A distributed radar multi-jamming suppression method based on spectrum reconstruction

Abstract

The present application provides a distributed radar multi-jamming suppression method of spectrum reconstruction, which estimates the time delay difference, phase difference and amplitude difference of the interference between different channels, and suppresses the interference after reconstructing the interference spectrum in the frequency domain according to the estimation results and the multi-node echo. The method can realize multi-jamming suppression under the condition that the time delays of each interference are inconsistent and difficult to align in the time domain, and the system phase correlation is reduced. The anti-jamming performance of the distributed radar system in the multi-jamming scene is improved.

Description

Technical Field

[0001] This invention belongs to the field of distributed radar anti-jamming technology, and particularly relates to a distributed radar multi-jamming suppression method based on spectrum reconstruction. Background Technology

[0002] Distributed radar is a radar system composed of multiple radars, distributed in a decentralized manner, working collaboratively. Signal-level coherent fusion can be achieved between individual radar units, such as... Figure 1 As shown. In the increasingly complex battlefield electromagnetic environment, distributed radar has certain advantages in airspace anti-jamming due to its high angular resolution.

[0003] Distributed radar requires ensuring signal coherence between nodes for interference suppression in the airspace. In single-interference scenarios, signal amplitude, phase, and time delay compensation can be performed by estimating parameters to achieve ideal signal coherence between nodes. However, in multi-interference scenarios, due to the different spatial distributions of interference and the different time delays of different interferences at different nodes (e.g., ... Figure 2 As shown in the diagram, even with amplitude, phase, and time delay compensation, nodes can only become coherent with respect to one interference source. Other interference sources remain uncoherent, reducing the correlation between nodes. In this situation, conventional array signal processing methods cannot effectively suppress interference.

[0004] This invention considers a distributed radar system with one transmitter and multiple receivers, and one main radar and multiple auxiliary radars. It proposes a method to separate interference with different time delays and then use the separated interference signals to assist the reference radar (main radar) in interference suppression. Summary of the Invention

[0005] This invention proposes a distributed radar multi-interference suppression method based on spectrum reconstruction. This method estimates the time delay difference, phase difference, and amplitude difference of interference across different channels, and designs a time delay reconstruction function based on the estimation results. To address the issues of accumulated error in parameter estimation and zero-points in the reconstruction function, a multi-time delay reconstruction function is proposed, replacing the minimum value with a fixed value. This is combined with multi-node echoes to reconstruct the interference spectrum in the frequency domain before transforming it to the time domain to suppress interference.

[0006] When a distributed radar system has a long baseline or the distance between interfering devices in the environment is large, the coherence parameters of each interfering device are inconsistent across different nodes. The method proposed in this invention can achieve multi-interference suppression by reconstructing the spectra of each interfering device when the time delays of each interfering device are inconsistent across nodes, making time-domain alignment difficult and reducing system coherence. This method does not require sub-band division, is applicable to any time delay difference between interfering devices, and estimates interfering parameters entirely based on data, making it insensitive to amplitude and phase delay errors. This effectively improves the anti-interference performance of distributed radar systems in multi-interference scenarios.

[0007] Algorithm Principles

[0008] The method proposed in this invention requires estimating the time delay difference, phase difference, and amplitude difference of interference in different nodes, and then separating the interference in the frequency domain based on the estimated parameters. Finally, the interference signal is reconstructed from multiple sets of auxiliary node signals and used by the reference node to cancel the interference. This invention only considers noise-type interference. For ease of understanding, this invention uses the example of one reference node and two auxiliary nodes suppressing two noise-type interferences to illustrate the algorithm principle. Theoretically, the number of auxiliary nodes is the maximum number of interferences that can be suppressed. The signal model is as follows:

[0009]

[0010] Where α i1 and α i2 These represent the amplitudes of the two interferences at the i-th node, determined by factors such as the jammer's transmit power and signal transmission distance loss. and s1(t) and s2(t) are the phases of the two interferences in the i-th channel, determined by the relative positions of the jammer and the radar; s1(t) and s2(t) are the envelopes of the two interferences, respectively. i1 and τ i2 These represent the time delays of the two interferences in the i-th channel, respectively; n i (t) represents the noise of the i-th channel.

[0011] Step 1: Delay Difference Estimation

[0012] Since noise interference signals have extremely low correlation when they are non-correlated in the time domain, the correlation between the signals at different time delays and the other signal can be obtained by performing frequency domain time delay on one of the signals. The time delay difference of the interference signals between different channels can then be measured by observing the position of the correlation peak.

