A moving platform distributed radar moving target rapid detection method, device and equipment

Abstract

The application provides a moving platform distributed radar moving target rapid detection method, device and equipment. The moving platform distributed radar moving target rapid detection method comprises the following steps: a moving platform distributed radar system model is constructed, and a low-bit quantized signal model is obtained based on the moving platform distributed radar system model; a generalized likelihood ratio test (GLRT) detector of the moving platform distributed radar system is generated through the low-bit quantized signal model; joint estimation processing is performed on the GLRT detector by using a differential evolution (DE) method and a batch gradient descent (BGDA) method, so that state data is obtained; the state data comprises a radar cross section (RCS), a target position, a target speed, a radar position and a radar speed. In the application, the joint estimation processing is performed by using the DE method and the BGDA method, so that the accuracy and the convergence speed of the moving platform distributed radar system for target parameter estimation in a dynamically changing environment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar signal processing, in particular to a dynamic platform distributed radar dynamic target rapid detection method, device and equipment. BACKGROUND

[0002] The dynamic platform distributed radar system is an important development direction of modern radar technology, and has great potential in many fields such as military reconnaissance, civil aviation monitoring and natural disaster warning due to its excellent multi-target complex scene detection capability and high robustness. The system can effectively improve the detection accuracy and parameter estimation capability of the target through the cooperative work of multiple radar nodes distributedly deployed, especially in the interference countermeasure environment. However, with the increasing complexity of modern battlefield environment and civil monitoring demand, the electromagnetic characteristics of targets and environment become more diversified, which puts higher requirements on the target detection and parameter estimation capability of the dynamic platform distributed radar system. In practical application, how to realize accurate tracking and parameter estimation of multiple dynamic targets under the condition of limited resources has become a key technical problem to be solved.

[0003] In order to cope with the hardware resource limitation of the radar system, the existing research generally adopts low-bit quantization technology, that is, before the data is transmitted from each radar node to the fusion center, the observation data is preprocessed by using a low-bit quantizer. This method significantly reduces the hardware resource consumption of data transmission, storage and processing, and improves the overall efficiency of the system. Especially in the fixed position radar system, for the observation task of a single static target, the low-bit quantization technology combined with a specific data processing algorithm has achieved relatively ideal results. These researches provide valuable experience for the data processing and resource optimization of the dynamic platform distributed radar system.

[0004] Although the existing technology has made progress in fixed radar systems and single target observation, it faces serious challenges when faced with more complex scenarios in practical application, that is, multiple dynamic targets exist in the alert area at the same time. On the one hand, the loss of information in the low-bit quantization process may exacerbate the mutual interference between multiple targets, affecting the accuracy of target detection and the precision of parameter estimation; on the other hand, the algorithm designed for static targets and fixed radar systems cannot be directly applied to dynamic targets and distributed radar networks, resulting in poor adaptability of the algorithm and failing to meet the actual needs in complex and variable scenarios. Therefore, how to improve the detection and parameter estimation capability of the dynamic platform distributed radar system for multiple dynamic targets while maintaining high efficient use of system resources has become a major problem in current research. SUMMARY

[0005] In order to solve the above problems existing in the prior art, the present application provides a dynamic platform distributed radar dynamic target rapid detection method based on low-bit quantization.

[0006] The technical problem to be solved by the present application is achieved by the following technical solutions:

[0007] In a first aspect, the present application provides a low-bit quantization-based moving platform distributed radar moving target rapid detection method, comprising:

[0008] A moving platform distributed radar system model is constructed, and a low-bit quantized signal model is obtained based on the moving platform distributed radar system model;

[0009] A generalized likelihood ratio test (GLRT) detector of the moving platform distributed radar system is generated through the low-bit quantized signal model;

[0010] Based on the GLRT detector, joint estimation processing is performed using a differential evolution (DE) method and a batch gradient descent (BGDA) method to obtain state data; the state data includes radar cross section (RCS), target position, target speed, radar position, and radar speed.

[0011] Optionally, constructing a moving platform distributed radar system model and obtaining a low-bit quantized signal model based on the moving platform distributed radar system model comprises:

[0012] Obtaining a target reflection signal received by a radar; the target reflection signal is a linear frequency modulation signal, and the radar is a moving platform distributed radar;

[0013] Sampling and processing the target reflection signal to obtain a target reflection discrete signal;

[0014] Pulse accumulation is performed on the target reflection discrete signal to obtain a moving platform distributed radar system model;

[0015] Low-bit quantization processing is performed on the moving platform distributed radar system model to obtain a low-bit quantized signal model.

[0016] Optionally, the moving platform distributed radar system model is represented as:

[0017]

[0018] where Y l represents the sampling data of the lth radar, α la represents the true radar cross section (RCS) of the ath target observed by the lth radar, H la represents the fast time response component of the ath target observed by the lth radar, D la represents the Doppler component of the ath target observed by the lth radar, W l represents the additive noise corresponding to the lth radar, represents the total number of targets in the monitoring area;

[0019] The low-bit quantized signal model is represented as:

[0020]

[0021] represents the low-bit quantized signal of the lth radar corresponding to the quantization bit number q, represents the low-bit quantizer when the input is Y l .

[0022] Optionally, the generalized likelihood ratio test (GLRT) detector of the moving platform distributed radar system is generated by the low-bit quantized signal model, comprising:

[0023] Under the low-bit quantized signal model, a binary hypothesis test corresponding to the low-bit quantized signal model is constructed according to the binary hypothesis test principle;

[0024] The GLRT detector is constructed by the binary hypothesis test.

