CN114895291A - Radar system configuration method and device based on fuzzy function local optimization - Google Patents
Radar system configuration method and device based on fuzzy function local optimization Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/28—Details of pulse systems
- G01S7/282—Transmitters
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/28—Details of pulse systems
- G01S7/285—Receivers
- G01S7/292—Extracting wanted echo-signals
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
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Abstract
The application relates to a radar system configuration method and device based on fuzzy function local optimization. By constructing a transmission waveform sequence and a reception filter bank according to the code length of a transmission waveform determined by the duration and bandwidth of a signal transmitted from the radar system, the receiving filter adopts a matched filter, simultaneously obtains the Doppler tolerance range and the signal-to-noise ratio loss threshold of the radar, constructs an optimization model of a transmitting waveform and a receiving filter, solves the optimization model to obtain an optimal transmitting waveform and an optimal receiving filter with ideal local fuzzy functions for configuring the radar system, and has higher flexibility compared with the existing method for designing the transmitting waveform and the receiving filter with ideal local AF.
Description
Technical Field
The application relates to the technical field of radar detection, in particular to a radar system configuration method and device based on fuzzy function local optimization.
Background
The Ambiguity Function (AF) plays an important role in pulse compression radar systems, often used to evaluate range, doppler resolution and interference suppression capability, while AF also represents the range-doppler response of the receive filter with respect to targets of different time delays and doppler shifts. The ideal AF shape should be a two-dimensional "pin" function, with the remaining side lobes being zero except at the origin in the range-doppler plane. However, since AF has volume invariance, i.e., the volume of the blur function is fixed, it is only energy dependent. It is therefore almost impossible to make the side lobes of all areas of AF at low levels by changing the signal form under the conditions of signal energy determination.
Currently, research on the design of local fuzzy functions can be divided into three major categories. The first is to design the transmission sequence based on a matched filter scheme, and f.arlery et al propose an Effective Gradient Descent (EGD) algorithm based on Fast Fourier Transform (FFT) operation to suppress the weighted side lobe energy of the single-mode sequence AF; the second type is to design a receiving filter for a fixed transmitting sequence to realize a desired AF shape, to suppress the Integral Side Lobe (ISL) or Peak Side Lobe (PSL) of AF, and to find the optimal receiving filter under the constraint of signal-to-noise ratio (SNR) loss, the performance mainly depends on the characteristics of the initialized transmitting sequence; a third method is to jointly design the transmit sequence and the receive filter, and He et al propose a round robin optimization (CO) algorithm to jointly design a pair of sequence and receive filter with the desired AF shape, since the joint design provides more freedom for local AF plasticity.
However, the above local fuzzy function design method still has the problems of low algorithm efficiency, uncontrollable loss of signal-to-noise ratio, not ideal fuzzy function and the like.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a radar system configuration method and apparatus based on fuzzy function local optimization.
A method for radar system configuration based on fuzzy function local optimization, the method comprising:
determining the code length of a transmitting waveform according to the time length and the bandwidth of a signal transmitted by a radar system, and constructing a transmitting waveform sequence and a receiving filter bank according to the code length; the receiving filter in the receiving filter bank is a non-matched filter;
obtaining a Doppler tolerance range and a signal-to-noise ratio loss threshold of a radar system;
according to the Doppler tolerance range, constructing an optimization model of a transmitting waveform and a receiving filter under the constraint of the signal-to-noise ratio loss threshold;
solving the optimization model to obtain an ideal optimal transmitting waveform and an optimal receiving filter of a local fuzzy function;
and configuring a radar system according to the optimal transmitting waveform and the optimal receiving filter.
In one embodiment, determining the code length of the transmitted waveform according to the duration and bandwidth of the signal transmitted by the radar system comprises:
determining the code length of a transmitting waveform according to the time length and the bandwidth of a signal transmitted by a radar system:
N＝B×T
wherein, N is the code length of the emission waveform, B is the emission signal bandwidth of the radar system, and T is the emission signal duration of the radar system.
In one embodiment, constructing the transmit waveform sequence and the receive filter bank according to the code length comprises:
constructing a transmitting wave form sequence and a receiving filter group according to the code length:
x＝[x 1 x 2 …x N ] T ，x n ＝e jφ(n) ,n＝1,…,N φ(n)∈[0,2π]
h＝[h 1 h 2 …h N ] T
where x is the transmit waveform sequence, h is the receive filter bank, and phi (n) is the phase of the nth transmit waveform in the transmit waveform sequence.
