoptimized waveform and the point target matched filtering output of the optimized short-time pulse train waveform under the same clutter environment, as shown in fig. 5 and fig. 6. Compared with the waveform which is not optimized, the matched filtering result obtained by transmitting the optimized short-time pulse string waveform has lower side lobe level, so the short-time pulse string waveform designed by the invention is beneficial to improving the target detection probability. To analyze the effect of signal length on target detection, the present invention provides a comparison of the SCNR of the matched filtered outputs at signal lengths of 100, 500, and 900, respectively, as shown in fig. 7. It can be seen that as the signal length increases, the SCNR increases, but the SCNR obtained by the burst waveform designed by the invention is better than the non-optimized transmit waveform, and when the signal length is 500, the performance of the burst waveform is significantly improved. Specific SCNR results are shown in table 1:
TABLE 1
Figure BDA0003545111370000111
In conclusion, the method considers the optimization design of the short-time pulse train waveform with narrow pulse width, high peak power and anti-interception capability, reduces the energy distribution of the short-time pulse train signal at a clutter energy concentration frequency band by utilizing the prior information of the environmental clutter, inhibits the interference of the clutter power to the target echo power while ensuring that the short-time pulse train signal is not interfered by a side lobe level, improves the target detection capability in the clutter environment, and has important application value in a complex detection scene.

Claims (2)

