A CDMA-DDMA Joint Inter-Pulse Orthogonal Phase Coding Design Method

Abstract

This application relates to a CDMA-DDMA joint inter-pulse orthogonal phase coding design method. The method includes: dividing a uniform DDMA code into two parts; distinguishing the two parts based on two sets of CDMA orthogonal phase codes to obtain a uniform DDMA-CDMA code; designing a non-uniform DDMA-CDMA code using a particle swarm optimization algorithm to obtain a non-uniform DDMA-CDMA code; performing modulation phase compensation and slow-time FFT on the uniform DDMA-CDMA code or the non-uniform DDMA-CDMA code to obtain two RD maps; subtracting the two RD maps modulowise and determining the first transmission channel based on strong scattering points; using the subtracted strong scattering points to represent the real target; using the real target position as the starting point to reconstruct the positions of the remaining transmission channels based on the DDMA code to obtain all transmission channels; and sampling all transmission channels to obtain the decoded signal. This method increases the maximum unambiguous speed compared to DDMA coding and reduces the computational load compared to CDMA coding.

Description

Technical Field

[0001] This application relates to the field of phase coding technology, and in particular to a CDMA-DDMA joint inter-pulse orthogonal phase coding design method. Background Technology

[0002] Multiple-Input Multiple-Output (MIMO) array radars can achieve an equivalent array with combined transmission and reception, thus obtaining greater spatial degrees of freedom and angular resolution, balancing imaging resolution and cost advantages. Real-time high-resolution imaging with MIMO array radars relies on single-shot angle measurement, and waveform orthogonality is a prerequisite for achieving MIMO single-shot imaging. Commonly used MIMO orthogonal waveforms include: Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Code Division Multiple Access (CDMA), and Doppler Division Multiple Access (DDMA). TDMA coding uses time diversity, which is simple to implement and has good separation, but it does not meet real-time requirements.

[0003] However, CDMA coding is divided into slow-time CDMA coding and fast-time CDMA coding. Fast-time CDMA adds orthogonal phase to the fast-time dimension, resulting in intra-pulse modulated orthogonal waveforms. Its reception process requires matched filtering, placing high demands on hardware implementation. Slow-time CDMA adds orthogonal phase to the slow-time dimension, resulting in inter-pulse modulated orthogonal waveforms. Its reception process can use deskewing reception, with lower hardware implementation requirements. Slow-time CDMA coding can use modulation phase compensation plus slow-time Fast Fourier Transform (FFT) to separate the transmit channels. The modulation phase compensation and the number of slow-time FFTs required for the transmit channel separation process are equal to the number of transmit channels. In DDMA coding, the inter-pulse modulation phase changes linearly with the number of transmit array elements and the number of pulses, that is, each transmit signal adds a set of linear Doppler frequencies in the slow-time dimension. All transmit channels can be separated by a single slow-time FFT, with a relatively small computational load. DDMA coding is very easy to implement in practical engineering applications and has good orthogonality, but DDMA coding suffers from a serious speed ambiguity problem. To address the speed ambiguity problem, scholars both domestically and internationally have proposed the concepts of frequency jitter and phase jitter. However, regardless of whether it's frequency jitter, phase jitter, or a combination of both, the computational load required for the transmit separation process is consistent with that of slow-time CDMA coding. FDMA waveforms achieve transmit channel separation by setting different frequencies for the transmit signals of each transmit array element and performing matched filtering at the receiver. While coded orthogonal waveforms have low speed tolerance, FDMA orthogonal waveforms can alleviate this problem. However, FDMA waveforms are also intra-pulse modulated orthogonal waveforms, making hardware implementation difficult. TDMA waveforms do not meet real-time requirements. The transmit channel separation process of intra-pulse modulated orthogonal waveforms such as FDMA and fast-time CDMA places high demands on hardware implementation. Therefore, achieving a balance between computational load and maximum unambiguous speed in inter-pulse orthogonal waveforms remains a challenge. Summary of the Invention

[0004] Based on this, it is necessary to provide a CDMA-DDMA joint inter-pulse orthogonal phase coding design method to address the above-mentioned technical problems. This method increases the maximum unambiguous speed compared to DDMA coding and reduces the computational load compared to CDMA coding.

