Integrated Signal Design and Processing Method Based on Filter Bank Multicarrier

Abstract

The present invention discloses an integrated signal design and processing method based on filter bank multi-carrier, which generates filter bank multi-carrier radar-communication integrated signals and uses the ISAR iterative entropy algorithm to estimate the motion parameters of radar detection targets. The specific steps include: generating filter bank multi-carrier radar-communication integrated signals; radiating the integrated signals into a channel to obtain echo signals; processing the communication information in the echo signals; using the ISAR iterative entropy algorithm to estimate the motion parameters of radar detection targets; performing Doppler frequency offset compensation on the echo signals; and performing radar imaging on the detection targets. The present invention solves the disadvantages that the cyclic prefix in traditional OFDM integrated signals occupies communication resources and the radar imaging accuracy of traditional SAR imaging methods decreases when detecting moving targets, improves the radar imaging accuracy, and reduces the communication error rate.

Description

Technical Field

[0001] The present invention belongs to the field of communication technologies, and further relates to an integrated signal design and processing method based on filter bank multicarrier in the field of radar communication technologies. The present invention can be used in an integrated radar communication system in a complex high-speed multipath scenario to transmit communication information and to obtain target radar information in an ISAR (Inverse Synthetic Aperture Radar) scenario. Background Art

[0002] In order for an integrated radar communication system to work efficiently, the key lies in designing an integrated fusion signal that makes full use of spectrum resources to achieve both radar and communication functions simultaneously. A typical method for generating an integrated signal is to use a communication signal to implement the radar function. A commonly used communication signal is an OFDM (Orthogonal-Frequency Division Multiplexing) signal. However, the introduction of a cyclic prefix in the signal will bring a series of problems. In a complex multipath scenario with a large time delay, the communication performance is not good. Therefore, a signal with good radar and communication performance is needed to complete the construction of the integrated system.

[0003] The University of Electronic Science and Technology disclosed a design and processing method for an integrated radar communication system based on OFDM carrier joint optimization in its patent document "An integrated radar communication system based on OFDM carrier joint optimization" (application number 201910025384.5, publication number CN 109688082 A). This method is based on a traditional OFDM system and uses data symbols and a random phase sequence to perform local reserved waveform design according to the data bandwidth ratio. This invention introduces a partial reserved cyclic prefix algorithm, which can flexibly allocate bandwidth and effectively reduce the PAPR (Peak to Average Power Ratio) while maintaining the performance of the communication system itself, improving the spectrum utilization rate. However, the disadvantage of this method is still that after reserving the cyclic prefix, not only will the resources of the communication system be occupied by the CP (Cyclic Prefix), but also in a scenario with a large time delay in a complex environment, the length of the CP often cannot meet the requirement to offset the influence of multipath, resulting in non-orthogonal subcarriers, making it difficult for the receiving end to accurately demodulate the communication signal and causing a high bit error rate.

[0004] Xidian University discloses a design method for a radar-communication signal sharing waveform based on Filter Bank Multi-Carrier (FBMC) in its patent document "A Method for Generating Radar-Communication Integrated Waveform Based on FBMC" (Application No. 202011074161.7, Publication No. CN 112363132 A). This method uses FBMC signals as radar-communication integrated signals, solving the problems that the cyclic prefix in the existing OFDM radar-communication integrated technology affects the detection ability and the adaptive power allocation under the constraint of limited transmission power. At the radar receiving end, Synthetic Aperture Radar (SAR) imaging technology is used to obtain target radar information, enabling accurate imaging of stationary targets. However, the disadvantage of this method is that the SAR imaging technology is mainly applied to the imaging of stationary targets, so the speed information of moving targets cannot be obtained, and the Doppler effect caused by moving targets is difficult to be compensated, resulting in a decrease in the accuracy of radar detection. Summary of the Invention

[0005] The object of the present invention is to propose an integrated signal design and processing method based on filter bank multi-carrier for the deficiencies of the existing technologies above, to solve the problems that the cyclic prefix in traditional OFDM signals occupies communication system resources, the sub-carriers are not orthogonal, and it is difficult for the receiving end to accurately demodulate communication signals, resulting in a high bit error rate, as well as the problem that the SAR imaging technology cannot obtain the speed information of moving targets and the Doppler effect caused by moving targets is difficult to be compensated.

