CN113589250A - Sparse learning coherent agile frequency conversion radar distance high-resolution processing method - Google Patents
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- 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
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Abstract
The invention discloses a sparse learning coherent agile frequency conversion radar distance high resolution processing method, wherein coherent refers to the fact that a determined phase relation exists among pulses, and the signal-to-noise ratio can be improved and high distance resolution can be obtained by means of coherent processing of the pulses. The frequency agility refers to that the carrier frequency of each pulse in a pulse train transmitted by a radar is randomly selected in a frequency band. Due to the fact that frequency spectrum of the frequency agile signal is discontinuous, the range sidelobe of the frequency agile signal is often high, and the problem that a weak target or a plurality of target sidelobes are synthesized into a false target by a strong target is generally caused. The invention synthesizes the coherent frequency agile signals into a high-resolution range profile as a sparse signal reconstruction problem, constructs a Bayesian model and estimates model parameters by utilizing a maximum posterior method. And solving a parameter updating expression by minimizing the target function, and finally realizing the distance high-resolution processing of the coherent agile frequency conversion radar. The method is suitable for the problems of range sidelobe suppression, synthesis of high-resolution range profile and the like of the coherent frequency agile radar or the discontinuous spectrum signal.
Description
Technical Field
The invention relates to the technical field of radar signal processing, in particular to a sparse learning coherent agile frequency conversion radar distance high-resolution processing method.
Background
The initial phase relation of each pulse in the coherent frequency agile radar transmitting pulse train is determined, and the carrier frequency randomly jumps in the frequency band range. The coherent agile frequency conversion radar has the following advantages: from the perspective of electronic countermeasure, the discontinuity of the signal spectrum avoids concentrating high energy in a narrow frequency band, thereby reducing the possibility of being discovered by an adversary; the coherent agile frequency conversion radar synthesizes large broadband signals by less pulses, and the system resolution can be ensured; the coherent agile frequency radar also has the advantages of pulse signals. However, due to the discontinuity of the signal spectrum, the range sidelobe is often high, which often causes the problem that the strong target sidelobe submerges the weak target or a plurality of target sidelobes synthesize a false target, and the like, thereby causing the situations of false target generation, target loss and the like, and affecting the radar working performance. In addition, the electromagnetic environment of the radar is increasingly complex at present, and a clean frequency band is difficult to find, so that the research on the coherent agile frequency conversion radar high-resolution distance image synthesis method has great significance.
Aiming at the problem of sidelobe suppression of coherent agile frequency conversion radar, the purpose of reducing distance sidelobe can be achieved by selecting the optimal frequency point combination through a signal waveform design approach (see the research of random frequency hopping signal design and processing technology, Pengjiang, national defense science and technology university, 2016), but the algorithm can not be used when the available frequency band is limited, and the algorithm usually needs to consume more calculation time; the matched filter can also be weighted in a frequency domain by utilizing a spectrum correction algorithm (see the application of the spectrum correction algorithm in sidelobe suppression of random frequency hopping interference, warm commander and the like, electronic warship countermeasure in 2018, and the 4 th period in detail), but the method has the cost of losing the signal-to-noise ratio; or an algorithm based on compressed sensing is utilized, for example, a cross vector is constructed by dividing a speed grid, a semi-definite programming (SDP) problem is constructed according to a signal model and a cross modulation vector, and a distance parameter estimation value is obtained by solving the SDP problem, but the construction of the cross modulation vector is based on the speed grid and is greatly influenced by grid intervals (see patent CN201910132135.6, a method and a device for fast-conversion radar speed-distance parameter joint estimation, yellow sky shine and the like). The invention provides a sparse learning coherent agile frequency conversion radar distance high-resolution processing method, aiming at solving the problems of strong range sidelobe randomness, large amplitude and the like of the coherent agile frequency conversion radar.
