CN113917421A - Distributed radar main lobe interference suppression method based on cascaded LMS filter - Google Patents
Distributed radar main lobe interference suppression method based on cascaded LMS filter Download PDFInfo
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Abstract
The invention discloses a distributed radar main lobe interference suppression method based on a cascaded LMS filter, which can effectively and steadily suppress main radar echo interference through two-stage filter cascade. Firstly, receiving a target and an interference signal by using a main radar, and receiving the interference signal by using a large-aperture coherent array with extremely narrow beams, which is formed by sparse deployment of a plurality of auxiliary radars; then, the main radar and the large-aperture coherent receiving array signals are used as input, and the fast time-slow time cascade LMS filter is adopted to carry out interference cancellation (wiener filtering) processing on the main radar echo. The fast time LMS filter can realize fast time stable tracking and filtering of the interference parameters, the slow time LMS filter can realize slow time stable tracking and filtering of the interference parameters, and the two-stage filter cascade can realize effective and steady suppression of the echo interference of the main radar.
Description
Technical Field
The invention relates to the field of radar main lobe interference suppression processing, in particular to a distributed radar main lobe interference suppression method based on a cascaded LMS filter.
Background
The electromagnetic environment of modern battlefield is increasingly complex, and electromagnetic interference becomes an important bottleneck problem limiting the working efficiency of radar. As a typical interference pattern, main lobe interference is modulated by the gain of a radar antenna, interference energy is larger, interference effect is more obvious, and the traditional side lobe interference suppression processing method cannot effectively suppress the main lobe interference.
Aiming at the problem of radar main lobe interference suppression, the traditional spatial domain processing method is to form a directional diagram zero point in the interference direction, but is limited by the aperture constraint of a single-base radar, the directional diagram after self-adaptive beam forming is distorted, and the target energy loss is large. In view of this, Yang x, Yin P, Zeng t, et al (Yang x, Yin P, Zeng t, et al. applying an automatic array to a present main interference for a group-based radar [ J ]. IEEE Antennas and Wireless transmission receivers, 2013,12: 433-. Zhang, j.luo (h.zhang, j.luo, x.chen, q.liu and t.zeng, whiting filter for mainloop interference suppression in distributed array RADAR, proc.cie int.conf.radar, pp.1-5,2016.) a Whitening filter is used to achieve robust main lobe interference suppression performance on the basis of distributed RADAR. Jian Tie Zhen, Liao Tongqing (Jian Tie Zhen, Liao Tongqing. distributed radar anti-main lobe interference method research [ J ]. proceedings of Chinese institute of electronic sciences, 2015,10(04): 389-. The researches show that the distributed radar system has obvious performance advantages in main lobe interference suppression and wide application prospects. On one hand, the distributed radar system can effectively improve the angular resolution of the system by increasing the array aperture, so that the width of a main lobe of a synthetic directional diagram is narrowed: when the spatial domain adaptive filtering processing is adopted, the interference signal originally positioned in the main lobe of the directional diagram of the single-base radar falls into the side lobe of the synthetic array directional diagram, so that the target energy loss caused by the interference suppression processing can be effectively reduced, and a better interference suppression effect is realized; when the time domain adaptive filtering processing is adopted, due to the angle high-resolution characteristic of the distributed radar, the interference and the target can be distinguished from the space domain, so that a purer interference sample can be obtained as a reference signal, and the target energy loss caused by the time domain filtering processing can be effectively reduced. On the other hand, since the interference signal may have slow changes of parameters such as doppler and power in a slow time, the problem cannot be effectively solved in both spatial domain and time domain adaptive filtering processing, which results in a loss of multi-frame accumulation processing performance.
Therefore, on the basis of a distributed radar system, the research on a robust main lobe interference suppression method has important practical significance and application value.
Disclosure of Invention
In view of this, the invention provides a distributed radar main lobe interference suppression method based on a cascaded LMS filter, which can realize effective and robust suppression of main radar echo interference through two-stage filter cascade.