[0025] Optionally, the binary hypothesis test is represented as:

[0026]

[0027] H0 represents that no target appears in the detection unit, and H1 represents that a target appears in the detection unit, represents the low-bit quantized signal of the lth radar corresponding to the quantization bit number q; represents the low-bit quantizer when the input is W l , W l represents the additive noise corresponding to the lth radar; represents the low-bit quantizer when the input is a la H la D la +W l , a la represents the real radar cross section (RCS) of the ath target observed by the lth radar, H la represents the fast time response component of the ath target observed by the lth radar, D la represents the Doppler component of the ath target observed by the lth radar, W l represents the additive noise corresponding to the lth radar.

[0028] Optionally, the GLRT detector is represented as:

[0029]

[0030] represents the low-bit quantized signal of the ath target observed by the lth radar corresponding to the quantization bit number q The corresponding GLRT detection statistics, p represents the pth radar pulse, i represents a real natural number, j represents a virtual natural number, i, j = 1, …, 2 q , k represents the kth sampling point number of the target reflection discrete signal, represents The corresponding real part, represents The corresponding imaginary part, γ Λ represents a preset detection threshold; L represents the total number of radars, P represents the total number of radar pulses of the lth radar, and K represents the total number of samples of the target reflection discrete signal.

[0031] Optionally, based on the GLRT detector, a differential evolution method DE and a batch gradient descent method BGDA are used for joint estimation processing to obtain state data, including:

[0032] A joint estimation optimization model is constructed based on the GLRT detector;

[0033] An initial value of a radar scattering cross-sectional area is obtained, and a DE algorithm is used to estimate and process a low-bit quantized signal in the GLRT detector to obtain a first related parameter estimate; the first related parameter estimate includes: a first target position, a first target speed, a first radar position, and a first radar speed;

[0034] Using the first related parameter estimate, a BGDA algorithm is used to optimize and process the initial value of the radar scattering cross-sectional area to obtain a first radar scattering cross-sectional area;

[0035] The first radar scattering cross-sectional area and the first related parameter estimate are jointly optimized based on the joint estimation optimization model to obtain state data.

[0036] Optionally, a joint estimation optimization model is constructed based on the GLRT detector, including:

[0037] The joint estimation optimization model is constructed based on the GLRT detection statistics in the GLRT detector;

[0038] The joint estimation optimization model is represented as:

[0039]

[0040] Where Δ represents the finally generated state data, represents contains and The corresponding GLRT detection statistics, represents a low-bit quantized signal corresponding to the lth radar when the quantization bit number is q, represents the initial position of the ath target in , denotes the initial position of the lth radar in the coordinate system, denotes the velocity of the ath target in the coordinate system, denotes the velocity of the lth radar in the coordinate system, denotes the radar cross section of the lth radar in the coordinate system, denotes the low-bit quantized signal corresponding to the lth radar when the quantization bit number is q.

[0041] In a second aspect, the present application provides a low-bit quantization-based fast moving target detection device for a moving platform distributed radar, which comprises a model construction unit, a detector generation unit and a state estimation unit.

[0042] The model construction unit is configured to construct a moving platform distributed radar system model and obtain a low-bit quantized signal model based on the moving platform distributed radar system model.

[0043] The detector generation unit is configured to generate a generalized likelihood ratio test (GLRT) detector for the moving platform distributed radar system based on the low-bit quantized signal model.

[0044] The state estimation unit is configured to perform joint estimation processing on the GLRT detector using a differential evolution (DE) method and a batch gradient descent algorithm (BGDA) to obtain state data, which includes radar cross section (RCS), target position, target velocity, radar position and radar velocity.

[0045] In a third aspect, the present application provides a low-bit quantization-based fast moving target detection device for a moving platform distributed radar, which comprises a processor, a storage medium and a bus. The storage medium stores machine-readable instructions executable by the processor. When the low-bit quantization-based fast moving target detection device for a moving platform distributed radar is running, the processor communicates with the storage medium through the bus. The processor executes the machine-readable instructions to perform the steps of the low-bit quantization-based fast moving target detection method for a moving platform distributed radar according to the first aspect.

[0046] ​​​​The application provides a moving platform distributed radar moving target rapid detection method, device and equipment. The moving platform distributed radar moving target rapid detection method comprises the following steps: a moving platform distributed radar system model is constructed, and a low-bit quantized signal model is obtained based on the moving platform distributed radar system model; a generalized likelihood ratio test (GLRT) detector of the moving platform distributed radar system is generated through the low-bit quantized signal model; joint estimation processing is performed on the GLRT detector by using a differential evolution (DE) method and a batch gradient descent (BGDA) method to obtain state data; the state data comprises a radar cross section (RCS), a target position, a target speed, a radar position and a radar speed. In the application, firstly, a low-bit quantized signal model strategy is proposed for a moving platform distributed radar system resource limited scene, which greatly reduces the overall resource consumption of the system; in addition, joint estimation processing is performed on the GLRT detector by using the DE method and the BGDA method, which improves the accuracy and convergence speed of the moving platform distributed radar system for target parameter estimation in a dynamically changing environment. Specifically, the BGDA finds the optimal solution by minimizing the cost function through an iterative process, while the DE uses the differences between individuals in the population to perform mutation and crossover operations to explore the solution space. The BGDA and DE algorithms are combined for state data estimation, which can fully utilize the efficiency of the BGDA in local search and the robustness of the DE in global search. Therefore, based on the method of the application, the accuracy and speed of parameter estimation of the moving platform distributed radar system are improved, and the resource consumption of the moving platform distributed radar system is reduced.