Disclosure of Invention
The invention aims to provide a high-resolution processing method for the distance of a coherent frequency agile radar for sparse learning, which is characterized in that a high-resolution range profile synthesized by the coherent frequency agile radar is used as a sparse signal reconstruction problem, a Bayesian model is constructed, model parameters are estimated by using a maximum posterior method, a target function is established, a formula is updated by using minimum solving parameters of the target function, and finally a sparse range profile is obtained, so that the high-resolution processing of the distance of the coherent frequency agile radar is realized.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a method for processing distance high resolution of a sparse learning coherent agile frequency conversion radar comprises the following steps:
s1, randomly selecting a certain number of frequencies from the frequency set as the frequency stepping quantity of each pulse relative to the central frequency, and constructing a coherent frequency agile transmitting signal of one period;
s2, constructing a sparse signal reconstruction model, and taking a DFT matrix as a dictionary matrix;
s3, initializing a P matrix, a range profile and noise power;
s4, updating the distance image, the noise power and the P matrix for each iteration;
s5, judging whether the iteration result is converged or whether the iteration frequency reaches the upper limit; if yes, go to S6; otherwise, go to S4;
and S6, stopping iteration and outputting a high-resolution range profile.
Optionally, the step S1 specifically includes:
let the center frequency be fcPulse width of TpWith a pulse repetition interval of TrRandomly selecting M (M < N) frequencies from a frequency set {0, delta f, …, (N-1) delta f } as frequency stepping f of each pulse relative to a center frequencymThen the carrier frequency of each pulse is Fm＝fc+fmM is 1, …, M; then the signal of one waveform period transmitted by the coherent agile frequency conversion radar is:
where rect [. cndot.) is a rectangular window function.
Optionally, the step S2 specifically includes:
considering the synthesized high-resolution range profile as a sparse signal reconstruction problem, the sparse signal reconstruction model is as follows:
y＝Ax+ε
wherein ε ∈ CM×1For noise, x ∈ CN×1For sparse range-images, y ∈ CM×1For frequency domain sample data of random frequency hopping echo signal, dictionary matrix A belongs to CM×NFor the DFT matrix:
optionally, the step S3 specifically includes:
initializing a P matrix, a range profile x and a noise power eta;
the P matrix is initialized with a matched filter:
wherein, anIs the nth column of the dictionary matrix a;
according to pn＝|xn|2-qEach element in the initialization range profile is
According to the expression of eta, eta is initially set to
Optionally, the step S4 specifically includes:
iteratively updating the distance image x, the noise power eta and the P matrix;
x(t+1)＝P(t)AH(AP(t)AH+η(t)I)-1y
P(t+1)＝diag{p(t+1)},p＝[p1,p2,...,pN],
optionally, the step S5 specifically includes:
judging whether iteration satisfies | | x(t)-x(t-1)||2/||x(t)||2< Δ (Δ is a minimum value greater than zero) or overlap
The generation number reaches the upper limit, if yes, go to S6; otherwise, go to S4 to continue the iteration.