In one embodiment, the obtaining the doppler tolerance range of the radar system includes:
obtaining a Doppler frequency shift interval of the radar system according to the detection target;
normalizing the Doppler frequency shift in the Doppler frequency shift interval to obtain a nominal Doppler frequency shift:
wherein, [ f ] 1 ，f 2 ]Is a nominal Doppler shift interval, f d Is the nominal doppler shift, f is the doppler shift;
forming a nominal Doppler frequency shift interval by the nominal Doppler frequency shift, and performing uniform discretization processing on the nominal Doppler frequency shift interval to obtain the Doppler tolerance range of the radar system:
f l ＝f l +(l-1)Δf,l＝1,...,L
wherein f is l The first discrete Doppler shift within the Doppler tolerance range is represented, and L is a preset value and represents the number of the discrete Doppler shifts.
In one embodiment, constructing an optimized model of the transmit waveform and the receive filter under the constraint of the maximum signal-to-noise ratio loss according to the maximum doppler tolerance interval includes:
obtaining the signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank;
constructing an objective function of an optimization model:
wherein the content of the first and second substances,
A x,h (n,f l )＝h H J N Diag[a(f l )]x
wherein, SNRL is the loss of SNR, mu is the loss threshold of SNR, n is the distance unit, i.e. the distance between the side lobe and the central position, w (n, f) l ) Representing the range bin n and the doppler shift f l ε is the pareto weight, Diag (a (f) l ) A (f) represents l ) The diagonal matrix of (a).
In one embodiment, obtaining the snr loss of the radar system according to the transmit waveform sequence and the receive filter bank includes:
and obtaining the signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank:
wherein | represents modulo operation, | | | | | | non-conducting phosphor 2 2-norm operation of a representation vector, (.) H Representing a conjugate operation.
In one embodiment, solving the optimization model to obtain an ideal transmit waveform and receive filter of the local ambiguity function includes:
obtaining a second objective function according to the initial objective function:
and obtaining a final objective function according to the second objective function by adopting an MM method:
wherein the content of the first and second substances,
constructing an alternate iteration minimization model according to the objective function:
s.t.h H h＝N f
wherein the content of the first and second substances,
solving the objective function according to the alternating iteration minimization model to obtain an optimal receiving filter and an optimal transmitting waveform:
a radar system configuration apparatus based on fuzzy function local optimization, the apparatus comprising:
the first construction module is used for determining the code length of a transmitted waveform according to the time length and the bandwidth of a signal transmitted by a radar system and constructing a transmitted waveform sequence and a receiving filter bank according to the code length; the receiving filter in the receiving filter bank is a non-matched filter;
the calculation module is used for obtaining the signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank;
the acquisition module is used for acquiring a Doppler tolerance range and a signal-to-noise ratio loss threshold of the radar system;
the second construction module is used for constructing an optimization model of a transmitting waveform and a receiving filter under the constraint of the signal-to-noise ratio loss threshold according to the Doppler tolerance range;
the solving module is used for solving the optimization model to obtain an optimal transmitting waveform and an optimal receiving filter of the local fuzzy function ideal;
and the configuration module is used for configuring the radar system according to the optimal transmitting waveform and the optimal receiving filter.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
determining the code length of a transmitting waveform according to the time length and the bandwidth of a signal transmitted by a radar system, and constructing a transmitting waveform sequence and a receiving filter bank according to the code length; the receiving filter in the receiving filter bank is a non-matched filter;
obtaining a Doppler tolerance range and a signal-to-noise ratio loss threshold of a radar system;
according to the Doppler tolerance range, constructing an optimization model of a transmitting waveform and a receiving filter under the constraint of the signal-to-noise ratio loss threshold;
solving the optimization model to obtain an ideal optimal transmitting waveform and an optimal receiving filter of a local fuzzy function;
and configuring a radar system according to the optimal transmitting waveform and the optimal receiving filter.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of:
determining the code length of a transmitting waveform according to the time length and the bandwidth of a signal transmitted by a radar system, and constructing a transmitting waveform sequence and a receiving filter bank according to the code length; the receiving filter in the receiving filter bank is a non-matched filter;
obtaining a Doppler tolerance range and a signal-to-noise ratio loss threshold of a radar system;
according to the Doppler tolerance range, constructing an optimization model of a transmitting waveform and a receiving filter under the constraint of the signal-to-noise ratio loss threshold;
solving the optimization model to obtain an ideal optimal transmitting waveform and an optimal receiving filter of a local fuzzy function;
and configuring a radar system according to the optimal transmitting waveform and the optimal receiving filter.
