CN115494461A - Viterbi algorithm-based intra-pulse transfer type interference time-frequency domain suppression method - Google Patents
Viterbi algorithm-based intra-pulse transfer type interference time-frequency domain suppression method Download PDFInfo
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Abstract
The invention discloses an intra-pulse transfer type interference time-frequency domain suppression method based on a Viterbi algorithm, which comprises the following steps of: acquiring radar echo data; performing short-time Fourier transform on the radar echo data to obtain time-frequency distribution; extracting a time-frequency curve from the time-frequency distribution based on a Viterbi algorithm; calculating the projection of the time-frequency curve in the frequency dimension to obtain a projection result; determining the number of interference slices and the bandwidth of the interference slices according to the projection result; constructing a time-frequency zero setting filter according to the time-frequency curve, the number of interference slices and the bandwidth of the interference slices; and filtering the time-frequency distribution by using a time-frequency zero setting filter, and obtaining radar echo data after interference suppression according to a filtering result. The invention can reserve the expected signal in the radar echo data without distortion, improves the signal-to-interference ratio improvement factor and has good interference suppression performance under the condition of low signal-to-noise ratio.
Description
Technical Field
The invention belongs to the technical field of radars, and particularly relates to an intra-pulse transfer interference time-frequency domain suppression method based on a Viterbi (Viterbi) algorithm.
Background
The radar is electronic equipment for estimating a target space position and a motion parameter by utilizing a reflection echo of electromagnetic waves, and the radar is used as sensing equipment for collecting information and is easily attacked by electronic interference equipment, so that the radar has better electronic anti-interference capability and is a powerful guarantee for realizing accurate detection and stable survival of a radar system in a complex electromagnetic environment.
Among a plurality of interferences, active deception interference is generated by intercepting, storing and analyzing parameters of radar transmitting signals by an interference machine, modulating information such as distance, speed and angle according to tactical requirements, is highly coherent with a target echo, and can mislead parameter estimation and target tracking of the radar. With the development of a high-speed Digital Signal Processor (DSP), active spoofing interference has stronger real-time response capability and adaptive capability, and can quickly analyze radar transmission Signal parameters and release interference in the current pulse repetition period. The most typical pattern of intra-pulse forward interference is intermittent sampling forward interference, which generates a plurality of leading or lagging false targets in the current repetition period by repeating the process of 'capture-slice-forward', which makes the techniques of radar pulse leading edge tracking, pulse-to-pulse waveform agility and the like difficult to work.
In order to effectively suppress the interference, the existing radar interference suppression method mainly includes: a time domain threshold crossing zero-setting method, a time domain filtering method based on time frequency analysis and a frequency domain filtering method based on energy detection.
The time domain threshold value zero crossing method carries out interference judgment and interference zero setting by calculating a radar single pulse echo threshold, and finally obtains a fast time domain accumulation result after interference suppression through pulse compression.
The time-domain filtering method based on time-frequency analysis mainly utilizes the characteristic that the time-frequency energy distribution after intermittent sampling and forwarding of interference pulse pressure is intermittent, accumulates the time-frequency transformation results after the pulse pressure containing interference echoes in a time dimension to obtain the projection of the time-frequency transformation results in a frequency dimension, searches frequency points only containing target signals in the projection results, namely minimum value points of the projection results, extracts time-dimension slices of the echo time-frequency transformation results in the minimum value points to obtain a time-domain filter with a main lobe pointing to a target position, and multiplies the echo pulse pressure results by the filter to achieve the purpose of interference suppression.
The frequency domain filtering method based on energy detection utilizes the difference of target echoes subjected to deskew (Dechirp) processing and intermittent sampling forwarding interference in an energy domain to construct a gating signal to extract a target echo band, performs Fourier transform on the extracted target band to obtain a frequency domain band-pass filter taking the target frequency subjected to deskew as the center frequency, and multiplies the frequency domain band-pass filter with the Fourier transform result of the radar echo subjected to deskew to obtain a fast time domain accumulation result subjected to interference suppression.
However, when the interference forwarding delay is small or the target signal-to-noise ratio is close to the dry-to-noise ratio, due to the limitation of the time domain single-dimensional discrimination, the target information is easily lost after the interference and target superposition echo is amplitude-zeroed, which results in the reduction of the output signal-to-interference ratio improvement factor. Under the condition of low signal-to-noise ratio of the time domain filtering method and the frequency domain filtering method, the main side lobe ratio of the constructed time domain or frequency domain filter is reduced, and the interference suppression capability is obviously deteriorated and even fails.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a Viterbi algorithm-based intra-pulse transmit interference time-frequency domain suppression method.
The technical problem to be solved by the invention is realized by the following technical scheme:
an intra-pulse transfer type interference time-frequency domain suppression method based on a Viterbi algorithm comprises the following steps:
acquiring radar echo data;
performing short-time Fourier transform on the radar echo data to obtain time-frequency distribution;
extracting a time-frequency curve from the time-frequency distribution based on a Viterbi algorithm;
calculating the projection of the time-frequency curve in the frequency dimension to obtain a projection result;
determining the number of interference slices and the bandwidth of the interference slices according to the projection result;
constructing a time-frequency zero setting filter according to the time-frequency curve, the number of the interference slices and the bandwidth of the interference slices;
and filtering the time-frequency distribution by using the time-frequency zero-setting filter, and obtaining radar echo data after interference suppression according to a filtering result.