[0005] A CDMA-DDMA joint inter-pulse orthogonal phase coding design method, the method comprising:

[0006] The uniform DDMA coding is divided into two parts, and the two parts are distinguished according to two sets of CDMA orthogonal phase coding to obtain uniform DDMA-CDMA coding.

[0007] The non-uniform DDMA-CDMA coding scheme is designed based on the particle swarm optimization algorithm, and the non-uniform DDMA-CDMA coding scheme is obtained.

[0008] Modulation phase compensation is performed on uniform DDMA-CDMA encoding or non-uniform DDMA-CDMA encoding to obtain the first half of the modulation phase-compensated signal and the second half of the modulation phase-compensated signal.

[0009] Slow-time FFT is performed on the first half of the modulated phase-compensated signal and the second half of the modulated phase-compensated signal to obtain two RD images.

[0010] The two RD images are subtracted by taking their modulo values, and the first transmission channel is determined based on the strong scattering points. The strong scattering points after subtraction are used to represent the real target. The positions of the remaining transmission channels are restored based on the DDMA encoding, using the real target position as the starting point to obtain all transmission channels. Channel sampling is performed on all transmission channels to obtain the decoded signal.

[0011] In one embodiment, the uniform DDMA coding is divided into two parts, and the two parts are distinguished according to two sets of CDMA orthogonal phase codes to obtain uniform DDMA-CDMA coding, including:

[0012] When the number of transmitter elements N is odd, the general form of uniform DDMA-CDMA coding is:

[0013]

[0014] When the number of transmitter elements N is even, the general form of uniform DDMA-CDMA coding is:

[0015]

[0016] Where i is the slow-time pulse number index, and I is the slow-time pulse number. and For two sets of orthogonal CDMA codes, α o,n For DDMA coding when N is odd, α e,n This refers to the DDMA encoding when N is even.

[0017] In one embodiment, non-uniform DDMA-CDMA coding is designed based on the particle swarm optimization algorithm to obtain non-uniform DDMA-CDMA coding, including:

[0018] Set the population size and spatial dimension; construct the Doppler spectrum matrix based on the population size and spatial dimension, perform matrix calculations on the Doppler spectrum matrix to obtain the elements and number of elements of the Doppler spectrum matrix;

[0019] The number of distinct elements in the Doppler spectrum matrix is ​​used as the objective function. The particle swarm optimization algorithm is used to optimize the objective function and obtain the maximum value of the number of distinct elements.

[0020] Non-uniform DDMA-CDMA coding is designed based on the maximum number of different elements in the Doppler spectrum matrix, resulting in non-uniform DDMA-CDMA coding.

[0021] In one embodiment, the Doppler spectrum matrix is ​​constructed based on population size and spatial dimension, including:

[0022] Construct the Doppler spectrum matrix based on population size and spatial dimension.

[0023]

[0024] Where the population size is W, the spatial dimension is d, and α w,1 This represents the DDMA encoding of the first spatial dimension of the w-th population.

[0025] In one embodiment, matrix calculations are performed on the Doppler spectrum matrix to obtain the elements and number of elements of the Doppler spectrum matrix, including:

[0026] Matrix calculations were performed on the Doppler spectrum matrix to obtain the elements and number of the Doppler spectrum matrix.

[0027] Γ" w =tabulate(Γ') w )

[0028] Here, tabulate(·) represents finding the distinct elements of the Doppler spectrum matrix and their number.

[0029] In one embodiment, the number of distinct elements in the Doppler spectrum matrix is ​​used as the objective function, including:

[0030] The number of distinct elements in the Doppler spectrum matrix is ​​used as the objective function.

[0031] f w =length[Γ" w (:,1)]

[0032] Here, length(·) represents calculating the length of a vector.

[0033] In one embodiment, channel sampling is performed on all transmission channels to obtain the decoded signal, including:

[0034] When the number of transmitting array elements N is odd, the sampling position of the transmitting channel is set to...

