CN118294908A - Frequency agility cognitive waveform design method for accurate detection of high-speed moving target - Google Patents
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Abstract
The invention provides a frequency agile cognitive waveform design method for accurately detecting a high-speed moving target, which constructs a random frequency coherent coding waveform signal with stronger anti-interference capability and can improve the detection probability of the high-speed target in an interference environment; then extracting frequency cognition waveform echo, performing motion compensation through fuzzy number estimation and trapezoidal transformation combined processing, and evaluating the compensated focusing performance based on a new peak sidelobe difference criterion to obtain a coarse evaluation value of the speed fuzzy number; the improved non-uniform discrete Fourier transform coherent accumulation method is utilized to realize high-speed moving object focusing, and compared with a maximum likelihood estimation method and a generalized trapezoidal transform method, the coherent accumulation method provided by the invention has higher speed deblurring and detection precision and lower calculation complexity.
Description
Technical Field
The invention belongs to the technical field of radar detection, and particularly relates to a frequency agile cognitive waveform design method for accurately detecting a high-speed moving target.
Background
In recent decades, high-speed target detection has received high attention in the fields of spaceborne, shore-based and airborne radars, and has important research value. With the rapid development of variable motion target technologies such as hypersonic missiles, near-air aircrafts and stealth fighters, high-speed target detection presents high challenges for modern radars. Range bin migration and velocity ambiguity are not negligible problems in high-speed motion, and many methods exist to solve this problem to some extent. Therefore, to achieve accurate detection of a high-speed target, it is necessary to directly face this problem.
Modern radars always operate in an increasingly complex electromagnetic environment, which is fraught with jamming by jammers. In particular, with the development of electronic support device technology, the identification and estimation of radar parameters becomes easier. The jammer is free to modulate these parameters of the intercepted signal and send it to the radar receiver, resulting in a resultant false target position and energy variation, confusing to detect the radar. Therefore, it is urgent to improve the anti-interference capability of modern radars. Thus, modern radars face the problem of upgrading high-speed target detection in complex electromagnetic environments. On the one hand, there is a need to improve its tamper resistance. On the other hand, the problems of inherent speed blurring and range cell migration need to be solved, so that a high-speed target accurate detection task can be completed.
In order to improve the anti-interference capability of modern multifunctional radars on moving target detection, an active interference countermeasure technology, namely waveform diversity, is used, and a frequency agility method is taken as a most representative waveform diversity method, and comprises a Step Frequency Waveform (SFW), a sparse SFW, a random SFW and a random sparse SFW. There are many speed estimation algorithms and coherent processing methods that are effective ways to improve the ability of frequency agile radar systems to detect high speed targets, including Radon transforms, maximum likelihood estimation methods, and generalized key transforms.
However, the Step Frequency Waveform (SFW) has weak interference resistance due to the fixed frequency switching. Sparse SFW, random SFW and random sparse SFW are proposed on the basis of a Step Frequency Waveform (SFW). Random phase jitter of echo signals of different frequencies causes severe distance-azimuth coupling while reducing the coherence of the echo. Due to the incoherence of the echo, the tasks of range cell migration correction, speed ambiguity resolution, target energy accumulation and the like of high-speed target detection become difficult. Furthermore, under the condition of low signal-to-noise ratio, incoherent radon transformation has difficulty in estimating the target motion parameters. For a high-speed target, the maximum likelihood estimation method has large calculated amount, and the long-time coherent accumulation method is difficult to ensure that the motion state of the target remains unchanged for a long time. The keystone transformation is a mature range cell migration correction algorithm in the field of synthetic aperture radar imaging, and the coherent accumulation of a high-speed target is realized through slow time scale transformation. However, since the carrier frequency and slow time change simultaneously, the keystone transform algorithm is difficult to apply directly to the frequency agile radar. Moreover, the above algorithm and waveform calculation is highly complex because of the speed ambiguity caused by high-speed motion.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a frequency agile cognitive waveform design method for accurately detecting a high-speed moving target. The technical problems to be solved by the invention are realized by the following technical scheme:
The invention provides a frequency agile cognitive waveform design method for accurate detection of a high-speed moving target, which comprises the following steps:
S100, constructing a random frequency coherent coding waveform signal based on a linear frequency modulation signal;
S200, extracting waveform signals with the same frequency from the random frequency coherent coding waveform signals, and forming the waveform signals with the same frequency into frequency cognitive waveform signals;
S300, performing pulse compression processing on the frequency cognitive waveform signal to obtain a frequency cognitive waveform signal after pulse compression, and determining a data relationship between a non-blurring speed of the frequency cognitive waveform signal and a non-blurring speed of the random frequency coherent coding waveform signal;
s400, compensating the frequency cognitive waveform signal after pulse compression for a blur-free speed, and performing keystone conversion to obtain a converted frequency cognitive waveform signal;
s500, evaluating the focusing performance of the transformed frequency cognitive waveform signal by using a new peak sidelobe difference criterion to obtain a speed fuzzy number evaluation value for evaluating the focusing performance;
S600, converting the converted frequency cognition waveform signals from an azimuth frequency domain to an azimuth time domain to obtain signal echoes after matched filtering and range migration correction;
S700, processing the coarse speed fuzzy number evaluation value by utilizing non-uniform discrete Fourier transform to obtain the speed fuzzy number of the random frequency coherent coding waveform;
s800, determining Doppler channel speed by using the speed fuzzy number of the random frequency coherent coding waveform, and determining the energy of the high-speed target by using the Doppler channel speed so as to obtain the actual position of the determined high-speed target to realize accurate target detection.
The beneficial effects are that:
The invention provides a frequency agile cognitive waveform design method for accurately detecting a high-speed moving target, which constructs a random frequency coherent coding waveform signal, has stronger anti-interference capability, and can improve the detection probability of the high-speed target in an interference environment; then extracting frequency cognition waveform echo from the random frequency coherent coding waveform signal, performing motion compensation on the frequency cognition waveform echo through combined processing of speed blur number estimation and trapezoidal transformation, and then evaluating the compensated focusing performance based on a new peak sidelobe difference criterion to obtain a coarse evaluation value of the speed blur number, wherein compared with the existing radon transformation, the combined processing of the speed blur number estimation and the trapezoidal transformation has better coarse speed estimation precision and noise robustness; and aiming at the incoherent echo of frequency agility, the improved nonuniform discrete Fourier transform coherent accumulation method is utilized to realize the focusing of the high-speed moving target, and compared with the maximum likelihood estimation method and the generalized trapezoidal transformation method, the coherent accumulation method provided by the invention has higher speed deblurring and detection precision and lower calculation complexity.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
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Fig. 1 is a flowchart of a frequency agile cognitive waveform design method for accurate detection of high speed moving objects.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but embodiments of the present invention are not limited thereto.
As shown in fig. 1, the present invention provides a frequency agile cognitive waveform design method for accurate detection of a high-speed moving object, comprising:
S100, constructing a random frequency coherent coding waveform signal based on a linear frequency modulation signal;
The random frequency coherent coding waveform signal of the invention is constructed on the basis of the traditional linear frequency modulation signal, wherein the traditional linear frequency modulation signal is that
Wherein, rect (·) is a rectangular window function, T is pulse time width, gamma is linear frequency adjustment, and τ is fast time;
The random frequency coherent encoded waveform signal in S100 is represented as:
s(t)＝p(τ)·exp(j2πflt) (2)
Wherein f l＝f0 +Δf (L) is the carrier frequency of the first pulse, l= [1,2, the L, L is the total number of pulses of all pulses; f 0 is the standard carrier frequency, Δf (l) is the frequency hopping interval of the first pulse; t=τ+l·t is the full time, T is the pulse repetition interval.
