An anti-interference method applied to millimeter wave radar

Abstract

The invention discloses an anti-interference method applied to millimeter wave radar, including interference detection, signal sorting, waveform control and interference migration, etc., which can reduce the probability of radar being interfered and reduce the influence of interference after the radar is interfered, thereby improving the anti-interference performance of the radar; directly detecting and estimating interference source parameters on a normal target echo signal without the need for additional detection waveforms, thereby greatly reducing the complexity and power consumption of the anti-interference system; using the mean, standard deviation and absolute deviation characteristics of ADC data and differential data for interference detection, thereby having stronger interference detection capability and robustness; estimating the original interfered signal in a manner of superimposing forward and backward time series models, thereby greatly reducing the influence on the system detection capability when interference occurs, thereby improving the system's anti-interference capability and detection stability.

Description

Technical Field

[0001] The present invention belongs to the technical field of millimeter wave radars, and in particular relates to an anti-interference method applied to millimeter wave radars. Background Art

[0002] With the continuous development of the automobile industry and the continuous improvement of automobile electrification, more and more cars will be equipped with millimeter-wave radars, and most vehicles will be equipped with multiple millimeter-wave radars for different functions, such as adaptive cruise control (ACC), blind spot detection (BSD), lane change assist (LCA), etc. When multiple millimeter-wave radars operate at the same or similar frequencies, co-frequency interference may occur.

[0003] At present, the mainstream anti-interference methods of automotive millimeter-wave radar are mainly divided into system-level and signal-level anti-interference: system-level anti-interference mainly reduces the power of interference signals entering the receiver or reduces the accumulated gain of interference signals by adjusting the antenna polarization direction, the frequency of the transmitted waveform and the modulation method; signal-level anti-interference reduces the impact of interference signals on effective echo signals through filtering, interference detection, interference zeroing, etc. after the interference signals enter the receiver.

[0004] If the millimeter-wave radars on vehicles in adjacent lanes have similar frequencies, the signals they transmit will overlap in space, affecting each other's detection accuracy. Therefore, the anti-interference function of vehicle-mounted millimeter-wave radars is becoming increasingly important. In some scenarios, even radars cannot work properly without anti-interference functions. Summary of the invention

[0005] The purpose of the present invention is to provide an anti-interference method applied to millimeter wave radar, which can reduce the probability of radar being interfered with and reduce the impact of interference after the radar is interfered with, thereby improving the anti-interference performance of the radar and solving the problems raised in the above background technology.

[0006] To achieve the above object, the present invention adopts the following technical solution: an anti-interference method applied to millimeter wave radar, comprising the following steps:

[0007] Analyze the real-time analog-to-digital converter data, including the mean, standard deviation, and mean absolute deviation characteristics, and detect the location of the interference signal according to the configured threshold value;

[0008] The interference position of each chirp is counted, and the characteristics of interference intensity, interference start time, interference duration, and interference bandwidth are calculated. Then, the parameters of the interference source are sorted out according to the characteristics.

[0009] The forward and backward recursive models of the received signal are constructed using the data before and after the interference. The forward and backward prediction results at the interference moment are obtained using the recursive model. The predicted signal result at the interference moment is obtained by weighting the two.

[0010] By constructing a time smoothing window, the interference source parameters at historical moments are stored. Before the radar starts to transmit, the interference source database is matched with the waveform configuration library parameters, and the waveform with a lower probability of being interfered with is selected for transmission.

[0011] Preferably, the analysis of the real-time analog-to-digital converter data further includes calculating differential data, and further counting the mean, standard deviation and mean absolute deviation characteristics of each chirp data based on the differential data, including:

[0012] In one frame time, the analog-to-digital converter data received by the millimeter-wave radar is represented as (x1, x2, x3, …, x N ), where x n =(x 1,n ,x 2,n ,x 3,n ,…,x K,n ) T , T represents the vector transpose symbol, represents the echo data of the nth chirp, x k,n represents the kth ADC sampling point of the nth chirp, N represents the number of chirps in a frame of echo data, and K represents the number of ADC points collected by a single chirp;

[0013] In each chirp data x n Calculate the difference data x' n =(|x 2,n -x 1,n |,|x 3,n -x 2,n |,|x 4,n -x 3,n |,…,|x K,n -x K-1,n |) T , and accordingly count each chirp data x n and x' n The mean, standard deviation and mean absolute deviation characteristics of

[0014]

[0015] in MD x Represents the mean, standard deviation and mean absolute deviation characteristics of ADC data, MD x' Represents the mean, standard deviation, and mean absolute deviation characteristics of the differenced data.

