A multi-target joint positioning method for distributed waveform diversity array radar

Abstract

The present invention discloses a distributed waveform diversity array radar multi-target joint positioning method, comprising: constructing a distributed waveform diversity array radar, the distributed waveform diversity array radar including an FDA-MIMO radar and an EPC-MIMO radar; constructing a signal model for the FDA-MIMO radar; constructing a signal model for the EPC-MIMO radar; performing two-dimensional parameter estimation of the angle and distance of each target in a multi-target according to the signal model of the FDA-MIMO radar and the signal model of the EPC-MIMO radar; and spatially positioning the multi-targets by iterative least squares fitting based on the two-dimensional parameter estimation of the angle and distance of the multi-targets to obtain a final fitted position of each target. The distributed waveform diversity array radar multi-target joint positioning method of the present invention can accurately fit the position parameters of the target and greatly reduce the amount of computation.

Description

Technical Field

[0001] The present invention belongs to the technical field of target positioning, and in particular relates to a multi-target joint positioning method of a distributed waveform diversity array radar. Background Art

[0002] Radar, with its ability to detect, identify, locate, track, and image targets within a designated detection area around the clock, plays a vital role in many fields, such as weapon guidance, environmental surveying, and target surveillance. However, with the advancement of the information age, higher requirements have been placed on radar's detection environment and capabilities. Given that a single high-precision radar is no longer sufficient, researchers are turning their attention to the coordinated use of multiple radars. Consequently, distributed radars, characterized by their miniaturization, distribution, and ease of organization, have become a popular research area.

[0003] Distributed radar systems network multiple radars in different locations, effectively coordinating the limited performance of individual radars to detect targets in the same area. This not only maximizes the high precision of individual radars, but also improves their anti-interference and anti-spoofing capabilities. As a new radar architecture, each radar node generally operates independently, with no close connections between them. During operation, each radar node processes the target echo independently at its receiving end, then transmits the local statistics obtained by each radar node to a fusion center, which performs final processing based on mission requirements.

[0004] Distributed Multiple-Input Multiple-Output (MIMO) radar, thanks to its waveform diversity, demonstrates great potential for high-precision target parameter measurement. Target localization, a key technology in distributed MIMO radar, is still in its infancy. Compared to direct localization, indirect localization methods offer lower data transmission costs and higher operational efficiency, attracting considerable attention in the field of distributed MIMO radar target localization.

[0005] Zhu Shixiang et al. proposed a robust target localization method using distributed MIMO radar. Due to its structural characteristics, distributed phased arrays can only measure azimuth angle. During target localization, the target's area is delineated using the angle information obtained from multiple local radars. The target's location is then fitted using the least squares method, which serves as the final target location. However, this distributed phased array method can only measure angle in a single dimension. In the presence of strong background noise, angle measurements can exhibit significant errors. The measurement accuracy of each radar unit directly impacts the accuracy of the final target localization. If the angle measurement accuracy of individual radar units is low, errors accumulate during target localization, increasing the deviation in the target area delineation and reducing target localization accuracy. Furthermore, Han Yahong proposed a multi-target localization method using distributed MIMO radar. This method primarily uses two-dimensional spatial search and concentrated area search to locate targets, but this method is computationally intensive. Summary of the Invention

[0006] To address the problems of single-dimensionality, low positioning accuracy, and high computational complexity in existing distributed phased array radar parameter estimation and joint positioning, the present invention provides a distributed waveform diversity array radar multi-target joint positioning method that can accurately fit the target's position parameters and reduce the computational complexity. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] The present invention provides a distributed waveform diversity array radar multi-target joint positioning method, comprising:

[0008] S1: constructing a distributed waveform diversity array radar, wherein the distributed waveform diversity array radar includes an FDA-MIMO radar and an EPC-MIMO radar;

[0009] S2: Constructing a signal model of the FDA-MIMO radar;

[0010] S3: Constructing a signal model of the EPC-MIMO radar;

[0011] S4: Based on the signal model of the FDA-MIMO radar and the signal model of the EPC-MIMO radar, two-dimensional parameter estimation of the angle and distance of each target in the multi-target is performed respectively;

[0012] S5: Based on the results of the two-dimensional parameter estimation of the angles and distances of the multiple targets, the multiple targets are spatially positioned by iterative least squares fitting to obtain a final fitting position of each target.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] 1. To address the problems of single dimension, low positioning accuracy and high computational complexity in parameter estimation and joint positioning of distributed phased arrays, the present invention proposes a distributed waveform diversity array radar multi-target joint positioning method, which can accurately fit the target's position parameters and improve the target's detection and positioning performance.

