Integrated method of target detection and parameter estimation for distributed waveform diversity array radar

Abstract

The present invention discloses a method for integrating target detection and parameter estimation of a distributed waveform diversity array radar, including: obtaining multiple FDA-MIMO radar received signals and multiple EPC-MIMO radar received signals; constructing an integrated target detection and parameter estimation algorithm, performing target detection and parameter estimation on the received signals, obtaining estimated values ​​of the angle and distance parameters of the target for the qth FDA-MIMO radar, as well as the local detection statistic of the qth FDA-MIMO radar, obtaining estimated values ​​of the angle and distance parameters of the target for the pth EPC-MIMO radar, as well as the local detection statistic of the pth EPC-MIMO radar; calculating the logarithm sum of multiple local detection statistics to obtain a global detection statistic; comparing the global detection statistic with a threshold value, and if the global detection statistic is greater than the threshold value, determining that a target is detected; otherwise, determining that no target is detected. The present invention can improve the detection performance of faint targets.

Description

Technical Field

[0001] The present invention belongs to the field of radar technology, and in particular relates to an integrated method for target detection and parameter estimation of a distributed waveform diversity array radar. Background Art

[0002] In recent years, distributed radar systems have garnered significant attention due to their improved performance in target detection, anti-interference, and target positioning. Compared to traditional monostatic radars, distributed radar systems utilize multiple, widely separated radar stations to achieve spatial diversity, resulting in superior performance. In distributed multiple-input and multiple-output (MIMO) radar systems, information acquired from multiple stations is integrated across the radar network, improving overall radar performance through information fusion. Information fusion is a multifaceted and multi-level information processing process that integrates data from multiple channels for a range of processing, including detection, parameter estimation, and target tracking.

[0003] Based on phased arrays, research on novel radar architectures such as frequency diverse array-multiple-input and multiple-output (FDA-MIMO) and element-pulse coding (EPC)-MIMO radars has emerged. By introducing tiny frequency increments between transmit array elements or performing simultaneous phase modulation in both the spatial and pulse domains, FDA-MIMO and EPC-MIMO radars can gain additional degrees of freedom in the range domain. Because FDA-MIMO radars can simultaneously acquire target range and angle information, they can simultaneously achieve multidimensional parameter estimation and target detection.

[0004] Although distributed radar systems achieve spatial diversity by utilizing multiple widely separated radar stations, and can improve detection performance by fusing individual station statistics, existing research has yet to explore the signal processing of distributed waveform diversity array radars, exploiting the dual advantages of waveform and spatial diversity. In reality, detecting weak targets in complex electromagnetic environments requires both a widely distributed array for multi-perspective detection and waveform diversity to increase the freedom of the radar system.

[0005] In the prior art, under uniform background conditions, Varshney et al. studied a distributed cell averaging (CA) constant false alarm rate (CFAR) detection algorithm. CA-CFAR was used at local sensors to obtain local decisions and design an optimal fusion criterion. Under non-uniform background conditions, each local sensor used an ordered statistics (OS) CFAR detector to obtain local decisions, which were then transmitted to a data fusion center for the final decision. Zhou et al. studied a distributed GLRT algorithm. Local detection statistics were obtained by fusing local radar stations at the fusion center to obtain global statistics for final detection. To simplify the calculation of the global constant false alarm threshold, the local detection statistics were weighted to ensure that all local detection statistics had the same mean, and the global statistics were then fused at the fusion center. Massimo Rosamilia et al. studied the integration of target detection and parameter estimation for polarimetric FDA-MIMO. Using coordinate descent (CD) and gradient projection method (GPM), they simultaneously estimated target parameters and obtained corresponding detection statistics for decision making. Compared to traditional distributed MIMO radars, distributed CA-CFAR suffers from significant detection performance degradation when fusing local decisions, as these decisions lose a significant portion of the data. In contrast, distributed detection methods based on local detection statistics retain more information, resulting in better detection performance than algorithms that only fuse local decisions. However, distributed GLRT algorithms cannot simultaneously achieve multidimensional parameter estimation and target detection. Furthermore, single-station waveform diversity radars exhibit relatively weak performance when detecting faint targets. Therefore, there is an urgent need to address these shortcomings in existing technologies. Summary of the Invention

[0006] To address the above-mentioned problems in the prior art, the present invention provides an integrated method for target detection and parameter estimation using a distributed waveform diversity array radar. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a method for integrating target detection and parameter estimation of a distributed waveform diversity array radar, comprising:

[0008] Obtain the received signal of the FDA-MIMO radar and the received signal of the EPC-MIMO radar;

[0009] Under Gaussian white noise, an integrated target detection and parameter estimation algorithm is constructed. Target detection and parameter estimation are performed simultaneously on the received signals of the FDA-MIMO radar and the EPC-MIMO radar. The estimated values ​​of the angle and range parameters of the target for the qth FDA-MIMO radar and the local detection statistics of the qth FDA-MIMO radar are obtained. The estimated values ​​of the angle and range parameters of the target for the pth EPC-MIMO radar and the local detection statistics of the pth EPC-MIMO radar are obtained.