[0016] Preferably, the detecting the location of the interference signal includes:

[0017] According to the conditions or Detect the interference position, where T1 and T2 represent the standard deviation threshold and mean absolute deviation threshold of ADC data, and T'1 and T'2 represent the standard deviation threshold and mean absolute deviation threshold of ADC differential data. At this time, the interference detection process of the nth chirp is completed.

[0018] Preferably, the parameters of the interference source are selected according to the characteristics, including:

[0019] According to the interference detection results, calculate the interference parameters in each chirp:

[0020] Interference intensity Where P is the interference detection time in the chirp data, and M is the interference duration;

[0021] Interference start time Where PRI is the pulse repetition period, F s Indicates the sampling rate;

[0022] Interference duration

[0023] Interference starting frequency Where f0 is the starting frequency of the radar transmission waveform, and K is the frequency modulation slope;

[0024] Interference frequency modulation slope difference Among them B IF is the receiver intermediate frequency bandwidth;

[0025] Use the calculated interference parameter as the feature F of the current interference pulse n ={e n ,t n ,τ n ,f inter ,ΔK n} T ,Using similar cumulative difference histogram method and sequence difference histogram method, the interference pulse sequence of the current frame is sorted, and the number of interference sources and their corresponding interference characteristics are determined.

[0026] Preferably, the method then uses the recursive model to obtain the forward and backward prediction results of the interference moment, and obtains the prediction signal result of the interference moment by weighting the two, including:

[0027] Using forward sequence Construct a p-order autoregressive process, that is, x t,n=w1x t-1,n +w2x t-2,n +…+w p x t-p,n +ε n , where w1,w2,…,w p is the p-order autoregressive coefficient, ε n is white noise, and the autoregressive coefficient can be obtained according to the Yule-Walker equation:

[0028] w=R -1 r

[0029] in r k For the sequence x n,forward The autocorrelation coefficient of

[0030] Using the backward sequence Construct a p-order autoregressive process;

[0031] According to the autoregressive coefficient w forward and w backward The sampling data when interference occurs can be predicted separately and Finally, we get the predicted data of the interference time.

[0032] Use the interference prediction data to replace the original interfered data to obtain a new sequence

[0033]

[0034] Preferably, the interference strength is determined by calculating the starting time and duration of the interference signal in the chirp data.

[0035] Preferably, the interference start time is determined by calculating a time offset of the interference signal relative to a pulse repetition period, wherein the time offset is determined by a sampling rate.

[0036] Preferably, the prediction signal result is obtained by weighting the forward prediction result and the backward prediction result in a certain ratio.

[0037] Preferably, the control of the waveform is based on the matching result of the historical interference source parameters and the waveform configuration library parameters, and the waveform with the lowest probability of being interfered is selected for transmission.

[0038] Preferably, the method further includes: placing the interference source information obtained by sorting into an interference source database for controlling the waveform parameters of the next frame.

[0039] Technical effects and advantages of the present invention: Compared with the prior art, the anti-interference method for millimeter wave radar proposed by the present invention has the following advantages:

[0040] The present invention constructs a complete set of anti-interference methods on the existing FMCW standard millimeter wave radar system, including interference detection, signal sorting, waveform control and interference migration, etc., which can reduce the probability of radar being interfered and reduce the influence of interference after the radar is interfered, thereby improving the anti-interference performance of the radar; directly detects and estimates the interference source parameters on the normal target echo signal without the need for additional detection waveforms, thereby greatly reducing the complexity and power consumption of the anti-interference system; uses the mean, standard deviation and absolute deviation characteristics of ADC data and differential data for interference detection, thereby having stronger interference detection capability and robustness; and superimposes the forward and backward time series models to estimate the original interfered signal, thereby greatly reducing the influence of interference on the system detection capability when interference occurs, thereby improving the system's anti-interference capability and detection stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of an anti-interference method applied to millimeter-wave radar in an embodiment of the present invention;