[0015] 2. In response to the method of two-dimensional search of the target area that has too much computational complexity, the present invention uses the CD method to reduce the dimensionality of the interval two-dimensional search. After verification by experimental data, the target location information can be accurately obtained, which greatly improves the computational efficiency and detection performance.

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a distributed waveform diversity array radar multi-target joint positioning method provided by an embodiment of the present invention;

[0018] Figure 2 This is a comparison chart of target positioning accuracy obtained using different methods;

[0019] Figure 3 is the root mean square error (RMSE) and signal-to-noise ratio curve of the distance obtained using different methods;

[0020] Figure 4 This is the root mean square error (RMSE) and signal-to-noise ratio curve of the angles obtained using different methods. DETAILED DESCRIPTION

[0021] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following is a detailed description of a distributed waveform diversity array radar multi-target joint positioning method proposed in accordance with the present invention, in conjunction with the accompanying drawings and specific implementation methods.

[0022] The aforementioned and other technical contents, features, and effects of the present invention are clearly presented in the following detailed description of the specific embodiments in conjunction with the accompanying drawings. Through the description of the specific embodiments, a deeper and more specific understanding of the technical means and effects adopted by the present invention to achieve the intended purpose can be obtained. However, the accompanying drawings are provided for reference and illustration purposes only and are not intended to limit the technical solutions of the present invention.

[0023] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations are intended to cover non-exclusive inclusion, such that an article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the article or device comprising the element.

[0024] Example 1

[0025] See Figure 1 , Figure 1 1 is a flow chart of a distributed waveform diversity array radar multi-target joint positioning method provided by an embodiment of the present invention. The method includes the following steps:

[0026] S1: Build a distributed waveform diversity array radar, which includes FDA-MIMO radar and EPC-MIMO radar.

[0027] It should be noted that the distributed waveform diversity array radar of this embodiment may include one or more FDA-MIMO radars, and may also include one or more EPC-MIMO radars.

[0028] S2: Construct the transmission signal model of FDA-MIMO radar.

[0029] Consider an arbitrary stationary target in the far field of space under narrowband conditions. The target has an angle θ0 and a distance r0 relative to the FDA-MIMO radar. The FDA-MIMO radar consists of M transmitting elements and N receiving elements. The spacing between adjacent transmitting and receiving elements is d. M, N, and d are all greater than 0. Specifically, step S2 of this embodiment includes:

[0030] S2.1: Under the background of Gaussian white noise, a frequency offset (FO) is introduced between the transmitting array elements of the FDA-MIMO radar to obtain the transmitting carrier frequency f of the mth transmitting array element of the FDA-MIMO radar m , which can be expressed as follows:

[0031] f m =f0+Δf m ,m=1,2,…,M,

[0032] Where f0 represents the transmitting carrier frequency, Δf mrepresents the frequency offset associated with the mth transmit array element. The vector form of the transmit carrier frequency and FO can be expressed as f = [f1, f2, ..., f m ,…,f M ] T ∈C M and Δf=[Δf1,Δf2,…,Δf m ,…,Δf M ] T ∈C M , C M represents an M-dimensional complex vector space.

[0033] S2.2: Obtain the complex envelope of the signal transmitted by the mth transmitting array element of the FDA-MIMO radar according to the transmitting carrier frequency:

[0034]

[0035] Where t represents fast time, T r is the radar pulse width, is an orthogonal waveform corresponding to the mth transmitting array element that satisfies the following conditions: τ represents time delay and * represents conjugation.

[0036] It is worth noting that it is unrealistic to obtain such an ideal orthogonal waveform in practice.

[0037] S2.3: Obtain the echo signal of the signals transmitted by all transmitting array elements of the FDA-MIMO radar and then reflected by the target and arriving at the nth receiving array element.

[0038] Specifically, the first transmitting element of the FDA-MIMO radar is selected as the reference element, and the time delay τ of the signal emitted by the mth transmitting element and reflected by the target to the nth receiving element is obtained. n,m , expressed as:

[0039]

[0040] Among them, r n Indicates the distance from the target to the nth receiving element, r m represents the distance from the target to the mth transmitting element, and c represents the speed of light.