[0013]

[0014] Let the measured delay difference between the j-th and i-th nodes be the first interference. Similarly.

[0015] Step 2: Phase Difference Estimation

[0016] Since the two noise interference signals have different time delays in different channels, when one interference is correlated, the other interference must be uncorrelated. Therefore, the estimated time delay can be used as a basis for determining the correlation between the two signals. Further estimation of the phase difference of inter-channel interference is performed.

[0017] Align the two channel signals relative to interference signal 1. Since the interference signal is random noise, the misaligned interference signal 2 is uncorrelated between the channels. and It can be treated as a high-power noise signal N i (t) and N j (t):

[0018]

[0019] Where Φ represents the phase and B is a constant real number that does not affect the result.

[0020] Step 3: Amplitude Difference Estimation

[0021] Given the phase difference and aligned with a certain interference delay, iterate through the amplitude ratio of the two sets of data within a certain range to find the value that makes the difference between the subtraction of the two sets of data equal to the reference channel x. m (t) The amplitude of the lowest correlation peak

[0022]

[0023] In the formula and The time delay difference and phase difference have been relative to the reference channel x. m (t) The supplementary auxiliary channel signal.

[0024] As a approaches the ratio of the true amplitude of the interference in the two channels, then x ij The lower the component of the interference in (t), the lower the correlation coefficient with the reference channel. Therefore, the amplitude difference can be estimated by iterating through a.

[0025] Step 4: Estimation of Multiple Interference Parameters

[0026] The above steps estimate the time delay difference, phase difference, and amplitude difference of one of the interference signals in the two channels. Similarly, by aligning different related peak points and repeating the above calculation steps, the parameters of the other interference signal in the two channels can be estimated.

[0027] Step 5: Frequency Domain Interference Signal Reconstruction

[0028] Using channel 1 as a reference, align the two channel signals relative to interference signal 1 and transform them to the frequency domain:

[0029] X1(w) = S1(w) + S2(w) + n′1(w)

[0030]

[0031] Where ⊙ is the Hadamard product, a x2 , τ x2 All of these can be calculated using the parameters estimated in the previous section.

[0032] Ignoring noise, for any frequency point wo We can obtain the following formula:

[0033]

[0034] Therefore, the spectrum of the interference can be calculated:

[0035]

[0036] in

[0037] Taking the interference spectrum as an example,

[0038]

[0039]

[0040] Equation (8) first suppresses interference 1 by canceling out interference between two channels, and then constructs the reconstruction function h(w) using the known time delay difference, phase difference, and amplitude difference. o ), to recover the original spectrum of interference 2 from two types of interference 2 with different time delays.

[0041] There are two main problems in this step: 1. Due to error accumulation τ x2 The estimate is inaccurate; 2.h(w o There exists a point 0, and dividing it will produce a maximum value.

[0042] These two problems are addressed by using a multi-delay reconstruction function and eliminating the minimum point in the denominator, respectively.

[0043] With τ x2 A multi-delay reconstruction function is formed within a certain time range nearby to reduce the impact of estimation errors; a fixed threshold is set to assign a fixed value to the minimum value in the reconstruction function to avoid the occurrence of maxima in the estimated spectrum.

[0044] Step 6: Interference Suppression Based on MMSE

[0045] The multi-delay reconstruction function forms multiple channels, which, after being transformed back to the time domain signal, can all be used as cancellation channels for interference cancellation. jam .

[0046] The minimum variance distortion-free criterion (MMSE) is used to suppress interference:

[0047]

[0048] In the formula r mjam To determine the cross-covariance matrix between the cancellation channel and the reference channel, R jam This is the autocovariance matrix of the cancellation channel. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a distributed radar system.

[0050] Figure 2 To address the issue of delay due to multiple interferences;

[0051] Figure 3 Flowchart of the multi-interference suppression algorithm for frequency domain reconstruction;

[0052] Figure 4 The time delay-correlation coefficient spectrum;

[0053] Figure 5 The amplitude ratio-correlation coefficient spectral line;

[0054] Figure 6 This represents the result of interference suppression. Detailed Implementation

[0055] The following is a simulation example of applying the present invention, and its specific process is explained.

[0056] The specific parameters of the one-dimensional distributed radar system used in the simulation are shown in the table below:

[0057] Table 1 Parameters of Distributed Radar System

[0058]

[0059] Consider the following simulation scenario:

[0060] Table 2 Source Parameters

[0061]

[0062] The flowchart of this invention is as follows Figure 3 As shown.