[0006] The specific idea to achieve the object of the present invention is that when designing the integrated signal based on filter bank multi-carrier, the present invention uses Filter Bank Multi-Carrier (FBMC) signals as radar-communication integrated signals to complete the functions of radar imaging and communication. Since FBMC signals do not contain cyclic prefixes, the problem of cyclic prefixes occupying communication resources in traditional OFDM signals is avoided. When processing the integrated signal based on filter bank multi-carrier, the present invention estimates the channel using radar signals as pilots during the transmission process, and obtains the channel estimation result of the communication signal after interpolating the channel estimation result of the radar signal, realizing channel equalization, ensuring the orthogonality of sub-carriers, improving indexes such as peak sidelobe ratio, integrated sidelobe ratio and communication bit error rate, and solving the problem of high communication bit error rate in traditional OFDM signals. When processing the integrated signal based on filter bank multi-carrier, the present invention uses the Inverse Synthetic Aperture Radar (ISAR) imaging algorithm for radar imaging of the target, estimates the speed and acceleration of the moving target, and compensates the Doppler frequency shift generated by the moving target with the estimated parameters, solving the problem of low accuracy of radar detection of moving targets by the SAR imaging algorithm.

[0007] The specific steps of the present invention for designing the integrated signal based on filter bank multi-carrier are as follows:

[0008] Step 1: Generate a PHYDAYS filter bank corresponding to the order K of the selected filter, and calculate the amplitude of the PHYDYAS filter bank at each sampling moment. The value of K is selected according to the amplitude of the sidelobe between the subcarriers of the integrated signal to be designed.

[0009] Step 2: Calculate the amplitude of the integrated filter bank multi-carrier radar communication signal at each sampling moment according to the following formula:

[0010]

[0011] where S(t2) represents the amplitude of the integrated filter bank multi-carrier radar communication signal at the t2-th sampling moment. The value of t2 is correspondingly equal to that of t1. Re(·) represents the operation of taking the real part. M represents the total number of subcarriers of the integrated filter bank multi-carrier signal. k represents the serial number of the subcarrier of the integrated filter bank multi-carrier signal. X k represents the modulation complex weight of the communication information modulated by OQAM (Offset Quadrature Amplitude Modulation) in the integrated filter bank multi-carrier radar communication signal on the k-th subcarrier. p(t1) represents the amplitude of the PHYDYAS filter bank at the t1-th sampling moment. exp(·) represents the exponential operation with the natural number e as the base. j represents the imaginary unit symbol. f k represents the center frequency of the k-th subcarrier in the integrated filter bank multi-carrier radar communication signal.

[0012] The specific steps for processing the designed integrated signal based on filter bank multi-carrier of the present invention are as follows:

[0013] Step 1: Obtain the echo signal reflected by the detection target:

[0014] Step 1.1: Radiate the designed integrated filter bank multi-carrier signal into the channels of each path of the channel model.

[0015] Step 1.2: Calculate the value of the channel model at each sampling moment in the channels of each path, and superimpose the values of all channels at each sampling moment to obtain the echo signal reflected by the detection target.

[0016] Step 2: Process the communication information in the echo signal:

[0017] Step 2.1: Perform symbol synchronization on the echo signal at the radar receiving end.

[0018] Step 2.2: Sample the communication signal contained in the subcarriers of the echo signal after symbol synchronization to obtain the sampled communication signal.

[0019] Step 2.3: Using the carrier correction algorithm, process each sampled communication signal to obtain the carrier frequency offset CFO value at this sampling point;

[0020] Step 2.4: Update the carrier frequency of the sampled communication signal with the CFO value at each sampling point to obtain a corrected communication signal without offset frequency;

[0021] Step 2.5: Estimate the channel response using the prior information of the radar signal, and interpolate the echo signal carrying communication information to obtain the channel response estimation result of the communication signal, realizing channel equalization;

[0022] Step 2.6: Demap the equalized communication signal and then perform channel decoding to obtain the original communication information;

[0023] Step 3: Use the ISAR iterative entropy algorithm to estimate the motion parameters of the radar detection target:

[0024] Step 3.1: Calculate the power-normalized image matrix of the target echo signal detected by the radar signal according to the following formula:

[0025]

[0026] where, represents the power-normalized matrix of the target echo signal detected by the radar, I represents the ISAR image matrix of the target detected by the radar, which is obtained by processing the echo signal using the RD (Range Doppler) imaging algorithm, I′ represents the matrix obtained by squaring each element value in matrix I, M represents the total number of rows in matrix I, N represents the total number of columns in matrix I, m represents the row number in matrix I, n represents the column number in matrix I, |·| represents the absolute value operation, and I m,n represents the element value at the m-th row and n-th column in matrix I;

[0027] Step 3.2: Calculate the image entropy of the target ISAR image matrix detected by the radar according to the following formula:

[0028]

[0029] where, IE represents the image entropy of the target ISAR image matrix detected by the radar, ln(·) represents the natural logarithm operation with base e, represents the element value at the m-th row and n-th column in matrix ;

[0030] Step 3.3: Calculate the estimated value of the target acceleration after the current iterative update according to the following formula:

[0031]

[0032] Among them, represents the estimated value of the target acceleration after the i-th iteration, and arg(·) represents the complex argument function. represents the operation of taking the minimum value of the function with as the variable. represents that the speed of the target detected by the radar is and the acceleration is the image entropy of the ISAR image matrix of the target; when i = 1, represents the initial value of the speed of the target detected by the radar obtained by processing the echo signal through the cross-correlation algorithm. a max represents the maximum value of the acceleration of the target detected by the radar; when i > 1, represents the estimated value of the target speed after the (i - 1)-th iteration. represents the estimated value of the target acceleration after the (i - 1)-th iteration, and α represents the maximum change value of the target acceleration.