Compared with the prior art, the invention has the following advantages:
according to the method, the high-resolution range profile synthesized by the coherent frequency agile radar is regarded as a sparse signal reconstruction problem, a Bayesian model is constructed to describe the sparse signal reconstruction problem, parameters are estimated by using a maximum posterior method, and the parameters are iteratively updated by using a minimum objective function solving parameter updating method to finally obtain the reconstructed sparse range profile. The method for restraining the range sidelobe is provided for solving the problems that the coherent frequency agile radar has high range sidelobe and strong randomness and is easy to generate a target sidelobe synthesis false target and a strong target sidelobe submerges a weak target, and can be used for synthesizing a high-resolution range profile by the coherent frequency agile radar.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention patent, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a distance image of a conventional NDFT synthesis;
FIG. 3 is a composite range image according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. It is to be noted that the drawings are in a very simplified form and are all used in a non-precise scale for the purpose of facilitating and distinctly aiding in the description of the embodiments of the present invention. To make the objects, features and advantages of the present invention comprehensible, reference is made to the accompanying drawings. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the implementation conditions of the present invention, so that the present invention has no technical significance, and any structural modification, ratio relationship change or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
Referring to fig. 1, the present embodiment provides a method for distance high-resolution processing of a coherent agile radar for sparse learning, where the coherent agile radar is a pulse system radar, coherent refers to a determined phase relationship between pulses, and coherent processing of the pulses can improve a signal-to-noise ratio and obtain high distance resolution. The frequency agility refers to that the carrier frequency of each pulse in a pulse train transmitted by a radar is randomly selected in a frequency band. Due to the fact that frequency spectrum of the frequency agile signal is discontinuous, the range sidelobe of the frequency agile signal is often high, and the problem that a weak target or a plurality of target sidelobes are synthesized into a false target by a strong target is generally caused. In order to solve the problem, the invention synthesizes the coherent frequency agile signals into a high-resolution range profile as a sparse signal reconstruction problem, constructs a Bayes model and estimates model parameters by using a maximum posterior method. And solving a parameter updating expression by minimizing the target function, and finally realizing the distance high-resolution processing of the coherent agile frequency conversion radar. The method is suitable for the problems of range sidelobe suppression, synthesis of high-resolution range profile and the like of the coherent frequency agile radar or the discontinuous spectrum signal.
The method for processing the distance of the sparse learning coherent agile frequency conversion radar in the embodiment specifically comprises the following steps:
s1, randomly selecting a certain number of frequencies from the frequency set as the frequency stepping quantity of each pulse relative to the central frequency, and constructing a coherent frequency agile transmitting signal of one period;
s2, constructing a sparse signal reconstruction model, and taking a DFT matrix as a dictionary matrix;
s3, initializing a P matrix, a range profile and noise power;
s4, updating the distance image, the noise power and the P matrix for each iteration;
s5, judging whether the iteration result is converged or whether the iteration frequency reaches the upper limit; if yes, go to S6; otherwise, go to S4;
and S6, stopping iteration and outputting a high-resolution range profile.
In this embodiment, the step S1 specifically includes:
let the center frequency be fcPulse width of TpWith a pulse repetition interval of TrRandomly selecting M (M < N) frequencies from a frequency set {0, delta f, …, (N-1) delta f } as frequency stepping f of each pulse relative to a center frequencymThen the carrier frequency of each pulse is Fm＝fc+fmM is 1, …, M; then the signal of one waveform period transmitted by the coherent agile frequency conversion radar is:
where rect [. cndot.) is a rectangular window function.
In this embodiment, the step S2 specifically includes:
considering the synthesized high-resolution range profile as a sparse signal reconstruction problem, the sparse signal reconstruction model is as follows:
y＝Ax+ε
wherein ε ∈ CM×1For noise, x ∈ CN×1For sparse range-images, y ∈ CM×1For frequency domain sample data of random frequency hopping echo signal, dictionary matrix A belongs to CM×NFor the DFT matrix:
in this embodiment, the step S3 specifically includes:
initializing a P matrix, a range profile x and a noise power eta;
the P matrix is initialized with a matched filter:
wherein, anIs the nth column of the dictionary matrix a;
according to pn＝|xn|2-qEach element in the initialization range profile is
According to the expression of eta, eta is initially set to
In this embodiment, the step S4 specifically includes:
iteratively updating the distance image x, the noise power eta and the P matrix;
x(t+1)＝P(t)AH(AP(t)AH+η(t)I)-1y
P(t+1)＝diag{p(t+1)},p＝[p1,p2,...,pN],
in this embodiment, the step S5 specifically includes:
judging whether iteration satisfies | | x(t)-x(t-1)||2/||x(t)||2If Δ is less than Δ (Δ is a minimum value greater than zero) or the number of iterations reaches an upper limit, go to S6; otherwise, go to S4 to continue the iteration.