[0041] Then, the echo signal of the signal transmitted by the mth transmitting array element and reflected by the target and reaching the nth receiving array element is obtained, which is expressed as:

[0042]

[0043] Where ξ is the complex echo amplitude (taking into account factors such as transmit power, phase, target reflectivity, and channel propagation effects), It represents the baseband signal of the signal transmitted by the mth transmitting array element and then reflected by the target and reaching the nth receiving array element.

[0044] Therefore, the echo signal of the transmitted signals of all transmitting array elements after being reflected by the point target and reaching the nth array element can be obtained, which is expressed as:

[0045]

[0046] Considering the narrowband condition, we have Where τ1 = 2r0 / c is the round-trip propagation delay between the antenna elements (including the transmitting and receiving elements) and the target. Therefore, the echo signal from all transmitting elements, after being reflected by the target and reaching the nth element, can be written as:

[0047]

[0048] S2.4: Perform digital mixing processing on the echo signal reaching the nth receiving array element to obtain a receiving signal after digital mixing processing of the FDA-MIMO radar.

[0049] Specifically, when processing the echo signal arriving at the nth receiving element, each receiving element needs to perform digital mixing related to Δf, that is, multiplying by e -j2πΔf(m-1)t , after digital mixing, the received signal of the nth receiving element can be expressed as:

[0050]

[0051] in,

[0052] S2.5: Obtain an output signal of the received signal of the nth receiving array element after matched filtering by the mth matched filter.

[0053] Specifically, the output signal of the signal of the nth receiving element after matched filtering by the mth matched filter is expressed as:

[0054]

[0055] in, Represents the matched filter for the mth transmit waveform in the nth receive element.

[0056] S2.6: Obtain echo data of the M transmitting array elements corresponding to the N receiving array elements.

[0057] Specifically, considering the ideal waveform orthogonality condition under the FDA-MIMO radar system, the target echo data of the M transmitting array elements corresponding to the N receiving array elements are arranged into a vector form to obtain:

[0058]

[0059] in, is the output signal of the signal of the nth receiving element after the matched filtering of the mth matched filter, (·) T represents the transpose operation, represents the Kronecker product, ⊙ represents the Hadamard product, and are the transmit steering vector and the receive steering vector, respectively, and are expressed as follows:

[0060]

[0061]

[0062] Furthermore, considering the background that the noise component satisfies zero mean and white Gaussian distribution, the actual received echo signal can be expressed as follows:

[0063] Y fda =Y a +Y n ,

[0064] in, is the noise component.

[0065] S3: Construct the signal model of EPC-MIMO radar.

[0066] Considering an arbitrary stationary target in the far field of space under narrowband conditions, with an angle θ1 and a distance r1 between it and the EPC-MIMO radar, the signal model process of the EPC-MIMO radar includes the following steps:

[0067] S3.1: Obtain a coded waveform of a kth transmit pulse transmitted by the EPC-MIMO radar within a coherent processing interval.

[0068] Specifically, consider a co-located MIMO radar system (EPC-MIMO radar) with P transmitting elements and Q receiving elements. Assuming that a total of K pulses are transmitted within a coherent processing interval (CPI), the code of the kth (k=1,2,...,K) transmitted pulse of the pth (p=1,2,...,P)th transmitting element is defined as:

[0069] Φ p,k =2πγ(p-1)(k-1),

[0070] Here, γ represents the coding coefficient.

[0071] It should be noted that the number of transmitting array elements of the FDA-MIMO radar may be the same as or different from the number of transmitting array elements of the EPC-MIMO radar; the number of receiving array elements of the FDA-MIMO radar may be the same as or different from the number of receiving array elements of the EPC-MIMO radar.

[0072] From the above steps, we can see that the coding phase is related to both the pulse and the transmitting array element, so different transmitting pulses will correspond to different transmitting steering vectors. The array element pulse coding vector of the kth pulse is expressed as:

[0073]

[0074] Furthermore, the coded waveform of the kth pulse can be written as:

[0075]

[0076] in, is the baseband signal corresponding to the pth transmitting array element of the EPC-MIMO radar.