Preferably, the method for extracting the time-frequency curve from the time-frequency distribution based on the Viterbi algorithm includes:
according to the time-frequency distribution, calculating an amplitude cost vector corresponding to each moment and a path cost matrix corresponding to every two adjacent moments;
calculating a joint cost matrix corresponding to every two adjacent moments and combining the amplitude cost and the path cost according to the calculated amplitude cost vector and the path cost matrix;
determining local optimal paths among all adjacent time points according to the calculated joint cost matrixes;
determining a global optimal path according to local optimal paths among all adjacent time points;
extracting a time-frequency curve from the time-frequency distribution according to the global optimal path;
the amplitude cost vector consists of amplitude cost values of all time frequency points at a single moment, and the amplitude cost values of the time frequency points are negatively correlated with the amplitude values of the time frequency points; the path cost matrix is composed of path cost values between each pair of time-frequency points of each adjacent moment, and the path cost values are used for representing the possibility that a previous time-frequency point in the adjacent moments jumps to a later time-frequency point.
Preferably, the calculation method of the magnitude cost vector includes:
sequencing the amplitude values of all time frequency points at any moment from large to small;
and setting the amplitude cost value of the time frequency point with the maximum amplitude value as 0, and setting the amplitude cost values of the rest time frequency points to be gradually increased according to the sequencing result.
Preferably, the calculation method of the path cost matrix includes:
aiming at every two adjacent moments, calculating the path cost value between each pair of time-frequency points between the two adjacent moments by using a path cost function to obtain path cost matrixes corresponding to the two adjacent moments;
wherein the path cost function is:
wherein x and y represent the frequency of a pair of time frequency points between any two adjacent time points, Δ represents a frequency jump tolerance, q is a preset constant, and Δ < q, and g (x, y) is the calculated path cost value.
Preferably, calculating a joint cost matrix corresponding to each two adjacent time points and combining the amplitude cost and the path cost according to the calculated amplitude cost vector and the path cost matrix, includes:
for each time t n Adding the amplitude cost vector to each column in the relevant path cost matrix to obtain the time t n To t n+1 A corresponding joint cost matrix; n = [1,2, \ 8230;, N]N is the number of time-domain sampling points;
wherein the relevant path cost matrix is: at time t n To t n+1 A corresponding path cost matrix.
Preferably, determining the local optimal path between all adjacent time points according to the calculated joint cost matrix includes:
for time t n To t n+1 Selecting the minimum element from each column of the combined cost matrix to form time t n To t n+1 A corresponding local optimal joint cost vector;
accumulating the local optimal joint cost vectors corresponding to all adjacent moments to obtain a global optimal joint cost vector;
and taking the frequency point corresponding to the minimum element in the global optimal joint cost vector as a global optimal point, and backtracking to obtain local optimal paths among all adjacent time points based on the global optimal point and all local optimal joint cost vectors.
Preferably, determining the number of interference slices and the bandwidth of the interference slices according to the projection result includes:
determining a gating threshold value according to the projection result;
carrying out binarization processing on the projection result according to the gating threshold value;
determining the interference frequency bandwidth and the number of interference slices according to the binarization processing result;
and dividing the interference frequency bandwidth by the number of the interference slices to obtain the interference slice bandwidth.
Preferably, the time-frequency zeroing filter is represented as:
wherein δ is the nulling frequency bandwidth of the time-frequency nulling filter, and δ is equal to the interference slice bandwidth,
indicating that the time-frequency zero-setting filter is at t n A center frequency of a null band of time instants, and
equal to said time-frequency curve at t n Frequency corresponding to time, H (t) n ,f m ) Representing the time in the time frequency distribution as t n Frequency of f m N = [1,2, \8230;, N]，m＝[1,2,…,M]N is the number of time domain sampling points, and M is the number of frequency points.
Preferably, obtaining the radar echo data after interference suppression according to the filtering result includes:
and performing pulse compression and inverse short-time Fourier transform on the filtering result to obtain radar echo data after interference suppression.
Preferably, the method further comprises:
and before extracting a time-frequency curve from the time-frequency distribution by using a Viterbi algorithm, performing time-frequency information enhancement processing on the time-frequency distribution.
In the method for inhibiting the intra-pulse transmission interference time-frequency domain based on the Viterbi algorithm, the Viterbi algorithm is used for extracting a time-frequency curve from time-frequency distribution, the amplitude value of a time-frequency point and the frequency continuity of the time-frequency curve are considered, so that a more accurate interference position is determined, and then a time-frequency zero-setting filter is constructed based on the extracted time-frequency curve, the determined number of interference slices and the interference slice bandwidth to filter out interference. Experimental results show that the method can remain the expected signal in the radar echo data without distortion, improve the signal-to-interference ratio improvement factor and have good interference suppression performance under the condition of low signal-to-noise ratio.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic flowchart of an intra-pulse transfer type interference time-frequency domain suppression method based on a Viterbi algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a process of extracting a time-frequency curve based on a Viterbi algorithm in an embodiment of the invention;
fig. 3 (a) to 3 (c) show the experimental results of performing a time-frequency curve on an LFM (linear frequency modulation) signal by using the existing time-frequency amplitude maximum value extraction method and the Viterbi algorithm used in the embodiment of the present invention;
fig. 4 (a) to fig. 4 (j) show experimental results of the suppression performance of the method provided by the embodiment of the present invention on the intermittent sampling direct-transfer Interference (ISDJ);
fig. 5 (a) -5 (c) show experimental results of interference suppression performance of the method provided by the embodiment of the present invention and three existing methods.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
In order to improve the interference suppression performance of the radar, an embodiment of the present invention provides an intra-pulse transfer interference time-frequency domain suppression method based on a Viterbi algorithm, as shown in fig. 1, where the method includes the following steps:
s1: and acquiring radar echo data.