Noteworthy are: since the sequence of both the hopping interval and the random frequency coherent encoded waveform is random. Compared with Step Frequency Waveforms (SFW), sparse SFW, random SFW and random sparse SFW, the provided waveforms have stronger anti-interference capability, and the detection probability of a high-speed target in an interference environment is improved.
S200, extracting waveform signals with the same frequency from the random frequency coherent coding waveform signals, and forming the waveform signals with the same frequency into frequency cognitive waveform signals;
S300, performing pulse compression processing on the frequency cognitive waveform signal to obtain a frequency cognitive waveform signal after pulse compression, and determining a data relationship between a non-blurring speed of the frequency cognitive waveform signal and a non-blurring speed of the random frequency coherent coding waveform signal;
due to frequency agility and speed ambiguity, conventional signal processing methods are difficult to apply to range migration correction. The frequency-aware waveform is a waveform with coherent characteristics that enable motion parameter estimation. The frequency-aware waveform signal after pulse compression is expressed as:
Where t m is slow time, σ 1 is the scattering coefficient of the target, R' (t m)＝R0+vtm is the pitch, t m = (m-1) ·nt, v is the target movement speed, f r is the distance frequency, R 0 is the initial pitch, and c is the light speed.
The random frequency coherent coding waveform signals comprise a plurality of groups of waveform signals, and each group of waveform signals comprises N linear frequency modulation signals with different frequencies; all groups have a chirp signal of the same frequency at the same location.
Noteworthy are: the pulse repetition interval of the frequency-aware waveform is N times that of the random frequency-coherent encoded waveform, so the maximum ambiguity-free velocity of the frequency-aware waveform is 1/N of v max, i.e
Where v max is the non-ambiguity speed of the random frequency coherent encoded waveform signal, v' max is the non-ambiguity speed of the frequency cognitive waveform signal, and f 3 is the waveform signal with the same frequency in S200.
S400, compensating the frequency cognitive waveform signal after pulse compression for a blur-free speed, and performing keystone conversion to obtain a converted frequency cognitive waveform signal;
the S400 of the present invention includes:
S410, constructing a fuzzy speed compensation function, which is expressed as:
Hk＝exp[j4πk(f3+fr)v′maxtm/c] (5)；
wherein K is a velocity fuzzy number, k= [1,2 …, K ], and K is the maximum iteration number;
S420, performing non-blurring speed compensation on the pulse compressed frequency cognitive waveform signal by using the blurring speed compensation function to obtain a compensated frequency cognitive waveform signal, wherein the compensated frequency cognitive waveform signal is obtained by multiplying (5) by (3), and is expressed as:
S430, correcting the frequency cognitive waveform signal after compensation by using a range migration correction formula of a keystone conversion algorithm to obtain a corrected frequency cognitive waveform signal;
then, for (6), the range migration can be corrected by using a keystone transformation algorithm, and the range migration correction formula is expressed as:
(f3+fr)·tm＝f3·t'm (7)；
The corrected frequency-aware waveform signal is expressed as:
Where v k＝v-k·v'max is the residual velocity and t' m is the new slow time after correction.
S440, performing Fourier transform on the corrected frequency cognition waveform signal in the azimuth direction, performing inverse Fourier transform on the frequency cognition waveform signal in the distance direction to obtain a frequency cognition waveform signal after transformation, performing azimuth FFT along tau m and distance inverse FFT along f r, and then performing the frequency cognition waveform signal after transformation, wherein the frequency cognition waveform signal comprises
Where σ 2 denotes the signal amplitude of the frequency-aware waveform in the τf a domain and f a denotes the Doppler frequency in azimuth.