The parameters of the radar signal are set in the embodiment as follows: center frequency of fcThe frequency stepping interval Δ f is 4MHz at 10GHz, the number of available frequency points N is 512, and the number of randomly extracted frequency points M is 64. The range resolution rho can be calculated according to the radar signal parametersrc/2/(N · Δ f) is 0.0732m, where c is the speed of light. The parameters of the target are: the number of the targets is 3, and the corresponding distances of the target pointsThe separation is [16.0000, 16.0659, 17.4648]. The upper limit of the number of iterations is set to 20, the norm q is 0.9, and Δ is 0.0001.
Fig. 2 shows that the phase-coherent agile frequency radar uses a conventional range profile synthesis method, that is, a range profile obtained by performing NDFT on a frequency domain sample, and it can be seen that under the simulation parameters, a side lobe of the range profile obtained by NDFT processing is higher, and a first target and a second target cannot be resolved because the interval is smaller than a resolution.
FIG. 3 shows a high-resolution range profile synthesized by the method of the present invention, from which it can be seen that range sidelobes are significantly suppressed and three targets can be resolved, demonstrating that the method of the present invention can achieve coherent agile frequency radar range high-resolution processing.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (6)
1. A method for processing the distance of a sparse learning coherent agile frequency conversion radar with high resolution is characterized by comprising the following steps:
s1, randomly selecting a certain number of frequencies from the frequency set as the frequency stepping quantity of each pulse relative to the central frequency, and constructing a coherent frequency agile transmitting signal of one period;
s2, constructing a sparse signal reconstruction model, and taking a DFT matrix as a dictionary matrix;
s3, initializing a P matrix, a range profile and noise power;
s4, updating the distance image, the noise power and the P matrix for each iteration;
s5, judging whether the iteration result is converged or whether the iteration frequency reaches the upper limit; if yes, go to S6;
otherwise, go to S4;
and S6, stopping iteration and outputting a high-resolution range profile.
2. The sparsely learned coherent agile frequency radar distance high resolution processing method according to claim 1, wherein the step S1 specifically comprises:
let the center frequency be fcPulse width of TpWith a pulse repetition interval of TrRandomly selecting M (M < N) frequencies from a frequency set {0, delta f, …, (N-1) delta f } as frequency stepping f of each pulse relative to a center frequencymThen the carrier frequency of each pulse is Fm＝fc+fmM is 1, …, M; then the signal of one waveform period transmitted by the coherent agile frequency conversion radar is:
where rect [. cndot.) is a rectangular window function.
3. The sparsely learned coherent agile frequency radar distance high resolution processing method according to claim 1, wherein the step S2 specifically comprises:
considering the synthesized high-resolution range profile as a sparse signal reconstruction problem, the sparse signal reconstruction model is as follows:
y＝Ax+ε
wherein ε ∈ CM×1For noise, x ∈ CN×1For sparse range-images, y ∈ CM×1For frequency domain sample data of random frequency hopping echo signal, dictionary matrix A belongs to CM×NFor the DFT matrix:
4. the sparsely learned coherent agile frequency radar distance high resolution processing method according to claim 1, wherein the step S3 specifically comprises:
initializing a P matrix, a range profile x and a noise power eta;
the P matrix is initialized with a matched filter:
wherein, anIs the nth column of the dictionary matrix a;
according to pn＝|xn|2-qEach element in the initialization range profile is
According to the expression of eta, eta is initially set to
5. The sparsely learned coherent agile frequency radar distance high resolution processing method according to claim 1, wherein the step S4 specifically comprises:
iteratively updating the distance image x, the noise power eta and the P matrix;
6. the sparsely learned coherent agile frequency radar distance high resolution processing method according to claim 1, wherein the step S5 specifically comprises:
determining whether iteration is satisfied
(Δ is a minimum value greater than zero) or the number of iterations reaches an upper limit, if yes, go to S6; otherwise, go to S4 to continue the iteration.
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