In order to achieve the purpose, the technical scheme of the invention is as follows: a distributed radar main lobe interference suppression method based on a cascaded LMS filter is disclosed, wherein a distributed radar system consists of a main radar and a plurality of auxiliary radars; the auxiliary radars are sparsely and dispersedly arranged around the main radar to form a sparse coherent receiving array, the aperture of the sparse coherent receiving array is larger than that of the main radar, the sparse dispersive arrangement of the auxiliary radars means that two adjacent auxiliary radars are mutually independent in space, the distance between antenna phase centers on the auxiliary radars is ten times to one hundred times of half wavelength, and the main radar is connected with each auxiliary radar through time-frequency and time-sequence synchronization equipment to ensure the working coherence; the distributed radar main lobe interference suppression method comprises the following steps:
step one, a main radar wave beam is aligned to a certain detection airspace emission signal, and a main radar echo signal is obtained; the sparse coherent receiving array forms a very narrow beam, scans a space domain in a main lobe beam range of the main radar and acquires an array echo of the sparse coherent receiving array under each beam pointing angle; the beam width of the extremely narrow beam of the sparse coherent array is far smaller than that of the main radar beam (far smaller in the embodiment of the present invention means that the extremely narrow beam width is one tenth of that of the main radar beam, and may be 0.05 degrees specifically).
Step two, carrying out interference identification on the sparse large-aperture coherent receiving array echo, and judging whether interference exists or not and the interference type; and if a plurality of interferences exist, giving the angle of each interference and using the sparse large-aperture coherent receiving array echo under the angle as a corresponding interference reference signal.
Step three, under each interference angle, executing the following steps:
the method comprises the steps of taking a main radar fast time echo as a main channel signal, taking a sparse coherent receiving array fast time echo as a reference signal, adopting a fast time LMS filter to execute a fast time LMS filtering process on the main radar fast time echo to obtain a filtering intermediate processing result, and circulating the fast time LMS filtering process for multiple times to obtain multiple pulse fast time LMS filtering processing results.
And then, taking the main radar slow time echo as a main channel signal, taking the slow time echo of the fast time LMS filtering intermediate processing result as a reference signal, adopting a slow time LMS filter to execute a slow time LMS filtering process on the main radar slow time echo, circulating the slow time LMS filtering process for multiple times, obtaining the slow time LMS filtering processing results of multiple sampling points, and further obtaining a final cascading filtering processing result.
And the cascade filtering processing results obtained under all the interference angles form a final interference cancellation processing result.
And step four, performing pulse compression processing on each sub pulse in the interference cancellation processing result to obtain a multi-frame one-dimensional range profile.
And fifthly, performing coherent processing on the multi-frame one-dimensional range profile to obtain a multi-frame joint accumulation processing result.
Further, the fast-time LMS filter is an N-order digital filter with step size μ.
Further, the fast-time LMS filtering process specifically includes the following steps:
step 1: setting the serial number of the current processing pulse as m, and setting the initial value of m as 1.
Step 2: note that the intermediate result of the fast time LMS filter weight vector operation at the nth sampling point of the mth pulse is
Wherein Q is the number of sampling points contained in the pulse, and the fast time sampling time is tn，n∈[1,…,Q]。
And t is not less than 01＜t2＜…＜tQT is less than or equal to T, and T is pulse repetition time PRT.
When n is 1, i.e. t1At the moment of time, the time of day,
the adaptive filtering output of the fast-time LMS filter is:
e(m,tn)＝d(m,tn)，d(m,tn) For the nth sampling point (t) of the mth pulsenTime) of the main channel signal of the fast-time LMS filter;
step 3: determining the nth sample point (t) of the mth pulsenTime) the reference signal of the fast-time LMS filter is:
x(m,tn)＝[x(m,tn) x(m,tn-1) … x(m,tn-N+1)]T；
wherein x (m, t)n) x(m,tn-1) … x(m,tn-N+1) Fast time echoes, t, respectively, for sparse coherent receive arraysn tn-1 … tn-N+1Fast time sampling points;
the intermediate result of the fast-time LMS filter weight vector operation at the (n + 1) th sampling point of the mth pulse is
then in the m pulseDash the n +1 th sampling point (t)n+1Time moment), the corresponding output signal after weighted summation of the fast-time LMS filter, namely the intermediate processing result of the fast-time LMS filter is y (m, t)n+1)：
N +1 sampling point (t) of mth pulsen+1Time of day) is e (m, t)n+1)＝d(m,tn+1)-y(m,tn+1)
Step 4: enabling n to be increased by 1, if n is Q, entering Step 5, and otherwise, returning to Step 3;
step 5: let the final fast-time LMS filter weight vector
Recalculating the nth sample point (t) of the mth pulsenTime of day) of the fast-time LMS filter is obtained as a result of the weighted summation of the fast-time LMS filter
Step 6: if M is equal to M, the process is terminated, otherwise, M is incremented by 1 and the process returns to Step 2.