[0010] The logarithmic sum of the local detection statistics of multiple FDA-MIMO radars and the local detection statistics of multiple EPC-MIMO radars is calculated to obtain a global detection statistic. The global detection statistic is compared with a threshold. If the global detection statistic is greater than the threshold, it is determined that a target is detected; otherwise, it is determined that no target is detected.

[0011] Beneficial effects of the present invention:

[0012] The present invention provides an integrated method for target detection and parameter estimation of a distributed waveform diversity array radar. Under a Gaussian white noise background, target detection and angle-range parameter estimation processing are performed on the echo signals of each receiving element of the distributed waveform diversity array radar to obtain estimated values ​​of the angle and range parameters of the target for the qth FDA-MIMO radar and the local detection statistics of the qth FDA-MIMO radar. The estimated values ​​of the angle and range parameters of the target for the pth EPC-MIMO radar and the local detection statistics of the pth EPC-MIMO radar are also obtained. Furthermore, at a fusion center, a global detection statistic for final detection judgment is constructed by calculating the logarithmic sum of multiple groups of local detection statistics. The global statistic is compared with a threshold. If the global statistic is greater than the threshold, it is determined that a target is present; otherwise, it is determined that no target is present. The present invention adopts a distributed waveform diversity array radar, which fully utilizes the dual advantages of spatial diversity and waveform diversity. On the one hand, the wide-area distributed array can detect weak targets from multiple angles. On the other hand, waveform diversity can increase the freedom of the radar system, optimize the distributed radar's capabilities in adaptive target detection and target multi-dimensional parameter estimation, and improve the inspection performance of weak targets.

[0013] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a method for integrating target detection and parameter estimation of a distributed waveform diversity array radar provided by an embodiment of the present invention;

[0015] Figure 2This is a schematic diagram of an integrated system for target detection and parameter estimation of a distributed waveform diversity array radar provided by an embodiment of the present invention;

[0016] Figure 3 This is a schematic diagram of a FDA-MIMO radar receiving and transmitting signal provided by an embodiment of the present invention;

[0017] Figure 4 This is a schematic diagram of an EPC-MIMO radar receiving and transmitting signal provided by an embodiment of the present invention;

[0018] Figure 5 This is a schematic diagram of a simulation experiment 1 provided by an embodiment of the present invention;

[0019] Figure 6 This is a schematic diagram of a simulation experiment 2 provided by an embodiment of the present invention;

[0020] Figure 7 This is a schematic diagram of a simulation experiment 3 provided by an embodiment of the present invention;

[0021] Figure 8 This is a schematic diagram of a simulation experiment 4 provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described in detail below with reference to specific examples, but the embodiments of the present invention are not limited thereto.

[0023] Aiming at the difficulty of detecting weak targets with traditional single-station radar, the present invention utilizes distributed array information modulation radar to give full play to the dual advantages of spatial diversity and waveform diversity, carries out research on adaptive target detection and multi-dimensional parameter estimation of angle-incremental distance-fuzzy distance, and proposes a distributed waveform diversity array radar target detection and parameter estimation method.

[0024] See Figure 1 and Figure 2 , Figure 1 This is a flow chart of a method for integrating target detection and parameter estimation of a distributed waveform diversity array radar provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of a distributed waveform diversity array radar target detection and parameter estimation integrated system provided by an embodiment of the present invention. The present invention provides a distributed waveform diversity array radar target detection and parameter estimation integrated method, including:

[0025] S101: Acquire a received signal of an FDA-MIMO radar and a received signal of an EPC-MIMO radar.

[0026] Specifically, in this embodiment, see Figure 3 and Figure 4 , Figure 3FIG1 is a schematic diagram of a FDA-MIMO radar receiving and transmitting signal provided by an embodiment of the present invention. Figure 4 This is a schematic diagram of an EPC-MIMO radar receiving and transmitting signals provided by an embodiment of the present invention. The FDA-MIMO radar and the EPC-MIMO radar can be co-located radars for transmission and reception. The total number of FDA-MIMO radars and EPC-MIMO radars is L, among which L1 and L2 FDA-MIMO radars and EPC-MIMO radars are composed of M transmitting array elements and N receiving array elements, with an array element spacing of d, and M, N and d greater than 0.

[0027] In this embodiment, obtaining received signals of multiple FDA-MIMO radars includes:

[0028] Construct the transmission signal x of the mth (m=1,2,...,M) transmitting element of the qth (q=1,...L1) FDA-MIMO radar m,q (t), its expression is:

[0029]

[0030] Where E represents the total energy of emission, T p Indicates the pulse width, f m =f0+(m-1)Δf represents the carrier frequency of the mth transmitting array element, f0 represents the reference carrier frequency, and Δf represents a very small frequency step (much smaller than the carrier frequency). represents the baseband waveform of the mth array element, j represents the imaginary number symbol, π represents the ratio of pi, L1 represents the total number of FDA-MIMO radars, and e represents the exponential operation with base 2.7;

[0031] For a target in the far field, the angle between the qth FDA-MIMO radar and the target is θ q , the distance is R q In the narrowband case, the nth (n=1,2,...,N) receiving element receives the echo signal y from the mth transmitting element m,n,q The expression of (t) is:

[0032]

[0033] Among them, β q Indicates the complex echo amplitude (taking into account factors such as transmit power, phase, target reflectivity, and channel propagation effects). represents the round trip time, c represents the speed of light, and d represents the array element spacing. Considering the narrowband assumption, then represents the public round-trip delay,

