CN115291185A

CN115291185A - Parameter detection method and device for radar target and electronic equipment

Info

Publication number: CN115291185A
Application number: CN202211223982.1A
Authority: CN
Inventors: 席峰; 宋俊飞
Original assignee: Nanjing University of Science and Technology
Current assignee: Nanjing University of Science and Technology
Priority date: 2022-10-09
Filing date: 2022-10-09
Publication date: 2022-11-04
Anticipated expiration: 2042-10-09
Also published as: CN115291185B

Abstract

The utility model provides a parameter detection method, a device and an electronic device of a radar target, wherein, a radar transmits a carrier frequency signal and receives a corresponding echo signal and separates out a sub-band signal, a single-bit sampling sub-band signal obtains a sampling signal, and a corresponding single-bit quantization value is determined; discretizing the detection range of the radar into a distance grid, and sparsely representing a sampling signal by adopting a coarse range gate indication vector; according to the single-bit quantized value, solving a coarse distance gate indication vector by adopting a convex optimization algorithm, and determining a target coarse distance gate where a target to be detected is located; respectively discretizing the range of various parameters to be measured of the target to be measured into corresponding sub-grids aiming at the target coarse distance gate, and sparsely representing a sampling signal by adopting a target indication vector; and recovering a target indication vector from the single-bit quantized value by adopting an iterative algorithm, and determining an actual parameter value according to the target indication vector. The accuracy of target parameter monitoring can be improved, the power consumption in the radar signal processing process is reduced, and the cost of a radar system is saved.

Description

Parameter detection method and device for radar target and electronic equipment

Technical Field

The disclosure relates to the technical field of radar measurement, in particular to a method and a device for detecting parameters of a radar target and electronic equipment.

Background

Frequency Agile Radar (FAR) is a relatively new technology developed in radar systems in recent years, and the carrier frequency of FAR varies with radar pulses, and the agile performance of the carrier frequency can bring many advantages to the radar system, including excellent electronic countermeasure characteristic, good electromagnetic compatibility, and the ability to synthesize a large and effective frequency band by transmitting a narrow-band signal, thereby improving the spectrum efficiency. The transmitting antenna of the Multiple Input Multiple Output (MIMO) radar system can simultaneously transmit different radar waveforms, the transmitting waveforms are mutually independent and meet the orthogonality, and meanwhile, the multiple antennas at the receiving end are used for receiving the reflected echo signals. By utilizing the multi-antenna characteristic of the transmitting terminal and the receiving terminal, the MIMO array can form a large-scale virtual array by using fewer antennas. The MIMO radar shows good performance in restraining attenuation, enhancing resolution and restraining interference.

In the application of radar signal processing, sampling and quantization are the first steps of processing echo signals, in the existing target parameter detection process, for example, a MIMO array and a carrier agile radar are combined, meanwhile, a transmitting end adopts a sparse array, namely, a plurality of transmitting antennas are randomly selected in each pulse transmission, firstly, pulse compression is performed on echo sampling signals by using inverse fourier transform (IDFT) to obtain coarse range gates (CRRP), then, a coefficient reconstruction algorithm is adopted in each coarse range gate to estimate the distance, speed, angle and the like of a target, and high-precision quantization is performed on signal amplitude on the assumption that infinite precise sampling is adopted, a certain error exists between the detected parameters and the actual condition of the target, and in practical application, when the signal bandwidth is large, a common analog-to-digital conversion (ADC) device needs to perform high-speed sampling and high-precision quantization with great power consumption and cost.

Disclosure of Invention

The embodiment of the disclosure at least provides a method and a device for detecting parameters of a radar target and an electronic device, which can improve the accuracy of monitoring the parameters of the radar target, reduce the power consumption in the radar signal processing process and save the cost of a radar system.