Further, the slow-time LMS filter is a K-order digital filter with a step size η.
Further, the slow-time LMS filtering process specifically includes the following steps:
SS 1: setting the sequence number of the sampling point of the current processing fast time as n, wherein the initial value of n is 1.
SS 2: the middle result of the slow time LMS filtering weight vector operation at the mth pulse of the nth sampling point is recorded as
Where M is the total number of pulses and the fast time sampling point time is tn，n∈[1,…,Q]Q is the number of sampling points contained in the pulse; and t is not less than 01＜t2＜…＜tQ≤T，T is the pulse repetition time PRT.
When m is 1, i.e. t1At the moment of time, the time of day,
t1the adaptive filtering output of the slow time LMS filter corresponding to the mth pulse at the moment is:
f(m,tn)＝d(m,tn)，d(m,tn) Is the nth sampling point (t)nTime instant) of the main channel signal of the slow-time LMS filter corresponding to the mth pulse.
SS 3: taking a slow time echo of a fast time LMS filtering intermediate processing result as a reference signal of a slow time LMS filter, specifically
y(m,tn)＝[y(m,tn) y(m-1,tn) … y(m-K+1,tn)]T；
Wherein y (m, t)n) y(m-1,tn) … y(m-K+1,tn) Are each tnK pulses before time m correspond to a value.
Then, at the m +1 th pulse of the nth sampling point, the intermediate result of the slow-time LMS filtering weight vector operation is:
Then at the (m + 1) th pulse at the nth sampling point, the result of the weighted summation of the slow-time LMS filter is:
wherein y (m +1, t)n) Is the nth sampling point (t)nTime) the reference signal corresponding to the m +1 th pulse;
the nth sample point (t)nTime) m +1 th pulse, the output result of the adaptive filter of the slow time LMS filter corresponding to the m +1 th pulse is as follows: f (m +1, t)n)＝d(m+1,tn)-z(m+1,tn)；d(m+1,tn) Is the nth sampling point (t)nTime) m +1 th pulse corresponding slow time LMS filteringThe main channel signal of the device.
SS 4: and (5) enabling M to be increased by 1, if M is equal to M, entering SS5, and otherwise, repeating SS 3.
SS 5: let the final slow-time LMS filter weight vector
Recalculating tnThe result of the weighted summation of the slow-time LMS filter corresponding to the mth pulse at the moment is:
recalculating tnThe adaptive filtering output of the slow time LMS filter corresponding to the mth pulse at the moment is:
f(m,tn)＝d(m,tn)-z(m,tn)m＝1,2,…,M
SS 6: if n is Q, the process is terminated, otherwise n is n +1, and the process returns to SS 2.
Has the advantages that:
the invention provides a robust distributed radar main lobe interference suppression method. Firstly, receiving a target and an interference signal by using a main radar, and receiving the interference signal by using a large-aperture coherent array with extremely narrow beams, which is formed by sparse deployment of a plurality of auxiliary radars; then, the main radar and the large-aperture coherent receiving array signals are used as input, and the fast time-slow time cascade LMS filter is adopted to carry out interference cancellation (wiener filtering) processing on the main radar echo. The fast time LMS filter can realize fast time stable tracking and filtering of the interference parameters, the slow time LMS filter can realize slow time stable tracking and filtering of the interference parameters, and the two-stage filter cascade can realize effective and steady suppression of the echo interference of the main radar.