[0042] Figure 2 is a curve diagram of disturbed data in an embodiment of the present invention;

[0043] Figure 3 It is a curve diagram after interference migration in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0045] The present invention provides an anti-interference method applied to millimeter wave radar, such as Figure 1 As shown, it includes the following four parts:

[0046] (1) Interference detection: First, the real-time ADC data is analyzed to collect statistics including mean, standard deviation, mean absolute deviation and other characteristics, and the location of the interference signal is detected according to the configured threshold value;

[0047] (2) Interference parameter estimation and signal sorting: the interference position of each chirp is counted, and the interference intensity, interference start time, interference duration, interference bandwidth and other characteristics are calculated. Then, the parameters of the interference source are sorted out according to the above characteristics. The interference intensity is determined by calculating the start time and duration of the interference signal in the chirp data. The interference start time is determined by calculating the time offset of the interference signal relative to the pulse repetition period, where the time offset is determined by the sampling rate.

[0048] (3) Interference signal migration: Use the data before and after the interference to build the forward and backward recursive models of the received signal. Then use the recursive model to obtain the forward and backward prediction results at the interference moment. By weighting the two, the predicted signal result at the interference moment is obtained. The predicted signal result is obtained by weighting the forward prediction result and the backward prediction result in a certain proportion.

[0049] (4) Waveform control: By building a time smoothing window, the interference source parameters at historical moments are stored. Before the radar starts to transmit, the interference source database is matched with the waveform configuration library parameters, and the waveform with the lowest probability of being interfered with is selected for transmission. The waveform control is based on the matching results of the historical interference source parameters and the waveform configuration library parameters, and the waveform with the lowest probability of being interfered with is selected for transmission.

[0050] Through the above contents of parts (1) and (3), when the radar is interfered, the impact of the interference signal can be reduced and a significant increase in the noise floor can be avoided. The contents of parts (2) and (4) can achieve a rough measurement of the interference signal parameters in the current working frequency band without additional wave transmission overhead, selectively control the radar's working waveform, and reduce the probability of the radar being interfered with by the same frequency.

[0051] The specific method is as follows:

[0052] Assume that within one frame, the ADC data received by the radar is represented as (x1, x2, x3, …, x N ), where x n =(x 1,n ,x 2,n ,x 3,n ,…,x K,n ) T , T represents the vector transpose symbol, represents the echo data of the nth chirp, x k,n represents the kth ADC sampling point of the nth chirp, N represents the number of chirps in a frame of echo data, and K represents the number of ADC points collected by a single chirp;

[0053] In each chirp data x n Calculate the difference data x' n =(|x2,n -x 1,n |,|x 3,n -x 2,n |,|x 4,n -x 3,n |,…,|x K,n -x K-1,n |) T , and accordingly count each chirp data x n and x' n The mean, standard deviation and mean absolute deviation characteristics of

[0054]

[0055] in MD x Represents the mean, standard deviation and mean absolute deviation characteristics of ADC data, MD x' Represents the mean, standard deviation, and mean absolute deviation characteristics of the difference data;

[0056] According to the conditions or Detect the interference position, where T1 and T2 represent the standard deviation threshold and mean absolute deviation threshold of ADC data, and T'1 and T'2 represent the standard deviation threshold and mean absolute deviation threshold of ADC differential data. At this point, the interference detection process of the nth chirp is completed;

[0057] According to the above interference detection results, the interference parameters in each chirp are calculated:

[0058] Interference intensity Where P is the interference detection time in the chirp data, and M is the interference duration;

[0059] Interference start time Where PRI is the pulse repetition period, F s Indicates the sampling rate;

[0060] Interference duration

[0061] Interference starting frequency Where f0 is the starting frequency of the radar transmission waveform, and K is the frequency modulation slope;

[0062] Interference frequency modulation slope difference Among them B IF is the receiver intermediate frequency bandwidth;