S2: and carrying out short-time Fourier transform on the radar echo data to obtain time-frequency distribution.
It is understood that the time-frequency distribution includes two data dimensions of frequency and time, wherein each element is an amplitude value at a corresponding time and frequency; wherein, the energy of the target echo and the interference is gathered at the respective instantaneous frequency ridge line, and the noise energy is dispersed on the whole time-frequency surface.
S3: and extracting a time-frequency curve from the time-frequency distribution based on a Viterbi algorithm.
The Viterbi algorithm was proposed since 1967 and is a special but most widely used dynamic programming algorithm. Based on the Viterbi algorithm, the shortest path problem in any graph can be solved. If the time-frequency distribution in the embodiment of the invention is output, a time-frequency graph can be obtained; the Viterbi algorithm can be used to extract the time-frequency curve from the time-frequency distribution.
Specifically, the step S3 includes:
(1) According to the time-frequency distribution, calculating an amplitude cost vector corresponding to each moment and a path cost matrix corresponding to every two adjacent moments;
(2) Calculating a combined cost matrix which corresponds to every two adjacent moments and combines the amplitude cost and the path cost according to the calculated amplitude cost vector and the calculated path cost matrix;
(3) Determining local optimal paths among all adjacent time points according to the calculated joint cost matrixes;
(4) Determining a global optimal path according to local optimal paths among all adjacent time points;
(5) Extracting a time-frequency curve from the time-frequency distribution according to the global optimal path;
the amplitude cost vector consists of amplitude cost values of the time frequency points at a single moment, and the amplitude cost values of the time frequency points are negatively correlated with the amplitude values of the time frequency points; the path cost matrix is composed of path cost values between each pair of time-frequency points of each adjacent time, and the path cost values are used for representing the possibility that a previous time-frequency point in the adjacent time jumps to a later time-frequency point.
Specifically, the calculation method of the amplitude cost vector in step (1) includes:
sequencing the amplitude values of all time frequency points at any moment from large to small; and setting the amplitude cost value of the time frequency point with the maximum amplitude value as 0, and setting the amplitude cost values of the rest time frequency points to be gradually increased according to the sequencing result.
For example, assuming that the dimension of time-frequency distribution is M × N, any time t n Arranging the amplitude values corresponding to all the time-frequency points from large to small to obtain:
TF(t n ,f 1 )>TF(t n ,f 2 )>…>TF(t n ,f m )>…>TF(t n ,f M )；
wherein M =1,2, \8230, M, N =1,2, \8230, N, M is the number of frequency points, N is the number of time-domain sampling points, f represents frequency, and t represents time. Setting the amplitude cost value of the time frequency point with the largest amplitude value as 0, setting the amplitude cost value of the time frequency point with the second largest amplitude value as 1, and sequentially setting the amplitude cost value of each time frequency point as follows:
h(TF(t n ,f m ))＝m-1。
the computed magnitude cost vector can be represented as:
H n ＝[h 1 ,h 2 ,…,h m ,…,h M ] T
wherein h is m Is shown at t n The amplitude cost value of the mth frequency point at time instant.
The calculation mode of the path cost matrix in the step (1) comprises the following steps:
aiming at every two adjacent moments, calculating the path cost value between each pair of time-frequency points between the two adjacent moments by using a path cost function to obtain a path cost matrix corresponding to the two adjacent moments;
wherein the path cost function is:
wherein x and y represent the frequency of a pair of time frequency points between any two adjacent time points, Δ represents a frequency hopping allowable value, q is a preset constant, and Δ < q, and g (x, y) is the calculated path cost value.
It can be seen that when the frequency jump between x and y is within the tolerance Δ, the cost value of the path between x and y is recorded as 0; when the allowable value delta is exceeded, the exceeded part is multiplied by a constant q to obtain a path cost value. It can be understood that, the smaller the frequency difference between two time frequency points at adjacent time, the smaller the path cost value between the two time frequency points.
The computed path cost matrix can be expressed as:
wherein,
it represents t n 1 st to M time points of time to t n+1 The path cost value of the kth time-frequency point at that time.
In the step (2), calculating a combined cost matrix combining the amplitude cost and the path cost corresponding to each two adjacent time points according to the calculated amplitude cost vector and the calculated path cost matrix, including:
for each time t n Adding the amplitude cost vector with each column in the related path cost matrix to obtain the time t n To t n+1 A corresponding joint cost matrix; n = [1,2, \8230 ], N]N is the number of time domain sampling points; wherein, the relevant path cost matrix is: at time t n To t n+1 A corresponding path cost matrix.
The computed joint cost matrix can be expressed as:
wherein,
p mk represents t n 1 st to M frequency points of time to t n+1 The joint cost value of the kth frequency point at time.