Drawings
FIG. 1 is a schematic diagram of a typical distributed radar system detection;
FIG. 2 is a schematic diagram of a baseline relationship between a main radar and a sparse large aperture coherent receiving array;
FIG. 3 is a schematic diagram of a fast time-slow time cascaded LMS filter structure;
FIG. 4 is a schematic diagram of a fast-time LMS filter;
FIG. 5 is a schematic diagram of a slow-time LMS filter;
fig. 6 is a schematic diagram of a one-dimensional distance image before and after interference suppression.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
A typical distributed radar system detection scenario is shown in fig. 1. The distributed radar system consists of a main radar and a plurality of auxiliary radars sparsely and dispersedly arranged around the main radar, wherein the plurality of auxiliary radars sparsely and dispersedly arranged around the main radar to form a sparse coherent receiving array, the aperture of the sparse coherent receiving array is larger than that of the main radar, the sparse dispersive arrangement of the plurality of auxiliary radars means that two adjacent auxiliary radars are mutually independent in space, the distance between antennas on the auxiliary radars is ten times to hundred times of half wavelength, the main radar and each auxiliary radar are connected by time-frequency and time-sequence synchronous equipment to ensure the working coherence, and the plurality of dispersedly arranged auxiliary radars can form a sparse large-aperture coherent receiving array with extremely narrow beams.
In consideration of the existence of grating lobes in a sparse large-aperture coherent receiving array pattern, array configuration optimization or other methods can be generally adopted to reduce the influence of the grating lobes, and in the invention, the sparse large-aperture coherent receiving array is assumed to have lower grating lobes. Accordingly, the distributed radar main lobe interference suppression method based on the cascaded LMS filter comprises the following specific steps:
step one, a main radar wave beam is aligned to a certain detection airspace emission signal, and a main radar echo signal is obtained; the sparse coherent receiving array forms a very narrow beam, scans a space domain in a main lobe beam range of the main radar and acquires an array echo of the sparse coherent receiving array under each beam pointing angle; the beam width of the extremely narrow beam of the sparse coherent array is far smaller than that of the main radar beam, which means that the extremely narrow beam is one tenth of that of the main radar beam, and may be 0.05 degrees.
And acquiring distributed radar echoes.
Let the main radar echo be
Where m is the slow time pulse number, t is the fast time axis, θ0Is the current beam pointing angle of the main radar, s (t) is the main radar transmission signal, Ak(m) is the echo amplitude of the kth target of the mth pulse of the main radar, f0For the system operating frequency, Rk(m) is the distance of the kth target of the mth pulse from the main radar, psik(m) is the echo Doppler and phase shift phase term of the kth target of the mth pulse of the main radar, Il(m, t) is the complex envelope of the interference signal of the mth pulse of the main radar,
in order to correspond to the power of the received interfering signal,
is Doppler and phase shift phase term, w, of the mth pulse of the main radarM(m, t) is the receiver background Gaussian white noise in the mth pulse of the main radar and satisfies E [ | wM(m,t)|2]＝σ2，σ2Is the noise power.
If the sparse large-aperture coherent receiving array includes N auxiliary radars, and the array baseline relationship between the N auxiliary radars and the main radar is shown in fig. 2, the steering vector of the target or interference signal entering the coherent receiving array at the angle θ is
Wherein, the lambda is the working wavelength of the system,
indicating the ith auxiliary radar under the condition of an angle thetaThe difference in propagation path from the main radar,
the propagation paths from the source to the main radar and the ith auxiliary radar respectively under the condition of an angle theta [ ·]TIs a matrix transposition operation. The sparse large aperture coherent receive array echo can be represented as
Wherein, the signal is a Hadamard product,
for the k-th target (angle)
) Under the condition, the sparse large-aperture coherent receiving array is used for receiving the target signal echo envelope of the mth pulse of each auxiliary radar,
for each auxiliary receiving radar antenna gain, Rk(m) definition and EM(m,t,θ0) In the same way, the first and second,
and echo Doppler and phase shift phase terms of the kth target of the mth pulse of the sparse large-aperture coherent receiving array.
For the first disturbance (angle)
) Under the condition, the m-th auxiliary radar of each sparse large-aperture coherent receiving arrayThe echo envelope of the interfering signal of the pulse,
and receiving Doppler and phase shift phase terms of the ith interference signal of the mth pulse of the array by using sparse large-aperture coherent reception. n (m, t) ═ …, ni(m,t),…]T，i∈[1,…,N]For m pulses of the sparse large-aperture coherent receiving array, the background noise signal of the radar is received in an auxiliary mode, ni(m, t) are independent of each other and have the same power.