[0035]

[0036] When the number of transmitting array elements N is even, the sampling position of the transmitting channel is set to...

[0037]

[0038] Where, k 2,max (k) corresponds to the target's true velocity.

[0039] In one embodiment, channel sampling is performed according to the set transmission channel sampling position to obtain the decoded signal.

[0040]

[0041] Where, k 1,max (k) represents the distance to the target, m represents the number of sequences for the receiving elements, n represents the number of sequences for the transmitting elements, k represents the number of sequences for the target, and R m R represents the distance from the m-th receiving element to the target. n λ represents the distance from the nth transmitting element to the target, and λ represents the wavelength.

[0042] The aforementioned CDMA-DDMA joint inter-pulse orthogonal phase coding design method first constructs a uniform DDMA-CDMA code by distinguishing the two parts based on the characteristics of DDMA and CDMA coding and two sets of CDMA orthogonal phase codes. Addressing the issue that the two RD map cancellation method is inapplicable under conditions of complete speed ambiguity, the method incorporates the DDMA frequency jitter concept and employs a particle swarm optimization algorithm to design the optimal non-uniform DDMA-CDMA code. Then, either the uniform DDMA-CDMA code or the non-uniform DDMA-CDMA code is decoded. Compared to uniform DDMA coding, the DDMA-CDMA joint coding can significantly improve the maximum unambiguous speed, while compared to CDMA coding, it can effectively reduce the computational load. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a CDMA-DDMA joint inter-pulse orthogonal phase coding design method in one embodiment;

[0044] Figure 2 This is a schematic diagram of the cancellation process of the uniform DDMA-CDMA joint coding RD diagram in one embodiment;

[0045] Figure 3 This is a schematic diagram of the RD graph cancellation process in a non-uniform DDMA-CDMA joint coding embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] In one embodiment, such as Figure 1 As shown, a CDMA-DDMA joint inter-pulse orthogonal phase coding design method is provided, including the following steps:

[0048] Step 102: Divide the uniform DDMA code into two parts, and distinguish the two parts according to the two sets of CDMA orthogonal phase codes to obtain the uniform DDMA-CDMA code.

[0049] DDMA-CDMA coding first divides DDMA coding into two parts, and then distinguishes the two parts by two sets of CDMA orthogonal phase coding and performs joint coding separately to obtain uniform DDMA-CDMA coding. The advantages of uniform DDMA-CDMA coding are: (1) Compared with CDMA coding, no matter how large the number of transmission channels N is, orthogonal phase coding only needs to consider two sets, which reduces the time for optimizing orthogonal phase coding; (2) Compared with CDMA coding, modulation phase compensation and slow-time FFT only need two sets, which reduces the amount of computation; (3) Compared with DDMA coding, the use of uniform DDMA-CDMA joint coding broadens the maximum unambiguous speed range.

[0050] Step 104: Design non-uniform DDMA-CDMA coding based on particle swarm optimization algorithm to obtain non-uniform DDMA-CDMA coding.

[0051] To address the issue that the two RD image cancellation method is inapplicable under conditions of complete velocity ambiguity, this paper combines the concept of DDMA frequency jitter and employs a particle swarm optimization algorithm to design optimal non-uniform DDMA-CDMA coding. The algorithm sets the population size and spatial dimension. Based on the population size and spatial dimension, a Doppler spectrum matrix is ​​constructed. Matrix calculations are performed on the Doppler spectrum matrix to obtain its elements and their number. The number of distinct elements in the Doppler spectrum matrix is ​​used as the objective function, and the particle swarm optimization algorithm is used to optimize the objective function to obtain the maximum value of the number of distinct elements. Based on the maximum value of the number of distinct elements in the Doppler spectrum matrix, a non-uniform DDMA-CDMA coding design is performed, resulting in the non-uniform DDMA-CDMA code.