[0077] S3.2: For a far-field target at an angle θ1 and a distance r1 from the EPC-MIMO radar, obtain the signal emitted by the p-th transmitting element and received by the q-th receiving element. Its complex envelope can be expressed as:

[0078]

[0079] Where A represents the complex amplitude of the point target (taking into account the transmission power, phase, target emissivity and channel propagation effects, etc.), represents the round-trip propagation delay, represents the common delay, d represents the array element spacing, c represents the speed of light, and p s Indicates the number of delayed pulses of the target, represents the Doppler frequency of the target, v s and represent the speed and wavelength of the target respectively.

[0080] S3.3 Multiply the received signal by Mixing is performed, and then the mixed signal is passed through M matched filters to achieve waveform separation. The received signal of the qth receiving element after being processed by the pth matched filter can be written as:

[0081]

[0082] in, represents the output of the pth matched filter, and T represents the pulse duration.

[0083] S3.4: Convert the k-th pulse signal processed by the p-th matched filter into an MN×1-dimensional vector, and we can get:

[0084]

[0085] S3.5: Receive signal x k (t) Matching is performed using the transmitted code, and then decoding is performed in slow time to obtain the decoded echo signal.

[0086] Specifically, in EPC-MIMO radar, since two-dimensional coding of pulses and transmit channels is performed, when processing echo signals, the received signal should first be matched with the transmit coding, and then decoded in slow time to distinguish echoes from different transmit pulses, that is:

[0087] y k (t)=(diag{g k}) H x k (t),

[0088] Among them, y k (t) represents the echo signal x for the kth pulse k (t) The decoded echo signal, represents the decoding vector within the k-th pulse, which can be written as:

[0089]

[0090] in, represents the Kronecker product, 1 Q represents the unit vector of Q dimension, c k Represents the array element pulse code vector of the th pulse.

[0091] S3.6: Obtain the echo signals of K pulses received by the EPC-MIMO radar, expressed as:

[0092]

[0093] in, represents the matched filter output vector, Represents the matched filter output vector of each filter, ⊙ represents the Hadamard product, and denote the transmit steering vector, receive steering vector and Doppler vector respectively, and The expressions are:

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] Among them, γ s =γp s .

[0100] S4: Based on the signal model of the FDA-MIMO radar and the signal model of the EPC-MIMO radar, two-dimensional parameter estimation of the angle and distance of each target in the multi-target is performed respectively.

[0101] Step S4 of this embodiment specifically includes:

[0102] S4.1: Obtain the signal covariance matrix and noise subspace of the matched filtered echo signal of the FDA-MIMO radar:

[0103] Assume that there are W targets in the far field of space, and their angle with the FDA-MIMO radar is θ w (w=1,2,...,W), distance is r w (w=1,2,...,W), then the echo signal after matched filtering at the FDA-MIMO radar receiver can be expressed as:

[0104]

[0105] Among them, ξ w is the complex scattering coefficient of the w-th target, A is the guidance vector matrix of W targets, l represents the l-th snapshot, L represents the number of snapshots, n is the Gaussian white noise vector, u(r w ,θ w ) is the receive-transmit joint steering vector of the FDA-MIMO radar at the w-th target, and its specific expression is:

[0106]

[0107] Among them, b(θ w ) is the receiving steering vector of the FDA-MIMO radar at the wth target, a(r w ,θ w ) is the transmission steering vector of the FDA-MIMO radar at the w-th target.

[0108] Furthermore, the covariance matrix of the echo signal can be expressed as:

[0109]

[0110] Among them, R s represents the covariance matrix of the target signal, is the noise power, I is the unit matrix, E{·} represents the expectation, (·) H is the conjugate transpose operation. In practice, the covariance matrix of the echo signal can only be estimated through snapshot data, that is:

[0111]

[0112] Performing eigenvalue decomposition on the above formula yields:

[0113]

[0114] Among them, Λ s =diag{λ1,λ2,...,λ W} is a diagonal matrix composed of W large eigenvalues ​​(large eigenvalues ​​correspond to target signals), is a matrix composed of eigenvectors corresponding to W large eigenvalues, i.e., the signal subspace of the echo signal; n =diag{λ K+1 ,...λ MN} is a diagonal matrix composed of small eigenvalues ​​(small eigenvalues ​​correspond to noise signals), The matrix composed of the eigenvectors corresponding to its eigenvalues ​​is the noise subspace of the echo signal.