Neglecting the propagation path difference time delay from the target and the interference to each auxiliary radar, the echo of the sparse large-aperture coherent receiving array can be simplified into
Wherein,
is composed of
A simplified complex envelope.
Is composed of
A simplified complex envelope.
The echo of the sparse large-aperture coherent receiving array after spatial domain matching filtering (spatial domain vector weighted summation) can be expressed as
Wherein, thetaAFor sparse large aperture coherent receive array beam pointing angle [ ·]HFor matrix conjugate transpose operation, m, t, s (t), Rk(m)、IlMeaning of (m, t) and main radar echo EM(m,t,θ0) In the same way, the first and second,
the echo amplitude of the kth target of the mth pulse after the sparse large-aperture coherent receiving array is subjected to spatial domain matching filtering,
the echo Doppler and phase shift phase item of the kth target of the mth pulse after the sparse large-aperture coherent receiving array is subjected to spatial domain matching filtering,
the interference signal power of the first interference signal received by the mth pulse after the sparse large-aperture coherent receiving array is subjected to spatial domain matching filtering,
doppler and phase shift phase terms, w, of the ith interference signal of the mth pulse after spatial domain matching filtering are carried out on the sparse large-aperture coherent receiving arrayA(m, t) is the receiver background Gaussian white noise in the mth pulse of the sparse large-aperture coherent receiving array and meets the requirement of E [ | w |)A(m,t)|2]＝σ2，σ2Is the noise power.
Varying thetaATraversing and scanning the airspace to obtain different angles thetaAAnd (3) receiving the array echo by using the lower sparse large aperture coherent receiver.
Step two, carrying out interference identification on the sparse large-aperture coherent receiving array echo, and judging whether interference exists or not and the interference type; and if a plurality of interferences exist, giving the angle of each interference and using the sparse large-aperture coherent receiving array echo under the angle as a corresponding interference reference signal.
Assuming that the main radar beam pointing angle is theta0When E is greaterM(m,t,θ0) All contain target and disturbance echoes, i.e. at this time Ak(m) and
are not 0. Sparse large aperture coherent receive array with main radar beam pointing angle theta0Centered, main radar 3dB beamwidth θwTo range, change thetaAAnd traversing and scanning a main lobe airspace of the main radar. For different thetaAAngular coherent receive array echo EA(m,t,θA) And performing interference identification to identify whether interference exists and the interference type.
Under the condition of extremely narrow beam of a sparse large-aperture coherent receiving array, P interferences in the range of main lobe beam of a main radar can be distinguished from the airspace by the coherent receiving array, and the corresponding angles are respectively
Selecting an angle
Sparse large aperture coherent receive array echo
As an interference reference signal, then
Can be approximated as
Wherein, Il(m, t) is the corresponding angle
Of the target echo amplitude Ak(m) echo power of the remaining disturbances
Are all approximately 0, i.e. a certain value can be obtained by sparse large aperture coherent receiving array space matching receivingA reference signal that interferes with the purer signal.
Step three, under each interference angle, executing the following steps:
the method comprises the steps of taking a main radar fast time echo as a main channel signal, taking a sparse coherent receiving array fast time echo as a reference signal, adopting a fast time LMS filter to execute a fast time LMS filtering process on the main radar fast time echo to obtain a filtering intermediate processing result, and circulating the fast time LMS filtering process for multiple times to obtain a multi-frame fast time LMS filtering processing result.
And then, taking the main radar slow time echo as a main channel signal, taking the slow time echo of the fast time LMS filtering intermediate processing result as a reference signal, adopting a slow time LMS filter to perform a slow time LMS filtering process on the main radar slow time echo, circulating the slow time LMS filtering process for multiple times, obtaining the slow time LMS filtering processing results of multiple sampling points, and further obtaining a final cascading filtering processing result.
And the cascade filtering processing results obtained under all the interference angles form a final interference cancellation processing result.