In the step (3), determining the local optimal path between all adjacent time points according to the calculated joint cost matrix includes:
(a) For time t n To t n+1 Selecting the minimum element from each column of the combined cost matrix to form a time t n To t n+1 Corresponding locally optimal joint cost vector L n 。
The L is n A column vector of dimension M × 1, expressed as:
L n ＝[l 1 ,l 2 ,…,l k ,…,l M ] T 。
wherein l k Represents t n+1 The kth e [1, M ] at time]The minimum joint cost value at each time-frequency point is recorded as
(b) Accumulating the local optimal joint cost vectors corresponding to all adjacent moments to obtain a global optimal joint cost vector;
wherein, the set formed by the local optimal joint cost vectors corresponding to all the adjacent time instants can be expressed as { L } 1 ,L 2 ,…,L n ,…,L N-1 }. Accumulating the vectors in the set to obtain a global optimal joint cost vector
Expressed as:
wherein,
represents the kth e [1, M ]]Global joint cost values at individual frequency points.
(c) And taking the frequency point corresponding to the minimum element in the global optimal joint cost vector as a global optimal point, and backtracking to obtain local optimal paths among all adjacent time points based on the global optimal point and all local optimal joint cost vectors.
Specifically, taking fig. 2 as an example, assuming that the dimension of time-frequency distribution is 9 × 3, the numbers beside the time-frequency points in fig. 2 represent the amplitude cost value thereof, and the numbers beside the black solid line represent the path cost value between two time-frequency points. First from t 1 Starting at time, setting the frequency point jump allowance value delta =1 and the constant q =2.5, then t can be calculated 1 Each time point of time reaches t 2 The cost values of all paths of each time frequency point at a moment; wherein for t 2 Selecting a path with the minimum joint cost value as the optimal path for each time frequency point at the moment, wherein the searched path is represented by a solid line, and sequentially searching t 1 Time t 2 The path with the minimum joint cost value at the moment is compared with the joint cost values among different frequency points, and the path with the minimum joint cost value is selected as t 1 Time to t 2 The local optimal path of the time instant (see bold solid line in sub-graph (a) in fig. 2). Repeating the above process to find t 2 Time t 3 The local optimal path for the time instant, see sub-graph (b) in fig. 2. At t 3 The point with the minimum joint cost value is selected from the time-frequency points, and the previous local optimal path is found in a path backtracking manner, so that the overall optimal path is obtained as the result of the time-frequency curve extraction, as shown by the bold solid line in the subgraph (c) in fig. 2.
The above backtracking procedure can be represented by a joint cost function defined as follows:
wherein p (m (n); n) 1 ,n 2 ) Representing the selection of a time-frequency distribution at a time t n1 And time t n2 The joint cost value of the paths m (n) between the nodes, g (m (n), m (n + 1)) represents the time t n To time t n+1 Path cost value of h (TF (t) n M (n))) represents t n The amplitude cost value corresponding to the path m (n) selected at the moment, K representsA set of possible paths.
S4: and calculating the projection of the time-frequency curve in the frequency dimension to obtain a projection result.
Converting the time-frequency curve into an M multiplied by N dimensional matrix Temp corresponding to time-frequency distribution; specifically, for the M N dimensional matrix, if at (t) n ,f m ) If the time-frequency point at the position corresponds to a point on the time-frequency curve, the element at the position in the matrix is assigned to 1, otherwise, the element is assigned to 0, and the process can be expressed as follows:
wherein,
represents a point on the time-frequency curve, temp (t) n ,f m ) Indicating that the matrix Temp is in position (t) n ,f m ) The elements of (1).
Then, temp (t) n ,f m ) Accumulating on the time dimension to obtain the projection Temp (f) of the time-frequency curve on the frequency dimension m ) Expressed as:
s5: determining the number of interference slices and the bandwidth of the interference slices according to the projection result;
it can be understood that the time-frequency curve reflects the change relation of the instantaneous frequency of the radar echo signal along with time, and comprises time-frequency two-dimensional information, and the interference parameters can be determined based on the information.
Specifically, the step S5 includes:
(1) And determining a gating threshold according to the projection result.
Specifically, the gating threshold Th 0 Determined by the mean of the time-frequency curve in the frequency dimension projection, can be expressed as:
wherein mean [. Cndot. ] represents the mean value.
(2) And carrying out binarization processing on the projection result according to the gating threshold value.
Specifically, the binarization processing procedure here can be expressed as:
wherein J (f) represents the result of the binarization processing, and the expression of the binarization processing procedure can be understood if Temp (f) m ) Greater than a threshold Th 0 If this point is selectively passed, the value is set to 1 when Temp (f) m ) Less than threshold Th 0 This point is blocked and cannot pass through, so the result of the binarization process may also be referred to as strobe signals J (f), each of which represents a sub-band of interference, i.e., a connected domain.
(3) And determining the interference frequency bandwidth and the number of interference slices according to the binarization processing result.
Specifically, the frequency width when the median of the gating signal J (f) is 1 is calculated, and the obtained result is the frequency bandwidth B of the interference, and the calculation process is expressed as follows:
wherein N is 1 Number of points representing a value of 1 in J (f), N FFT For the number of Fourier transform points in a short-time Fourier transform time window, f s Is the sampling rate.
J (f) is a one-dimensional vector, and is marked by using a 2-connectivity criterion, and if two adjacent points are 1 and are adjacent in the horizontal direction, the two points are considered to belong to the same region. Therefore, the number N of the connected domains can be determined through the connected domain analysis J I.e. the number of interfering slices.
(4) And dividing the interference frequency bandwidth by the number of the interference slices to obtain the interference slice bandwidth.
This step is formulated as: b is J ＝B/N J . Thus, an interference parameter estimation result is obtained.