The embodiment of the disclosure provides a method for detecting parameters of a radar target, which comprises the following steps:

controlling a radar to transmit a carrier frequency signal and receive a corresponding echo signal;

separating out a sub-band signal after frequency modulation from the echo signal, sampling the sub-band signal by a single bit to obtain a sampling signal, and determining a single bit quantization value corresponding to the sampling signal;

discretizing the detection range of the radar into a distance grid, and sparsely representing the sampling signal by adopting a coarse range gate indication vector;

according to the single-bit quantized value, solving the coarse distance gate indication vector by adopting a convex optimization algorithm, and determining a target coarse distance gate where a target to be detected is located;

respectively discretizing the range of various parameters to be measured of the target to be measured into corresponding sub-grids aiming at the target coarse distance gate, and sparsely representing the sampling signal by adopting a target indication vector indicating the actual parameter values of the parameters to be measured;

and recovering the target indication vector from the single-bit quantized value corresponding to the target coarse range gate by adopting an iterative algorithm, and determining the actual parameter value according to the target indication vector.

In an optional embodiment, the discretizing a detection range of the radar into a range grid, and sparsely representing the sampling signal by using a coarse range gate indication vector includes:

discretizing a detection range of the radar into a range grid, and constructing a coarse range gate indication vector indicating the position of the target coarse range gate according to the sampling signal;

constructing a frequency grid corresponding to the sub-band signal on a fast time domain, and constructing a dictionary matrix according to elements in the frequency grid;

and representing the sampling signal by adopting the dictionary matrix and the coarse distance gate indication vector, wherein the coarse distance gate indication vector is a sparse vector.

In an optional implementation manner, the solving, according to the single-bit quantized value, the coarse distance gate indication vector by using a convex optimization algorithm to determine a target coarse distance gate where a target to be measured is located specifically includes:

constructing a first single-bit compressed sensing model according to the single-bit quantized value and the sampling signal after sparse representation;

solving the first single-bit compressed sensing model by adopting a convex optimization algorithm, and recovering the coarse distance gate indication vector from the single-bit quantized value;

performing modulus extraction on the coarse range gate indicating vector, and determining a target position indicated by a peak value after modulus extraction of the coarse range gate indicating vector, wherein the target position is a coarse range gate position corresponding to the target coarse range gate;

and determining the target coarse range gate in the range grid according to the position of the coarse range gate.

In an optional implementation manner, the discretizing, for the target coarse range gate, ranges of multiple parameters to be measured of the target to be measured into corresponding sub-grids, and sparsely representing the sampling signal by using a target indication vector indicating actual parameter values of the parameters to be measured, specifically includes:

for each of the sub-grids, determining a normalized reflection factor corresponding to the sampled signal and a target coarse range frequency corresponding to the sampled signal at the target coarse range gate;

constructing the target indication vector by the normalized reflection factor and the frequency component corresponding to the target coarse distance frequency and the sampling signal;

and sparsely representing the sampling signal by adopting the target indication vector.

In an optional implementation manner, the recovering the target indication vector from the single-bit quantized value corresponding to the target coarse range gate by using an iterative algorithm specifically includes:

combining the real part and the imaginary part of the single-bit quantized value to generate a single-bit quantized vector;

constructing an observation matrix corresponding to the sampling signal according to a vector corresponding to the sampling signal at the target coarse range gate, and constructing a noise matrix corresponding to the sampling signal according to a noise component in the sampling signal;

constructing a reconstruction indication vector matrix with the target indication vector as an element;

constructing a second single-bit compressed sensing model for a reconstruction indicating vector in the reconstruction indicating vector matrix based on the single-bit quantized vector, the reconstruction indicating vector matrix, the observation matrix and the noise matrix;

solving the second single-bit compressed sensing model by adopting a binary soft threshold algorithm, and determining an optimal reconstruction indication vector corresponding to the sampling signal at the target coarse distance gate;

and representing the real part and the imaginary part of the target indication vector by the optimal reconstruction indication vector, and determining the target indication vector.

In an optional implementation manner, the determining the actual parameter value according to the target indication vector specifically includes:

determining a target parameter index corresponding to a maximum element in the target indication vector;

and aiming at each sub-grid, taking the parameter value corresponding to the target parameter index in the sub-grid as the parameter value of the to-be-measured parameter type corresponding to the sub-grid.