[0034] The nth receiving element receives the echo signal y from the M transmitting elements n The expression of (t) is:

[0035]

[0036] in, Indicates wavelength;

[0037] The echo signal y n (t) multiplied by Mixing is performed, and the echo signal received by each receiving array element is processed by M groups of matched filters. Among them, the representation of the lth (l=1,2,...,M) matched filter is Get the received signal y received by the qth FDA-MIMO radar q , whose expression is:

[0038]

[0039] Where, represents the Kronecker product, ⊙ represents the Hadamard product operation, represents the complex coefficient of the target after digital mixing, β q represents the complex-valued coefficient of the target, τ 0,q represents the common envelope time delay of the qth FDA-MIMO radar, s(θ q ,Δτ q ) represents the first transceiver steering vector, b(θ q ) represents the angle-dependent receiving steering vector, c(θ q ) represents the launch steering vector related to the angle, a(Δτ q ) represents the launch guidance vector related to the incremental distance, and its specific expression is:

[0040]

[0041] Where, (·) T represents the transpose operation, M represents the number of transmitting elements, N represents the number of receiving elements, λ0 represents the wavelength, f0 represents the reference carrier frequency, d represents the spacing between transmitting elements or receiving elements, c represents the speed of light, and θ q represents the target angle, Δf represents the frequency step introduced by the FDA-MIMO radar between the transmitting array elements, and Δτ q represents the incremental range of the target to the qth FDA-MIMO radar.

[0042] In this embodiment, obtaining multiple EPC-MIMO radar receive signals includes:

[0043] Construct K transmit pulses within the radar coherent integration time of the p-th (p=1,...,L2) EPC-MIMO radar, where the transmit signal s of the m-th transmit element of the k-th (k=1,...,K) pulse is m,k,q The expression of (t) is:

[0044]

[0045] Where, t∈(0,T r ) represents the EPC-MIMO radar pulse repetition time T r Time variable within, c m,k =e j2πγ(m-1)(k-1) represents the phase coefficient, γ∈(0,1) represents the adjustable coding parameter, represents the baseband waveform of the mth array element, f0 represents the reference carrier frequency, T p Indicates pulse width;

[0046] For a target in the far field, the angle between the pth EPC-MIMO radar and the target is θ p , the distance is R p , the nth (n=1,...N) receiving element receives the echo signal x from the mth transmitting element n,m,k,p The expression of (t) is:

[0047]

[0048] Among them, α p represents the complex amplitude of the target, p s,p represents the number of delayed pulses of the target for the pth EPC-MIMO radar, τ 0,p represents the common round-trip delay, τ n,m,p Indicates round trip time;

[0049] The echo signal x n,m,k,p (t) multiplied by It should be noted that the echo signal of each receiving array element passes through M matched filters to achieve waveform separation, where the lth matched filter is expressed as The echo signal received by the nth receiving element is processed by the mth group of matched filters. The echo signal of each receiving element needs to pass through M matched filters to achieve waveform separation. The lth matched filter is expressed as Get the processed echo signal Its expression is:

[0050]

[0051] Among them, α 0,prepresents the complex echo power after digital mixing, Represents the output result of the mth group of matched filtering

[0052] Get the processed echo signal x corresponding to all echo signals of the kth pulse k,p (t), its expression is:

[0053]

[0054] The processed echo signal x corresponding to the kth pulse k,p (t) is decoded to obtain the decoded echo signal y k,p (t), its expression is:

[0055]

[0056] in, represents the conjugate transpose operation, represents the decoding vector within the kth pulse, 1 N represents an N×N matrix of all 1s, c k =[1,c 1,k ,...,c M,k ] T represents the EPC vector of the kth pulse;

[0057] According to the decoded echo signal, the received signal y of the EPC-MIMO radar received by the pth EPC-MIMO radar is obtained. p , whose expression is:

[0058]

[0059] Where, represents the complex amplitude of the target after digital mixing, α p represents the complex amplitude of the target, τ 0,p represents the common envelope time delay of the p-th EPC-MIMO radar, is the matched filter output vector, represents the launch steering vector, represents the receiving steering vector, and its specific expression is:

[0060]

[0061] Where θ p represents the target angle, γ s,p =γp s,p represents the adjustable encoding parameter, γ∈(0,1), p s,p It represents the number of delayed pulses of the target for the p-th EPC-MIMO radar.

[0062] S102. Under Gaussian white noise, construct an integrated target detection and parameter estimation algorithm, and simultaneously perform target detection and parameter estimation on the FDA-MIMO radar received signals and the EPC-MIMO radar received signals, thereby obtaining estimated values ​​of the angle and range parameters of the target for the qth FDA-MIMO radar and the local detection statistics of the qth FDA-MIMO radar, and obtaining estimated values ​​of the angle and range parameters of the target for the pth EPC-MIMO radar and the local detection statistics of the pth EPC-MIMO radar.