S6: constructing a time-frequency zero setting filter according to the time-frequency curve, the number of the interference slices and the bandwidth of the interference slices;
specifically, the time-frequency zero-setting filter is represented as:
wherein, delta is the zero frequency bandwidth of the time-frequency zero filter, and delta is equal to the interference slice bandwidth,
is a time-frequency curve at t n Frequency corresponding to time, time-frequency zero setting filter at t n The center frequency of the zero-set frequency band of the time is equal to
f m Represents the frequency corresponding to the time frequency point in the time frequency distribution, H (t) n ,f m ) Representing a time t in the time-frequency distribution n Frequency of f m N = [1,2, \8230 ], N]，m＝[1,2,…,M]N is the number of time domain sampling points, and M is the number of frequency points.
It can be understood that, the interference slice bandwidth is used as the zero setting bandwidth of the time-frequency filter, which is beneficial to obtaining better interference suppression effect.
S7: and filtering the time-frequency distribution by using a time-frequency zero setting filter, and obtaining radar echo data after interference suppression according to a filtering result.
It can be understood that, the time-frequency distribution is filtered by using the time-frequency zero-setting filter, the interference area in the time-frequency distribution can be set to 0, interference suppression is realized, and the obtained time-frequency distribution after interference suppression is the filtering result.
The filtering process can be expressed as:
TF anti (t n ,f m )＝TF(t n ,f m )·H(t n ,f m )；
wherein, TF (t) n ,f m ) Represents the time-frequency distribution, H (t) n ,f m ) As a function of the pulse, TF anti (t n ,f m ) For the time-frequency distribution after interference suppression, H (t) n ,f m ) Representing a time-frequency nulling filter.
Then, obtaining radar echo data after interference suppression according to the filtering result, including:
and performing pulse compression and inverse short-time Fourier transform on the filtering result to obtain radar echo data after interference suppression.
The pulse compression and inverse short-time fourier transform performed on the filtering result may be represented as:
wherein, y pc (t) shows interference suppressed radar echo data, STFT -1 {. Cndot. } denotes a short-time inverse fourier transform,
and (2) representing convolution operation, namely realizing the pulse compression process, wherein S (t) is a radar transmitting signal, and after the signal is sent out by a radar, an echo signal is formed and received by a receiver, and down-conversion and sampling are carried out to obtain radar echo data in the step S1.
In the method for inhibiting intra-pulse transmission interference time-frequency domain based on the Viterbi algorithm, provided by the embodiment of the invention, the Viterbi algorithm is used for extracting a time-frequency curve from time-frequency distribution, the amplitude value of a time-frequency point and the frequency continuity of the time-frequency curve are considered, so that a more accurate interference position is determined, and then a time-frequency zero-setting filter is constructed based on the extracted time-frequency curve, the determined number of interference slices and the interference slice bandwidth to filter out interference.
It can be understood that a common way to extract a time-frequency curve is to use a point with the largest amplitude value at each moment as an instantaneous frequency ridge line of a radar echo signal, but under a strong noise background, if the amplitude of noise at a certain moment is greater than the amplitude of an expected signal, a noise frequency point is extracted by mistake, which may cause an instantaneous frequency curve to be unsmooth; the accuracy of the time-frequency curve extraction result directly influences the accuracy of instantaneous frequency estimation, so that the accuracy of subsequent interference parameter estimation is influenced, and the subsequent interference suppression effect is further influenced. Therefore, when the Viterbi algorithm is used for extracting the time-frequency curve, in order to ensure the continuity of the extracted curve, the amplitude cost value of the time-frequency point and the path cost value between adjacent time-frequency points are considered, so that the extracted time-frequency curve is smooth, and the interference suppression performance is improved.
In addition, the Viterbi algorithm mainly adopts the idea of dynamic programming, and the whole optimal path is obtained by the path selection result determined by the current path selection result and finally by a path backtracking mode. Compared with the method for calculating the joint cost value of all possible paths by adopting an exhaustion method, the method for calculating the joint cost value of all possible paths by adopting the Viterbi algorithm has the advantage that the operation amount is greatly reduced, so that the real-time processing performance of the method provided by the embodiment of the invention is ensured to a certain extent.
In an optional implementation manner, the method for suppressing intra-pulse transfer interference time-frequency domain based on the Viterbi algorithm according to the embodiment of the present invention may further include:
before extracting the time-frequency curve from the time-frequency distribution by using a Viterbi algorithm, the time-frequency information of the time-frequency distribution is enhanced. Therefore, the time-frequency distribution of the salient object and the interference time-frequency support domain can be obtained.
Correspondingly, the time-frequency distribution used in step S3 is the time-frequency distribution subjected to the time-frequency information enhancement processing.
Specifically, the time-frequency information enhancement processing is expressed as;
of these, TF' (t) n ,f m ) Is element TF (t) in time-frequency distribution n ,f m ) And (3) squaring the modulus value, wherein in order to improve the accuracy of a time-frequency curve extraction result, a mode of taking the modulus and solving the square is adopted to enhance the time-frequency information of the target and the interference in the echo. In addition, in order to further highlight the time-frequency support domain of the target and the interference, a threshold Th is adopted 1 To TF' (t) n ,f m ) The noise reduction processing is carried out, and the processing result is TF' (t) n ,f m ). Wherein, the threshold value Th 1 The method can be determined by a maximum inter-class variance method (Otsu), which divides an image into two classes of a target and a background according to the gray characteristic of a time-frequency image (obtained by outputting time-frequency distribution), and takes the maximum inter-class variance of the target and the background as an image segmentation threshold.