The embodiment of the present disclosure further provides a device for detecting parameters of a radar target, where the device includes:

the transmitting and receiving module is used for controlling the radar to transmit carrier frequency signals and receive corresponding echo signals;

the single-bit sampling module is used for separating out the sub-band signals after frequency modulation from the echo signals, sampling the sub-band signals by single bits to obtain sampling signals, and determining single-bit quantization values corresponding to the sampling signals;

the distance grid division module is used for discretizing the detection range of the radar into distance grids and sparsely representing the sampling signals by adopting coarse range gate indication vectors;

the coarse distance gate determining module is used for solving the coarse distance gate indicating vector by adopting a convex optimization algorithm according to the single-bit quantized value and determining a target coarse distance gate where the target to be detected is located;

the sub-grid division module is used for respectively discretizing the range of various parameters to be measured of the target to be measured into corresponding sub-grids aiming at the target coarse distance gate, and sparsely representing the sampling signal by adopting a target indication vector indicating the actual parameter value of the parameter to be measured;

and the parameter determining module is used for recovering the target indication vector from the single-bit quantized value corresponding to the target coarse range gate by adopting an iterative algorithm, and determining the actual parameter value according to the target indication vector.

In an optional implementation manner, the distance meshing module is specifically configured to:

In an optional implementation manner, the coarse distance gate determining module is specifically configured to:

In an optional implementation manner, the sub-meshing module is specifically configured to:

constructing the target indication vector by the normalized reflection factor and the target coarse distance frequency;

and sparsely representing the sampling signals by adopting the target indication vector.

In an optional implementation manner, the parameter determining module is specifically configured to:

constructing a reconstruction indication vector matrix taking the target indication vector as an element;

solving the second single-bit compressed sensing model by adopting a binary soft threshold algorithm, and determining an optimal reconstruction indication vector corresponding to the sampling signal at the target coarse range gate;

In an optional implementation manner, the parameter determining module is specifically further configured to:

An embodiment of the present disclosure further provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate with each other via the bus when the electronic device runs, and the machine-readable instructions are executed by the processor to perform the above method for detecting parameters of a radar target, or the steps of any one of the possible embodiments of the above method for detecting parameters of a radar target.

The embodiments of the present disclosure also provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the method for detecting the parameter of the radar target, or the steps in any possible implementation manner of the method for detecting the parameter of the radar target.

According to the parameter detection method, the parameter detection device and the electronic equipment for the radar target, a radar is controlled to transmit a carrier frequency signal and receive a corresponding echo signal; separating out the sub-band signal after frequency modulation from the echo signal, obtaining a sampling signal by using a single-bit sampling sub-band signal, and determining a single-bit quantization value corresponding to the sampling signal; discretizing the detection range of the radar into a distance grid, and sparsely representing a sampling signal by adopting a coarse range gate indication vector; according to the single-bit quantized value, solving a coarse distance gate indication vector by adopting a convex optimization algorithm, and determining a target coarse distance gate where a target to be detected is located; respectively discretizing the range of various parameters to be measured of the target to be measured into corresponding sub-grids aiming at the target coarse range gate, and sparsely representing a sampling signal by adopting a target indication vector for indicating the actual parameter value of the parameter to be measured; and restoring a target indication vector from the single-bit quantized value corresponding to the target coarse range gate by adopting an iterative algorithm, and determining an actual parameter value according to the target indication vector. The accuracy of monitoring the radar target parameters can be improved, the power consumption in the radar signal processing process is reduced, and the cost of a radar system is saved.

In order to make the aforementioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly described below, and the drawings herein incorporated in and forming a part of the specification illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the technical solutions of the present disclosure. It is appreciated that the following drawings depict only certain embodiments of the disclosure and are therefore not to be considered limiting of its scope, for those skilled in the art will be able to derive additional related drawings therefrom without the benefit of the inventive faculty.

Fig. 1 shows a flowchart of a method for detecting parameters of a radar target according to an embodiment of the present disclosure;

FIG. 2 is a flow chart illustrating another method for detecting parameters of a radar target according to an embodiment of the disclosure;

fig. 3 is a schematic diagram illustrating a parameter detection apparatus for a radar target according to an embodiment of the present disclosure;

fig. 4 shows a schematic diagram of an electronic device provided by an embodiment of the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. The components of the embodiments of the present disclosure, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the disclosure, provided in the accompanying drawings, is not intended to limit the scope of the disclosure, as claimed, but is merely representative of selected embodiments of the disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the disclosure without making any creative effort, shall fall within the protection scope of the disclosure.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

The term "and/or" herein merely describes an associative relationship, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of a, B, and C, and may mean including any one or more elements selected from the group consisting of a, B, and C.