[0115] S4.2: Construct the spatial spectrum function of the multiple signal classification algorithm of the FDA-MIMO radar and estimate the angle and distance of each target in the multi-target system.

[0116] The multiple signal classification algorithm (MUSIC) is used to estimate along the two dimensions of angle and distance. First, the spatial spectrum function of the MUSIC of the FDA-MIMO radar is constructed as follows:

[0117]

[0118] in, It is a matrix composed of eigenvectors corresponding to MN-W small eigenvalues, that is, the noise subspace of the echo signal; represents the receive-transmit joint steering vector of the FDA-MIMO radar.

[0119] Furthermore, the spatial spectrum function of the FDA-MIMO radar is used to estimate the angle and distance of the target:

[0120]

[0121]

[0122] in, represents the estimated distance of the w-th target relative to the current FDA-MIMO radar, Indicates that the r value corresponding to the maximum value of P(r,θ) is assigned to represents the estimated angle of the w-th target relative to the current FDA-MIMO radar, Indicates that the value of θ corresponding to the maximum value of P(r,θ) is assigned to

[0123] Similarly, the above method is used to obtain the estimated angle and distance of each target in the multi-target relative to each FDA-MIMO radar.

[0124] S4.3: Construct the spatial spectrum function of the EPC-MIMO multiple signal classification algorithm and estimate the angle and distance of each target in the multi-target.

[0125] This step also uses the MUSIC algorithm to estimate the angle and ambiguity number of EPC-MIMO. First, the spatial spectrum function is constructed:

[0126]

[0127] in, is a matrix composed of eigenvectors corresponding to MN-W small eigenvalues, that is, the noise subspace of the echo signal. represents the receive-transmit joint steering vector of the EPC-MIMO radar, b'(θ) represents the receive steering vector of the EPC-MIMO radar, and a(p,θ) represents the transmit steering vector of the EPC-MIMO radar.

[0128] Furthermore, the spatial spectrum function of the EPC-MIMO radar is used to estimate the angle and distance of the target:

[0129]

[0130]

[0131] in, represents the estimated distance of the w-th target relative to the current EPC-MIMO radar, represents the estimated angle of the wth target relative to the current EPC-MIMO radar.

[0132] Similarly, the above method is used to obtain the estimated values ​​of the angle and distance of each target in the multi-target relative to each EPC-MIMO radar.

[0133] S5: Based on the results of the two-dimensional parameter estimation of the angles and distances of the multiple targets, spatially locate the multiple targets by iterative least squares fitting.

[0134] Based on the target parameter information, target angle and distance information obtained in step S4, and combined with the known position information of multiple radars (including at least one FDA-MIMO radar and at least one EPC-MIMO radar), it is assumed that u=[u1,...,u j ,...,u J ] T , where u j =[u jx ,u jy ], j=1,...,J, represents the position coordinates of the jth radar, and the target's observation position information e can be obtained j =(e jx ,e jy ):

[0135] e jx =u jx +r jgu sin(θ jgu )

[0136] e jy =u jy +r jgu sin(θ jgu ),

[0137] Among them, e j =(e jx ,e jy ) represents the observation position information of the current target obtained by the jth radar, r jgu and θ jgu represent the estimated distance and angle of the current target relative to the j-th radar respectively.

[0138] S5.2: Based on the above formula, we can obtain the observation position information of multiple radar stations on the same target. On this basis, we can fit the final observation position of the target by iterative least squares method. Here, we assume that the fitting position of the target is (x nh ,y nh ), the objective function f is:

[0139]

[0140] S5.3: Search the objective function using a coordinate descent method, converting the two-dimensional search into two one-dimensional searches, thereby obtaining a final fitting position for each target in the multiple targets.

[0141] Specifically, the objective function requires a two-dimensional grid search, which is computationally intensive. Using the Coordinate Descent (CD) method, this two-dimensional search is converted into two one-dimensional searches, significantly reducing the computational effort. Coordinate descent is an iterative optimization method that gradually approaches the optimal solution by sequentially minimizing the value of the objective function in each coordinate direction.

[0142] The target search area is divided into two one-dimensional search dimensions, X and Y. The initial iteration point is set to (0,0). The search process is as follows:

[0143]

[0144]

[0145] By simplifying the above calculations using the CD method, the final position of the target can be accurately obtained.