[0063] Specifically, in this embodiment, the process of obtaining the estimated values ​​of the angle and distance parameters of the target with respect to the qth FDA-MIMO radar, and the local detection statistics of the qth FDA-MIMO radar includes:

[0064] Under the background of Gaussian white noise, a set of training samples x is obtained for the qth FDA-MIMO radar. q,k ,k=1,2,...K q ≥MN, K q Indicates the number of snapshots, x q represents the echo vector of the unit to be detected, the echo vector x of the unit to be detected q Acquire according to the FDA-MIMO radar receiving signal; Based on the training sample x q,k , construct the target detection problem, and express the target detection problem as a binary hypothesis detection problem, which is expressed as:

[0065]

[0066] Among them, H0 represents the hypothesis that the target does not have a unit to be detected, H1 represents the hypothesis that the target has a unit to be detected, and n q and represents complex Gaussian noise with zero mean that satisfies the independent and identically distributed conditions and is cyclically symmetric. represents the positive definite noise covariance matrix, Complex coefficients of the target after digital mixing;

[0067] Since the variable β q , M, θ p and Δτ p Unknown, based on the GLRT criterion, obtain the local detection statistic z of the qth FDA-MIMO radar q , whose expression is:

[0068]

[0069] in, represents θ q All possible sets, Denotes Δτ q All possible sets of g(·|·;H i ), i = 0, 1 represents the joint probability density under the H0 hypothesis and the H1 hypothesis;

[0070] By introducing the matrix Maximize the numerator and denominator with respect to M, and then maximize the β in the denominator. q Maximize the local detection statistic z for the qth FDA-MIMO radar q Convert it to get the equivalent local detection statistic η of the qth FDA-MIMO radar q , whose expression is:

[0071]

[0072] Where,

[0073] Will and As the initial estimated value, optimize with the angle as the initial search direction, and then optimize with the distance increment as the initial search direction to obtain the estimated values ​​of the angle and distance parameters of the target for the qth FDA-MIMO radar; the estimated values ​​of the angle and distance parameters of the target for the qth FDA-MIMO radar are substituted into the equivalent local detection statistic η of the qth FDA-MIMO radar q The expression of is used to obtain the local detection statistic of the qth FDA-MIMO radar.

[0074] In this embodiment, and As the initial estimated value, the angle is used as the initial search direction for optimization, and then the distance increment is used as the initial search direction for optimization to obtain the estimated values ​​of the angle and distance parameters of the target for the qth FDA-MIMO radar, including:

[0075] Optimize with angle as initial search direction and fix the incremental distance of the nth estimate In the discrete interval I θ Find the angle estimated for the n+1th time, and its expression is:

[0076]

[0077] Where, Discrete interval I θ There is N θ interval

[0078] Fix the angle of the n+1th estimate In the discrete interval I ΔτFind the incremental distance of the n+1th estimate, and its expression is:

[0079]

[0080] in, B1 represents the bandwidth of FDA-MIMO radar, and the discrete interval I Δτ There is N Δτ intervals;

[0081] The n+1th angle estimate and incremental estimates Substitute the objective function and we get:

[0082]

[0083] if ε1 represents a constant, which means the optimization is over. Otherwise, continue optimizing until

[0084] Optimize the initial search direction with the distance increment, and fix the angle of the nth estimate In the discrete interval I Δτ Find the incremental distance of the n+1th estimate, and its expression is:

[0085]

[0086] Fixed incremental distance of the n+1th estimate In the discrete interval I θ Find the angle estimated for the n+1th time, and its expression is:

[0087]

[0088] The n+1th angle estimate and incremental estimates Substitute the objective function and we get:

[0089]

[0090] if ε1>0, it means the optimization is finished, let Otherwise, continue optimizing until

[0091] if but Will and As the estimated value of the angle and distance parameters of the target for the qth FDA-MIMO radar; otherwise, Will and As the estimated value of the angle and distance parameters of the target for the qth FDA-MIMO radar.

[0092] In this embodiment, obtaining the estimated values ​​of the angle and distance parameters of the target with respect to the p-th EPC-MIMO radar, and the local detection statistics of the p-th EPC-MIMO radar includes:

[0093] Under the background of Gaussian white noise, for the p-th EPC-MIMO radar, a set of training samples x is obtained. p,k ,k=1,2,...K p ≥MN, K p Indicates the number of snapshots, x p represents the echo vector of the unit to be detected, the echo vector x of the unit to be detected p According to the received signal of the EPC-MIMO radar, the p,k , construct the target detection problem, and express the target detection problem as a binary hypothesis detection problem, which is expressed as:

[0094]

[0095] Among them, H0 represents the hypothesis that the target does not have a unit to be detected, H1 represents the hypothesis that the target has a unit to be detected, and n p and represents complex Gaussian noise with zero mean that satisfies the independent and identically distributed conditions and is cyclically symmetric. represents the positive definite noise covariance matrix, Indicates the complex echo power after digital mixing;

[0096] Based on the GLRT criterion, the local detection statistic z of the p-th EPC-MIMO radar is obtained p , whose expression is:

[0097]

[0098] in, represents θ p All possible sets, Indicates p s,p All possible sets of g(·|·;H i ), i = 0, 1 represents the joint probability density under the H0 hypothesis and the H1 hypothesis;

[0099] By introducing the matrix Maximize the numerator and denominator with respect to M, and then maximize the α in the denominator pMaximize the local detection statistic z for the p-th EPC-MIMO radar p Convert it to get the equivalent detection statistic η of the p-th EPC-MIMO radar p , whose expression is:

[0100]

[0101] Where,

[0102] Will and As the initial estimate, optimize with the angle as the initial search direction, and then optimize with the range ambiguity number as the initial search direction to obtain the estimated values ​​of the angle and distance parameters of the target for the p-th EPC-MIMO radar; and bring the estimated values ​​of the angle and distance parameters of the target for the p-th EPC-MIMO radar into the equivalent detection statistic η of the p-th EPC-MIMO radar p The expression of is used to obtain the local detection statistic of the p-th EPC-MIMO radar.