Research shows that in the existing target parameter detection process, for example, a MIMO array and a carrier agile radar are combined, a transmitting end adopts a sparse array, namely, a plurality of transmitting antennas are randomly selected in each pulse transmission, firstly, an echo sampling signal is subjected to pulse compression by using inverse fourier transform (IDFT) to obtain a coarse range gate (CRRP), then, a coefficient reconstruction algorithm is adopted in each coarse range gate to estimate the distance, speed, angle and the like of a target, and high-precision quantization is performed on the signal amplitude on the assumption that infinite precision sampling is based.

Based on the research, the present disclosure provides a method, an apparatus, and an electronic device for detecting parameters of a radar target, wherein a radar is controlled to transmit a carrier frequency signal and receive a corresponding echo signal; separating out sub-band signals after frequency modulation from the echo signals, acquiring sampling signals from the single-bit sampling sub-band signals, and determining single-bit quantization values corresponding to the sampling signals; discretizing a detection range of the radar into a distance grid, and sparsely representing a sampling signal by adopting a coarse range gate indication vector; according to the single-bit quantized value, solving a coarse distance gate indication vector by adopting a convex optimization algorithm, and determining a target coarse distance gate where a target to be detected is located; respectively discretizing the range of various parameters to be measured of the target to be measured into corresponding sub-grids aiming at the target coarse range gate, and sparsely representing a sampling signal by adopting a target indication vector for indicating the actual parameter value of the parameter to be measured; and restoring a target indication vector from the single-bit quantized value corresponding to the target coarse range gate by adopting an iterative algorithm, and determining an actual parameter value according to the target indication vector. The accuracy of monitoring the radar target parameters can be improved, the power consumption in the radar signal processing process is reduced, and the cost of a radar system is saved.

To facilitate understanding of the present embodiment, first, a method for detecting a parameter of a radar target disclosed in the embodiments of the present disclosure is described in detail, where an execution subject of the method for detecting a parameter of a radar target provided in the embodiments of the present disclosure is generally a computer device with certain computing capability, and the computer device includes, for example: a terminal device, which may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle mounted device, a wearable device, or a server or other processing device. In some possible implementations, the method for detecting the parameter of the radar target may be implemented by a processor calling computer readable instructions stored in a memory.

Referring to fig. 1, a flowchart of a method for detecting parameters of a radar target according to an embodiment of the present disclosure is shown, where the method includes steps S101 to S106, where:

s101, controlling the radar to transmit carrier frequency signals and receive corresponding echo signals.

In the specific implementation, a transmitting end of the radar transmits a carrier frequency signal to a target to be detected, the carrier frequency signal is reflected by the target to be detected after reaching the target to be detected and then becomes an echo signal, the echo signal is received by a receiving end of the radar, and parameter information such as a distance, a movement speed and a direction angle corresponding to the target to be detected can be obtained by analyzing the echo signal.

Specifically, the radar system may adopt an MIMO array, the transmitting end may be an even linear array having a plurality of antennas, the receiving end may also be an even linear array having a plurality of antennas, each transmitting antenna transmits a different carrier frequency signal, and the carrier frequency selected by the carrier frequency signal is changed randomly along with the pulse change.

Here, the baseband signal corresponding to the carrier Frequency signal transmitted by the radar may be a Frequency Modulated Continuous Wave (FMCW), and may be represented by the following formula:

wherein, the first and the second end of the pipe are connected with each other,

represents a rectangular function when

When the temperature of the water is higher than the set temperature,

otherwise

；

Representing the frequency modulation rate of the FMCW,

in which

Representing the bandwidth of the baseband signal and,

representing the pulse width.

Further, a set of carrier frequencies is defined

Comprises the following steps:

wherein, in the process,

which represents the initial carrier frequency of the carrier frequency,

that is, there are M different carrier frequencies to select;

indicating the spacing between adjacent carrier frequencies.