[0047] The application will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 A flowchart of a low-bit quantization based moving platform distributed radar moving target rapid detection method provided by the application is shown;

[0049] Figure 2 An example of a process diagram for determining a low-bit quantized signal model based on a moving platform distributed radar system model is shown;

[0050] Figure 3 An example of a simulation scene of each node and target position of the moving platform distributed radar system is shown;

[0051] Figure 4 An example of a detection performance curve corresponding to the average detection probability of multiple targets is shown;

[0052] Figure 5 An example of a root mean square error curve corresponding to the multiple target speeds determined by the basic method of the application is shown;

[0053] Figure 6 A root mean square error curve corresponding to a plurality of target positions determined by the basic method of the application is exemplarily shown;

[0054] Figure 7 A structural schematic diagram of a low-bit quantization-based moving platform distributed radar moving target rapid detection device provided for an embodiment of the application is shown in FIG. 1.

[0055] Figure 8 A structural schematic diagram of a low-bit quantization-based moving platform distributed radar moving target rapid detection device provided for an embodiment of the application is shown in FIG. 1. DETAILED DESCRIPTION

[0056] The application will be further described in detail below with reference to specific embodiments, but the embodiments of the application are not limited thereto.

[0057] In order to improve the accuracy and speed of parameter estimation of a moving platform distributed radar system and reduce resource consumption of the moving platform distributed radar system, an embodiment of the application provides a low-bit quantization-based moving platform distributed radar moving target rapid detection method. Figure 1 A flowchart of a low-bit quantization-based moving platform distributed radar moving target rapid detection method provided for an embodiment of the application is shown in FIG. 2. Figure 1 As shown in the figure, the low-bit quantization-based moving platform distributed radar moving target rapid detection method includes the following steps.

[0058] S101, a moving platform distributed radar system model is constructed, and a low-bit quantized signal model is obtained based on the moving platform distributed radar system model.

[0059] Optionally, S101 can specifically include the following steps.

[0060] A target reflection signal received by a radar is acquired; the target reflection signal is a linear frequency modulation signal, and the radar is a moving platform distributed radar;

[0061] The target reflection signal is sampled and processed to obtain a target reflection discrete signal;

[0062] The target reflection discrete signal is pulse accumulated to obtain the moving platform distributed radar system model;

[0063] The moving platform distributed radar system model is low-bit quantized to obtain the low-bit quantized signal model.

[0064] Optionally, the moving platform distributed radar system model is expressed as:

[0065]

[0066] Y = HX + N, wherein Y represents a target reflection signal, H represents a channel matrix of the moving platform distributed radar system, X represents a target reflection signal matrix, and N represents a noise matrix. lα represents the sampled data of the l-th radar. la H represents the true radar cross section (RCS) of the a-th target when observed by the l-th radar. la D represents the fast time response component when the a-th target is observed by the l-th radar. la W represents the Doppler component when the a-th target is observed by the l-th radar. l This represents the additive noise corresponding to the l-th radar. Indicates the total number of targets in the monitored area;

[0067] The signal model after low-bit quantization is represented as:

[0068]

[0069] This represents the low-bit quantized signal corresponding to the l-th radar when the quantization bit depth is q. This indicates that the input is Y. l A low-bit quantizer for time, where q∈{1,2,3}.

[0070] It should be noted that the low-bit quantizer It can be represented as:

[0071]

[0072] in, This represents a low-bit quantizer when the input is a complex variable x. Let x represent the real part of the complex variable x. Represents the imaginary part of the complex variable x. Represents the imaginary unit. This indicates that the input is x. R Low-bit quantizer at that time This indicates that the input is x. I The low-bit quantizer is used when the complex variable x is in the context of the time-varying quantizer. In this embodiment, the complex variable x can specifically be Y. l .

[0073] Additionally, when the low-bit quantizer When the input is a complex variable x, x can be represented as follows after being quantized by the quantizer:

[0074]

[0075] in, This represents the quantization result of the complex variable x when the quantization bit depth is q. The input x is given by a set of monotonically increasing 2. q +1 quantization threshold In comparison, the output b j and b jThe real part and the imaginary part of the q-bit binary can be represented as a real natural number i and an imaginary natural number j, respectively, wherein, is a real part quantization threshold of i, is an imaginary part quantization threshold of j, x R represents the real part of a complex variable x, x I represents the imaginary part of a complex variable x.

[0076] In addition, Figure 2 An exemplary process diagram for determining a low-bit quantized signal model based on a moving platform distributed radar system model is shown. As Figure 2 shown, it is assumed that the moving platform distributed radar system is composed of L radars in total, wherein the movement speed of the lth radar is V l , and there are targets in the monitoring area, wherein the speed of the ath target is V a First, the speed data of each target is sampled to obtain a target reflection discrete signal; then, the target reflection discrete signal is pulse accumulated to obtain a moving platform distributed radar system model (the radars in the moving platform distributed radar system model are moving); finally, the moving platform distributed radar system model is low-bit quantized (the quantization process here includes: 1-bit quantization, 2-bit quantization, and 3-bit quantization) to obtain a low-bit quantized signal model. Based on the low-bit quantized signal model, a binary hypothesis test (H0\H1) corresponding to the low-bit quantized signal model is obtained according to the binary hypothesis test principle. In the moving platform distributed radar system model, the true initial position and the true movement speed of the lth radar are denoted as Θ l (0) = [X l , Y l ] Τ and V l = [v lX , v lY ] Τ , respectively, wherein l = 1, …, L. X l , Y l represent the transverse initial position of the lth radar and the longitudinal initial position of the lth radar, respectively. v lX , v lY represent the transverse initial speed of the lth radar and the longitudinal initial speed of the lth radar, respectively, and T represents the transposition process.