The radar system configuration method and device based on fuzzy function local optimization construct the transmitting waveform sequence and the receiving filter group according to the code length of the transmitting waveform determined by the time length and the bandwidth of the transmitting signal of the radar system, wherein the receiving filter adopts the matched filter, compared with the matched filter, the non-matched filter is used to sacrifice the signal-to-noise ratio of the receiving end, so that the greater signal processing freedom can be obtained, thereby obtaining better sidelobe and anti-interference performance, simultaneously obtaining the Doppler tolerance range and the signal-to-noise ratio loss threshold of the radar, constructing the optimization model of the transmitting waveform and the receiving filter, solving the optimization model to obtain the optimal transmitting waveform and the optimal receiving filter of the local fuzzy function ideal for configuring the radar system, compared with the existing local AF ideal transmitting waveform design and receiving filter design method, the flexibility is higher, the shapes of the designed transmitting waveform and the AF of the receiving filter in a preset area are more ideal, and the method has the advantages of wider application range, higher operation efficiency, controllable signal-to-noise ratio loss, lower receiving end pulse pressure output side lobe and the like.
Drawings
FIG. 1 is a flow diagram illustrating a method for radar system configuration based on fuzzy function local optimization in one embodiment;
FIG. 2 is a diagram illustrating the results of a blur function in one embodiment;
FIG. 3 is a diagram illustrating the results of a zero Doppler cut with a blur function according to an embodiment;
FIG. 4 is a diagram illustrating the results of a blur function in another embodiment;
FIG. 5 is a diagram illustrating a result of a zero-Doppler cut with a blur function according to another embodiment;
FIG. 6 is a block diagram of a radar system configuration device based on fuzzy function local optimization in another embodiment;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a radar system configuration method based on fuzzy function local optimization, including the following steps:
and 102, determining the code length of a transmitting waveform according to the time length and the bandwidth of a signal transmitted by the radar system, and constructing a transmitting waveform sequence and a receiving filter bank according to the code length.
For a given radar system, the transmit signal duration/duration and the transmit signal bandwidth can be directly known from the system parameters of the radar system itself.
Compared with the matched filter, the receiving filter in the receiving filter bank is a non-matched filter, and the non-matched filter is used to sacrifice the signal-to-noise ratio of the receiving end, so that the larger self-degree of local AF design can be obtained, and better related performance can be obtained.
The transmit waveform refers to the interference-free waveform to be transmitted.
And 104, acquiring a Doppler tolerance range and a signal-to-noise ratio loss threshold of the radar system.
Different radar systems are endowed with different task requirements, and the determined task requirements determine the input, output and signal processing performances required by the radar systems, so that the maximum signal-to-noise ratio loss allowed by the radar systems, namely the signal-to-noise ratio loss threshold, can be directly provided according to the task requirements of the radar systems.
In an actual radar system, the movement speed of a detection target can be known in advance by means of environmental cognition and the like through an auxiliary channel, so that the range of Doppler frequency shift brought by the movement of the target can be determined. The Doppler frequency shift range can limit a Doppler tolerance region of the joint design of the transmitting waveform and the receiving filter of the radar system, and a fuzzy function of the transmitting waveform and the receiving filter of the radar in the region is required to have an ideal shape.
And 106, constructing an optimized model of the transmitting waveform and the receiving filter under the constraint of the loss threshold of the signal-to-noise ratio according to the Doppler tolerance range.
And 108, solving the optimization model to obtain the optimal transmitting waveform and the optimal receiving filter of the local fuzzy function.
And step 110, configuring the radar system according to the optimal transmitting waveform and the optimal receiving filter.