Here, in order to ensure orthogonality between transmitted carrier frequency signals, let

. Assuming that a Coherent Processing Interval (CPI) of a radar system includes N pulses, for the nth pulse, the transmitted carrier frequency signal on the pth transmit antenna can be represented as:

the corresponding carrier frequency can be expressed as:

wherein the content of the first and second substances,

a carrier frequency signal representing the transmission on the nth pulse, the pth transmit antenna;

representing the carrier frequency corresponding to the carrier frequency signal transmitted on the nth pulse and the pth transmitting antenna;

and indicates the index corresponding to the carrier frequency selected by the p-th transmitting antenna for the n-th pulse.

Further, for the receiving end of the radar system, it is assumed thatThe distance, the movement speed and the direction angle of the first target to be measured are respectively expressed as

For the nth pulse, its echo delay between the pth transmitting antenna and the qth receiving antenna can be expressed as:

wherein the content of the first and second substances,

represents an echo time delay of the nth pulse between the p-th transmitting antenna and the q-th receiving antenna; c represents the speed of light;

representing the spacing between the radar transmit antennas,

representing the spacing between the radar receiving antennas, the echo signal of the qth receiving antenna corresponding to the nth transmitted pulse can therefore be expressed as:

wherein the content of the first and second substances,

representing the reflection factor of the ith target to be measured;

representing additive white gaussian noise in the echo signal; p represents the total number of antennas at the transmitting end of the radar.

S102, sub-band signals after frequency modulation are separated from the echo signals, the sub-band signals are sampled by single bits to obtain sampling signals, and single-bit quantization values corresponding to the sampling signals are determined.

In specific implementation, after receiving an echo signal, a radar receiving end needs to perform frequency modulation removal processing on the echo signal, then the echo signal after frequency modulation removal passes through a low-pass filter to obtain a separated sub-band signal, then after the separated sub-band signal is obtained, nyquist sampling is performed on the sub-band signal to obtain a sampling signal, finally single-bit quantization is performed on the sampling signal, and a single-bit quantization value corresponding to the sampling signal is determined.

Specifically, the subband signal between the p-th transmitting antenna and the q-th receiving antenna can be expressed as:

wherein, LPF stands for low-pass filtering, and approximately, the above equation can be expressed as:

wherein the content of the first and second substances,

a subband signal representing between the p-th transmit antenna and the q-th receive antenna;

represents a normalized reflection factor;

respectively representing a normalized velocity frequency and a normalized angle frequency;

representing the relative factor between the actual carrier frequency and the initial carrier frequency;

representing noise; q represents the total number of antennas at the radar receiving end.

Further, the sub-band signals

Nyquist sampling to obtain sampling signal

Wherein G represents the number of Nyquist samples;

representing a set of complex numbers. The g-th sample value can then be represented as follows:

a sampling interval representing nyquist sampling; the coarse range resolution of the system is determined by the bandwidth of the baseband signal transmitted by the radar transmitting end as follows:

，

representing the bandwidth of the baseband signal, assuming that the target to be measured is a low-speed moving target and the transmitted baseband signal is a narrow-band signalNo. i.e. satisfy the condition

And

the distance offset caused by the pulses:

and distance offset caused by different receive antennas:

it can be ignored, and therefore, the sampling signal can be simplified as:

further, to the sampling signal

Performing single bit quantization to obtain

Representing the real part of the single-bit quantized value,

represents the imaginary part of a single-bit quantized value, wherein

As a function of sign, it can be expressed as:

co-generation of NPQ-to-single-contrast in a CPIThe quantization value of each target to be measured can be estimated by analyzing the NPQ to single-bit quantization value

Namely three parameters of distance, movement speed and direction angle.

Therefore, when the signal bandwidth is large, a large power consumption and cost are needed for realizing high-speed sampling and high-precision quantization by a common analog-to-digital conversion (ADC) device, a single-bit ADC can realize the quantization function of the signal by only one comparator, and under the condition of considering the power consumption and the cost, the power consumption in the radar signal processing process can be reduced by the single-bit sampling subband signal, and the cost of a radar system is saved.