[0077] In addition, in this embodiment, the transmission signal s l (t) of the lth radar can be represented as:

[0078]

[0079] wherein t represents a fast time variable, E represents the energy of the transmission signal, and fl c denotes the carrier frequency of the lth radar, μ denotes the slope, denotes the initial phase of the transmitted signal of the lth radar, denotes the imaginary unit.

[0080] According to the radar range equation, for the pth pulse, the lth radar receives the signal s lpa (t) is denoted as:

[0081]

[0082] where, α la denotes the true radar cross section (RCS) of the ath target when observed by the lth radar, G la denotes the propagation gain of the ath target when observed by the lth radar, E denotes the transmitted signal energy, T denotes the pulse width, τ lpa denotes the signal propagation delay of the ath target when observed by the pth pulse of the lth radar, f l c denotes the carrier frequency of the lth radar, μ denotes the slope, denotes the initial phase of the transmitted signal of the lth radar, denotes the imaginary unit, exp denotes the exponential function.

[0083] where, τ lpa can be denoted as:

[0084]

[0085] c denotes the speed of light, ||·||2 represents the 2-norm of a vector, Θ l (p) is the true position of the lth radar when transmitting the pth pulse, Θ a (p) is the true position of the ath target at the pth pulse.

[0086] It is assumed that the approximate motion models of all targets are uniform linear motion, and the target motion error Δ v obeys the same distribution, denoted as The motion variance of the target is The motion equation of the ath target is:

[0087] Θ a (p+1) = Θ a (p) + V a T + Δ v ;

[0088] where, Θ a (p+1) is the true position of the ath radar at the pth+1 pulse, Θ l(p) represents the actual position of the l-th radar when the p-th pulse is transmitted, V a Let Δ represent the true velocity of the a-th target. v This represents the target motion error.

[0089] For s lpa (t) is stretched to obtain the following low-frequency signal:

[0090]

[0091] Among them, y lp (t) represents s lpa (t) The low-frequency reflected signal after broadening, s lpa (t) represents the signal received by the l-th radar from itself and reflected by the a-th target, w lp (t) represents the additive noise received by the l-th radar under the p-th radar pulse.

[0092] For y lp (t) is sampled and processed to obtain the discrete data y of the l-th radar. lp The fast time response discrete component h when the a-th target is observed by the p-th radar pulse of the l-th radar lpa The discrete value of additive noise w corresponding to the l-th radar under the p-th radar pulse. lp The Doppler discrete component d when the a-th target is observed by the p-th pulse of the l-th radar lpa .

[0093] By accumulating the above data into pulses, we can obtain the dynamic platform distributed radar system model Y. l Y l It can characterize each channel The vector sum of all reflected signals and additive noise from a target. This invention assumes that all discrete values ​​of additive noise w lp It can be modeled as an independent and identically distributed circularly symmetric complex Gaussian variable.

[0094] in addition, Figure 3 An illustrative simulation scenario of the nodes and target positions of a dynamic platform distributed radar system is shown. For example... Figure 3 As shown, blue represents radar nodes, red represents targets, and arrows indicate the direction of the speed of movement of radar nodes or targets.

[0095] S102. Generate a generalized likelihood ratio test (GLRT) detector for a dynamic platform distributed radar system by generating a signal model after low-bit quantization.

[0096] Optionally, S102 may specifically include:

[0097] Under the low-bit quantized signal model, a binary hypothesis test principle is used to construct a binary hypothesis test corresponding to the low-bit quantized signal model.

[0098] The GLRT detector is constructed by using the binary hypothesis test.

[0099] Optionally, the binary hypothesis test is represented as:

[0100]

[0101] H0 represents that no target appears in the detection unit, and H1 represents that a target appears in the detection unit, represents the low-bit quantized signal of the lth radar corresponding to the quantization bit number q; represents the low-bit quantizer when the input is W l , W l represents the additive noise corresponding to the lth radar; represents the low-bit quantizer when the input is α la H la D la +W l , and α la represents the true radar cross section (RCS) when the ath target is observed by the lth radar, H la represents the fast-time response component when the ath target is observed by the lth radar, D la represents the Doppler component when the ath target is observed by the lth radar, W l represents the additive noise corresponding to the lth radar.

[0102] Optionally, the GLRT detector is represented as:

[0103]

[0104] represents the low-bit quantized signal of the ath target observed by the lth radar when the quantization bit number is q , p represents the pth radar pulse, i represents a real natural number, j represents an imaginary natural number, i, j = 1, …, 2 q , k represents the kth sampling point number of the target reflection discrete signal, represents the corresponding real part, represents the corresponding imaginary part, γ Λ represents a preset detection threshold; L represents the total number of radars, P represents the total number of radar pulses of the lth radar, and K represents the total number of samplings of the target reflection discrete signal.