In this embodiment, the fast-time-slow-time cascaded LMS filtering process specifically includes:
let d (m, t) be EM(m,t,θ0)，
And taking x (m, t) as a reference signal, and performing fast time-slow time cascade LMS filtering (wiener filtering) processing on d (m, t) to realize the self-adaptive cancellation of interference signals in d (m, t). Let the number of sampling points contained in each PRT echo be Q and the sampling time be tn(n∈[1,…,Q]And t is not less than 01＜t2＜…＜tQT is less than or equal to T, T is PRT), the actual digital echo is d (m, T)n) And x (m, t)n) Is { (m, t)n) 1, | M ═ 1,2, …, M; n is 1,2, …, Q, M is the number of the coherent processing pulses of the multi-frame.
A fast-slow cascaded LMS filter structure is shown in fig. 3. The fast-time LMS filter is an N-order digital filter with step size μ, and the processing procedure is shown in fig. 4. y (m,tn) Is x (m, t)n) Corresponding output signals after weighted summation of the fast time LMS filter
Wherein, wm＝[wm0,wm1,…,wm(N-1)]TFor the m-th pulse fast time LMS filter weight vector, w0,w1,…,wN-1Is the corresponding nth order weight vector coefficient. x (m, t)n)＝[x(m,tn) x(m,tn-1) … x(m,tn-N+1)]T。
e(m,tn) Is an adaptive filtering output, which satisfies
e(m,tn)＝d(m,tn)-y(m,tn)m＝1,2,…,M；n＝1,2,…,Q
The fast-time LMS filtering process flow is as follows:
in this embodiment, the slow time LMS filter is a K-order digital filter with a step length η, and the processing procedure is as shown in fig. 5. Let z (m, t)n) Is y (m, t)n) Corresponding output signals after weighted summation by the slow time LMS filter
Wherein v isn＝[vn0,vn1,…,vn(K-1)]TFor the n sample point slow time LMS filter weight vector, vn0,vn1,…,vn(K-1)Is the corresponding K-order weight vector coefficient. y (m, t)n)＝[y(m,tn) y(m-1,tn) … y(m-K+1,tn)]T。
f(m,tn) Is an adaptive filterWave output of which satisfies
f(m,tn)＝d(m,tn)-z(m,tn)
The slow-time LMS filtering process flow is as follows:
z (m, t) is the result of the approximation of the interference component in d (m, t) by x (m, t) in the slow-time independent variable m domain and the fast-time independent variable t domain respectively, so that the adaptive cancellation of the interference component in d (m, t) can be realized.
Returning to the step two to select the angle
Lower sparse large aperture coherent receive array echo
As a new interference reference signal.
Returning to the step three, and changing d (m, t) into f (m, t),
taking x (m, t) as a reference signal, and performing fast time-slow time cascade LMS filtering (wiener filtering) processing on d (m, t) to obtain an angle
New filtering result f (m, t) after interference cancellation.
And (3) repeating the step two and the step three for multiple times, traversing P interference angles in the main lobe beam range of the main radar, acquiring an interference reference signal of a corresponding angle, inputting a last filtering result f (m, t) as a new main radar echo, and performing fast time-slow time cascade LMS filtering processing until the P interferences are subjected to filtering cancellation processing, thereby obtaining a final echo f (m, t) after main lobe interference cancellation.
And step four, echo pulse compression.
Transmitting with a primary radarTaking the signal s (t) as a reference signal, performing pulse compression processing on each sub-pulse in the f (m, t) result after the fast time-slow time cascade LMS filtering in the step three to obtain a multi-frame coherent one-dimensional range profile P after the interference cancellation is finishedc(m,t)。
And step five, multi-frame joint accumulation.
Coherent one-dimensional range profile Pc(m, t) performing multi-frame joint accumulation processing to obtain a multi-frame joint processing range profile Pc-I(m,t)。Pc-I(m, t) can be represented as
It should be noted that the interference assumed in the above-mentioned "step two" and "step three" is located within the main radar main lobe beam. If the angle of disturbance thetaJThe interference identification and reference signal selection range in the step two is only required to be expanded to the whole airspace, namely, the processing method can simultaneously deal with the interference of a plurality of main lobes and side lobes.
The following examples are given to illustrate the inventive process:
the verification conditions of the present invention are shown in table 1. Here, the sparse large-aperture coherent receiving array only comprises one auxiliary radar, and the receiving direction diagram of the sparse large-aperture coherent receiving array does not have an extremely narrow beam condition, namely, certain target energy loss exists in the fast time-slow time cascade LMS filtering processing in the invention; in addition, the type of interference used in the implementation example is noise suppression, but the method related to the present invention is also applicable to deceptive interference. Namely, the relevant conditions of the embodiment do not affect the concrete implementation and the technical essence of the invention.