[0052] By using particle swarm optimization and other algorithms to find the maximum number of different elements in the Doppler spectrum matrix, the Doppler frequency components in the Doppler spectrum matrix are spread out as evenly as possible across the entire spectrum after slow-time FFT, without overlapping. This reduces the probability of transmission channel coupling, lowers the degree of phase coupling in the transmission spatial domain, and reduces the probability of horizontal rise in the sidelobes of the spatial pattern, thereby significantly improving the maximum unambiguous velocity.

[0053] Step 106: Perform modulation phase compensation on uniform DDMA-CDMA encoding or non-uniform DDMA-CDMA encoding to obtain the first half of the modulation phase-compensated signal and the second half of the modulation phase-compensated signal.

[0054] Step 108: Perform slow-time FFT on the first half of the modulated phase-compensated signal and the second half of the modulated phase-compensated signal to obtain two RD maps.

[0055] The process of separating the transmit channel in DDMA-CDMA joint coding is as follows:

[0056] (1) Modulation phase compensation (taking an odd number of transmission channels as an example)

[0057] In DDMA-CDMA joint coding, there are only two sets of CDMA codes, so only two sets of modulation phase compensation are needed. The exponent terms of the two sets of modulation phases are respectively and It consists of two sets of orthogonal phase codes.

[0058] Let the signal expression after fast time compression be:

[0059]

[0060] In the formula: A(k1) is the time-domain signal after fast time compression, L FFT R represents the number of points in a fast-time FFT. m +R n For MIMO two-way distance delay, Where V is the wave number, M is the target velocity, and T is the number of receiving channels. r This represents the pulse repetition time.

[0061] The signal expression after the first modulation phase compensation:

[0062]

[0063] In the formula: This refers to the modulation phase corresponding to the first half of the CDMA encoding. When At that time, separated Corresponding to the previous The Doppler linear phase of each transmission channel forms a peak after a slow-time FFT. When At that time, due to The slow-time linear phase caused by the Doppler velocity is disrupted. After a slow-time FFT, the subsequent... Instead of forming a peak value, the transmission channel becomes a side lobe.

[0064] The signal expression after the second modulation phase compensation:

[0065]

[0066] In the formula: This refers to the modulation phase corresponding to the latter half of the CDMA encoding. When... At that time, due to The slow-time linear phase caused by the Doppler velocity is disrupted. After a slow-time FFT, the preceding... Each transmission channel does not form a peak value, but instead becomes a side lobe. When At that time, separated After corresponding The Doppler linear phase of each transmission channel, after a slow-time FFT, is then... Each launch channel generates a peak value.

[0067] (2) Slow-time FFT

[0068]

[0069] In the formula: I FFT For slow-time FFT points, This represents the amplitude and phase of the slow-time FFT after the first phase compensation:

[0070]

[0071] This indicates the amplitude and phase of the slow-time FFT after the second phase compensation:

[0072]

[0073] The joint coding transmit channel separation process requires two modulation phase compensations, two slow-time FFTs, two sets of data modulus taking, and one subtraction of the two sets of data, totaling [number missing]. Multiplication by a second time Addition.

[0074] Table 1 lists the computational complexity comparison results of DDMA, CDMA and joint coding. It can be found that when the number of transmit array elements N>2, DDMA coding has the smallest computational complexity, CDMA coding has the largest computational complexity, and it increases with the number of transmit channels. The computational complexity of joint coding is in the middle and is independent of the number of transmit channels. Therefore, joint coding is a compromise between computational complexity and maximum unambiguous speed.

[0075] Table 1

[0076]

[0077] For speed less than V o,max / V e,maxFor targets with high speed and relative motion, the entire transmission channel can be separated by two modulation phase compensations and slow-time FFT. For targets with high speed and relative motion, two more steps are required: RD map cancellation and transmission channel sampling.

[0078] Step 110: Subtract the modulo values ​​of the two RD images and determine the first transmission channel based on the strong scattering points. Use the strong scattering points after subtraction to represent the real target. Using the real target position as the starting point, restore the positions of the remaining transmission channels according to DDMA encoding to obtain all transmission channels. Perform channel sampling on all transmission channels to obtain the decoded signal.