[0105] wherein, and The correspondence can be represented as:

[0106]

[0107]

[0108] Indicates signal The real part of the probability mass function under the H1 hypothesis, F i (0) represents the real part of the probability quality function of the low-bit quantized signal under the H0 assumption, F j (0) represents the imaginary part of the probability quality function of the signal after low-bit quantization under the H0 assumption. express The imaginary part of the probability mass function under the H1 hypothesis. This represents the low-bit quantized estimate of the signal when the a-th target is observed by the p-th radar pulse of the l-th radar. The real part, This represents the low-bit quantized estimate of the signal when the a-th target is observed by the p-th radar pulse of the l-th radar. The imaginary part of , k represents the number of the kth sampling point of the discrete signal reflected by the target.

[0109] Correspondingly, for the a-th objective, Under the binary assumptions, the probability mass function (PMF) can be expressed as follows:

[0110]

[0111] in, This represents the low-bit quantized signal of the l-th radar observation. The probability mass function under the H1 hypothesis, This represents the low-bit quantized signal of the l-th radar observation. The probability mass function under the H0 hypothesis express The real part of the probability mass function, Indicates signal The imaginary part of the probability mass function, θ lpa [k] represents the true value of the low-bit quantized signal when the a-th target is observed by the p-th radar pulse of the l-th radar. Represents θ lpa The real part of [k] Represents θ lpa The imaginary part of [k]. F i (0) represents the real part of the probability quality function of the low-bit quantized signal under the H0 assumption, F j (0) represents the imaginary part of the probability quality function of the signal after low-bit quantization under the H0 assumption.

[0112] discrete signal components in h and can be expressed as:

[0113]

[0114] where, represents the real part of the low-bit quantizer output when the input is la h lpa [k]d lpa , represents the imaginary part of the low-bit quantizer output when the input is la h lpa [k]d lpa , lpa [k] represents the kth sample point in h lpa , lpa represents the fast-time response discrete component when the ath target is observed by the lth radar, lpa represents the Doppler discrete component when the ath target is observed by the pth pulse of the lth radar.

[0115] In addition, can be defined according to the following formula:

[0116]

[0117] where, F i (u R ) represents the real part of the probability mass function of the signal u R , is the real part quantization threshold of i, is the imaginary part quantization threshold of j, is the real part quantization threshold of i-1, u R represents the real part of the input signal u, t represents the fast-time variable, and pT<t≤(p+1)T, represents the noise variance.

[0118] According to the binary hypothesis of the probability mass function PMF corresponding to the ath target, the log of the likelihood function is:

[0119]

[0120]

[0121] where, represents the log of the PMF corresponding to the ath target under the H1 hypothesis according to the low-bit quantized signal , ​denotes the real part of the probability mass function of the low-bit quantized signal under the H0 hypothesis, F denotes the log of the PMF under the H0 hypothesis, F i (0) denotes the real part of the probability mass function of the low-bit quantized signal under the H0 hypothesis, F j (0) denotes the imaginary part of the probability mass function of the low-bit quantized signal under the H0 hypothesis.

[0122] The first-order partial derivative of the unknown parameter, the true radar cross section α la of the a-th target observed by the l-th radar, can be respectively denoted as the partial derivative of the real part la and the imaginary part of α , since is a two-dimensional convex function of the unknown variables and , the BGDA algorithm can be used to quickly solve , and obtain as the final estimate of α la .

[0123] The above derivation is for a target with known position and velocity. If the possible states of all targets in space are checked, i.e., the multi-channel time delay and Doppler frequency shift are unknown, the estimate of is calculated using the DE algorithm.

[0124] S103, based on the GLRT detector, joint estimation processing is performed using the differential evolution method DE and the batch gradient descent method BGDA to obtain state data.

[0125] The state data includes: radar cross section RCS, target position, target speed, radar position, and radar speed.

[0126] Optionally, S103 can specifically include:

[0127] Constructing a joint estimation optimization model based on the GLRT detector;

[0128] Obtaining an initial value of the radar cross section, performing estimation processing on the low-bit quantized signal in the GLRT detector using the DE algorithm to obtain a first related parameter estimate; the first related parameter estimate includes: a first target position, a first target speed, a first radar position, and a first radar speed;

[0129] Using the first related parameter estimate, performing optimization processing on the initial value of the radar cross section using the BGDA algorithm to obtain a first radar cross section;

[0130] Performing joint optimization processing on the first radar cross section and the first related parameter estimate based on the joint estimation optimization model to obtain state data.

[0131] First, in this embodiment, with The representation is similar, let the given l-th radar be... Doppler frequency shift in and Radar cross section of the l-th radar The corresponding candidate detection statistic is a two-dimensional function as follows:

[0132]

[0133] express Includes as well as The corresponding GLRT detection statistics at that time express The Doppler frequency shift of the l-th radar in the middle, express The corresponding real part, express The corresponding imaginary part, express The radar cross section of the l-th radar.