Table 1 example verification conditions
Fig. 6 shows one-dimensional distance images before and after the interference suppression, and it is understood from the images that a desired signal component in the original echo signal is annihilated by the interference signal. Comparing the time domain LMS and the cascade LMS filtering results, the target peak value after cascade LMS is more obvious, and the method has better interference suppression effect. Table 2 shows the improved statistics of the signal to interference plus noise ratio of different filtering processing methods, and it can be seen that the SINR after fast time-slow time cascade LMS filtering is improved by more than about 7dB compared with the SINR after time domain LMS filtering.
TABLE 2 Signal to interference and noise ratio (SINR) improvement for different filtering processes
From the above results, it can be seen that after the method provided by the present invention is used for interference suppression, indexes such as SINR improvement are significantly improved, and the superiority of the method of the present invention is shown.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A distributed radar main lobe interference suppression method based on a cascaded LMS filter is characterized in that a distributed radar system is composed of a main radar and a plurality of auxiliary radars; the auxiliary radars are sparsely and dispersedly arranged around the main radar to form a sparse coherent receiving array, the aperture of the sparse coherent receiving array is larger than that of the main radar, the sparse dispersive arrangement of the auxiliary radars means that two adjacent auxiliary radars are mutually independent in space, the distance between antenna phase centers on the auxiliary radars is ten times to one hundred times of half wavelength, and the main radar is connected with each auxiliary radar through time-frequency and time-sequence synchronization equipment to ensure the working coherence; the distributed radar main lobe interference suppression method comprises the following steps:
step one, a main radar wave beam is aligned to a certain detection airspace emission signal, and a main radar echo signal is obtained; the sparse coherent receiving array forms a very narrow beam, scans a space domain in a main lobe beam range of the main radar and acquires an array echo of the sparse coherent receiving array under each beam pointing angle; wherein the beam of the sparse coherent array is a very narrow beam, the beam width of which is much smaller than the main radar beam width;
step two, carrying out interference identification on the sparse large-aperture coherent receiving array echo, and judging whether interference exists or not and the interference type; if a plurality of interferences exist, giving an angle of each interference and taking a sparse large-aperture coherent receiving array echo under the angle as a corresponding interference reference signal;
step three, under each interference angle, executing the following steps:
taking a main radar fast time echo as a main channel signal, taking a sparse coherent receiving array fast time echo as a reference signal, adopting a fast time LMS filter to execute a fast time LMS filtering process on the main radar fast time echo to obtain a filtering intermediate processing result, and circulating the fast time LMS filtering process for multiple times to obtain multiple pulse fast time LMS filtering processing results;
then, taking the main radar slow time echo as a main channel signal, taking the slow time echo of the fast time LMS filtering intermediate processing result as a reference signal, adopting a slow time LMS filter to execute a slow time LMS filtering process on the main radar slow time echo, circulating the slow time LMS filtering process for multiple times, obtaining the slow time LMS filtering processing results of multiple sampling points, and further obtaining a final cascade filtering processing result;
the cascade filtering processing results obtained under all the interference angles form a final interference cancellation processing result;
step four, carrying out pulse compression processing on each sub pulse in the interference cancellation processing result to obtain a multi-frame one-dimensional range profile;
and fifthly, performing coherent processing on the multi-frame one-dimensional range profile to obtain a multi-frame joint accumulation processing result.