[0079] A drawback of DDMA encoding is the difficulty in finding the true target velocity location. DDMA-CDMA joint encoding addresses this issue by canceling two RD maps. The two sets of data are modulo-subtracted; theoretically, only the true velocity location (zero Doppler encoded channel) is positive, while other ambiguous velocity locations are zero. Using DDMA-CDMA encoding combined with the two RD map cancellation method, for general targets, the maximum unambiguous velocity is no longer limited to V. o,max or V e,max Instead, it extends to the entire Doppler spectrum, that is...

[0080] After the two RD maps cancel each other out, the remaining strong scattering points represent the real target. Using the real target location as the starting point, the locations of the remaining transmission channels can be reconstructed using DDMA encoding, thus obtaining all transmission channels. Let the k-th strong scattering point after the RD map cancellation be represented as [k...]. 1,max (k),k 2,max [(k)], where k 1,max (k) represents the distance to the target, k 2,max (k) corresponds to the target's true velocity.

[0081] When the number of transmitting array elements N is odd, the sampling position of the transmitting channel is:

[0082]

[0083] In the formula:

[0084] When the number of transmitting array elements N is even, the sampling position of the transmitting channel is:

[0085]

[0086] In the formula:

[0087] The first half of the transmission channel data comes from Mid-sampling, the latter half of the transmission channel data from By sampling, the signal expression after sampling from the transmission channel is obtained, which is the decoded signal.

[0088]

[0089] In the formula: when N is odd, When N is even

[0090] In the aforementioned CDMA-DDMA joint inter-pulse orthogonal phase coding design method, firstly, based on the characteristics of DDMA and CDMA coding, a uniform DDMA-CDMA coding is constructed by distinguishing the two parts according to two sets of CDMA orthogonal phase coding. Addressing the issue that the two RD map cancellation method is not applicable under conditions of complete speed ambiguity, the optimal non-uniform DDMA-CDMA coding is designed using a particle swarm optimization algorithm, combined with the DDMA frequency jitter concept. Then, either the uniform DDMA-CDMA coding or the non-uniform DDMA-CDMA coding is decoded. Compared to uniform DDMA coding, the DDMA-CDMA joint coding can significantly improve the maximum unambiguous speed, and compared to CDMA coding, it can effectively reduce the computational load.

[0091] In one embodiment, the uniform DDMA coding is divided into two parts, and the two parts are distinguished according to two sets of CDMA orthogonal phase codes to obtain uniform DDMA-CDMA coding, including:

[0092] When the number of transmitter elements N is odd, the general form of uniform DDMA-CDMA coding is:

[0093]

[0094] When the number of transmitter elements N is even, the general form of uniform DDMA-CDMA coding is:

[0095]

[0096] Where i is the slow-time pulse number index, and I is the slow-time pulse number. and For two sets of orthogonal CDMA codes, α o,n For DDMA coding when N is odd, α e,n This refers to the DDMA encoding when N is even.

[0097] In a specific embodiment, the maximum unambiguous speed of uniform DDMA-CDMA joint coding is:

[0098]

[0099] In the formula: V o,max V represents the maximum unambiguous speed when N is odd. e,max This represents the maximum unambiguous speed when N is an even number.

[0100] When two or more targets in the same distance cell have the same velocity: ξ is an integer, and this special case is referred to as velocity complete fuzziness in this paper.

[0101] The following simple example illustrates this problem: The number of transmitter elements N = 7, and the DDMA encoding is: the first half is... The Doppler spectrum is divided into four uniform Doppler intervals, with the latter half being... Let the velocity of target T1 be V1, and the velocity of target T2 be V1. The conditions for complete velocity ambiguity are met, and the two targets are located in the same range cell. The transmitted signal returns to the receiving array element after being scattered by both targets. The received signal undergoes de-skewing reception, modulation phase compensation, and slow-time FFT to obtain two RD maps. Target T1 forms 4 peaks in the first RD map, and target T2 also forms 4 peaks in the first RD map. However, the peaks of target T2 are shifted two Doppler intervals backward relative to the peaks of target T1 (equivalent to a two-bit backward shift in uniform DDMA coding). Since the RD map represents the superposition of the amplitudes of the two targets, the superimposed amplitude is less than or equal to the sum of the amplitudes of the two targets. Target T1 forms 3 peaks in the second RD map, and target T2 also forms 3 peaks in the second RD map. Again, the peaks of target T2 are shifted two Doppler intervals backward relative to the peaks of target T1. Similarly, the superimposed amplitude is less than or equal to the sum of the amplitudes of the two targets. Assuming that the transmit power of each transmitting element is stable and consistent, the target scattering coefficient is ignored, and the signal amplitude of each transmit channel is 1, the cancellation process of the two RD maps can be... Figure 2 This indicates that after the two RD diagrams cancel each other out, four strong scattering points may be generated:

[0102] The first possible scattering point: After sampling the transmission channel according to the formula of the decoded signal, the correct transmission channel of target T1 is obtained, and the spatial pattern forms a spectral peak;

[0103] The second possible scattering point: After sampling the transmission channel according to the formula of the decoded signal, an incorrect but complete transmission channel is obtained. The spatial pattern does not produce spectral peaks, and the side lobes of the pattern increase horizontally.

[0104] The third possible scattering point: After sampling the transmission channel according to the formula of the decoded signal, the correct transmission channel of target T2 is obtained, and the spatial pattern forms a spectral peak;

[0105] The fourth possible scattering point: After sampling the transmission channel according to the formula of the decoded signal, an incorrect but complete transmission channel is obtained. The spatial pattern does not produce spectral peaks, and the side lobes of the pattern increase horizontally.

[0106] Among the four strong scattering points generated by the cancellation of the RD diagram, two are real targets and two are false targets. After equivalent array matching + azimuth / height FFT, the reason for the increase in the level of the sidelobe of the spatial pattern is: (1) For the false targets after the cancellation of the RD diagram, they share the transmission channel sampling position with the real targets. The transmission spatial phase of the false targets is equivalent to the transmission spatial phase of the real targets that is complete but misaligned. In this case, firstly, no obvious spectral peak will be formed, and secondly, the level of the sidelobe of the spatial pattern will be significantly increased. (2) For the real targets, the transmission channel sampling position is complete and correct, which enables the real targets to form spectral peaks. However, the transmission spatial phase of the real targets carries the transmission spatial phase of all other real targets or false targets, which increases the level of the sidelobe. Therefore, non-uniform DDMA-CDMA coding design is needed to reduce the increase in the level of the sidelobe of the spatial pattern caused by the complete ambiguity of the velocity.

[0107] In one embodiment, non-uniform DDMA-CDMA coding is designed based on the particle swarm optimization algorithm to obtain non-uniform DDMA-CDMA coding, including:

[0108] Set the population size and spatial dimension; construct the Doppler spectrum matrix based on the population size and spatial dimension, perform matrix calculations on the Doppler spectrum matrix to obtain the elements and number of elements of the Doppler spectrum matrix;

[0109] The number of distinct elements in the Doppler spectrum matrix is ​​used as the objective function. The particle swarm optimization algorithm is used to optimize the objective function and obtain the maximum value of the number of distinct elements.

[0110] Non-uniform DDMA-CDMA coding is designed based on the maximum number of different elements in the Doppler spectrum matrix, resulting in non-uniform DDMA-CDMA coding.

[0111] In specific embodiments, the non-uniform DDMA-CDMA coding design based on the number of different elements in the Doppler spectrum matrix is ​​prior art and will not be elaborated further in this application.

[0112] In one embodiment, the Doppler spectrum matrix is ​​constructed based on population size and spatial dimension, including:

[0113] Construct the Doppler spectrum matrix based on population size and spatial dimension.

[0114]

[0115] Where the population size is W, the spatial dimension is d, and α w,1 This represents the DDMA encoding of the first spatial dimension of the w-th population.

[0116] In one embodiment, matrix calculations are performed on the Doppler spectrum matrix to obtain the elements and number of elements of the Doppler spectrum matrix, including:

[0117] Matrix calculations were performed on the Doppler spectrum matrix to obtain the elements and number of the Doppler spectrum matrix.