[0134] The Derivative Algorithm (DE) is a stochastic search algorithm based on individual differences within a population. It aims to optimize individual fitness and is a multi-objective optimization algorithm used to find the global optimum in a multi-dimensional space. In the considered scenario, the equation... Unknown parameter Doppler frequency shift By Θ a (0), Θ l (0), V a and V l It was jointly determined, and based on this, it can be... Represented as:

[0135]

[0136] The true initial position and true velocity of the l-th radar are denoted as Θ. l (0) = [X l ,Y l ] T and V l =[v lx ,v ly ] T The true initial position and true velocity of the a-th target are respectively: Θ a (0) = [X a ,Y a ] T and V a =[vax ,v ay ] T , c represents the speed of light, f l c represents the carrier frequency of the lth radar, X l ,Y l represent the lateral and longitudinal true initial positions of the lth radar, respectively, v lx ,v ly represent the lateral and longitudinal true initial velocities of the lth radar, respectively, X a ,Y a represent the lateral and longitudinal true initial positions of the ath target, respectively, v ax ,v ay represent the lateral and longitudinal true initial velocities of the ath target, respectively, T represents the transpose of a matrix, thus, the detection statistic in equation can be further expressed as follows:

[0137]

[0138] wherein, represents contains and , the corresponding GLRT detection statistic, represents the initial position of the ath target in , the initial position corresponding to the lth radar in , the velocity of the ath target in , the velocity corresponding to the lth radar in , the radar cross section (RCS) of the lth radar in , the radar cross section (RCS) of the lth radar in , the radar cross section (RCS) of the lth radar in , the radar cross section (RCS) of the lth radar in , the radar cross section (RCS) of the lth radar in , the radar cross section (RCS) of the lth radar in all represent the corresponding data in the low-bit quantized signal of the lth radar when the quantization bit number is q, for the sake of simplicity of expression, it is to be noted that in the present embodiment

[0139] all represent the corresponding estimated data after optimization, (·) all represent the corresponding radar true observation data. Exemplarily, as described in the above embodiment, a la represents the true radar cross section (RCS) of the ath target when observed by the lth radar, represents the radar cross section (RCS) of the ath target when observed by the lth radar in the low-bit quantized signal , the radar cross section (RCS) of the ath target when observed by the lth radar in the low-bit quantized signal The final estimated value is obtained through optimization processing.

[0140] for The corresponding real part.

[0141] for The corresponding imaginary part.

[0142] Therefore, the detection statistic Defined as the objective function, the differential optimization (DE) algorithm is used to optimize the four variables (Θ) related to the Doppler frequency shift. a (0), Θ l (0), V a and V l To perform a search, among which, Batch gradient descent can be used for fast solution. Differential evolution includes steps such as population initialization, mutation, repair, crossover, and selection. The final state data Δ is obtained. Furthermore, in this embodiment, the vector dimension D = 4 in the differential evolution (DE) algorithm.

[0143] Optionally, a joint estimation optimization model is constructed based on the GLRT detector, including:

[0144] A joint estimation optimization model is constructed based on the GLRT detection statistics in the GLRT detector;

[0145] The joint estimation optimization model is expressed as:

[0146]

[0147] Where Δ represents the final generated state data, express Includes as well as The corresponding GLRT detection statistics at that time This represents the low-bit quantized signal corresponding to the l-th radar when the quantization bit depth is q. express The initial position of the a-th target. express The initial position corresponding to the l-th radar in the middle, express The speed of the a-th target in the middle, express The speed corresponding to the l-th radar in the middle, express The radar cross section of the l-th radar. This represents the low-bit quantized signal corresponding to the l-th radar when the quantization bit depth is q.

[0148] The embodiment of the present application provides a kind of dynamic platform distributed radar dynamic target rapid detection method, first for dynamic platform distributed radar system resource limited scene, the strategy of low bit quantization signal model is proposed, greatly reduce the overall resource consumption of system;In addition, on the basis of GLRT detector, differential evolution DE and batch gradient descent method BGDA are used for joint estimation processing, improve the accuracy and convergence speed of dynamic platform distributed radar system for target parameter estimation in dynamic environment.Specifically, since BGDA finds the optimal solution by minimizing the cost function through iteration process, while DE uses the difference between individuals in population to perform mutation and crossover operation, so as to explore the solution space.Combining BGDA and DE algorithm, state data estimation can fully utilize the efficiency of BGDA in local search and the robustness of DE in global search.Therefore, based on the method of the present application, the accuracy and speed of parameter estimation of dynamic platform distributed radar system are improved, and the resource consumption of dynamic platform distributed radar system is reduced.

[0149] To verify the effectiveness of the low-bit quantization-based dynamic platform distributed radar dynamic target rapid detection method provided by the embodiment of the present application, simulation experiments are also carried out as follows:

[0150] The number of nodes (radars) of the dynamic platform distributed radar system is L=8. The speed direction and position of each node are set as random numbers satisfying the boundary restriction in this experiment. There are moving targets, and the initial positions of the targets are Θ1(0)=[15,12] T km, Θ2(0)=[10,10] T km and Θ3(0)=[5,15] T km, and the corresponding speeds are V1=[40,-35] T m / s, V2=[-40,25] T m / s and V3=[45,15] T m / s. The simulation scene of the positions of each node and target is shown in Figure 3 , where the corresponding arrows represent the respective vector speeds.

[0151] As shown in Figures 4-6 , the statistical performance of the method is verified by the detection performance curve and the root mean square error (RMSE) curve. For intuitive display, 2-bit quantization data is used for corresponding detection and estimation simulation in this experiment. Figure 4 The detection performance curve corresponding to the average detection probability of multiple targets is exemplarily shown, Figure 5 The root mean square error curve of the speeds of multiple targets determined by the basic method of the present application is exemplarily shown, Figure 6The root mean square error curve of a plurality of target positions determined by the basic method of the application is exemplarily shown. Figures 4-6 As can be seen from the results shown in Table 1, the output of the detector is basically consistent with the simulation setting, that is, all targets in the multi-target scene can be robustly detected, and the detection capability is improved with the improvement of the signal-to-noise ratio.