[0103] In this embodiment, and As the initial estimated value, the angle is used as the initial search direction for optimization, and then the range ambiguity number is used as the initial search direction for optimization to obtain the estimated values ​​of the angle and distance parameters of the target for the p-th EPC-MIMO radar, including:

[0104] Optimize with angle as the initial search direction and fix the distance ambiguity number of the nth estimate In the discrete interval I θ Find the angle estimated for the n+1th time, and its expression is:

[0105]

[0106] Where, Discrete interval I θ There is N θ interval

[0107] Fix the angle of the n+1th estimate In the discrete interval I Δτ Find the distance fuzzy number of the n+1th estimate, and its expression is:

[0108]

[0109] in, Discrete interval exist interval;

[0110] The n+1th angle estimate and distance fuzzy number Substitute the objective function and we get:

[0111]

[0112] if ε2>0, ε2 represents a constant, which means the optimization is over. Otherwise, continue optimizing until

[0113] Optimize the initial search direction using the distance fuzzy number and fix the angle of the nth estimate In the discrete interval I Δτ Find the distance fuzzy number of the n+1th estimate, and its expression is:

[0114]

[0115] Fixed the distance ambiguity number of the n+1th estimate In the discrete interval I θ Find the angle estimated for the n+1th time, and its expression is:

[0116]

[0117] The n+1th angle estimate and distance fuzzy number Substitute the objective function and we get:

[0118]

[0119] if ε2>0, it means the optimization is finished, let Otherwise, continue optimizing until

[0120] if but Will and As the estimated value of the angle and distance parameters of the target for the pth EPC-MIMO radar; otherwise, Will and As the estimated value of the angle and distance parameters of the target for the p-th EPC-MIMO radar.

[0121] S103. Calculate the logarithm sum of the local detection statistics of the multiple FDA-MIMO radars and the local detection statistics of the multiple EPC-MIMO radars to obtain a global detection statistic; compare the global detection statistic with a threshold; if the global detection statistic is greater than the threshold, determine that a target is detected; otherwise, determine that no target is detected.

[0122] Specifically, in this embodiment, a relationship between global detection statistics and local radar detection statistics is established based on the GLRT criterion. The process of obtaining the global detection statistics includes:

[0123] Under the condition that the noises of L1 FDA-MIMO radars and L2 EPC-MIMO radars are statistically independent, the joint probability density under the H0 hypothesis and the H1 hypothesis is updated according to the local detection statistics X of all FDA-MIMO radars and all EPC-MIMO radars. The expression is:

[0124]

[0125] According to the GLRT criterion, the global detection statistic is obtained, and its expression is:

[0126]

[0127] Where γ represents the threshold, L = L1 + L2;

[0128] The maximization of the global detection statistic is converted into the maximization of L local detection statistics, and the expression of the global detection statistic is updated to obtain:

[0129]

[0130] Among them, z i represents the local detection statistic of the i-th FDA-MIMO radar or EPC-MIMO radar;

[0131] The updated global detection statistic is taken in logarithmic form to obtain the final global detection statistic, which is expressed as:

[0132]

[0133] Here, ρ represents the final global detection statistic.

[0134] In summary, the present invention provides an integrated method for target detection and parameter estimation of a distributed waveform diversity array radar. Under a Gaussian white noise background, target detection and angle-range parameter estimation processing are performed on the echo signals of each receiving element of the distributed waveform diversity array radar, and estimated values ​​of the angle and range parameters of the target for the qth FDA-MIMO radar and the local detection statistics of the qth FDA-MIMO radar are obtained. Estimated values ​​of the angle and range parameters of the target for the pth EPC-MIMO radar and the local detection statistics of the pth EPC-MIMO radar are obtained. Furthermore, at the fusion center, a global detection statistic for final detection judgment is constructed by calculating the logarithmic sum of multiple groups of local detection statistics. The global statistic is compared with a threshold. If the global statistic is greater than the threshold, it is determined that a target is present; otherwise, it is determined that there is no target. The present invention adopts a distributed waveform diversity array radar, which fully utilizes the dual advantages of spatial diversity and waveform diversity. On the one hand, the wide-area distributed array can detect weak targets from multiple angles. On the other hand, waveform diversity can increase the freedom of the radar system, optimize the distributed radar's capabilities in adaptive target detection and target multi-dimensional parameter estimation, and improve the inspection performance of weak targets.