In another optional implementation manner, after the time-frequency curve is obtained by performing step S3, before continuing to perform the subsequent steps by using the time-frequency curve, a threshold Th may be set 2 Non-interfering time frequency points in the time frequency curve are filtered out, and are represented as:
wherein,
represents the points on the time-frequency curve at TF' (t) n ,f m ) Amplitude at the middle corresponding time-frequency point, threshold Th 2 ＝K 2 ·max|TF″(t n ,f m )|，K 2 The value range is 0-1 for the preset threshold coefficient; max | TF ″ (t) n ,f m ) L is TF' (t) n ,f m ) The largest element in (1).
It can be understood that, because the interference received by the pulse system radar only exists in a period of the pulse repetition period, the accuracy of the extracted time-frequency curve can be further ensured by filtering the non-interference time-frequency points in the time-frequency curve.
In order to verify the effectiveness of the method for inhibiting the intra-pulse transfer type interference time-frequency domain based on the Viterbi algorithm provided by the embodiment of the invention, the method is further explained by the following experimental simulation results:
simulation conditions are as follows:
setting a short-time Fourier transform time window as a 128-point Hamming window, and the sampling rate f s 100MHz, 2048 FFT points, Δ =1, q =3.5, and a threshold coefficient K 2 Is 0.15. Considering that a single jammer releases interference in a single mode during the radar work, the repetition period of radar pulses is set to be 40us, the pulse width of signals transmitted by the radar is set to be 10us, and the bandwidth of the transmitted signals is set to be 20MHz.
Simulation experiment 1:
the conventional time-frequency amplitude value maximum value extraction method and the Viterbi algorithm used in the embodiment of the present invention are respectively adopted to perform time-frequency curve extraction on an LFM (linear frequency modulation) signal with a signal-to-noise ratio of-10 dB, and the experimental results are shown in fig. 3 (a) -3 (c).
Fig. 3 (a) is a result of short-time fourier transform of an LFM signal, and it can be seen that under the influence of strong noise, a time-frequency curve of the LFM signal is blurred; fig. 3 (b) is a result of performing time-frequency curve extraction on the LFM signal in fig. 3 (a) by using the existing time-frequency amplitude maximum method, which shows that more burrs are generated in the extraction result, and a part of frequency points generate extraction errors and generate jump; fig. 3 (c) is a result of extracting a time-frequency curve from the LFM signal in fig. 3 (a) by using the Viterbi algorithm used in the embodiment of the present invention, which shows that since the algorithm comprehensively considers the continuity of the time-frequency amplitude and the frequency, the time-frequency curve of the expected signal can still be accurately extracted under the background of strong noise.
Simulation experiment 2:
the effectiveness of the method provided by the embodiment of the invention on the suppression of the intermittent sampling direct forward Interference (ISDJ) is verified, and the ISDJ interference parameters and the target echo are set as follows:
see fig. 4 (a) -4 (j):
fig. 4 (a) is a result of short-time fourier transform of an echo containing an ISDJ signal, it can be seen that the ISDJ directly forwards multiple slices of a radar transmission signal and then represents a discontinuous bright line in time-frequency distribution, and 3 high-brightness interference signal segments parallel to a target echo are formed in a time-frequency image, which indicates that the amplitude of each interference slice is greater than a target and the frequency modulation slope is the same as the target.
Fig. 4 (b) shows the time-frequency distribution obtained by performing the time-frequency information enhancement processing in the embodiment of the present invention, so that the time-frequency support domain of the target and the ISDJ is more prominent, which is beneficial to the accurate extraction of the time-frequency curve.
Fig. 4 (c) shows the result of the ISDJ time-frequency curve in the echo obtained by the step of extracting the time-frequency curve in the embodiment of the present invention. Wherein, since the threshold Th is set 2 And the extraction result is the time-frequency curve of the ISDJ in the current echo.
Fig. 4 (d) is a projection result of the extracted time-frequency curve in fig. 4 (c) in the frequency dimension, which can be seen to form a plurality of sub-bands in the frequency domain. Because the interference energy in the time-frequency transformation result is gathered near the ridge line and has a certain width, the time-frequency point of a certain frequency can be extracted as a local optimal point at a plurality of moments, so that the passband in the frequency dimension projection result has fluctuation and a peak appears at the boundary of the passband.
Then, according to the gating threshold Th 0 Processing the projection result, and respectively showing the obtained gating signal and the connected domain marking result thereof as shown in fig. 4 (e) and 4 (f); the maximum label in the result of the connected component labeling is 3, which indicates that the gating signal contains 3 connected components, and the number of interference slices is 3. The interference bandwidth is calculated to be 9.94MHz, and the bandwidth of each slice is B J At 3.31MHz, it can be seen that the bandwidth is less than half of the bandwidth of the radar transmitted signal.
Setting the null frequency width δ = B based on the time-frequency curve extraction result shown in fig. 4 (c) and the above-described estimated interference slice bandwidth J =3.31MHz, and the constructed time-frequency zero setting filter is shown in fig. 4 (g). It can be seen from the figure that 3 zero-set regions are formed in total, the zero-set central frequency of each region corresponds to the instantaneous frequency of the ISDJ, and the zero-set bandwidth corresponds to the slice bandwidth estimation value. Zeroing the time-frequency filter with that of FIG. 4 (a)The result of the short-time fourier transform is multiplied, and the result of the time-frequency domain interference suppression is obtained, as shown in fig. 4 (h), it can be seen that the highlight area where the ISDJ exists in the time-frequency distribution is darkened, which represents that the area signal is set to zero.