The radar system configuration method based on fuzzy function local optimization constructs a transmitting waveform sequence and a receiving filter group according to the code length of a transmitting waveform determined by the time length and the bandwidth of a signal transmitted by a radar system, wherein the receiving filter adopts a matched filter, compared with the matched filter, a non-matched filter is used to sacrifice the signal-to-noise ratio of a receiving end, and larger signal processing freedom can be obtained, thereby obtaining better side lobe and anti-interference performance, simultaneously obtaining the Doppler tolerance range and the signal-to-noise ratio loss threshold of the radar, constructing an optimization model of the transmitting waveform and the receiving filter, solving the optimization model to obtain the optimal transmitting waveform with ideal local fuzzy function and the optimal receiving filter for configuring the radar system, compared with the existing local AF ideal transmitting waveform design and receiving filter design method, the flexibility is higher, the designed transmitting waveform and AF of the receiving filter are more ideal in shape in a preset area, and the method has the advantages of wider application range, higher operation efficiency, controllable signal-to-noise ratio loss, lower receiving end pulse pressure output side lobe and the like.
In one embodiment, determining the code length of the transmitted waveform based on the duration and bandwidth of the signal transmitted by the radar system comprises:
determining the code length of a transmitting waveform according to the time length and the bandwidth of a signal transmitted by a radar system:
N＝B×T
wherein, N is the code length of the emission waveform, B is the emission signal bandwidth of the radar system, and T is the emission signal duration of the radar system.
In one embodiment, constructing the transmit waveform sequence and the receive filter bank from code lengths comprises: constructing a transmitting wave form sequence and a receiving filter group according to the code length:
x＝[x 1 x 2 …x N ] T ，x n ＝e jφ(n) ,n＝1,…,N φ(n)∈[0,2π]
h＝[h 1 h 2 ...h N ] T
where x is the transmit waveform sequence, h is the receive filter bank, and phi (n) is the phase of the nth transmit waveform in the transmit waveform sequence.
In one embodiment, obtaining a doppler tolerance range of a radar system comprises:
obtaining a Doppler frequency shift interval of the radar system according to the detection target, and normalizing Doppler frequency shift in the Doppler frequency shift interval to obtain a nominal Doppler frequency shift:
wherein, [ f ] 1 ，f 2 ]Is a nominal Doppler shift interval, f d Is the nominal doppler shift and f is the doppler shift.
Forming a nominal Doppler frequency shift interval by the nominal Doppler frequency shift, and performing uniform discretization processing on the nominal Doppler frequency shift interval to obtain the Doppler tolerance range of the radar system:
f l ＝f l +(l-1)Δf,l＝1,...,L
wherein f is l The first discrete Doppler shift within the Doppler tolerance range is represented, and L is a preset value and represents the number of the discrete Doppler shifts.
In one embodiment, an optimization model of transmit waveform and receive filter is constructed under the constraint of maximum signal-to-noise ratio loss according to a maximum doppler tolerance interval, comprising:
obtaining the signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank;
constructing an objective function of an optimization model:
wherein the content of the first and second substances,
A x,h (n,f l )＝h H J N Diag[a(f l )]x
wherein, SNRL is the loss of SNR, mu is the loss threshold of SNR, n is the distance unit, i.e. the distance between the side lobe and the central position, w (n, f) l ) Representing the range bin n and the doppler shift f l ε is the pareto weight, Diag (a (f) l ) A represents a (f) l ) The diagonal matrix of (a).
In one embodiment, obtaining a signal-to-noise ratio loss for a radar system from a transmit waveform sequence and a receive filter bank comprises:
obtaining the signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank:
wherein | represents modulo operation, | | | | | | non-conducting phosphor 2 2-norm operation of a representation vector, (.) H Representing a conjugate operation.
In one embodiment, solving the optimization model to obtain the transmit waveform and receive filter of the local ambiguity function includes:
obtaining a second objective function according to the initial objective function:
and obtaining a final objective function according to the second objective function by adopting an MM method:
wherein the content of the first and second substances,
wherein
Is a matrix
The maximum eigenvalue of (c).