1. A method for designing short-time pulse train waveform for resisting clutter interference is characterized in that the short-time pulse train waveform is a signal model formed by a plurality of sub-pulses with the same duty ratio, each sub-pulse is formed by M chips, and T is set1For the duration of the pulse train, τ2Is the pulse repetition interval of the sub-pulse, tau3For sub-pulse width, the signal bandwidth is B, assuming T1The burst emission waveform during the time period is s (t), and s (t) is expressed as:
Figure FDA0003545111360000011
wherein N is the number of sub-pulses, xij(t) is the ith chip in the jth sub-pulse, with a chip width of τ1Let M be τ3/τ1＝Bτ3Then the transmit burst discrete vector is expressed as:
Figure FDA0003545111360000012
wherein ,
Figure FDA0003545111360000013
representing a vector of complex field dimension K x 1, 01×PAn all-zero matrix with a dimension of 1 × P is represented, and K ═ N (M + P) represents the total number of chips of the burst sequence;
the method specifically comprises the following steps:
the method comprises the following steps: the optimal waveform frequency spectrum is solved,
let echo signal y (t) be:
y(t)＝s(t)*g(t)+s(t)*c(t)+n(t) (3)
wherein g (t) is a target parameter, c (t) is a clutter pulse response, and n (t) is noise;
t0the SCNR at time is:
Figure FDA0003545111360000014
wherein ,Pc(f) Is the power spectral density of the clutter, Pn(f) Is the power spectral density of the noise, s (f) is the transmitted signal spectrum, g (f) is the target parameter spectrum, h (f) is the receive filter spectrum;
energy constraint is carried out on the frequency spectrum of the optimized waveform, and the energy E of the optimized waveformsExpressed as:
Figure FDA0003545111360000015
constructing a problem model that maximizes the SCNR criterion:
Figure FDA0003545111360000021
order to
Figure FDA0003545111360000022
The echo signal to noise ratio is then expressed as:
Figure FDA0003545111360000023
wherein ,L-1(f) Is the reciprocal of L (f);
and (3) contracting the formula (7) by using a Schwarz inequality to obtain:
Figure FDA0003545111360000024
if and only if
Figure FDA0003545111360000025
If the equal sign is true, the receiving filter expression is:
Figure FDA0003545111360000026
wherein ,α1Is constant, when the SCNR is expressed as:
Figure FDA0003545111360000027
the problem model of equation (6) can be converted to:
Figure FDA0003545111360000028
and constructing a function by using a Lagrange multiplier method, and converting the constrained optimization problem into an unconstrained optimization problem, wherein the constructed function is as follows:
Figure FDA0003545111360000029
wherein ,α2Is a constant in Lagrange multiplier method, J | S (f) is not calculation2]To routing electricity through | S (f)2A general function of (a); formula (12) to y2And (4) obtaining a derivative, wherein the derivative is 0, and solving the optimal frequency spectrum S of the transmitting waveform based on the maximum signal-to-noise-ratio criteriono(f)：
Figure FDA0003545111360000031
wherein ,α2The value of (c) is determined by the energy of the transmitted signal if:
Figure FDA0003545111360000032
the maximum signal-to-noise-plus-noise ratio is:
Figure FDA0003545111360000033
step two: the short-time pulse train waveform design is adopted,
frequency spectrum S of short-time pulse string signalt(f) The frequency spectrum of the optimal transmitting waveform obtained based on the maximum signal-to-noise-and-noise ratio criterion is represented as So(f) Then the objective function can be expressed as the squared error of both:
SE＝|St(f)-So(f)|2 (16)
constructing a cost function, and simultaneously optimizing the frequency spectrum and the integral sidelobe level of the short-time pulse string, wherein the cost function is expressed as:
C＝λSE+(1-λ)ISL (17)
wherein λ represents the weight of the squared error function, ISL is the integral side lobe of the short-time pulse train, ISL is:
Figure FDA0003545111360000034
where R is the autocorrelation function of the burst s, and R (k) is the kth side lobe of the autocorrelation function, which is represented as:
Figure FDA0003545111360000035
wherein ,snThe nth element of the sequence representing the burst,
Figure FDA0003545111360000036
is the conjugate of the (n-k) th element of the burst sequence, the discrete phase parameter corresponding to the burst sequence of equation (2) is expressed as:
Figure FDA0003545111360000037
wherein, the problem model based on the criterion of the least square error can be expressed as:
Figure FDA0003545111360000041
solving the formula (21) by using a pattern search algorithm, namely performing traversal search on the phase parameters of the waveform, converting the multidimensional optimization search problem into a plurality of one-dimensional optimization search problems in each iteration process by means of an iteration mode, and obtaining the optimal phase parameter phi through a plurality of iterations, namely completing the design of the short-time pulse train waveform.
2. The method according to claim 1, wherein the pattern search algorithm comprises the following steps:
(1) initializing a phase parameter phi;
(2) phase parameter phi for the nth phase of the transmit waveformnThe objective function SE is expressed as a univariate function SE [ phi ] of the phase parametern]The multidimensional optimization problem of the formula (21) is converted into a one-dimensional optimization problem:
Figure FDA0003545111360000042
updating the phase parameter Φ using the one-dimensional optimization search result of equation (22) until all phase parameters of all waveforms are updated once;
(3) and (5) repeating the step (2) until the set stop condition is reached.
CN202210246873.5A 2022-03-14 2022-03-14 Short-time pulse train waveform design method for resisting clutter interference Active CN114594425B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210246873.5A CN114594425B (en) 2022-03-14 2022-03-14 Short-time pulse train waveform design method for resisting clutter interference

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210246873.5A CN114594425B (en) 2022-03-14 2022-03-14 Short-time pulse train waveform design method for resisting clutter interference

Publications (2)

Publication Number Publication Date
CN114594425A true CN114594425A (en) 2022-06-07
CN114594425B CN114594425B (en) 2023-05-16

Family

ID=81817401

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210246873.5A Active CN114594425B (en) 2022-03-14 2022-03-14 Short-time pulse train waveform design method for resisting clutter interference

Country Status (1)

Country Link
CN (1) CN114594425B (en)

Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3995271A (en) * 1975-08-20 1976-11-30 The United States Of America As Represented By The Secretary Of The Air Force Adaptive clutter cancellation and interference rejection system for AMTI radar
US20030069504A1 (en) * 2001-10-05 2003-04-10 Siemens Medical Solutions Usa, Inc. Receive filtering and filters for phase or amplitude coded pulse sequences
US20030189512A1 (en) * 2002-04-04 2003-10-09 Hai-Wai Chen System and method for detecting and estimating the direction of near-stationary targets in monostatic clutter using phase information
JP2014044193A (en) * 2012-07-31 2014-03-13 Mitsubishi Electric Corp Clutter suppressing device
CN103777183A (en) * 2014-01-27 2014-05-07 西安电子科技大学 Minimum redundance extended F$A clutter suppression method
CN108562901A (en) * 2018-05-14 2018-09-21 西安电子科技大学 ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion
CN110161478A (en) * 2019-06-14 2019-08-23 西安电子科技大学 A kind of waveform design method based on clutter power spectrum density self-optimizing
CN110471034A (en) * 2019-09-19 2019-11-19 上海无线电设备研究所 A kind of ULTRA-WIDEBAND RADAR waveform design method
CN110488227A (en) * 2019-09-20 2019-11-22 上海无线电设备研究所 Sourceless seism suppressing method under complex environment based on cognition radar waveform design
EP3572828A1 (en) * 2018-05-24 2019-11-27 The Boeing Company Combined radar and communications system using common signal waveform
CN111830480A (en) * 2020-07-09 2020-10-27 中国人民解放军海军航空大学 Radar sea clutter short-time spectrum characteristic parameter estimation method and system
CN113239554A (en) * 2021-05-21 2021-08-10 电子科技大学 Wave form optimization design method for resisting sidelobe shielding interference
CN113296073A (en) * 2021-07-05 2021-08-24 西安多芬睿达科技有限公司 Amplitude-phase joint blade clearance measurement method based on envelope constraint
CN113656913A (en) * 2021-08-19 2021-11-16 电子科技大学 Distributed foresight radar geometric configuration optimization design method
CN113702961A (en) * 2021-07-15 2021-11-26 浙江理工大学 MIMO-STAP radar transmit-receive space-time resource configuration method in clutter scene

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3995271A (en) * 1975-08-20 1976-11-30 The United States Of America As Represented By The Secretary Of The Air Force Adaptive clutter cancellation and interference rejection system for AMTI radar
US20030069504A1 (en) * 2001-10-05 2003-04-10 Siemens Medical Solutions Usa, Inc. Receive filtering and filters for phase or amplitude coded pulse sequences
US20030189512A1 (en) * 2002-04-04 2003-10-09 Hai-Wai Chen System and method for detecting and estimating the direction of near-stationary targets in monostatic clutter using phase information
JP2014044193A (en) * 2012-07-31 2014-03-13 Mitsubishi Electric Corp Clutter suppressing device
CN103777183A (en) * 2014-01-27 2014-05-07 西安电子科技大学 Minimum redundance extended F$A clutter suppression method
CN108562901A (en) * 2018-05-14 2018-09-21 西安电子科技大学 ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion
EP3572828A1 (en) * 2018-05-24 2019-11-27 The Boeing Company Combined radar and communications system using common signal waveform
CN110161478A (en) * 2019-06-14 2019-08-23 西安电子科技大学 A kind of waveform design method based on clutter power spectrum density self-optimizing
CN110471034A (en) * 2019-09-19 2019-11-19 上海无线电设备研究所 A kind of ULTRA-WIDEBAND RADAR waveform design method
CN110488227A (en) * 2019-09-20 2019-11-22 上海无线电设备研究所 Sourceless seism suppressing method under complex environment based on cognition radar waveform design
CN111830480A (en) * 2020-07-09 2020-10-27 中国人民解放军海军航空大学 Radar sea clutter short-time spectrum characteristic parameter estimation method and system
CN113239554A (en) * 2021-05-21 2021-08-10 电子科技大学 Wave form optimization design method for resisting sidelobe shielding interference
CN113296073A (en) * 2021-07-05 2021-08-24 西安多芬睿达科技有限公司 Amplitude-phase joint blade clearance measurement method based on envelope constraint
CN113702961A (en) * 2021-07-15 2021-11-26 浙江理工大学 MIMO-STAP radar transmit-receive space-time resource configuration method in clutter scene
CN113656913A (en) * 2021-08-19 2021-11-16 电子科技大学 Distributed foresight radar geometric configuration optimization design method