Fig. 4 (i) and 4 (j) are pulse compression results before and after ISDJ suppression, respectively; the comparison shows that two peak values exist in the pulse pressure result without interference suppression, and the peak amplitude of the false target is higher than that of the real target. The pulse pressure result after interference suppression only has a real target peak at 2km, and the false target peak is completely suppressed, so that the effectiveness of the method provided by the embodiment of the invention for suppressing the ISDJ is verified. Statistically, the signal to interference ratio improvement factor in fig. 4 (j) is 24.51dB.
Simulation experiment 3:
the performance of the interference suppression method provided by the embodiment of the invention and three existing methods is verified by comparison; the method comprises the steps that an interference machine is configured to respectively release intermittent sampling direct forwarding Interference (ISDJ), intermittent sampling repeated forwarding Interference (ISRJ) and intermittent sampling cyclic forwarding Interference (ISJJ) under the same condition, the interference-to-noise ratio is set to be 10dB, the signal-to-noise ratio is-15 dB-5 dB, 2dB is taken as the step length to increase progressively, and 100 Monte Carlo experiments are carried out; the ISDJ interference parameters and the ISRJ interference parameters and the target echo of the simulation experiment 2, ISRJ and ISJJ are set as follows:
the experimental results are shown in FIG. 5 (a) to FIG. 5 (c), wherein the Viterbi-TFF is an embodiment of the present invention, and the remaining three conventional methods are the three methods mentioned in the background of the invention section of the specification.
Fig. 5 (a) is the statistical result of the signal-to-interference ratio improvement factors of the above four methods for ISDJ suppression, and it can be seen from the figure that the embodiment of the present invention can losslessly retain the target signal and filter out the interference, the performance is not obviously deteriorated under the condition of low signal-to-noise ratio, and the signal-to-interference ratio improvement factor is stabilized above 19.63 dB. When the signal-to-noise ratio is less than-1 dB, the improvement factor of the embodiment of the invention is superior to 2 dB-4.4 dB of a time domain threshold zero-crossing method, superior to more than 10.74dB of a time domain filtering method and superior to more than 4.71dB of a frequency domain filtering method; when the signal-to-noise ratio is larger than 1dB, the discrimination of the time domain threshold crossing zero method and the frequency domain filtering method to the target and the interference is reduced, the improvement factor of the time domain threshold crossing zero method and the frequency domain filtering method is in an obvious reduction trend, and the improvement factor of the embodiment of the invention is not obviously reduced and is superior to other three existing methods.
Fig. 5 (b) is the signal to interference ratio improvement factor statistics for ISRJ suppression of the above four methods; the signal-to-interference ratio improvement factor of the embodiment of the invention is stabilized above 19.2 dB. Under the condition of low signal-to-noise ratio, the embodiment of the invention still keeps good interference suppression effect, is obviously superior to a time domain filtering method and a frequency domain filtering method, and has an improvement factor higher than a time domain threshold crossing method by more than 4.31 dB. When the signal-to-noise ratio is larger than 1dB, the difference between the improvement factor of the embodiment of the invention and the improvement factor of the time domain filtering method and the frequency domain filtering method is not large.
FIG. 5 (c) is the signal to interference ratio improvement factor statistics for IScJ suppression by the above four methods; the signal-to-interference ratio improvement factor of the embodiment of the invention is stabilized above 20.18 dB. When the signal-to-noise ratio is-15 dB, the improvement factor of the time domain filtering method and the frequency domain filtering method is below 5dB, the interference performance is seriously deteriorated, and the improvement factor of the embodiment of the invention is 20.93dB, which is obviously superior to the time domain filtering method and the frequency domain filtering method and is 6.11dB superior to the time domain threshold-crossing method.
In summary, the method for inhibiting intra-pulse transfer type interference time-frequency domain based on the Viterbi algorithm provided in the embodiments of the present invention has stable interference inhibition performance, and still maintains good interference inhibition capability under the condition of low snr, and the sir improvement factor after interference inhibition is maintained above 19.2dB, so that the desired signal in the radar echo data can be retained without distortion, the sir improvement factor is improved, and good interference inhibition performance is also achieved under the condition of low snr.
It should be noted that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the description of the specification, reference to the description of the term "one embodiment", "some embodiments", "an example", "a specific example", or "some examples", etc., means that a particular feature or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples described in this specification can be combined and combined by those skilled in the art.
While the present application has been described 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 review of the drawings, the disclosure, and the appended claims.
The foregoing is a further detailed description of the invention in connection with specific preferred embodiments and it is not intended to limit the invention to the specific embodiments described. For those skilled in the art to which the invention pertains, numerous simple deductions or substitutions may be made without departing from the spirit of the invention, which shall be deemed to belong to the scope of the invention.
Claims (10)
1. An intra-pulse transfer type interference time-frequency domain suppression method based on a Viterbi algorithm is characterized by comprising the following steps of:
acquiring radar echo data;
carrying out short-time Fourier transform on the radar echo data to obtain time-frequency distribution;
extracting a time-frequency curve from the time-frequency distribution based on a Viterbi algorithm;
calculating the projection of the time-frequency curve in the frequency dimension to obtain a projection result;
determining the number of interference slices and the bandwidth of the interference slices according to the projection result;
constructing a time-frequency zero setting filter according to the time-frequency curve, the number of the interference slices and the bandwidth of the interference slices;
and filtering the time-frequency distribution by using the time-frequency zero setting filter, and obtaining radar echo data after interference suppression according to a filtering result.