[0135] Specifically, this embodiment utilizes a distributed waveform diversity array radar, leveraging the dual advantages of spatial diversity and waveform diversity. On the one hand, the widely distributed array enables multi-angle detection of faint targets. On the other hand, waveform diversity increases the radar system's degrees of freedom, optimizing the distributed radar's adaptive target detection and multi-dimensional target parameter estimation capabilities, thereby improving weak target detection performance. The distributed waveform diversity array radar leverages both waveform diversity and spatial diversity, significantly improving target detection performance compared to single-station radars. Compared to distributed phased array radar systems, the distributed waveform diversity array radar significantly improves target detection probability and parameter estimation performance.

[0136] In an optional embodiment of the present invention, the effect of the integrated method of target detection and parameter estimation of the distributed waveform diversity array radar provided in the above embodiment is verified through simulation experiments, specifically:

[0137] 1. Simulation conditions

[0138] The simulation parameters of the distributed waveform diversity array radar system and the target are set as shown in Table 1 and Table 2.

[0139] Table 1 System simulation parameters

[0140] parameter Numerical parameter Numerical Bandwidth B 10MHz <![CDATA[Carrier frequency f0]]> 1GHz Unit element spacing d 0.15m Number of transmitting units M 8,10 Number of receiving units N 8,10 Pulse repetition frequency 6KHz

[0141] Table 2 Target parameters

[0142]

[0143] 2. Simulation content and result analysis

[0144] See Figure 5 , Figure 5 This is a schematic diagram of a simulation experiment 1 provided by an embodiment of the present invention. Figure 5 The detection probabilities of distributed waveform diversity array radars, distributed phased arrays, and various radar stations using different numbers of transmitting array elements are compared under frequency offset Δf = B / M. Figure 5 As shown in the figure, under the same number of transmitting array elements, the detection probability increases with the increase of signal-to-noise ratio (SNR); under the same SNR, the detection probability increases with the increase of the number of transmitting array elements. (a) and (b) respectively show the detection probability of distributed waveform diversity array radar, distributed phased array, and each radar station when M=8 and M=10 transmitting array elements. Figure 5 As shown in (a), compared with individual radar stations and distributed phased arrays, distributed waveform diversity array radar has a higher d = 0.9, the detection performance is improved by about 3.5dB and 8dB respectively; Figure 5 As shown in (b), compared with the distributed phased array, the distributed waveform diversity array radar has a d =0.9, the detection performance is improved by about 8.5dB.

[0145] See Figure 6 , Figure 6 This is a schematic diagram of a simulation experiment 2 provided by an embodiment of the present invention. Figure 6 The angle RMSE of distributed waveform diversity array radar and distributed phased array radar with different numbers of transmitting array elements are compared under the frequency offset Δf=B / M. Figure 6 At the same number of transmitting elements, the angle root mean square error (RMSE) decreases with increasing SNR. At the same SNR, the angle RMSE decreases with increasing number of transmitting elements. The angle RMSEs are nearly consistent across radar stations. When the SNR is sufficiently high, the angle estimation RMSE is comparable to the CRB (Cramér-Rao bound). Figure 6 The angle RMSE of the distributed waveform diversity array radar and the distributed phased array radar are evaluated in (a) and (b) respectively when M = 8 and M = 10 transmitting array elements. Figure 6As shown in (a), when SNR = -6dB, the angle RMSE accuracy obtained by the distributed waveform diversity array radar through the CD algorithm can be improved by 73.5% compared with the distributed phased array radar; at high signal-to-noise ratio, the difference in angle estimation performance between the distributed waveform diversity array radar and the distributed phased array radar decreases as the signal-to-noise ratio increases. Figure 6 As shown in (b), when SNR = -6dB, the angle RMSE accuracy obtained by the distributed waveform diversity array radar through the CD algorithm can be improved by 81.1% compared with the distributed phased array radar.

[0146] See Figure 7 , Figure 7 This is a schematic diagram of a simulation experiment 3 provided by an embodiment of the present invention. Figure 7 The incremental range RMSE of distributed waveform diversity array radars with different numbers of transmitting array elements under frequency offset Δf=B / M is compared. Figure 7 Figures (a) and (b) evaluate the incremental range RMSE of distributed waveform diversity array radar and distributed phased array radar when M = 8 and M = 10 transmitting array elements, respectively. Under the same number of transmitting array elements, as the SNR increases, the incremental range RMSE obtained by the distributed waveform diversity array radar through the CD algorithm decreases; under the same SNR, the incremental range RMSE decreases with the increase of the number of transmitting array elements.

[0147] See Figure 8 , Figure 8 This is a schematic diagram of a simulation experiment 4 provided by an embodiment of the present invention. Figure 8 The range ambiguity RMSE of distributed waveform diversity array radars with different numbers of transmitting array elements under frequency offset Δf=B / M is compared. Figure 8 Figures (a) and (b) evaluate the RMSE of the range ambiguity number of the distributed waveform diversity array radar and the distributed phased array radar when the number of transmitting array elements is M=8 and M=10, respectively. Under the same number of transmitting array elements, as the SNR increases, the RMSE of the range ambiguity number obtained by the CD algorithm decreases; under the same signal-to-noise ratio, the RMSE of the range ambiguity number decreases with the increase of the number of transmitting array elements; when the SNR is high enough, the RMSE of the range ambiguity number is lower than the CRB level.

[0148] It should be noted that the CD algorithm described above is an integrated target detection and parameter estimation algorithm constructed by the present invention.