Constructing an alternative iteration minimization model according to an objective function:
s.t.h H h＝N f
wherein the content of the first and second substances,
solving an objective function according to the alternating iteration minimization model to obtain an optimal receiving filter and an optimal transmitting waveform:
specifically, the transmit waveform or the receive filter may be fixed alternately, the corresponding receive filter or the transmit waveform may be optimized, an optimization model for joint design of the transmit waveform and the receive filter may be solved under the constraint of the loss threshold of the signal-to-noise ratio, and the transmit waveform and the receive filter group that satisfy the constraint of the loss of the maximum signal-to-noise ratio and are ideal for the local AF may be obtained after a predetermined number of iterations.
The model for the alternate iteration minimization can be summarized as:
h (i) ＝argminΓ(x (i-1) ,h)
x (i) ＝argminΓ(x,h (i) )
wherein h is (i) And x (i) Respectively, the optimal solution of the objective function at the ith iteration. Meanwhile, the MM algorithm is used for solving the two sub-optimization problems, and the optimization efficiency is improved.
The solving algorithm for the transmit waveform and the receive filter that summarize the local AF ideal and satisfy the maximum signal-to-noise ratio loss constraint is: firstly, determining the number of alternate iterations, the code length N of a transmitting waveform and a receiving filter, and the energy N of the receiving filter f A maximum signal-to-noise ratio loss, i.e., signal-to-noise ratio loss threshold μ, and a weighting coefficient { w (n, f) }; the times of which are calculated
Initializing i to 0 to obtain x (0) And h (0) And is calculated to obtain
Again, by x (i) Is calculated to obtain
For updating the receive filter to obtain h (i+1) Through h (i+1) Is calculated to obtain
For updating the transmit waveform to obtain x (i+1) (ii) a And finally, repeating the steps until the iteration times i reach a set value, and obtaining the transmitting waveform and the receiving filter which are ideal for local AF and meet the maximum signal-to-noise ratio loss constraint.
After the local AF ideal radar system to-be-transmitted waveform and the receiving filter bank of the receiving end are obtained, the transmitting signal and the receiving end parameter of the radar system can be configured according to the design result, and therefore the radar system can accurately measure the high-dynamic target in the actual application scene.
The method can control the loss of the signal-to-noise ratio of the non-matched filtering signal processing by adjusting the model parameters according to the actual task requirements, and obtains the optimal anti-interference performance on the premise of ensuring the normal work of the radar system.
In order to more intuitively and fully illustrate the above-mentioned ideal transmit waveform and receive filter joint design method for local AF, a specific embodiment is given below:
the radar emission waveform has a width T of 25.6 μ s, a bandwidth B of 20MHz, and a code length N of 512 bxt. The number of alternating iterations is set to 10000 under the condition that the signal-to-noise ratio loss constraint SNRL is less than 0.5dB, and the results of the ambiguity function and the ambiguity function zero doppler cut map of the receive filter and the transmit sequence obtained through the above step S20 are shown in fig. 2 to 3, respectively.
When the time width is further changed to be 51.2 μ S, the bandwidth is B20 MHz, that is, the code length is N × T1024, and the remaining parameters are not changed, the results of the ambiguity function and the ambiguity function zero doppler cut map of the reception filter and the transmission sequence obtained in step S20 are shown in fig. 4 to 5, respectively.
As can be seen from fig. 2-3, the blur function is an ideal "spike" shape with zero doppler cut pattern sidelobes below-60 dB over a predetermined range, doppler interval. Further comparing the results of fig. 2-3 and fig. 4-5, it can be seen that the local performance of the long code length signal ambiguity function is better.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in a strict order unless explicitly stated herein, and may be performed in other orders. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 6, there is provided a radar system configuration apparatus based on fuzzy function local optimization, including: the device comprises a first construction module, an acquisition module, a second construction module, a solving module and a configuration module, wherein:
the first construction module is used for determining the code length of a transmitting waveform according to the time length and the bandwidth of a signal transmitted by the radar system and constructing a transmitting waveform sequence and a receiving filter bank according to the code length; the receive filters in the receive filter bank are non-matched filters.
And the acquisition module is used for acquiring the Doppler tolerance range and the signal-to-noise ratio loss threshold of the radar system.
And the second construction module is used for constructing an optimization model of the transmitting waveform and the receiving filter under the constraint of the loss threshold of the signal-to-noise ratio according to the Doppler tolerance range.
And the solving module is used for solving the optimization model to obtain the optimal transmitting waveform and the optimal receiving filter of the local fuzzy function.