[0152] The low-bit quantization data detection algorithm provided by the application still maintains excellent detection performance under the condition of greatly reducing the data amount, and the algorithm based on DE and BGDA has a greatly reduced calculation amount compared with the traditional state traversal algorithm, faster convergence speed, and better detection performance.

[0153] The method provided by the embodiment of the application can be applied to an electronic device.

[0154] Based on the same inventive concept, the embodiment of the application further provides a low-bit quantization-based moving platform distributed radar moving target rapid detection device. Figure 7 A structure schematic diagram of the low-bit quantization-based moving platform distributed radar moving target rapid detection device provided by the embodiment of the application is shown in FIG. 1. Figure 7 As shown in FIG. 1, the low-bit quantization-based moving platform distributed radar moving target rapid detection device includes a model construction unit 601, a detector generation unit 602, and a state estimation unit 603.

[0155] The model construction unit 601 is configured to construct a moving platform distributed radar system model and obtain a low-bit quantized signal model based on the moving platform distributed radar system model.

[0156] The detector generation unit 602 is configured to generate a generalized likelihood ratio test (GLRT) detector of the moving platform distributed radar system through the low-bit quantized signal model.

[0157] The state estimation unit 603 is configured to perform joint estimation processing on the GLRT detector by using a differential evolution (DE) method and a batch gradient descent (BGDA) method to obtain state data.

[0158] Figure 8A structural diagram of a low-bit quantization-based moving platform distributed radar moving target rapid detection device provided by an embodiment of the present application includes a processor 710, a storage medium 720, and a bus 730. The storage medium 720 stores machine-readable instructions executable by the processor 710. When the low-bit quantization-based moving platform distributed radar moving target rapid detection device is running, the processor 710 communicates with the storage medium 720 through the bus 730. The processor 710 executes the machine-readable instructions to perform the steps of the above method embodiments. The specific implementation manners and technical effects are similar, and will not be described here again.

[0159] The storage medium can include a random access memory (RAM) and can also include a non-volatile memory (NVM), such as at least one disk memory. Optionally, the storage medium can also be at least one storage device located away from the aforementioned processor.

[0160] The processor described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc. It can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0161] It should be noted that the terms "first", "second", and the like are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The implementation described in the following exemplary embodiments does not represent all implementations consistent with the present application. Rather, they are merely examples of devices and methods consistent with some aspects of the present application.

[0162] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the description of the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in the specification.

[0163] Although the present application is described herein in conjunction with various embodiments, those skilled in the art, with reference to the appended drawings and the disclosure, can understand and implement other variations of the disclosed embodiments in implementing the claimed application. In the description of the present application, the word "comprising" does not exclude other components or steps, "a" or "one" does not exclude a plurality, and "plurality" means two or more, unless otherwise expressly specified. In addition, some measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0164] The above is a further detailed description of the present application in conjunction with specific preferred embodiments, and cannot be considered as limiting the specific implementation of the present application to these descriptions. For those skilled in the art, without departing from the concept of the present application, a number of simple deductions or substitutions can be made, which should be considered as falling within the scope of protection of the present application.

Claims

1. A method for rapid detection of moving targets using a distributed radar system on a moving platform based on low-bit quantization, characterized in that, include: A dynamic platform distributed radar system model is constructed, and a low-bit quantized signal model is obtained based on the dynamic platform distributed radar system model; The generalized likelihood ratio test (GLRT) detector for the dynamic platform distributed radar system is generated by the low-bit quantized signal model. Based on the GLRT detector, the differential evolution method (DE) and the batch gradient descent method (BGDA) are used for joint estimation to obtain state data. The state data includes: radar cross section (RCS), target position, target velocity, radar position, and radar velocity. Specifically, based on the GLRT detector, the state data obtained by joint estimation using differential evolution (DE) and batch gradient descent (BGDA) methods includes: A joint estimation optimization model is constructed based on the GLRT detector; An initial value of the radar cross section is obtained, and the low-bit quantized signal in the GLRT detector is estimated using the DE algorithm to obtain a first correlation parameter estimate; the first correlation parameter estimate includes: first target position, first target velocity, first radar position, and first radar velocity; Using the first relevant parameter estimate, the initial value of the radar cross section is optimized using the BGDA algorithm to obtain the first radar cross section; Based on the joint estimation optimization model, the first radar cross section and the first relevant parameter estimate are jointly optimized to obtain the state data; The construction of the joint estimation optimization model based on the GLRT detector includes: The joint estimation optimization model is constructed based on the GLRT detection statistics in the GLRT detector. The joint estimation optimization model is expressed as follows: ； in, This represents the final generated state data. express Includes , , , as well as The corresponding GLRT detection statistics at that time Indicates the number of quantization bits. Time The low-bit quantized signal corresponding to each radar express The Middle The initial position of each target express The Middle The initial position corresponding to each radar express The Middle The speed of the target express The Middle The speed corresponding to each radar express The Middle The radar cross section of each radar. Indicates the number of quantization bits. Time The low-bit quantized signal corresponding to each radar.