2. The Viterbi-algorithm-based intra-pulse retransmission type interference time-frequency domain suppression method according to claim 1, wherein the Viterbi algorithm-based method for extracting the time-frequency curve from the time-frequency distribution includes:
calculating an amplitude cost vector corresponding to each moment and a path cost matrix corresponding to every two adjacent moments according to the time-frequency distribution;
calculating a joint cost matrix corresponding to every two adjacent moments and combining the amplitude cost and the path cost according to the calculated amplitude cost vector and the path cost matrix;
determining local optimal paths among all adjacent time points according to the calculated joint cost matrixes;
determining a global optimal path according to local optimal paths among all adjacent time points;
extracting a time-frequency curve from the time-frequency distribution according to the global optimal path;
the amplitude cost vector consists of amplitude cost values of all time frequency points at a single moment, and the amplitude cost values of the time frequency points are negatively correlated with the amplitude values of the time frequency points; the path cost matrix is composed of path cost values between each pair of time-frequency points of each adjacent moment, and the path cost values are used for representing the possibility that a previous time-frequency point in the adjacent moments jumps to a later time-frequency point.
3. The viterbi-algorithm-based intra-pulse-transfer-based interference time-frequency-domain suppression method according to claim 2, wherein the magnitude cost vector is calculated in a manner including:
sequencing the amplitude values of all time frequency points at any moment from large to small;
and setting the amplitude cost value of the time frequency point with the maximum amplitude value as 0, and setting the amplitude cost values of the rest time frequency points to be gradually increased according to the sequencing result.
4. The viterbi algorithm-based intra-pulse retransmission interference time-frequency domain suppression method according to claim 2, wherein the path cost matrix is calculated in a manner including:
aiming at every two adjacent moments, calculating the path cost value between each pair of time-frequency points between the two adjacent moments by using a path cost function to obtain a path cost matrix corresponding to the two adjacent moments;
wherein the path cost function is:
wherein x and y represent the frequency of a pair of time frequency points between any two adjacent time points, Δ represents a frequency hopping allowable value, q is a preset constant, and Δ < q, and g (x, y) is the calculated path cost value.
5. The viterbi-algorithm-based intra-pulse retransmission interference time-frequency domain suppression method according to claim 2, wherein calculating a joint cost matrix combining the amplitude cost and the path cost corresponding to each two adjacent time instants according to the calculated amplitude cost vector and the path cost matrix comprises:
for the purpose ofEach time t n Adding the amplitude cost vector with each column in the related path cost matrix to obtain the time t n To t n+1 A corresponding joint cost matrix; n = [1,2, \ 8230;, N]N is the number of time-domain sampling points;
wherein the relevant path cost matrix is: at time t n To t n+1 A corresponding path cost matrix.
6. The viterbi-algorithm-based intra-pulse retransmission interference time-frequency domain suppression method according to claim 5, wherein the determining a local optimal path between all neighboring time points according to the calculated joint cost matrix comprises:
for time t n To t n+1 Selecting the minimum element from each column of the combined cost matrix to form time t n To t n+1 A corresponding local optimal joint cost vector;
accumulating the local optimal joint cost vectors corresponding to all adjacent moments to obtain a global optimal joint cost vector;
and taking the frequency point corresponding to the minimum element in the global optimal joint cost vector as a global optimal point, and backtracking to obtain local optimal paths between all adjacent time points based on the global optimal point and each local optimal joint cost vector.
7. The viterbi-algorithm-based intra-pulse retransmission-based interference time-frequency domain suppression method according to claim 1, wherein determining the number of interference slices and the interference slice bandwidth according to the projection result comprises:
determining a gating threshold value according to the projection result;
carrying out binarization processing on the projection result according to the gating threshold value;
determining the interference frequency bandwidth and the number of interference slices according to the binarization processing result;
and dividing the interference frequency bandwidth by the number of the interference slices to obtain the interference slice bandwidth.
8. The viterbi algorithm based intra-pulse retransmission interference time-frequency domain suppression method according to claim 1, wherein the time-frequency nulling filter is represented as:
wherein δ is the nulling frequency bandwidth of the time-frequency nulling filter, and δ is equal to the interference slice bandwidth,
indicating that the time-frequency zero-setting filter is at t n The center frequency of the zero-set frequency band of the time, and
equal to said time-frequency curve at t n Frequency corresponding to time, H (t) n ,f m ) Representing the time in the time frequency distribution as t n Frequency of f m N = [1,2, \8230;, N]，m＝[1,2,…,M]N is the number of time domain sampling points, and M is the number of frequency points.
9. The viterbi-algorithm-based intra-pulse retransmission interference time-frequency domain suppression method according to claim 1, wherein obtaining the radar echo data after interference suppression according to the filtering result includes:
and performing pulse compression and inverse short-time Fourier transform on the filtering result to obtain radar echo data after interference suppression.
10. The viterbi algorithm-based intra-pulse retransmission interference time-frequency domain suppression method according to claim 1, further comprising:
and before extracting a time-frequency curve from the time-frequency distribution by using a Viterbi algorithm, performing time-frequency information enhancement processing on the time-frequency distribution.
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