[0063] Use the interference parameter calculated in step 4) as the feature F of the current interference pulse n ={e n,t n ,τ n ,f inter ,ΔK n} T , using methods similar to the cumulative difference histogram (CDIF) and sequential difference histogram (SDIF) methods to sort the interference pulse sequence of the current frame, and determine the number of interference sources and their corresponding interference characteristics, and finally put the sorted interference source information into the interference source database to control the waveform parameters of the next frame. The method of sorting radar parameters by CDIF and SDIF has been widely disclosed in the prior art, so it will not be elaborated here;

[0064] Assume that the data x of the nth chirp n In the case of interference, the interference occurs at the sampling point In the range, L represents the number of interfered points detected by the interference, and the range K / 2 to K / 2+L is the interfered range. In order to reduce the increase of the interference signal on the background noise, the present invention proposes to use the forward and backward time series model superposition method to estimate the original signal. Here, the autoregressive model (AR) is taken as an example:

[0065] Using forward sequence Construct a p-order autoregressive process, that is, x t,n =w1x t-1,n +w2x t-2,n +…+w p x t-p,n +ε n , where w1,w2,…,w p is the p-order autoregressive coefficient, ε n is white noise, and the autoregressive coefficient can be obtained according to the Yule-Walker equation

[0066] w=R -1 r

[0067] in r k For the sequence x n,forward The autocorrelation coefficient of

[0068] Similarly, using the backward sequence Construct a p-order autoregressive process and calculate the backward autoregressive coefficient w backward The process is the same, so I won’t go into details.

[0069] According to the autoregressive coefficient w forward and w backward The sampling data when interference occurs can be predicted separately and Finally, we get the predicted data of the interference time.

[0070] Use the interference prediction data to replace the original interfered data to obtain a new sequence

[0071]

[0072] Before the radar starts to configure the next frame of waveform, it first finds the recent interference source parameters from the interference database, matches the interference source parameters with the waveform configuration library parameters, and selects the waveform with a lower probability of being interfered with for transmission. At this time, a cycle of anti-interference is completed.

[0073] like Figure 2 As shown, curves 1 and 2 are the chirp data of the millimeter wave radar under actual working environment. It can be seen that the echo amplitude is abnormally elevated at the 201st to 211th sampling points, that is, the radar is interfered during this period. Curve 3 is the result of the interference detection method mentioned in the present invention, which can correctly output the interference status detection at different times.

[0074] like Figure 3 As shown, Figure 3 This is the data after interference migration. It can be clearly seen that the echo amplitude at the interference moment returns to normal.

[0075] In summary, the present invention constructs a complete set of anti-interference methods on the existing FMCW standard millimeter wave radar system, including interference detection, signal sorting, waveform control and interference migration, etc., which can reduce the probability of radar being interfered and reduce the impact of interference after the radar is interfered, thereby improving the anti-interference performance of the radar; directly detect and estimate the interference source parameters on the normal target echo signal without the need for additional detection waveforms, which greatly reduces the complexity and system power consumption of the anti-interference system;

[0076] Interference detection uses the mean, standard deviation and absolute deviation characteristics of ADC data and differential data, which has stronger interference detection capability and robustness; the forward and backward timing models are superimposed to estimate the original interfered signal, which can greatly reduce the impact of interference on the system detection capability when it occurs, and improve the system's anti-interference ability and detection stability.

[0077] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An anti-interference method applied to millimeter wave radar, characterized in that: The following steps are involved: Analyze the real-time analog-to-digital converter data, including the mean, standard deviation, and mean absolute deviation characteristics, and detect the location of the interference signal according to the configured threshold value; The interference position of each chirp is counted, and the characteristics of interference intensity, interference start time, interference duration, and interference bandwidth are calculated. Then, the parameters of the interference source are sorted out according to the characteristics. The forward and backward recursive models of the received signal are constructed using the data before and after the interference. The forward and backward prediction results at the interference moment are obtained using the recursive model. The predicted signal result at the interference moment is obtained by weighting the two. By constructing a time smoothing window, the interference source parameters at historical moments are stored. Before the radar starts to transmit, the interference source database is matched with the waveform configuration library parameters, and the waveform with a lower probability of being interfered with is selected for transmission.