[0118] Γ" w =tabulate(Γ') w )

[0119] Here, tabulate(·) represents finding the distinct elements of the Doppler spectrum matrix and their number.

[0120] In one embodiment, the number of distinct elements in the Doppler spectrum matrix is ​​used as the objective function, including:

[0121] The number of distinct elements in the Doppler spectrum matrix is ​​used as the objective function.

[0122] f w =length[Γ" w (:,1)]

[0123] Here, length(·) represents calculating the length of a vector.

[0124] In a specific embodiment, the reason why the spatial pattern rises due to the complete ambiguity of velocity is that real targets share the same transmission spatial phase with each other and with false targets. If non-uniform DDMA coding is used, the sampling positions of the transmission channels between real targets and between real targets and false targets will be inconsistent, the degree of transmission spatial phase coupling will be reduced, and the probability of horizontal rise of the sidelobes of the spatial pattern will be reduced.

[0125] This article uses a simple example to illustrate the principle of non-uniform DDMA-CDMA joint coding in mitigating speed-related ambiguity. Non-uniform DDMA-CDMA joint coding primarily improves upon DDMA coding, transforming uniform DDMA coding into non-uniform DDMA coding, while the CDMA coding remains unchanged. Its decoding process is consistent with uniform DDMA-CDMA coding. Assume the non-uniform DDMA coding is as follows: the first half is... The second half is The Doppler spectrum is divided into 8 uniform Doppler intervals. Non-uniform DDMA coding involves sampling these 8 intervals at unequal intervals. Let the velocity of target T1 be V1, and the velocity of target T2 be... The conditions for complete velocity ambiguity are met, and the two targets are located within the same distance cell. The cancellation process of the two RD maps can be achieved using... Figure 3 express.

[0126] Figure 3In the middle, the first arrow indicates a strong scattering point that must be generated in the RD diagram after cancellation. After sampling the transmission channel according to the formula of the decoded signal, the correct transmission channel of target T1 is obtained, and the spatial pattern forms a spectral peak.

[0127] The second arrow indicates a strong scattering point that may be generated in the RD diagram after cancellation. After sampling the transmission channel according to the formula of the decoded signal, an incorrect and incomplete transmission channel is obtained, which does not form a spectral peak in the radiation pattern.

[0128] The third arrow indicates a strong scattering point that must be generated in the RD diagram after cancellation. After sampling the transmission channel according to the formula of the decoded signal, the correct transmission channel of target T2 is obtained, and the spatial pattern forms a spectral peak.

[0129] The fourth arrow indicates a strong scattering point that may be generated in the RD diagram after cancellation. After sampling the transmission channel according to the formula of the decoded signal, an incorrect and incomplete transmission channel is obtained, which does not form a spectral peak in the radiation pattern.

[0130] exist Figure 3 In this model, the sampling positions of the launch channels for real and false targets completely overlap. The coupling degree between launch channels of real targets and between real and false targets is significantly reduced, which helps to reduce the increase in the horizontal level of the spatial pattern sidelobes caused by the complete ambiguity of velocity.

[0131] In one embodiment, channel sampling is performed on all transmission channels to obtain the decoded signal, including:

[0132] When the number of transmitting array elements N is odd, the sampling position of the transmitting channel is set to...

[0133]

[0134] When the number of transmitting array elements N is even, the sampling position of the transmitting channel is set to...

[0135]

[0136] Where, k 2,max (k) corresponds to the target's true velocity.

[0137] In one embodiment, channel sampling is performed according to the set transmission channel sampling position to obtain the decoded signal.

[0138]

[0139] Where, k 1,max (k) represents the distance to the target, m represents the number of sequences for the receiving elements, n represents the number of sequences for the transmitting elements, k represents the number of sequences for the target, and R m R represents the distance from the m-th receiving element to the target.n λ represents the distance from the nth transmitting element to the target, and λ represents the wavelength.