S500, evaluating the focusing performance of the transformed frequency cognitive waveform signal by using a new peak sidelobe difference criterion to obtain a speed fuzzy number evaluation value for evaluating the focusing performance;
Although the doppler focus positions of all the velocity blur numbers are shown in (9), range migration correction can be actually achieved only by correct velocity blur compensation, and then precise focusing in azimuth can be achieved. Therefore, we can determine the blur number of the velocity by evaluating the focus quality of the target. For detection radars, since there are generally few targets or a single target, the focused image and the image contrast change little, and thus cannot be used to evaluate the focusing performance of the target. We propose a new peak sidelobe difference criterion to determine the focus quality of the target. The new peak sidelobe difference criterion is expressed as:
Px＝(Pmain-Pleft)+(Pmain-Pright)) (10)；
Wherein P main is the peak value of the main lobe of the range, P left is the peak value P right from the left lobe and P right from the right lobe;
The P x values of the focused images after the fuzzy speed is compensated by the K speed fuzzy numbers are compared, and the speed fuzzy number with the maximum P x value is selected as the estimated speed fuzzy number of the target K e. From the estimated speed blur number and the blur-free speed obtained by the expression (9), the speed blur number evaluation value is expressed as:
Ve＝v′max·ke+ve (11)；
Where v e is the blur-free speed relative to k e and satisfies F c is carrier frequency, and k e is speed ambiguity number.
Because the pulse repetition frequency of the frequency-aware waveform is low, the v e error obtained in the formula (9) is small, and the high-precision speed ambiguity can be obtained by adopting a peak sidelobe difference criterion. Obviously, we can conclude that under the peak sidelobe difference criterion, the fuzzy number estimation of the speed is processed in combination with the keystone transformation, so that the coarse non-fuzzy speed with higher precision can be obtained.
S600, converting the converted frequency cognition waveform signal from an azimuth frequency domain to an azimuth time domain to obtain a signal echo after matched filtering and range migration correction, wherein the signal echo is expressed as follows:
where σ 3 is the signal amplitude of the random frequency coherent encoded waveform in the τ -t l domain, B is the signal bandwidth, and t l is the slow time.
Noteworthy are: aiming at the range unit migration and speed ambiguity correction in the frequency agility system, coarse speed estimation is carried out in the frequency cognitive waveform echo through combined processing of speed ambiguity estimation and trapezoidal transformation, and then the coarse speed estimation is realized based on a new peak sidelobe difference criterion. Thus, range bin migration correction can be successfully accomplished in random frequency coherently encoded waveform echoes. Compared with the radon transformation, the combined processing of the velocity fuzzy number estimation and the trapezoidal transformation has better coarse velocity estimation precision and noise robustness.
S700, processing the coarse speed fuzzy number evaluation value by utilizing non-uniform discrete Fourier transform to obtain the speed fuzzy number of the random frequency coherent coding waveform;
for frequency agile incoherent echoes, we use the improved non-uniform discrete fourier transform concept to achieve accurate detection of high speed targets, achieving high practicality and fast deblurring.
S710, defining a non-uniform discrete Fourier transform formula expressed as:
Wherein ω n＝0,1…,L,s1(tl) is
S720, processing the velocity ambiguity rough evaluation value by utilizing non-uniform discrete Fourier transform to obtain velocity ambiguity of the random frequency coherent encoding waveform, wherein the velocity ambiguity of the random frequency coherent encoding waveform is obtained based on the rough velocity V e
S800, determining Doppler channel speed by using the speed fuzzy number of the random frequency coherent coding waveform, and determining the energy of the high-speed target by using the Doppler channel speed so as to obtain the actual position of the determined high-speed target to realize accurate target detection.
The S800 of the present invention includes:
S810, determining any Doppler channel speed by using the speed ambiguity of the random frequency coherent code waveform, wherein the first Doppler channel speed can be expressed as
S820, replacing the phase term in the signal echo after the matched filtering and the range migration correction by Doppler channel speed to obtain a non-uniform discrete Fourier transform factor, and replacing the phase term in (12) with (15), wherein the non-uniform discrete Fourier transform factor can be deduced as
W(l,x)＝exp(-j4πflvltl/c) (16)；
S830, performing non-uniform discrete Fourier transform on the signal echo after the matched filtering and the range migration correction by using a non-uniform discrete Fourier transform factor to obtain the energy of the high-speed target, and after the non-uniform discrete Fourier transform is performed on the step (12), fully accumulating the energy of the high-speed target, namely:
Where σ 4 is the amplitude of the signal in the τ -x domain.