2. The method of claim 1, wherein the fast-time LMS filter is an N-order digital filter with step size μ.
3. The method as claimed in claim 2, wherein said fast-time LMS filtering process specifically comprises the steps of:
step 1: setting the serial number of the current processing pulse as m, and setting the initial value of m as 1;
step 2: note that the intermediate result of the fast time LMS filter weight vector operation at the nth sampling point of the mth pulse is
n＝1,2,…,Q；
Wherein Q is the number of sampling points contained in the pulse, and the fast time sampling time is tn，n∈[1,…,Q]；
And t is not less than 01＜t2＜…＜tQT is less than or equal to T, and T is pulse repetition time PRT;
when n is 1, i.e. t1At the moment of time, the time of day,
the adaptive filtering output of the fast-time LMS filter is e (m, t)n)＝d(m,tn)，d(m,tn) The main channel signal of the fast time LMS filter at the nth sampling point of the mth pulse;
step 3: determining the reference signal of the fast time LMS filter of the nth sampling point of the mth pulse as follows:
x(m,tn)＝[x(m,tn) x(m,tn-1) … x(m,tn-N+1)]T；
wherein x (m, t)n) x(m,tn-1) … x(m,tn-N+1) Fast time echoes, t, respectively, for sparse coherent receive arraysn tn-1… tn-N+1Fast time sampling points;
the intermediate result of the fast-time LMS filter weight vector operation at the (n + 1) th sampling point of the mth pulse is
then at tn+1At the moment, the (n + 1) th sampling point of the mth pulse corresponds to an output signal after weighted summation of the fast time LMS filter, namely the intermediate processing result of the fast time LMS filter is y (m, t)n+1)：
tn+1At the moment, the adaptive filtering output of the (n + 1) th sampling point of the mth pulse is e (m, t)n+1)＝d(m,tn+1)-y(m,tn+1)；
Step 4: enabling n to be increased by 1, if n is Q, entering Step 5, and otherwise, returning to Step 3;
step 5: let the final fast-time LMS filter weight vector
Recalculating tnAt the moment, the result of the weighted summation of the fast time LMS filter corresponding to the nth sampling point of the mth pulse is
Step 6: if M is equal to M, the process is terminated, otherwise, M is incremented by 1 and the process returns to Step 2.
4. The method of claim 1, wherein the slow-time LMS filter is a K-order digital filter with a step size η.
5. The method as claimed in claim 4, wherein said slow-time LMS filtering process specifically comprises the steps of:
SS 1: setting the serial number of a sampling point of the current processing fast time as n, wherein the initial value of n is 1;
SS 2: the middle result of the slow time LMS filtering weight vector operation at the mth pulse of the nth sampling point is recorded as
m＝1,2,…,M；
Where M is the total number of pulses and the time of the fast time sampling point is tn，n∈[1,…,Q]Q is the number of sampling points contained in the pulse; and t is not less than 01＜t2＜…＜tQT is less than or equal to T, and T is pulse repetition time PRT;
when m is 1, i.e. t1At the moment of time, the time of day,
t1the adaptive filtering output of the slow time LMS filter corresponding to the mth pulse at the moment is:
f(m,tn)＝d(m,tn)，d(m,tn) A main channel signal of a slow time LMS filter corresponding to the mth pulse at the nth sampling point for the mth pulse;
SS 3: taking the slow time echo of the intermediate processing result of the fast time LMS filtering as a reference signal of the slow time echo slow time LMS filter, specifically
y(m,tn)＝[y(m,tn) y(m-1,tn) … y(m-K+1,tn)]T；
Wherein y (m, t)n) y(m-1,tn) … y(m-K+1,tn) Are each tnCorresponding values of K pulses before the time m;
then, at the m +1 th pulse of the nth sampling point, the intermediate result of the slow-time LMS filtering weight vector operation is:
then at the nth sampling point m +1The result of weighted summation of the slow-time LMS filter is as follows:
wherein y (m +1, t)n) Is the nth sampling point (t)nTime) the reference signal corresponding to the m +1 th pulse;
the output result of the slow time LMS filter adaptive filter corresponding to the (m + 1) th pulse of the nth sampling point is as follows: f (m +1, t)n)＝d(m+1,tn)-z(m+1,tn)；d(m+1,tn) The main channel signal of the slow time LMS filter corresponding to the (m + 1) th pulse of the nth sampling point.
SS 4: enabling M to be increased by 1, if M is equal to M, entering SS5, otherwise, repeating SS 3;
SS 5: let the final slow-time LMS filter weight vector
Recalculating tnThe result of the weighted summation of the slow-time LMS filter corresponding to the mth pulse at the moment is:
recalculating tnThe adaptive filtering output of the slow time LMS filter corresponding to the mth pulse at the moment is:
f(m,tn)＝d(m,tn)-z(m,tn)m＝1,2,…,M
SS 6: if n is Q, the process is terminated, otherwise n is n +1, and the process returns to SS 2.
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