[0033] Step 3.4, calculate the estimated value of the target speed after the current iteration update according to the following formula:

[0034]

[0035] Among them, represents the estimated value of the target speed after the i-th iteration. represents the operation of taking the minimum value of the function with as the variable. represents that the speed of the target detected by the radar is and the acceleration is the image entropy of the ISAR image matrix of the target. represents the estimated value of the target speed after the (i - 1)-th iteration, and β represents the maximum change amount of the speed.

[0036] Step 3.5, determine whether the estimated value of the target speed after the current iteration update meets the termination condition. If so, after obtaining the accurate value of the target speed detected by the radar, execute Step 4; otherwise, execute Step 3.3.

[0037] Step 4, perform Doppler frequency offset compensation on the echo signal using the accurate target speed value:

[0038] Step 4.1, adopt range-direction Fourier transform to transform the echo signal in the time domain to the two-dimensional frequency domain, and then adopt the range-direction pulse compression algorithm to process the echo signal in the two-dimensional frequency domain to obtain the range-compressed signal.

[0039] Step 4.2: Using the range migration correction algorithm, process the range-compressed signal to obtain the range-migration-corrected signal;

[0040] Step 4.3: Using the inverse Fourier transform corresponding to Step 4.1 in the range direction, transform the range-migration-corrected signal into the time domain;

[0041] Step 4.4: Using the formula calculate the Doppler frequency offset of the time-domain signal after range migration correction, where f′ d represents the Doppler frequency offset in the echo signal, v p represents the accurate value of the target velocity, and λ represents the wavelength of the echo signal;

[0042] Step 4.5: Using the Doppler frequency offset compensation algorithm, process the time-domain echo signal after range migration correction to eliminate the Doppler frequency offset f′ d in the echo signal, and obtain an echo signal without Doppler frequency offset;

[0043] Step 5: Perform radar imaging on the detected target:

[0044] Step 5.1: Input the echo signal without Doppler frequency offset into the azimuth compression matching filter, and output the azimuth-compressed echo signal;

[0045] Step 5.2: Using the inverse Fourier transform algorithm in the azimuth direction, process the azimuth-compressed echo signal to obtain the radar imaging points of the target in the range-azimuth plane.

[0046] Compared with the prior art, the present invention has the following advantages:

[0047] First, in the design of the integrated signal, the present invention uses the multi-carrier filter bank signal FBMC as the radar-communication integrated signal. Since the FBMC signal itself does not carry a cyclic prefix, it avoids the deficiency of the cyclic prefix carried by the traditional OFDM signal, which leads to the occupation of communication resources. This enables the communication resources in the signal transmission process of the present invention to be fully used to transmit effective communication information, improves the utilization rate of communication resources, and reduces the bit error rate.

[0048] Second, when processing the designed integrated signal based on filter bank multi-carriers, the present invention uses the ISAR iterative entropy algorithm to estimate the motion parameters of the radar detection target, so that the Doppler frequency shift caused by the target motion can be compensated. It overcomes the disadvantage that the Doppler frequency shift cannot be compensated in the traditional SAR imaging method when detecting moving targets, resulting in a reduction in the accuracy of radar imaging. This makes the present invention have a high accuracy in radar imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1is the flowchart of the present invention;

[0050] Figure 2 is the simulation comparison diagram of the frequency domain results of three prototype filters in the simulation experiment of the present invention;

[0051] Figure 3 is the simulation comparison diagram of the communication bit error rate results of OFDM signal and FBMC signal in the simulation experiment of the present invention;

[0052] Figure 4 is the simulation comparison diagram of the range slice and Doppler slice of the ambiguity function of OFDM signal and FBMC signal in the simulation experiment of the present invention;

[0053] Figure 5 is the simulation comparison diagram of the ISAR imaging results of OFDM signal and FBMC signal in the simulation experiment of the present invention; Detailed implementation manners

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] Refer to Figure 1 and the embodiments to further describe the implementation steps of the present invention.

[0056] Step 1: Generate the integrated signal of filter bank multi-carrier radar communication.