2. The method for rapid detection of moving targets by distributed radar on a moving platform based on low-bit quantization according to claim 1, characterized in that, The construction of the dynamic platform distributed radar system model, and the obtaining of the low-bit quantized signal model based on the dynamic platform distributed radar system model, includes: Acquire the target reflection signal received by the radar; the target reflection signal is a linear frequency modulated signal, and the radar is a moving platform distributed radar; The target reflection signal is sampled and processed to obtain a discrete target reflection signal; The discrete signal reflected from the target is accumulated by pulses to obtain the model of the dynamic platform distributed radar system. The dynamic platform distributed radar system model is subjected to low-bit quantization processing to obtain the low-bit quantized signal model.

3. The method for rapid detection of moving targets by distributed radar on a moving platform based on low-bit quantization according to claim 2, characterized in that, The model of the dynamic platform distributed radar system is represented as follows: ； in, Indicates the first The sampling data of each radar Indicates the first The first goal was the The actual radar cross section (RCS) during radar observation. Indicates the first The first goal was the The fast time response component during radar observation Indicates the first The first goal was the Doppler components during radar observation Indicates the first Additive noise corresponding to each radar, Indicates the total number of targets in the monitored area; The low-bit quantized signal model is represented as follows: ； Indicates the number of quantization bits. Time The low-bit quantized signal corresponding to each radar Indicates that the input is Low-bit quantizer.

4. The method for rapid detection of moving targets by distributed radar on a moving platform based on low-bit quantization according to claim 2, characterized in that, The generalized likelihood ratio test (GLRT) detector for the dynamic platform distributed radar system generated through the low-bit quantized signal model includes: Under the low-bit quantized signal model, a binary hypothesis test corresponding to the low-bit quantized signal model is constructed based on the binary hypothesis testing principle. The GLRT detector is constructed using the binary hypothesis test.

5. The method for rapid detection of moving targets by distributed radar on a moving platform based on low-bit quantization according to claim 4, characterized in that, The binary hypothesis test is expressed as follows: ； This indicates that no target was detected in the detection unit. This indicates that a target has been detected in the detection unit. Indicates the number of quantization bits. Time The low-bit quantized signal corresponding to each radar. Indicates that the input is Low-bit quantizer at that time Indicates the first Additive noise corresponding to each radar; Indicates that the input is Low-bit quantizer at that time Indicates the first The first goal was the The actual radar cross section (RCS) during radar observation. Indicates the first The first goal was the The fast time response component during radar observation Indicates the first The first goal was the Doppler components during radar observation Indicates the first Additive noise corresponding to each radar.

6. The method for rapid detection of moving targets by distributed radar on a moving platform based on low-bit quantization according to claim 5, characterized in that, The GLRT detector is represented as: ； Indicates the number of quantization bits. Time The radar observation of the first The low-bit quantized signal of the target The corresponding GLRT detection statistics, Indicates the first One radar pulse, Represents natural numbers with real parts. represents an imaginary natural number, , The first discrete signal reflected by the target represents the... Number of sampling points express The corresponding real part, express The corresponding imaginary part, Indicates the preset detection threshold; Indicates the total number of radars. Indicates the first The total number of radar pulses for each radar. This represents the total number of samples of the discrete signal reflected by the target.

7. A rapid moving target detection device for a distributed radar platform based on low-bit quantization, characterized in that, The dynamic platform distributed radar moving target rapid detection device based on low-bit quantization includes: a model building unit, a detector generation unit, and a state estimation unit; The model building unit is used to: build a dynamic platform distributed radar system model, and obtain a low-bit quantized signal model based on the dynamic platform distributed radar system model; The detector generation unit is used to: generate a generalized likelihood ratio test (GLRT) detector for a dynamic platform distributed radar system based on the low-bit quantized signal model. The state estimation unit is used to: perform joint estimation processing using the differential evolution method (DE) and the batch gradient descent method (BGDA) based on the GLRT detector to obtain state data; the state data includes: radar cross section (RCS), target position, target velocity, radar position, and radar velocity; Specifically, based on the GLRT detector, the state data obtained by joint estimation using differential evolution (DE) and batch gradient descent (BGDA) methods includes: A joint estimation optimization model is constructed based on the GLRT detector; An initial value of the radar cross section is obtained, and the low-bit quantized signal in the GLRT detector is estimated using the DE algorithm to obtain a first correlation parameter estimate; the first correlation parameter estimate includes: first target position, first target velocity, first radar position, and first radar velocity; Using the first relevant parameter estimate, the initial value of the radar cross section is optimized using the BGDA algorithm to obtain the first radar cross section; Based on the joint estimation optimization model, the first radar cross section and the first relevant parameter estimate are jointly optimized to obtain the state data; The construction of the joint estimation optimization model based on the GLRT detector includes: The joint estimation optimization model is constructed based on the GLRT detection statistics in the GLRT detector. The joint estimation optimization model is expressed as follows: ； in, This represents the final generated state data. express Includes , , , as well as The corresponding GLRT detection statistics at that time Indicates the number of quantization bits. Time The low-bit quantized signal corresponding to each radar express The Middle The initial position of each target express The Middle The initial position corresponding to each radar express The Middle The speed of the target express The Middle The speed corresponding to each radar express The Middle The radar cross section of each radar. Indicates the number of quantization bits. Time The low-bit quantized signal corresponding to each radar.

8. A rapid moving target detection device for a distributed radar platform based on low-bit quantization, characterized in that, include: The device includes a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the low-bit quantization-based distributed radar moving target rapid detection device is running, the processor communicates with the storage medium via the bus. The processor executes the machine-readable instructions to perform the steps of the low-bit quantization-based distributed radar moving target rapid detection method as described in any one of claims 1-6.

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