[0149] It should be noted that, in this document, relational terms such as first and second are used solely to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. Furthermore, the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that an article or device comprising a list of elements includes not only those elements but also other elements not explicitly listed. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of additional identical elements in the article or device comprising the element. Terms such as "connected" or "connected" are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. References to orientations or positional relationships, such as "upper," "lower," "left," and "right," are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate description and simplify the description of the present invention. They do not indicate or imply that the device or element referred to must have, be constructed, or operate in a specific orientation, and are therefore not to be construed as limiting the present invention.

[0150] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification.

[0151] 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 method for integrating target detection and parameter estimation of a distributed waveform diversity array radar, characterized in that: include: Obtain the received signal of the FDA-MIMO radar and the received signal of the EPC-MIMO radar; Under Gaussian white noise, an integrated algorithm of target detection and parameter estimation is constructed. Target detection and parameter estimation are performed on the received signals of the FDA-MIMO radar and the EPC-MIMO radar at the same time. The target is obtained for the first The estimated values ​​of the angle and distance parameters of the first FDA-MIMO radar, and the The local detection statistics of the FDA-MIMO radar are obtained for the target The estimated values ​​of the angle and distance parameters of the EPC-MIMO radar, and the Local detection statistics of an EPC-MIMO radar; Calculating the logarithmic sum of the local detection statistics of multiple FDA-MIMO radars and the local detection statistics of multiple EPC-MIMO radars to obtain a global detection statistic; comparing the global detection statistic with a threshold; if the global detection statistic is greater than the threshold, it is determined that a target is detected; otherwise, it is determined that there is no target; wherein, The process of obtaining the global detection statistic includes: In the FDA-MIMO radar and the Under the condition that the noise of each EPC-MIMO radar is statistically independent, according to the local detection statistics of all FDA-MIMO radars and all EPC-MIMO radars , updated in Assume and The joint probability density under the assumption is expressed as: ； According to the GLRT criterion, the global detection statistic is obtained, and its expression is: ； in, represents the threshold value, ; Maximization of the global detection statistic is converted into Maximize the local detection statistics and update the expression of the global detection statistics to obtain: ； in, Indicates the The local detection statistics of an FDA-MIMO radar or an EPC-MIMO radar; The updated global detection statistic is taken in logarithmic form to obtain the final global detection statistic, which is expressed as: ； in, represents the final global detection statistic.

2. The integrated method for target detection and parameter estimation of a distributed waveform diversity array radar according to claim 1, characterized in that: No. The received signal of an FDA-MIMO radar The expression is: ； Where, represents the Kronecker product, represents the Hadamard product operation, represents the complex coefficient of the target after digital mixing, represents the complex-valued coefficient of the target, Indicates the The common envelope time delay of the FDA-MIMO radar, represents the first transmit / receive steering vector, represents the receive steering vector associated with the angle, represents the launch steering vector associated with the angle, represents the launch steering vector associated with the incremental distance; ； ； ； Where, represents the transpose operation, represents the number of transmitting array elements, represents the number of receiving array elements, represents the wavelength, represents the reference carrier frequency, Indicates the spacing between transmitting or receiving array elements. represents the speed of light, represents the target angle, It represents the frequency step introduced by FDA-MIMO radar between transmitting array elements. Indicates the target for Incremental range of an FDA-MIMO radar.

3. The integrated method for target detection and parameter estimation of a distributed waveform diversity array radar according to claim 2, characterized in that: No. The received signal of an EPC-MIMO radar The expression is: ； Where, represents the complex amplitude of the target after digital mixing, represents the complex amplitude of the target, Indicates the The common envelope time delay of the EPC-MIMO radar, represents the matched filter output vector, represents the launch steering vector, represents the receiving steering vector; ； ； Where, represents the target angle, Indicates adjustable encoding parameters, , Indicates the target for The number of delayed pulses of an EPC-MIMO radar.

4. The integrated method for target detection and parameter estimation of a distributed waveform diversity array radar according to claim 1, characterized in that: Target for The estimated values ​​of the angle and distance parameters of the first FDA-MIMO radar, and the The process of obtaining the local detection statistics of an FDA-MIMO radar includes: In the background of Gaussian white noise, FDA-MIMO radar, obtain a set of training samples , , Indicates the number of snapshots. Represents the echo vector of the unit to be detected, the echo vector of the unit to be detected According to the received signal of the FDA-MIMO radar; based on the training sample , construct the target detection problem, and express the target detection problem as a binary hypothesis detection problem, which is expressed as: ； in, Indicates the assumption that the target does not have a unit to be detected, Indicates the hypothesis that there is a unit to be detected in the target, and represents complex Gaussian noise with zero mean that satisfies the independent and identically distributed conditions and is cyclically symmetric. , represents the positive definite noise covariance matrix, Complex coefficients of the target after digital mixing; Based on the GLRT criterion, obtain the Local detection statistics of a FDA-MIMO radar , whose expression is: ； in, express All possible sets, express All possible sets of Indicates Assume and Joint probability density under the assumptions; By introducing the matrix And maximize the numerator and denominator, for the Local detection statistics of a FDA-MIMO radar Convert to get the equivalent Local detection statistics of a FDA-MIMO radar , whose expression is: ； Where, ; Will and As the initial estimate, optimize with the angle as the initial search direction, and then optimize with the distance increment as the initial search direction, and get the target for the first The estimated values ​​of the angle and range parameters of the FDA-MIMO radar; The target is for the The estimated values ​​of the angle and distance parameters of the first FDA-MIMO radar are substituted into the equivalent Local detection statistics of a FDA-MIMO radar The expression of Local detection statistics of a FDA-MIMO radar.