[0146] The performance of the distributed waveform diversity array radar joint positioning method of the present invention is further illustrated by simulation experiments below.

[0147] Simulation 1: Performance of the Joint Localization Method with Distributed Waveform Diversity Array Radar

[0148] Three targets with different distributions are considered, and their parameters relative to four different radars are shown in Tables 1 to 3.

[0149] Table 1. Basic parameters

[0150] Number of transmitting array elements M 15 Number of receiving array elements N 15 <![CDATA[Carrier frequency f0]]> 1GHz bandwidth 10MHz Frequency offset 12.5KHz Quick Shot K 30 Pulse repetition frequency (PRF) 6KHz Coding coefficient γ 1 / 15

[0151] Table 2. Target angle information relative to each local radar (°)

[0152] Target A Target B Objective-C Radar 1 -68.5 21.5 75 Radar 2 -77 7.5 80.5 Radar 3 0 9 16 Radar 4 -16 4 20

[0153] Note: The above angle information is approximate. When measuring in the angle dimension, the angle search interval is 0.5°.

[0154] Table 3. Target distance information relative to each local radar (km)

[0155] Target A Target B Objective-C Radar 1 10.1 4 10.5 Radar 2 10.9 2.5 9.1 Radar 3 68.8 69.7 70.6 Radar 4 31.7 30.6 31.4

[0156] Radars 1 and 2 are FDA-MIMO radars; Radars 3 and 4 are EPC-MIMO radars. The ambiguity numbers of the three targets relative to the two radars are 2 and 1, respectively.

[0157] See Figure 2 , Figure 2This is a comparison chart of target positioning accuracy obtained using different methods, specifically comparing the distributed waveform diversity array radar multi-target joint positioning method of the present invention with the distributed phased array under the same conditions. Figure 2 It can be seen that the joint positioning accuracy of the target by the distributed waveform diversity array radar of the present invention is improved by more than 50% compared with the distributed accuracy of the phased array under the same conditions, and the position information of the target can be accurately estimated.

[0158] Simulation 2: Root mean square error (RMSE) and signal-to-noise ratio curves of distance and angle

[0159] See Figure 3 , Figure 3 is the root mean square error (RMSE) and signal-to-noise ratio curve of the distance obtained using different methods. Figure 3 It can be seen that the RMSE of the distance dimension of the distributed waveform diversity array radar joint parameter estimation method of the present invention decreases with the increase of the input signal-to-noise ratio, and is significantly improved compared with the RMSE of the distributed phased array.

[0160] See Figure 4 , Figure 4 The following are the root mean square error (RMSE) and signal-to-noise ratio curves of the angles obtained using different methods. Figure 4 It can be seen that the RMSE of the angular dimension of the distributed waveform diversity array radar joint parameter estimation method of the present invention decreases with the increase of the input signal-to-noise ratio, and is significantly improved compared with the RMSE of the distributed phased array.

[0161] To address the problems of single dimension, low positioning accuracy and high computational complexity when performing parameter estimation and joint positioning with distributed phased arrays, the present invention proposes a distributed waveform diversity array radar multi-target joint positioning method, which can accurately fit the target's position parameters, greatly reduce the computational complexity, and improve the target's detection and positioning performance.

[0162] Aiming at the method of excessive computational complexity in the two-dimensional search of the target area, the present invention uses the CD method to perform dimensionality reduction processing on the interval two-dimensional search. After verification by experimental data, the target location information can be accurately obtained, which greatly improves the computational efficiency and detection performance.

[0163] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A distributed waveform diversity array radar multi-target joint positioning method, characterized in that: include: S1: constructing a distributed waveform diversity array radar, wherein the distributed waveform diversity array radar includes an FDA-MIMO radar and an EPC-MIMO radar; S2: Constructing a signal model of the FDA-MIMO radar; S3: Constructing a signal model of the EPC-MIMO radar; S4: Based on the signal model of the FDA-MIMO radar and the signal model of the EPC-MIMO radar, two-dimensional parameter estimation of the angle and distance of each target in the multi-target is performed respectively; S5: Based on the results of the two-dimensional parameter estimation of the angles and distances of the multiple targets, the multiple targets are spatially positioned by iterative least squares fitting to obtain a final fitting position of each target.