[0057] Step 1.1: Generate the PHYDYAS filter bank corresponding to the order K of the selected filter, and calculate the amplitude of the PHYDYAS filter bank at each sampling moment. The value of K is obtained by selecting the amplitude of the sidelobe between subcarriers of the integrated signal to be designed. Since the integrated signal designed in the embodiment of the present invention requires a sidelobe attenuation of about -40 dB to meet the performance requirements, K = 4 is taken.

[0058] Calculate the amplitude of the PHYDYAS filter bank at each sampling moment according to the following formula:

[0059]

[0060] where p(t1) represents the amplitude of the PHYDYAS filter bank at the t1-th sampling moment, K represents the order of the filter, i represents the serial number of the filter order, the value of l is equal to the corresponding serial number i of the filter order, and b i represents the filter coefficient of the i-th order filter, cos(·) represents the cosine function, π represents the pi, and T0 represents the time scaling parameter equal to the period T of the FBMC signal.

[0061] Step 1.2: Calculate the amplitude of the integrated signal of filter bank multi-carrier radar communication at each sampling moment according to the following formula:

[0062]

[0063] Among them, S(t2) represents the amplitude of the FBMC radar-communication integrated signal at the t2-th sampling moment, the value of t2 is correspondingly equal to that of t1, Re(·) represents the operation of taking the real part, M represents the total number of subcarriers of the FBMC signal, k represents the serial number of the subcarriers of the FBMC signal, X k represents the modulation complex weight of the communication information modulated by OQAM (Offset Quadrature Amplitude Modulation) in the FBMC radar-communication integrated signal on the k-th subcarrier, p(t1) represents the amplitude of the PHYDYAS filter bank at the t1-th sampling moment, exp(·) represents the exponential operation with the natural number e as the base, j represents the imaginary unit symbol, f k represents the center frequency of the k-th subcarrier in the FBMC radar-communication integrated signal.

[0064] Step 2: Radiate the integrated signal into the channel and obtain the echo signal.

[0065] Step 2.1: The channel model is an expression describing the channel characteristics for transmitting the FBMC integrated signal containing L paths, including parameters such as the total number of channel paths, path delay, and frequency shift.

[0066] Calculate the value of the channel model at each sampling moment according to the following formula:

[0067]

[0068] Among them, h(t3) represents the value of the channel model at the t3-th sampling moment, the value of t3 is correspondingly equal to that of t2, L represents the total number of channel paths, p represents the serial number of the channel path, a p represents the channel coefficient corresponding to the p-th channel path, which is determined by the transmission performance of this channel path, δ(·) represents the impulse function, τ p represents the delay generated when the signal is transmitted in the p-th channel path, f d (p) represents the frequency shift generated when the signal is transmitted in the p-th channel path.

[0069] Step 2.2: Radiate the FBMC integrated signal into the channels of each path of the channel model to obtain the echo signal reflected by the detection target.

[0070] Step 3: Process the communication information in the echo signal.

[0071] Step 3.1: Perform symbol synchronization on the echo signal at the radar receiving end.

[0072] Step 3.2: Sample the communication signal contained in the subcarriers of the echo signal after symbol synchronization to obtain the sampled communication signal.

[0073] Step 3.3: Use the carrier correction algorithm to process each sampled communication signal to obtain the carrier frequency offset CFO (Carrier Frequency Offset) value at this sampling point.

[0074] Step 3.4: Update the carrier frequency of the sampled communication signal with the CFO value at each sampling point to obtain a corrected communication signal without offset frequency.

[0075] Step 3.5: Estimate the channel using the prior information of the radar signal, and obtain the channel estimation result of the communication signal after interpolation to achieve channel equalization.

[0076] Step 3.6: Demap the equalized communication signal and then perform channel decoding to obtain the original communication information.

[0077] Step 4: Use the ISAR iterative entropy algorithm to estimate the motion parameters of the radar detection target.

[0078] Step 4.1: Calculate the power-normalized image matrix of the target echo signal detected by the radar signal according to the following formula:

[0079]

[0080] where, represents the power-normalized matrix of the target echo signal detected by the radar, I represents the ISAR image matrix of the target detected by the radar, which is obtained by processing the echo signal using the RD (Range Doppler) imaging algorithm, I′ represents the matrix obtained by squaring each element value in matrix I, M represents the total number of rows in matrix I, N represents the total number of columns in matrix I, m represents the row number in matrix I, n represents the column number in matrix I, |·| represents the absolute value operation, and I m,n represents the element value at the m-th row and n-th column in matrix I.