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
A. AUBRY: "Optimizing radar waveform and doppler filter bank via generalized fractional programming", 《IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING》 *
BRENNER, T., GOCA, J., KLEMBOWSKI, W., PIETRASINSKI, J., & RAPACKI, T: "Researching of selected anti-clutter and anti-interference algorithms using recorded radar signals. In 13th International Conference on Microwaves, Radar and Wireless Communications", 《MIKON-2000. CONFERENCE PROCEEDINGS》 *
S C PARK: "Super-resolution image reconstruction: a technical overview", 《SIGNAL PROCESSING MAGAZINE, IEEE》 *
李江源;王建国;杨建宇;: "基于参数引导的对宽带SAR的类杂波干扰", 《电子与信息学报》 *
李江源;王建国;杨建宇;: "基于数字储频式的对SAR类杂波干扰", 《电子科技大学学报》 *
王龙岗,岳显昌,吴雄斌等: "基于奇异值分解的空域海杂波抑制算法", 《电波科学学报》 *

Also Published As

Publication number Publication date
CN114594425B (en) 2023-05-16

Similar Documents

Publication Publication Date Title
CN110095765B (en) High-resolution Doppler reconstruction method of frequency agile radar
CN103364764B (en) Airborne radar non-stationary clutter suppression method
CN113608180B (en) Array element-pulse coding MIMO radar main lobe deception jamming inhibition method
CN101881822B (en) Method for inhibiting same frequency interference of shared-spectrum radars
CN113376601B (en) Frequency agile radar sidelobe suppression method based on CLEAN algorithm
CN106970368B (en) Radar waveform design method based on fuzzy function local optimization
CN112799023A (en) Multi-false-target interference method for fast forwarding
CN113721216B (en) Target detection waveform optimization and processing method of agile coherent radar
CN113884992A (en) Self-adaptive anti-interference method of frequency agile radar
CN111239698B (en) Drag type interference suppression method based on complementary discrete chaotic phase coding waveform design
CN107180259A (en) A kind of STAP training sample selection methods based on System Discrimination
Li et al. Random-frequency-Coded waveform optimization and signal coherent accumulation against compound deception jamming
CN107102305A (en) Sane cognitive radar emission receives co-design method under a kind of clutter environment
CN113376607B (en) Airborne distributed radar small sample space-time self-adaptive processing method
CN113671450A (en) Chaos coding-based detection and interference integrated waveform design method and device
CN114594425A (en) Clutter interference resistant short-time pulse train waveform design method
CN114696788B (en) Wave form and filter combined cognition design method for resisting multi-main lobe interference
Shi et al. A novel jamming method against LFM radar using pseudo-random code phase modulation
Zhao et al. Range-Doppler sidelobe and clutter suppression via time-range adaptive processing
Li et al. A Radar Waveform Design of MCPC Method for Interrupted Sampling Repeater Jamming Suppression via Fractional Fourier Transform.
Xu et al. Cognitive SAR waveform design method based on joint optimization criteria
Li et al. Shared Waveform Design for Integrated Detection and Jamming Signal Based on Smart Noise Jamming of Convolution Modulation
CN117269897A (en) Radar intelligent interference waveform design optimization method and system based on cross-correlation function and genetic algorithm
Nuthalapati Design of mismatched filters for long binary codes
Callahan et al. Simulated & theoretical SNR in passive bistatic noise radar processing

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
TR01 Transfer of patent right

Effective date of registration: 20231212

Address after: 218, 2nd Floor, Block B, Building 5, No. 200 Tianfu Fifth Street, High tech Zone, Chengdu City, Sichuan Province, 610095

Patentee after: Sichuan jianweizhishu Technology Co.,Ltd.

Address before: 611731, No. 2006, West Avenue, Chengdu hi tech Zone (West District, Sichuan)

Patentee before: University of Electronic Science and Technology of China

TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20240516

Address after: Room 1701H25, Building C, Tian'an International Building, No. 3012 Renmin South Road, Jiabei Community, Nanhu Street, Luohu District, Shenzhen City, Guangdong Province, 518002

Patentee after: Shenzhen Zhentai Technology Co.,Ltd.

Country or region after: China

Address before: 218, 2nd Floor, Block B, Building 5, No. 200 Tianfu Fifth Street, High tech Zone, Chengdu City, Sichuan Province, 610095

Patentee before: Sichuan jianweizhishu Technology Co.,Ltd.

Country or region before: China

TR01 Transfer of patent right