And the configuration module is used for configuring the radar system according to the optimal transmitting waveform and the optimal receiving filter.
In one embodiment, the first building block is further configured to determine a code length of the transmit waveform according to a duration and a bandwidth of a signal transmitted by the radar system:
N＝B×T
wherein, N is the code length of the emission waveform, B is the emission signal bandwidth of the radar system, and T is the emission signal duration of the radar system.
In one embodiment, the first constructing module is further configured to construct the transmit waveform sequence and the receive filter bank according to code length, including:
constructing a transmitting wave form sequence and a receiving filter group according to the code length:
x＝[x 1 x 2 ...x N ] T ，x n ＝e jφ(n) ,n＝1,...,N φ(n)∈[0,2π]
h＝[h 1 h 2 …h N ] T
where x is the transmit waveform sequence, h is the receive filter bank, and phi (n) is the phase of the nth transmit waveform in the transmit waveform sequence.
In one embodiment, the obtaining module is further configured to obtain a doppler shift interval of the radar system according to the detected target;
normalizing the Doppler frequency shift in the Doppler frequency shift interval to obtain a nominal Doppler frequency shift:
wherein, [ f ] 1 ，f 2 ]Is a nominal Doppler shift interval, f d Is the nominal doppler shift and f is the doppler shift.
Forming a nominal Doppler frequency shift interval by the nominal Doppler frequency shift, and performing uniform discretization processing on the nominal Doppler frequency shift interval to obtain the Doppler tolerance range of the radar system:
f l ＝f l +(l-1)Δf,l＝1,…,L
wherein f is l The first discrete Doppler shift within the Doppler tolerance range is represented, and L is a preset value and represents the number of the discrete Doppler shifts.
In one embodiment, the second building module is further configured to obtain a signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank;
constructing an objective function of an optimization model:
wherein the content of the first and second substances,
A x,h (n,f l )＝h H J N Diag[a(f l )]x
wherein, SNRL is the loss of SNR, mu is the loss threshold of SNR, n is the distance unit, i.e. the distance between the side lobe and the central position, w (n, f) l ) Representing a distance element nAnd Doppler shift f l ε is the pareto weight, Diag (a (f) l ) A represents a (f) l ) The diagonal matrix of (a).
In one embodiment, the second building block is further configured to obtain a signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank, and includes:
and obtaining the signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank:
wherein | represents modulo operation, | | | | | | non-conducting phosphor 2 2-norm operation of a representation vector, (.) H Representing a conjugate operation.
In one embodiment, the solving module is further configured to obtain a second objective function from the initial objective function:
and obtaining a final objective function according to the second objective function by adopting an MM method:
wherein the content of the first and second substances,
constructing an alternative iteration minimization model according to an objective function:
s.t.h H h＝N f
wherein the content of the first and second substances,
solving an objective function according to the alternating iteration minimization model to obtain an optimal receiving filter and an optimal transmitting waveform:
for specific definition of the radar system configuration device based on the fuzzy function local optimization, refer to the above definition of the radar system configuration method based on the fuzzy function local optimization, and details are not repeated here. The modules in the radar system configuration device based on the fuzzy function local optimization can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for radar system configuration based on a fuzzy function local optimization. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.
Claims (10)
1. A radar system configuration method based on fuzzy function local optimization is characterized by comprising the following steps:
determining the code length of a transmitting waveform according to the time length and the bandwidth of a signal transmitted by a radar system, and constructing a transmitting waveform sequence and a receiving filter bank according to the code length; the receiving filter in the receiving filter bank is a non-matched filter;
obtaining a Doppler tolerance range and a signal-to-noise ratio loss threshold of a radar system;
according to the Doppler tolerance range, constructing an optimization model of a transmitting waveform and a receiving filter under the constraint of the signal-to-noise ratio loss threshold;
solving the optimization model to obtain an ideal optimal transmitting waveform and an optimal receiving filter of a local fuzzy function;
and configuring a radar system according to the optimal transmitting waveform and the optimal receiving filter.