[0081] Step 4.2: Calculate the image entropy of the target ISAR image matrix detected by the radar according to the following formula:

[0082]

[0083] where, IE represents the image entropy of the target ISAR image matrix detected by the radar, ln(·) represents the logarithm operation with the natural number e as the base, represents the matrix the element value at the m-th row and n-th column.

[0084] Step 4.3: Calculate the estimated value of the target acceleration after the current iterative update according to the following formula:

[0085]

[0086] Among them, represents the estimated value of the target acceleration after the i-th iteration, and arg(·) represents the complex argument function. represents the operation of taking the minimum value of the function with as the variable. represents that the speed of the target detected by the radar is and the acceleration is at this time, the image entropy of the ISAR image matrix of the target; when i = 1, represents the initial value of the speed of the target detected by the radar obtained after processing the echo signal through the cross-correlation algorithm. a max represents the maximum value of the acceleration of the target detected by the radar; when i > 1, represents the estimated value of the target speed after the (i - 1)-th iteration. represents the estimated value of the target acceleration after the (i - 1)-th iteration, and α represents the maximum change value of the target acceleration.

[0087] Step 4.4, calculate the estimated value of the target speed after the current iterative update according to the following formula:

[0088]

[0089] Among them, represents the estimated value of the target speed after the i-th iteration. represents the operation of taking the minimum value of the function with as the variable. represents that the speed of the target detected by the radar is and the acceleration is at this time, the image entropy of the ISAR image matrix of the target. represents the estimated value of the target speed after the (i - 1)-th iteration, and β represents the maximum change amount of the speed.

[0090] Step 4.5, determine whether the estimated value of the target speed after the current iterative update satisfies the termination condition. If so, obtain the accurate value v p of the speed of the target detected by the radar, and then execute Step 5; otherwise, execute Step 4.3.

[0091] The termination condition is that the following formula holds:

[0092]

[0093] Among them, max(·) represents the operation of taking the maximum value, and η represents the slow time of the radar pulse. represents the phase difference between the radar pulses after the p-th iteration update and those after the (p - 1)-th iteration update, and θ represents the accuracy threshold of the phase error.

[0094] Step 5: Perform Doppler frequency offset compensation on the echo signal.

[0095] Step 5.1: Using range Fourier transform, transform the echo signal in the time domain to the two-dimensional frequency domain, and then use the range pulse compression algorithm to process the echo signal in the two-dimensional frequency domain to obtain the signal after range compression.

[0096] Step 5.2: When the target is in a moving state, the pulse peaks of the echo signal will be distributed in several adjacent range gates, which will have a greater impact on the imaging quality. Therefore, use the range migration correction algorithm to process the signal after range compression, so that all the pulse peaks of the echo signal of the target detected by the radar are distributed on the same range gate to obtain the signal after range migration correction.

[0097] Step 5.3: Use the inverse range Fourier transform corresponding to Step 5.1 to transform the signal after range migration correction to the time domain.

[0098] Step 5.4: When the target detected by the radar is in a moving state, a Doppler frequency offset related to the moving speed will be generated in the echo signal, which will affect the imaging quality.

[0099] Use the formula to calculate the Doppler frequency offset in the echo signal, where f′ d represents the Doppler frequency offset in the echo signal, and λ represents the wavelength of the echo signal.

[0100] Step 5.5: Use the Doppler frequency offset compensation algorithm to process the echo signal in the time domain after range migration correction to eliminate the Doppler frequency offset f′ d in the echo signal to obtain an echo signal without Doppler frequency offset.

[0101] Step 6: Perform radar imaging on the detected target.

[0102] Step 6.1: Input the echo signal without Doppler frequency offset into the azimuth compression matching filter and output the echo signal after azimuth compression.

[0103] Step 6.2: Use the inverse azimuth Fourier transform algorithm to process the echo signal after azimuth compression to obtain the radar imaging points of the target in the range - azimuth plane.

[0104] The present invention will be further described below in combination with simulation experiments.

[0105] The effects of the present invention will be further described below in combination with the simulation experiment of the present invention.

[0106] 1. Simulation experiment conditions.

[0107] The hardware platform for the simulation experiment of the present invention: The CPU is Intel Core i7-7700, and the RAM is 8GB.

[0108] The software platform for the simulation experiment of the present invention: Windows 10 operating system and Matlab R2019a.

[0109] For the simulation of the present invention to verify the effectiveness of the ISAR and communication integrated waveform processing algorithm based on the FBMC waveform, the simulation parameters of the integrated signal are set as follows. The pulse width of the signal is 4 μs, the pulse repetition frequency is 800 Hz, the signal bandwidth is 150 MHz, and the number of subcarriers is 512; the initial distance of the target is 5 km, the moving speed is 30 m / s, and the acceleration is -1.9 m / s 2 . There are a total of 512 echo pulses, each echo pulse contains N = 512 subcarriers, the number of range cells is 74, and the subcarrier spacing is Δf = B / N = 0.58 MHz.