2. The distributed waveform diversity array radar multi-target joint positioning method according to claim 1, characterized in that: The S2 includes: S2.1: Under the background of Gaussian white noise, a frequency offset is introduced between the transmitting array elements of the FDA-MIMO radar to obtain the transmitting carrier frequency f of the mth transmitting array element of the FDA-MIMO radar m , m=1,2,...,M, where M represents the number of transmitting array elements of the FDA-MIMO radar; S2.2: Obtaining a complex envelope of a signal transmitted by the mth transmitting array element of the FDA-MIMO radar according to the transmitting carrier frequency; S2.3: Obtain a target echo signal of the nth receiving element after the signals transmitted by all transmitting elements of the FDA-MIMO radar are reflected by the target; S2.4: Performing digital mixing processing on the echo signal to obtain a digitally mixed received signal of the nth receiving element of the FDA-MIMO radar; S2.5: Obtain an output signal of the received signal of the nth receiving element after matched filtering by the mth matched filter; S2.6: Obtain echo data of the M transmitting array elements corresponding to the N receiving array elements.

3. The distributed waveform diversity array radar multi-target joint positioning method according to claim 2, characterized in that: Said S2.3 includes: The first transmitting array element of the FDA-MIMO radar is selected as the reference array element, and the time delay τ of the signal emitted by the mth transmitting array element and then reflected by the target to the nth receiving array element is obtained. n,m : Among them, r n Indicates the distance from the target to the nth receiving element, r m represents the distance from the target to the mth transmitting element, c represents the speed of light, θ0 represents the angle of the target relative to the current FDA-MIMO radar, r0 represents the distance of the target relative to the current FDA-MIMO radar, n = 1, 2, ..., N, N represents the number of receiving elements of the FDA-MIMO radar; The echo signal of the signal transmitted by the mth transmitting element of the current FDA-MIMO radar and then reflected by the target and reaching the nth receiving element is obtained, which is expressed as: Where ξ is the complex echo amplitude, It represents the baseband signal of the signal transmitted by the mth transmitting array element and then reflected by the target and reaching the nth receiving array element, and t represents the time; The echo signal of the transmitted signals of all transmitting array elements reaching the nth array element after being reflected by the target is obtained, which is expressed as: in, Represents the round-trip propagation delay between the transmitting array element, the receiving array element and the target; The echo signal of the transmitted signals of all transmitting array elements reaching the nth receiving array element after being reflected by the target is expressed as: Where d represents the spacing between adjacent receiving array elements, f0 represents the transmitting carrier frequency, and Δf represents the frequency offset between two adjacent transmitting array elements.

4. The distributed waveform diversity array radar multi-target joint positioning method according to claim 3, characterized in that: The received signal of the digital mixing processing of the nth receiving element of the FDA-MIMO radar is expressed as: in, 5. The distributed waveform diversity array radar multi-target joint positioning method according to claim 4, characterized in that: The S2.6 includes: Arrange the target echo signals of the M transmitting array elements corresponding to the N receiving array elements into vector form: in,(·) T represents the transpose operation, represents the Kronecker product, ⊙ represents the Hadamard product, and are the transmitting steering vector and the receiving steering vector, is the output signal of the signal of the nth receiving array element after the matched filtering by the mth matched filter; Considering that the noise component satisfies zero mean and white Gaussian distribution, the actual received echo signal is expressed as: AND fda =And a +Y n , in, is the noise component.

6. The distributed waveform diversity array radar multi-target joint positioning method according to claim 5, characterized in that: The S3 includes: S3.1: Obtain a coded waveform of the kth transmit pulse transmitted by the EPC-MIMO radar within a coherent processing interval, where k = 1, 2, ..., K; S3.2: For a target at an angle θ1 and a distance r1 from the EPC-MIMO radar, obtain the complex envelope of a signal transmitted by the pth transmitting element and received by the qth receiving element of the EPC-MIMO radar; S3.3 Multiply the received signal by Perform frequency mixing, pass the mixed signal through M matched filters for waveform separation, and obtain the signal of the k-th pulse signal of the q-th receiving array element after being processed by the p-th matched filter: in, represents the output of the pth matched filter, T represents the pulse duration; S3.4: Convert the k-th pulse signal processed by the p-th matched filter into an MN×1-dimensional vector to obtain: S3.5: Receive signal x k (t) Use the transmitted code to match, and then decode in slow time to obtain the decoded echo signal: y k (t)=(diag{g k }) H x k (t), Among them, y k (t) represents the echo signal x for the kth pulse k (t) The decoded echo signal, diag{·} represents the creation of a diagonal matrix; Represents the decoded vector within the kth pulse: in, represents the Kronecker product, 1 Q represents the unit vector of Q dimension, c k represents the array element pulse code vector of the th pulse; S3.6: Obtain the echo signals of K pulses received by the EPC-MIMO radar, expressed as: in, represents the matched filter output vector, Represents the matched filter output vector of each filter, ⊙ represents the Hadamard product, and represent the transmit steering vector, receive steering vector, and Doppler vector, respectively.