[0063] Step 1: Delay Difference Estimation

[0064] The time delay-correlation coefficient spectral line is calculated according to equation (2), as follows: Figure 4 As shown, the time delay difference sequence τ of each node relative to the reference node for the k-th interference is obtained based on the peak value. k .

[0065] Step 2: Phase Difference Estimation

[0066] First, based on the time delay difference τ k Time delay compensation is performed on each node, and then the phase difference sequence of the kth interference relative to the reference node is estimated according to equation (3).

[0067] Step 3: Amplitude Difference Estimation

[0068] Based on the time delay difference τ kPhase difference Time delay compensation is performed on each node, and then the amplitude difference is traversed according to equation (4), such as... Figure 5 As shown. Based on the minimum point, the amplitude difference sequence a of the k-th disturbance relative to the reference node is estimated. k .

[0069] Step 4: Estimate all interference parameters

[0070] Repeat steps one through three until all disturbance parameters have been estimated.

[0071] Step 5: Frequency Domain Interference Signal Reconstruction

[0072] The reconstruction function is calculated according to equation (7). A multi-delay reconstruction function is constructed using the estimated delay neighborhood to ensure robustness (generally, a granularity of one-thousandth of the sampling interval is used, and 10 delays in the neighborhood are sufficient). Points whose reconstruction function modulus is less than 5% of the maximum value are assigned a modulus of 5% of the maximum value. Each interference cancellation group is obtained using the multi-delay reconstruction function.

[0073] Step 6: Interference Suppression Based on MMSE

[0074] Using the reference channel as the main channel and the signals of each interference cancellation group as the cancellation channel, the MMSE weights are calculated according to equation (10) to suppress interference. The output results are as follows: Figure 6 As shown.

[0075] In summary, the above are merely implementation examples of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed radar multi-interference suppression method based on spectrum reconstruction, characterized in that, Includes the following steps: Step 1, Delay Difference Estimation: Delay one of the signals in the frequency domain, correlate the delayed signal with the other signal, observe the position of the correlation peak, and measure the delay difference of the interference signal between the channels; Step 2, Phase Difference Estimation: Based on the time delay difference estimated in Step 1, perform time delay compensation on the signals of different channels, align the signals of the two channels with respect to the interference signal to be estimated, and use the uncorrelated characteristics of the unaligned interference signal between channels to estimate the phase difference of the interference signal between channels. Step 3, Amplitude Difference Estimation: Given the phase difference and time delay alignment of the interference signal to be estimated, traverse the amplitude ratios of the two sets of auxiliary channel data within a certain range, find the amplitude ratio that minimizes the correlation peak with the reference channel data after subtracting the two sets of data, and estimate the amplitude difference of the interference signal between the channels. Step 4, Multi-interference parameter estimation: Align different related peak points, and repeat steps 1 to 3 to estimate the time delay difference, phase difference and amplitude difference of all interference signals between channels in sequence; Step 5, Frequency Domain Interference Signal Reconstruction: Based on the estimated time delay difference, phase difference, and amplitude difference, using the reference channel as a benchmark, the auxiliary channel signal is aligned with a certain interference signal and transformed to the frequency domain. Combined with the reconstruction functions of other interference signals, the original spectrum of each interference signal is restored from the aliased interference by frequency domain division. When calculating the reconstruction function, a multi-time delay reconstruction function is constructed with the estimated time delay as the center, and a fixed threshold is set to assign a fixed value to the minimum value in the reconstruction function. Step 6: Interference suppression based on MMSE: The multiple channels formed by the multi-delay reconstruction function obtained in Step 5 are transformed back into time domain signals and used as interference cancellation channels. The minimum variance distortion-free criterion (MMSE) is used for interference suppression.

2. The method as described in claim 1, characterized in that, In step five, when calculating the reconstruction function, a multi-delay reconstruction function is formed using a certain range of time near the estimated delay, in order to reduce the impact of delay estimation error on the reconstruction of the interference spectrum; A fixed threshold is set to assign a fixed value to the minimum value in the reconstruction function in order to avoid the estimated spectrum from producing a maximum value when dividing in the frequency domain.

3. The method as described in claim 1, characterized in that, In step six, the cross-covariance matrix of the cancellation channel and the reference channel, as well as the autocovariance matrix of the cancellation channel, are calculated respectively, and the MMSE criterion is used for interference suppression.

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