[0110] 2. Simulation content and result analysis.

[0111] The simulation experiment of the present invention uses the method proposed by the present invention to generate a radar-communication integrated waveform, simulates the signal transmission scenario among the radar, communication target, and detection target, randomly generates a string of binary signals as communication information, transmits the modulated shared waveform signal, simulates the radar transmitting a detection signal and processing the expected echo containing point target information, and the communication side performs communication-related processing on the communication signal. Through Matlab R2019a to simulate this process, the frequency-domain waveform diagrams of the PHYDAYS filter, Hermite filter, and rectangular filter are compared as shown in Figure 2 ; the bit error rate diagrams of OFDM and FBMC signals under multipath are compared as shown in Figure 3 ; the range slice and Doppler slice of the ambiguity function diagram of the radar-communication integrated waveform of the present invention and the traditional OFDM integrated waveform are compared as shown in Figure 4; the ISAR imaging results of the traditional OFDM signal and FBMC signal are compared as shown in Figure 5.

[0112] In the simulation experiment of the present invention, the method of the present invention is used. The main difference between OFDM and FBMC is that the former uses a rectangular window as the prototype pulse, and its frequency-domain sub-band is a Sinc function, which has a relatively high sidelobe, while the latter uses a carefully designed filter as the prototype pulse and basically has no out-of-band leakage. Thus, the Figure 2 comparison diagram of the frequency-domain responses of the PHYDAYS filter, Hermite filter, and rectangular filter shown inFigure 2 It can be seen that the sidelobe of the rectangular filter subcarrier is relatively high, at -13.26 dB. The sidelobe of the FBMC subcarrier passing through the Hermite prototype filter is -34.30 dB, and the sidelobe of the FBMC subcarrier passing through the PHYDYAS prototype filter is extremely low, at -39.86 dB. Therefore, FBMC can provide better out-of-band rejection.

[0113] In the simulation experiment of the present invention, using the method of the present invention, due to the complex target in the integrated scenario, the received communication echo has different time delays, and the integrated platform will introduce multipath effects during the transmission process. The bit error rates of the two waveforms under multipath effects are obtained by simulation Figure 3 The bit error rate diagrams of the two signal waveforms shown in. When the OFDM waveform has spectral leakage, due to the serious out-of-band interference of its subcarriers, it will cause serious interference to the remaining subcarriers during broadening, thus affecting the waveform orthogonality. FBMC has good out-of-band rejection. At the same time, using OQAM coding, it can perform equalization separately on the subcarriers and use the analysis filter at the receiving end to avoid interference between subcarriers and achieve multipath suppression. From Figure 3 It can be seen that when the time delay exceeds the CP length, OFDM cannot suppress the multipath effect, and the bit error rate cannot decrease with the increase of the signal-to-noise ratio. Based on good out-of-band rejection, the FBMC waveform avoids interference between carriers through OQAM coding on the subcarriers. Combining with the subcarrier equalization operation, it can effectively suppress the multipath phenomenon.

[0114] In the simulation experiment of the present invention, using the method of the present invention, the range slices and Doppler slices of the ambiguity functions of the traditional OFDM signal and the FBMC signal are simulated to obtain the comparison diagram of the range slices and Doppler slices of the ambiguity functions of the two signals in Fig. 4. Fig. 4(a) shows the range slice and Doppler slice of the OFDM signal, and Fig. 4(b) shows the result diagram of the range slice and Doppler slice of the FBMC integrated signal. It can be seen from Fig. 4(b) that due to the multi-carrier modulation signal structure, similar to OFDM in Fig. 4(a), the ambiguity function of the FBMC waveform is also in the shape of a thumbtack. In the time dimension, the OFDM waveform and the FBMC waveform have the same range resolution, and both depend on the signal bandwidth. However, the CP insertion of OFDM results in two side lobes at the position of the CP length, which will affect the imaging quality. If the maximum time delay of the echo is to be limited within the CP length to avoid the influence of false targets, this will seriously affect the mapping bandwidth of the imaging result. In the Doppler frequency shift dimension, the Doppler frequency shift resolution is affected by the CP length and the prototype filter design. Therefore, the Doppler bandwidth of FBMC is greater than that of OFDM and OFDM, but FBMC uses a specially designed prototype filter to ensure that there are no side lobes. Therefore, the FBMC waveform has different azimuth resolutions from the OFDM waveform, and has a wider Doppler bandwidth and lower out-of-band leakage.