7. The distributed waveform diversity array radar multi-target joint positioning method according to claim 1, characterized in that: The S4 includes: S4.1: Obtain the signal covariance matrix and noise subspace of the matched filtered echo signal of the FDA-MIMO radar; S4.2: Construct the spatial spectrum function of the multiple signal classification algorithm of the FDA-MIMO radar and estimate the angle and distance of each target in the multi-target system; S4.3: Construct the spatial spectrum function of the multiple signal classification algorithm of the EPC-MIMO radar and estimate the angle and distance of each target in the multi-target system; S4.4: Using the angle and distance estimated by the FDA-MIMO radar and the angle and distance estimated by the EPC-MIMO radar, and combining the positions of the FDA-MIMO radar and the EPC-MIMO radar, obtain observation position information of each target for multiple radar station pairs, wherein the multiple radar stations include at least one FDA-MIMO radar and at least one EPC-MIMO radar.

8. The distributed waveform diversity array radar multi-target joint positioning method according to claim 7, characterized in that: Said S4.2 includes: The spatial spectrum function of the multiple signal classification algorithm for constructing FDA-MIMO radar is: Among them, E n is the noise subspace of the echo signal; represents the receive-transmit joint steering vector of the FDA-MIMO radar, b(θ) represents the receive steering vector of the FDA-MIMO radar, and a(r,θ) represents the transmit steering vector of the FDA-MIMO radar; The spatial spectrum function of the FDA-MIMO radar is used to estimate the angle and distance of the target: in, represents the estimated distance of the w-th target relative to the current FDA-MIMO radar, Indicates that the r value corresponding to the maximum value of P(r,θ) is assigned to represents the estimated angle of the w-th target relative to the current FDA-MIMO radar; Obtain estimates of the angle and distance of each target in the multi-target array relative to each FDA-MIMO radar.

9. The distributed waveform diversity array radar multi-target joint positioning method according to claim 7, characterized in that: The S4.3 includes: The spatial spectrum function of the multiple signal classification algorithm for constructing EPC-MIMO radar is: Among them, E n is the noise subspace of the echo signal, represents the receive-transmit joint steering vector of the EPC-MIMO radar, b'(θ) represents the receive steering vector of the EPC-MIMO radar, and a(p,θ) represents the transmit steering vector of the EPC-MIMO radar; The spatial spectrum function of the EPC-MIMO radar is used to estimate the angle and distance of the target: in, represents the estimated distance of the w-th target relative to the current EPC-MIMO radar, represents the estimated angle of the w-th target relative to the current EPC-MIMO radar; Obtain estimates of the angle and distance of each target in the multi-target set relative to each EPC-MIMO radar.

10. The distributed waveform diversity array radar multi-target joint positioning method according to claim 7, characterized in that: The S5 includes: S5.1: Obtain the observation position information of multiple radar stations on the same target: e jx =u jx +r jgu sin(θ jgu ), e jy =u jy +r jgu sin(θ jgu ), Among them, e j =(e jx ,e jy ) represents the observation position information of the current target obtained by the jth radar among the multiple radar stations, r jgu and θ jgu represent respectively the estimated distance and angle of the current target relative to the j-th radar of the plurality of radar stations, wherein the plurality of radar stations include at least one FDA-MIMO radar and at least one EPC-MIMO radar; S5.2: Construct the objective function: Among them, (x nh ,y nh ) represents the final fitting position of the current target; S5.3: Search the objective function using a coordinate descent method, converting the two-dimensional search into two one-dimensional searches, thereby obtaining a final fitting position for each target in the multiple targets.

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