[0140] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0141] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0142] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A CDMA-DDMA joint inter-pulse orthogonal phase coding design method, characterized in that, The method includes: The uniform DDMA coding is divided into two parts, and the two parts are distinguished according to two sets of CDMA orthogonal phase coding to obtain uniform DDMA-CDMA coding. The non-uniform DDMA-CDMA coding scheme is designed based on the particle swarm optimization algorithm, and the non-uniform DDMA-CDMA coding scheme is obtained. Modulation phase compensation is performed on the uniform DDMA-CDMA code or the non-uniform DDMA-CDMA code to obtain the first half of the modulation phase-compensated signal and the second half of the modulation phase-compensated signal. Slow-time FFT is performed on the first half of the modulated phase-compensated signal and the second half of the modulated phase-compensated signal to obtain two RD diagrams. The two RD images are subtracted by taking their modulo values, and the first transmission channel is determined based on the strong scattering points. The strong scattering points after subtraction are used to represent the real target. The positions of the remaining transmission channels are restored based on the DDMA encoding, using the real target position as the starting point to obtain all transmission channels. Channel sampling is performed on all transmission channels to obtain the decoded signal. The non-uniform DDMA-CDMA coding scheme is designed based on the particle swarm optimization algorithm, resulting in the following: Set the population size and spatial dimension; construct a Doppler spectrum matrix based on the population size and spatial dimension; perform matrix calculations on the Doppler spectrum matrix to obtain the distinct elements and the number of elements of the Doppler spectrum matrix. The number of different elements in the Doppler spectrum matrix is ​​used as the objective function. The particle swarm optimization algorithm is used to optimize the objective function to obtain the maximum value of the number of different elements. Non-uniform DDMA-CDMA coding is designed based on the maximum number of different elements in the Doppler spectrum matrix, resulting in non-uniform DDMA-CDMA coding.

2. The method according to claim 1, characterized in that, The uniform DDMA coding is divided into two parts, and the two parts are distinguished according to two sets of CDMA orthogonal phase codes, resulting in uniform DDMA-CDMA coding, including: When the number of launch elements N When the number is odd, the general form of uniform DDMA-CDMA coding is: When the number of launch elements N When the number is even, the general form of uniform DDMA-CDMA coding is: in, This is the sequence number of the slow-time pulse. I The number of slow-time pulses. and Two sets of CDMA orthogonal phase codes, for N DDMA encoding when the number is odd. for N DDMA encoding when the number is even.

3. The method according to claim 1, characterized in that, Constructing a Doppler spectrum matrix based on the population size and spatial dimension includes: Construct the Doppler spectrum matrix based on the population size and spatial dimension. Among them, the population size is W Spatial dimension is d , Indicates the first DDMA encoding of the first spatial dimension of each population.

4. The method according to claim 3, characterized in that, Matrix calculations are performed on the Doppler spectrum matrix to obtain the distinct elements and the number of elements of the Doppler spectrum matrix, including: Matrix calculations are performed on the Doppler spectrum matrix to obtain the distinct elements and the number of elements of the Doppler spectrum matrix. in, This indicates the search for distinct elements of the Doppler spectrum matrix and their number.

5. The method according to claim 4, characterized in that, The number of distinct elements in the Doppler spectrum matrix is ​​used as the objective function, including: The number of distinct elements in the Doppler spectrum matrix is ​​used as the objective function. in, This indicates finding the length of a vector.

6. The method according to claim 2, characterized in that, Channel sampling is performed on all the transmission channels to obtain the decoded signal, including: When the number of launch elements N When the number is odd, the sampling position of the transmission channel is set to... When the number of launch elements N When the number is even, set the sampling position of the transmission channel to... in, Corresponding to the target's actual speed, This represents the number of points in a slow-time FFT.

7. The method according to claim 6, characterized in that, The method further includes: Channel sampling is performed according to the set transmission channel sampling position to obtain the decoded signal. in, Distance to the target This indicates the sequence number of the receiving array elements. This indicates the sequence number of the transmitting array elements. The number of sequences representing the target. Indicates the first The distance from each receiving element to the target. Indicates the first The distance from each launch element to the target Indicates wavelength.

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