[0115] In the simulation experiment of the present invention, the method of the present invention is used to respectively transmit OFDM signals and FBMC signals to perform relevant imaging processing on the echoes in the ISAR scenario, thereby obtaining the comparison diagram of the ISAR imaging results of the two signals in Fig. 5. Fig. 5(a) is the ISAR imaging result of the OFDM waveform, and Fig. 5(b) is the imaging result of the FBMC waveform. As can be seen from Fig. 5(a), due to the sensitivity of OFDM to Doppler frequency offset and multipath effects, the integral sidelobe ratio of point targets is too low, and obvious range sidelobes appear, which will cause weak targets to be masked by the sidelobes of strong targets, resulting in relatively strong blurred energy. As can be seen from Fig. 5(b), due to the good out-of-band suppression of the FBMC waveform, the inter-carrier interference is avoided through the OQAM coding on the sub-carriers. Combining the sub-carrier equalization operation, the multipath phenomenon can be effectively suppressed. At this time, the range sidelobe is extremely low, which is better than the ISAR imaging result of the traditional OFDM waveform, and the image effect is good.

Claims

1. An integrated signal design method based on filter bank multicarrier, characterized in that, Generate a corresponding PHYDAYS filter bank according to the order K of the filter and calculate the amplitude of the integrated signal at each sampling moment; the steps of this signal design method are as follows: Step 1: Generate a PHYDAYS filter bank corresponding to the selected order K of the filter, and calculate the amplitude of the PHYDYAS filter bank at each sampling moment. The value of K is selected according to the amplitude of the sidelobe between subcarriers of the integrated signal to be designed; Step 2: Calculate the amplitude of the filter bank multi-carrier radar communication integrated signal at each sampling moment according to the following formula: Among them, S(t2) represents the amplitude of the integrated filter bank multi-carrier radar communication signal at the t2-th sampling moment. The value of t2 is correspondingly equal to that of t1. Re(·) represents the operation of taking the real part. M represents the total number of sub-carriers of the filter bank multi-carrier signal, k represents the serial number of the sub-carriers of the filter bank multi-carrier signal, and X k represents the modulation complex weight of the communication information modulated by OQAM (Offset Quadrature Amplitude Modulation) in the integrated filter bank multi-carrier radar communication signal on the k-th sub-carrier. p(t1) represents the amplitude of the PHYDYAS filter bank at the t1-th sampling moment. exp(·) represents the exponential operation with the natural number e as the base, and j represents the imaginary unit symbol, f k represents the center frequency of the k-th sub-carrier in the integrated filter bank multi-carrier radar communication signal.

2. The integrated signal design method based on filter bank multi-carrier according to claim 1, characterized in that The calculation of the amplitude of the PHYDYAS filter bank at each sampling moment described in Step 1 is obtained by the following formula: Among them, p(t1) represents the amplitude of the PHYDYAS filter bank at the t1-th sampling moment, K represents the order of the filter, i represents the serial number of the filter order, the value of l is correspondingly equal to the serial number i of the filter order, and b i represents the filter coefficient of the i-th order filter, cos(·) represents the cosine function, π represents the pi, and T0 represents the time scaling parameter whose value is equal to the period T of the filter bank multi-carrier signal.