S840, determining the actual position of the high-speed target by using the energy of the high-speed target. It can be found here that the high-speed target energy is concentrated in x columns, denoted as:
Aiming at the incoherent echo of frequency agility, the invention provides an improved non-uniform discrete Fourier transform coherent accumulation method, which realizes the focusing of a high-speed moving target. Compared with a maximum likelihood estimation method and a generalized trapezoidal transformation method, the coherent accumulation method provided by the invention has higher speed deblurring and detection precision and lower calculation complexity.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Although the application is described herein in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (10)
1. A frequency agile cognitive waveform design method for accurate detection of a high speed moving object, comprising:
S100, constructing a random frequency coherent coding waveform signal based on a linear frequency modulation signal;
S200, extracting waveform signals with the same frequency from the random frequency coherent coding waveform signals, and forming the waveform signals with the same frequency into frequency cognitive waveform signals;
S300, performing pulse compression processing on the frequency cognitive waveform signal to obtain a frequency cognitive waveform signal after pulse compression, and determining a data relationship between a non-blurring speed of the frequency cognitive waveform signal and a non-blurring speed of the random frequency coherent coding waveform signal;
s400, compensating the frequency cognitive waveform signal after pulse compression for a blur-free speed, and performing keystone conversion to obtain a converted frequency cognitive waveform signal;
s500, evaluating the focusing performance of the transformed frequency cognitive waveform signal by using a new peak sidelobe difference criterion to obtain a speed fuzzy number evaluation value for evaluating the focusing performance;
S600, converting the converted frequency cognition waveform signals from an azimuth frequency domain to an azimuth time domain to obtain signal echoes after matched filtering and range migration correction;
S700, processing the coarse speed fuzzy number evaluation value by utilizing non-uniform discrete Fourier transform to obtain the speed fuzzy number of the random frequency coherent coding waveform;
s800, determining Doppler channel speed by using the speed fuzzy number of the random frequency coherent coding waveform, and determining the energy of the high-speed target by using the Doppler channel speed so as to obtain the actual position of the determined high-speed target to realize accurate target detection.
2. The frequency agile cognitive waveform design method for accurate detection of a high speed moving object of claim 1, wherein the random frequency coherently encoded waveform signals comprise a plurality of sets of waveform signals, each set of waveform signals comprising N frequency diverse chirp signals; all groups have a chirp signal of the same frequency at the same location.
3. The frequency agile cognitive waveform design method for accurate detection of high speed moving objects of claim 2, wherein the chirp signal in S100 is represented as:
wherein, rect (·) is a rectangular window function, T is pulse time width, gamma is linear frequency adjustment, and τ is fast time;
The random frequency coherent encoded waveform signal in S100 is represented as:
s(t)＝p(τ)·exp(j2πflt) (2)
Wherein f l＝f0 +Δf (L) is the carrier frequency of the first pulse, l= [1,2, the L, L is the total number of pulses of all pulses; f 0 is the standard carrier frequency, Δf (l) is the frequency hopping interval of the first pulse; t=τ+l·t is the full time, T is the pulse repetition interval.
4. The frequency agile cognitive waveform design method for accurate detection of a high speed moving object according to claim 3, wherein the pulse compressed frequency cognitive waveform signal in S300 is represented as: :
Where t m is slow time, σ 1 is the scattering coefficient of the target, R' (t m)＝R0+vtm is the pitch, t m = (m-1) ·nt, v is the target movement speed, f r is the distance frequency, R 0 is the initial pitch, and c is the light speed.
5. The frequency agile cognitive waveform design method for accurate detection of high speed moving objects of claim 4, wherein the data relationship is that the non-ambiguous speed of the frequency cognitive waveform signal is 1/N of the non-ambiguous speed of a random frequency coherently encoded waveform signal, expressed as:
Where v max is the non-ambiguity speed of the random frequency coherent encoded waveform signal, v' max is the non-ambiguity speed of the frequency cognitive waveform signal, and f 3 is the waveform signal with the same frequency in S200.