2. The method of claim 1, wherein determining the code length of the transmit waveform based on the duration and bandwidth of the signal transmitted by the radar system comprises:
determining the code length of a transmitting waveform according to the time length and the bandwidth of a signal transmitted by a radar system:
N＝B×T
wherein, N is the code length of the emission waveform, B is the emission signal bandwidth of the radar system, and T is the emission signal duration of the radar system.
3. The method of claim 2, wherein constructing a transmit waveform sequence and a receive filter bank based on the code length comprises:
constructing a transmitting wave form sequence and a receiving filter group according to the code length:
x＝[x 1 x 2 ...x N ] T ，x n ＝e jφ(n) ,n＝1,...,N φ(n)∈[0,2π]
h＝[h 1 h 2 ...h N ] T
where x is the transmit waveform sequence, h is the receive filter bank, and phi (n) is the phase of the nth transmit waveform in the transmit waveform sequence.
4. The method of claim 3, wherein obtaining the Doppler tolerance range of the radar system comprises:
obtaining a Doppler frequency shift interval of the radar system according to the detection target;
normalizing the Doppler frequency shift in the Doppler frequency shift interval to obtain a nominal Doppler frequency shift:
wherein f is d Is the nominal doppler shift, f is the doppler shift;
forming a nominal Doppler frequency shift interval by the nominal Doppler frequency shift, and performing uniform discretization processing on the nominal Doppler frequency shift interval to obtain a Doppler tolerance range of the radar system:
f l ＝f 1 +(l-1)Δf,l＝1,...,L
wherein, [ f ] 1 ，f 2 ]Is a nominal DopplerFrequency shift interval, f l The first discrete Doppler shift within the Doppler tolerance range is represented, and L is a preset value and represents the number of the discrete Doppler shifts.
5. The method of claim 4, wherein constructing an optimized model of transmit waveforms and receive filters under the constraint of the maximum signal-to-noise ratio loss according to the maximum Doppler tolerance interval comprises:
obtaining the signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank;
constructing an objective function of an optimization model:
s.t.SNRL＜μ h H h＝N h 
wherein the content of the first and second substances,
A x,h (n,f l )＝h H J N Diag[a(f l )]x
wherein, SNRL is SNR loss, μ represents SNR loss threshold, n represents range unit, i.e. distance of side lobe from center position, w (n, f) l ) Represents a distance element n anddoppler shift f l ε is the pareto weight, Diag (a (f) l ) A represents a (f) l ) The diagonal matrix of (a).
6. The method of claim 5, wherein deriving a signal-to-noise ratio loss for the radar system from the transmit waveform sequence and the receive filter bank comprises:
and obtaining the signal-to-noise ratio loss of the radar system according to the transmitting waveform sequence and the receiving filter bank:
wherein | represents modulo operation, | | | | | | non-conducting phosphor 2 2-norm operation of a representation vector, (.) H Representing a conjugate operation.
7. The method of claim 6, wherein solving the optimization model to obtain the transmit waveform and receive filter for which the local ambiguity function is ideal comprises:
obtaining a second objective function according to the initial objective function:
s.t.
and obtaining a final objective function according to the second objective function by adopting an MM method:
s.t.SNRL＜μ h H h＝N h 
wherein the content of the first and second substances,
constructing an alternate iteration minimization model according to the objective function:
s.t.h H h＝N f
s.t.
wherein the content of the first and second substances,
p,q＝1,...,N
p,q＝1,...,N；
solving the objective function according to the alternating iteration minimization model to obtain an optimal receiving filter and an optimal transmitting waveform:
8. a radar system configuration apparatus based on fuzzy function local optimization, the apparatus comprising:
the first construction module is used for determining the code length of a transmitted waveform according to the time length and the bandwidth of a signal transmitted by a radar system and constructing a transmitted waveform sequence and a receiving filter bank according to the code length; the receiving filter in the receiving filter bank is a non-matched filter;
the acquisition module is used for acquiring a Doppler tolerance range and a signal-to-noise ratio loss threshold of the radar system;
the second construction module is used for constructing an optimization model of a transmitting waveform and a receiving filter under the constraint of the signal-to-noise ratio loss threshold according to the Doppler tolerance range;
the solving module is used for solving the optimization model to obtain an optimal transmitting waveform and an optimal receiving filter of the local fuzzy function ideal;
and the configuration module is used for configuring the radar system according to the optimal transmitting waveform and the optimal receiving filter.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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