3. An integrated signal processing method based on filter bank multi-carrier for designing signals according to claim 1, characterized in that, Process the communication information and radar information in the echo signal of the transmitted signal designed at the radar receiving end respectively, and use the ISAR iterative entropy algorithm to estimate the motion parameters of the radar detection target and perform radar imaging on the detection target; the steps of this processing method are as follows: Step 1: Obtain the echo signal reflected by the detection target: Step 1.1: Radiate the designed filter bank multi-carrier integrated signal into the channels of each path of the channel model; Step 1.2: Calculate the value at each sampling moment in the channels of each path of the channel model, and superimpose the values at each sampling moment of all channels to obtain the echo signal reflected by the detection target; Step 2: Process the communication information in the echo signal: Step 2.1: Perform symbol synchronization on the echo signal at the radar receiving end; Step 2.2: Sample the communication signal contained in the subcarriers of the echo signal after symbol synchronization to obtain the sampled communication signal; Step 2.3: Use the carrier correction algorithm to process each sampled communication signal to obtain the carrier frequency offset CFO value at this sampling point; Step 2.4: Update the carrier frequency of the sampled communication signal with the CFO value at each sampling point to obtain the corrected communication signal without the offset frequency; Step 2.5: Estimate the channel response using the prior information of the radar signal, and interpolate the echo signal carrying the communication information to obtain the estimated result of the channel response of the communication signal, realizing channel equalization; Step 2.6: Demap the equalized communication signal and then perform channel decoding to obtain the original communication information; Step 3: Use the ISAR iterative entropy algorithm to estimate the motion parameters of the radar detection target: Step 3.1: Calculate the power-normalized image matrix of the target echo signal detected by the radar signal according to the following formula: Among them, represents the normalized matrix of the target echo signal detected by the radar. I represents the ISAR image matrix of the target detected by the radar. This matrix is obtained by processing the echo signal using the RD (Range Doppler) imaging algorithm. I' represents the matrix obtained by squaring each element value in matrix I. M represents the total number of rows in matrix I, N represents the total number of columns in matrix I, m represents the row number in matrix I, n represents the column number in matrix I, |·| represents the absolute value operation, and I m,n represents the element value of the m-th row and the n-th column in matrix I; Step 3.2: Calculate the image entropy of the ISAR image matrix of the target detected by the radar according to the following formula: Among them, IE represents the image entropy of the ISAR image matrix of the target detected by the radar, and ln(·) represents the logarithmic operation with the natural number e as the base. represents the matrix the element value of the m-th row and the n-th column in; Step 3.3: Calculate the estimated value of the target acceleration after the current iterative update according to the following formula: Among them, represents the estimated value of the target acceleration after the i-th iteration, and arg(·) represents the complex argument function. represents the operation of taking the minimum value of the function with as the variable. represents that the speed of the target detected by the radar is and the acceleration is the image entropy of the ISAR image matrix of the target; when i = 1, represents the initial value of the speed of the target detected by the radar obtained after the echo signal is processed by the cross-correlation algorithm. a max represents the maximum value of the acceleration of the target detected by the radar; when i > 1, represents the estimated value of the target speed after the (i - 1)-th iteration, represents the estimated value of the target acceleration after the (i - 1)-th iteration, and α represents the maximum change value of the target acceleration. Step 3.4: Calculate the estimated value of the target speed after the current iterative update according to the following formula: Among them, represents the estimated value of the target speed after the i-th iteration, represents the operation of taking the minimum value of the function with as the variable, represents that the speed of the target detected by the radar is the acceleration is when the image entropy of the ISAR image matrix of the target, represents the estimated value of the target speed after the (i - 1)-th iteration, and β represents the maximum change in speed; Step 3.5: Judge whether the estimated value of the target speed after the current iterative update meets the termination condition. If so, after obtaining the accurate value of the target speed detected by the radar, execute Step 4; otherwise, execute Step 3.3; Step 4: Perform Doppler frequency offset compensation on the echo signal using the accurate value of the target speed Step 4.1: After transforming the echo signal in the time domain to the two-dimensional frequency domain by range-direction Fourier transform, the echo signal in the two-dimensional frequency domain is processed using the range-direction pulse compression algorithm to obtain the signal after range compression; Step 4.2: The signal after range compression is processed using the range migration correction algorithm to obtain the signal after range migration correction; Step 4.3: The signal after range migration correction is transformed to the time domain using the inverse range-direction Fourier transform corresponding to Step 4.1; Step 4.4, using the formula calculate the Doppler frequency offset of the time-domain signal after range migration correction, where f d ' represents the Doppler frequency offset in the echo signal, v p represents the accurate value of the target velocity, and λ represents the wavelength of the echo signal; Step 4.5, using the Doppler frequency offset compensation algorithm, process the time-domain echo signal after range migration correction to eliminate the Doppler frequency offset f d ' in the echo signal, and obtain an echo signal without Doppler frequency offset; Step 5: Radar imaging is performed on the detection target: Step 5.1: The echo signal without Doppler frequency offset is input into the azimuth compression matching filter, and the echo signal after azimuth compression is output; Step 5.2: The echo signal after azimuth compression is processed using the inverse azimuth-direction Fourier transform algorithm to obtain the radar imaging points of the target in the range-azimuth plane.

4. The integrated signal processing method based on filter bank multi-carrier according to claim 3, characterized in that The value of the calculation channel model at each sampling moment in each path of the channel described in Step 1.2 is obtained by the following formula: h p (t3) = a p δ(t3 - τ p )·exp[j2πf d (p)t3] where h p (t3) represents the value of the p-th path of the channel model at the t3-th sampling moment, where the value range of t3 is the same as that of t2, and p represents the serial number of the channel path, a p represents the channel coefficient corresponding to the p-th channel path, which is determined by the transmission performance of this channel path. δ(·) represents the impulse function, τ p represents the time delay generated by the signal transmission in the p-th channel path, and f d (p) represents the frequency shift generated by the signal transmission in the p-th channel path.

5. The integrated signal processing method based on filter bank multicarrier according to claim 3, wherein The termination conditions described in Step 3.5 are as follows: where, max(·) represents the maximum value operation, η represents the slow time of the radar pulse, represents the phase difference of the radar pulse after the p-th iterative update and after the (p - 1)-th iterative update, and θ represents the accuracy threshold of the phase error.

Images