2. The anti-interference method applied to millimeter wave radar according to claim 1, characterized in that: The analysis of the real-time analog-to-digital converter data also includes calculating differential data, and further calculating the mean, standard deviation and mean absolute deviation characteristics of each chirp data, including: In one frame time, the analog-to-digital converter data received by the millimeter-wave radar is represented as (x1, x2, x3, …, x N ), where x n =(x 1,n ,x 2,n ,x 3,n ,…,x K,n ) T , T represents the vector transpose symbol, represents the echo data of the nth chirp, x k,n represents the kth ADC sampling point of the nth chirp, N represents the number of chirps in a frame of echo data, and K represents the number of ADC points collected by a single chirp; In each chirp data x n Calculate the difference data x' n =(|x 2,n -x 1,n | , |x 3,n -x 2,n | , |x 4,n -x 3,n |,…,|x K,n -x K-1,n |) T , and accordingly count each chirp data x n and x' n The mean, standard deviation and mean absolute deviation characteristics of in MD x Represents the mean, standard deviation and mean absolute deviation characteristics of ADC data, MD x' Represents the mean, standard deviation, and mean absolute deviation characteristics of the differenced data.

3. The anti-interference method applied to millimeter wave radar according to claim 2, characterized in that: The detecting the location of the interference signal includes: According to the conditions or Detect the interference position, where T1 and T2 represent the standard deviation threshold and mean absolute deviation threshold of ADC data, and T'1 and T'2 represent the standard deviation threshold and mean absolute deviation threshold of ADC differential data. At this time, the interference detection process of the nth chirp is completed.

4. The anti-interference method applied to millimeter wave radar according to claim 3, characterized in that: The parameters for selecting interference sources according to the characteristics include: According to the interference detection results, calculate the interference parameters in each chirp: Interference intensity 1≤m≤K, where P is the interference detection time in the chirp data, and M is the interference duration; Interference start time Where PRI is the pulse repetition period, F s Indicates the sampling rate; Interference duration Interference starting frequency Where f0 is the starting frequency of the radar transmission waveform, and K is the frequency modulation slope; Interference frequency modulation slope difference Among them B IF is the receiver intermediate frequency bandwidth; Use the calculated interference parameter as the feature F of the current interference pulse n ={e n ,t n ,τ n ,f inter ,ΔK n } T ,Using similar cumulative difference histogram method and sequence difference histogram method, the interference pulse sequence of the current frame is sorted, and the number of interference sources and their corresponding interference characteristics are determined.

5. The anti-interference method applied to millimeter wave radar according to claim 4, characterized in that: The recursive model is then used to obtain the forward and backward prediction results at the interference moment, and the prediction signal result at the interference moment is obtained by weighting the two, including: Using forward sequence Construct a p-order autoregressive process, that is, x t,n =w1x t-1,n +w2x t-2,n +…+w p x t-p,n +ε n , where w1,w2,…,w p is the p-order autoregressive coefficient, ε n is white noise, and the autoregressive coefficient can be obtained according to the Yule-Walker equation: w=R -1 r in r k For the sequence x n,forward The autocorrelation coefficient of Using the backward sequence Construct a p-order autoregressive process; According to the autoregressive coefficient w forward and w backward The sampling data when interference occurs can be predicted separately and Finally, we get the predicted data of the interference time. Use the interference prediction data to replace the original interfered data to obtain a new sequence 6. The anti-interference method applied to millimeter wave radar according to claim 1, characterized in that: The interference strength is determined by calculating the starting time and duration of the interference signal in the chirp data.

7. The anti-interference method applied to millimeter wave radar according to claim 1, characterized in that: The interference start time is determined by calculating the time offset of the interference signal relative to the pulse repetition period, wherein the time offset is determined by the sampling rate.

8. The anti-interference method applied to millimeter wave radar according to claim 1, characterized in that: The prediction signal result is obtained by weighting the forward prediction result and the backward prediction result in a certain ratio.

9. The anti-interference method applied to millimeter wave radar according to claim 1, characterized in that: The control of the waveform is based on the matching result of the historical interference source parameters and the waveform configuration library parameters, and the waveform with the lowest probability of being interfered is selected for transmission.

10. The anti-interference method applied to millimeter wave radar according to claim 4, characterized in that: Also includes: The sorted interference source information is placed in the interference source database to control the waveform parameters of the next frame.

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