6. The frequency agile cognitive waveform design method for accurate detection of high speed moving objects of claim 5, wherein S400 comprises:
S410, constructing a fuzzy speed compensation function, which is expressed as:
Hk＝exp[j4πk(f3+fr)v′maxtm/c] (5)；
wherein K is a velocity fuzzy number, k= [1,2 …, K ], and K is the maximum iteration number;
s420, performing non-blurring speed compensation on the pulse compressed frequency cognitive waveform signal by using the blurring speed compensation function to obtain a compensated frequency cognitive waveform signal, wherein the compensated frequency cognitive waveform signal is expressed as:
S430, correcting the frequency cognitive waveform signal after compensation by using a range migration correction formula of a keystone conversion algorithm to obtain a corrected frequency cognitive waveform signal;
wherein, the range migration correction formula is expressed as:
(f3+fr)·tm＝f3·t'm (7)；
The corrected frequency-aware waveform signal is expressed as:
where v k＝v-k·v'max is the residual velocity and t' m is the new slow time after correction;
S440, performing Fourier transform on the corrected frequency cognitive waveform signal in the azimuth direction and performing inverse Fourier transform on the corrected frequency cognitive waveform signal in the distance direction to obtain a transformed frequency cognitive waveform signal, wherein the transformed frequency cognitive waveform signal is expressed as:
Where σ 2 denotes the signal amplitude of the frequency-aware waveform in the τf a domain and f a denotes the Doppler frequency in azimuth.
7. The frequency agile cognitive waveform design method for accurate detection of high speed moving objects of claim 6, wherein the new peak sidelobe difference criterion in S500 is expressed as:
Px＝(Pmain-Pleft)+(Pmain-Pright)) (10)；
Wherein P main is the peak value of the main lobe of the range, P left is the peak value P right from the left lobe and P right from the right lobe;
The speed ambiguity estimation value is expressed as:
Ve＝v′max·ke+ve (11)；
Where v e is the blur-free speed relative to k e and satisfies F c is carrier frequency, and k e is speed ambiguity number.
8. The frequency agile cognitive waveform design method for accurate detection of high speed moving objects of claim 7, wherein the matched filtered and range migration corrected signal echoes in S600 are represented as:
where σ 3 is the signal amplitude of the random frequency coherent encoded waveform in the τ -t l domain, B is the signal bandwidth, and t l is the slow time.
9. The frequency agile cognitive waveform design method for accurate detection of high speed moving objects of claim 8, wherein S700 comprises:
S710, defining a non-uniform discrete Fourier transform formula expressed as:
Wherein ω n＝0,1…,L,s1(tl) is
S720, processing the coarse speed fuzzy number evaluation value by utilizing non-uniform discrete Fourier transform to obtain the speed fuzzy number of the random frequency coherent coding waveform, wherein the speed fuzzy number is expressed as:
10. the frequency agile cognitive waveform design method for accurate detection of high speed moving objects of claim 9, wherein S800 comprises:
S810, determining any Doppler channel speed by using the speed fuzzy number of the random frequency coherent coding waveform, wherein the first Doppler channel speed is expressed as:
s820, replacing a phase term in the signal echo after the matched filtering and the range migration correction by Doppler channel speed to obtain a non-uniform discrete Fourier transform factor, wherein the non-uniform discrete Fourier transform factor is expressed as follows:
W(l,x)＝exp(-j4πflvltl/c) (16)；
s830, performing non-uniform discrete Fourier transform on the signal echo after the matched filtering and the range migration correction by using a non-uniform discrete Fourier transform factor to obtain energy of a high-speed target, wherein the energy is expressed as follows:
Where σ 4 is the amplitude of the signal in the τ -x domain;
S840, determining the actual position of the high-speed target by using the energy of the high-speed target, wherein the actual position is expressed as:
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