5. The integrated method for target detection and parameter estimation of a distributed waveform diversity array radar according to claim 4, characterized in that: Will and As the initial estimate, optimize with the angle as the initial search direction, and then optimize with the distance increment as the initial search direction, and get the target for the first The estimated values ​​of the angle and distance parameters of the FDA-MIMO radar include: Optimize with angle as the initial search direction, fix the The estimated incremental distance , in the discrete interval Find the The estimated angle is expressed as: ； Where, , discrete interval exist intervals; Fixed Second estimated angle , in the discrete interval Find the The incremental distance of the second estimate is expressed as: ； Where, , Indicates the The bandwidth of an FDA-MIMO radar, discrete interval exist intervals; The first Secondary angle estimate and incremental estimates Substitute the objective function and we get: ； if , , represents a constant, indicating the end of optimization, let 、 、 Otherwise, continue to optimize until ; Optimize the initial search direction with the distance increment, fix the Second estimated angle , in the discrete interval Find the The incremental distance of the second estimate is expressed as: ； Fixed The estimated incremental distance , in the discrete interval Find the The estimated angle is expressed as: ； The first Secondary angle estimate and incremental estimates Substitute the objective function and we get: ； if , , it means the optimization is finished, let 、 、 Otherwise, continue to optimize until ; if ,but , ,Will and As a goal for The estimated values ​​of the angle and distance parameters of the FDA-MIMO radar; conversely, , ,Will and As a goal for Estimated values ​​of angle and range parameters of a FDA-MIMO radar.

6. The integrated method for target detection and parameter estimation of a distributed waveform diversity array radar according to claim 1, characterized in that: Get the target for The estimated values ​​of the angle and distance parameters of the EPC-MIMO radar, and the The local detection statistics of an EPC-MIMO radar include: In the background of Gaussian white noise, EPC-MIMO radar, obtain a set of training samples , , Indicates the number of snapshots. Represents the echo vector of the unit to be detected, the echo vector of the unit to be detected According to the received signal of the EPC-MIMO radar, the , construct the target detection problem, and express the target detection problem as a binary hypothesis detection problem, which is expressed as: ； in, Indicates the assumption that the target does not have a unit to be detected, Indicates the hypothesis that there is a unit to be detected in the target, and represents complex Gaussian noise with zero mean that satisfies the independent and identically distributed conditions and is cyclically symmetric. , represents the positive definite noise covariance matrix, Represents the complex amplitude of the target after digital mixing; Based on the GLRT criterion, obtain the Local detection statistics of an EPC-MIMO radar , whose expression is: ； in, express All possible sets, express All possible sets of Indicates Assume and Joint probability density under the assumptions; By introducing the matrix And maximize the numerator and denominator, for the Local detection statistics of an EPC-MIMO radar Convert the equivalent Detection statistics of an EPC-MIMO radar , whose expression is: ； Where, ; Will and As the initial estimate, the angle is used as the initial search direction for optimization, and then the distance fuzzy number is used as the initial search direction for optimization, and the target is obtained for the first Estimated values ​​of the angle and distance parameters of the EPC-MIMO radar; and the target The estimated values ​​of the angle and distance parameters of the EPC-MIMO radar are substituted into the equivalent Detection statistics of an EPC-MIMO radar The expression of Local detection statistics of an EPC-MIMO radar.

7. The integrated method for distributed waveform diversity array radar target detection and parameter estimation according to claim 6, characterized in that: Will and As the initial estimate, the angle is used as the initial search direction for optimization, and then the distance fuzzy number is used as the initial search direction for optimization, and the target is obtained for the first Estimated values ​​of angle and distance parameters of each EPC-MIMO radar, including: Optimize with angle as the initial search direction, fix the The estimated distance ambiguity number , in the discrete interval Find the The estimated angle is expressed as: ； Where, , discrete interval exist intervals; Fixed Second estimated angle , in the discrete interval Find the The estimated distance ambiguity number is expressed as: ； Where, , discrete interval exist intervals; The first Secondary angle estimate and distance fuzzy number Substitute the objective function and we get: ； if , , represents a constant, indicating the end of optimization, let 、 、 Otherwise, continue to optimize until ; Optimize the initial search direction with the distance fuzzy number, fix the Second estimated angle , in the discrete interval Find the The estimated distance ambiguity number is expressed as: ； Fixed Estimated distance ambiguity number , in the discrete interval Find the The estimated angle is expressed as: ； The first Secondary angle estimate and distance fuzzy number Substitute the objective function and we get: ； if , , it means the optimization is finished, let 、 、 Otherwise, continue to optimize until ; if ,but , ,Will and As a goal for The estimated values ​​of the angle and distance parameters of the EPC-MIMO radar; otherwise, , ,Will and As a goal for Estimated values ​​of angle and distance parameters of an EPC-MIMO radar.

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