S103, discretizing the detection range of the radar into a distance grid, and sparsely representing the sampling signals by adopting coarse range gate indication vectors.

In the step, the detectable range of the radar, that is, the distance range in which the target to be detected may exist, is uniformly discretized into a plurality of distance grids, the distance represented by each grid point may be determined according to the interval between adjacent carrier frequencies, and when the distance represented by the target to be detected falls within a certain distance grid, the position represented by the target to be detected is the corresponding range gate.

It should be noted that the coarse range gate indication vector is a vector for indicating the position of the coarse range gate where the target to be measured is located in the range grid, and may be formed by terms other than the noise term in the expression of the sampling signal.

In specific implementation, the distance range of the target to be measured which may appear is dispersed into G distance grids, and the width of each grid in the distance grids is

Distance represented by the g-th grid point

Can be expressed as:

g =0 represents the first grid point, when the distance of the object to be measured is within the range

When the position of the target to be measured is higher than the g-th range gate, the position of the target to be measured is shown to be the g-th range gate.

Here, the sampled signal in step S102 is observed

Frequency component in the expression of (1)

Which can be represented as

Wherein

For representing a distance observation for an object to be measured, and

is the actual distance value of the target to be measured, thereby showing that the maximum unambiguous distance of the radar system is

Therefore, only need to estimate

And

the actual distance value of the target to be measured can be obtained

And then to the sampling point

The estimate of (a) is converted into an estimate for the coarse range gate within the range grid at which the object to be measured is located.

Specifically, step S103 may be implemented by steps S1031 to S1033 as follows:

and S1031, discretizing the detection range of the radar into a distance grid, and constructing the coarse range gate indication vector indicating the position of the target coarse range gate according to the sampling signal.

S1032, constructing a frequency grid corresponding to the sub-band signal on a fast time domain, and constructing a dictionary matrix according to elements in the frequency grid.

S1033, representing the sampling signal by using the dictionary matrix and the coarse distance gate indication vector, wherein the coarse distance gate indication vector is a sparse vector.

In particular, for sampling signals

Its coarse range gate indicates the vector

Can be expressed as:

wherein the content of the first and second substances,

represents a coarse range gate indication vector; further sampling the signal

Can be rewritten as:

wherein the content of the first and second substances,

and is used to represent the normalized coarse distance frequency.

Further, a frequency grid in a fast time domain is constructed

The g-th element in the frequency grid is:

dictionary matrices constructed from elements in a frequency grid

Can be expressed as:

further, a dictionary matrix is adopted

And coarse range gate indication vector

The sampled signal represented may be represented as:

representing the sampled signal after sparse representation;

representing a coarse range gate indication vector,

；

representing a dictionary matrix;

representing gaussian noise.

Here, when

When the temperature of the water is higher than the set temperature,

and the number of targets

Thus a vector of

Is a sparse vector.

And S104, solving the coarse range gate indication vector by adopting a convex optimization algorithm according to the single-bit quantization value, and determining a target coarse range gate where the target to be detected is located.

In specific implementation, the problem of recovering the coarse range gate indication vector from the single-bit quantization value corresponding to the sampling signal can be understood as a single-bit compressed sensing model problem, and a convex optimization algorithm can be used in a solving process of the problem, so that a coarse range gate corresponding to the position of the obtained coarse range gate indication vector indicated in the range grid is solved, that is, the coarse range gate of the target where the target to be detected is located.

Specifically, step S104 can be implemented by steps S1041 to S1044 as follows:

s1041, according to the single-bit quantized value and the sampling signal after sparse representation, constructing a first single-bit compressed sensing model.

S1042, solving the first single-bit compressed sensing model by adopting a convex optimization algorithm, and recovering the coarse distance gate indication vector from the single-bit quantized value.

And S1043, performing modulus extraction on the coarse range gate indication vector, and determining a target position indicated by a peak value after modulus extraction of the coarse range gate indication vector, wherein the target position is a coarse range gate position corresponding to the target coarse range gate.

And S1044, determining the target coarse range gate in the range grid according to the position of the coarse range gate.

In specific implementation, a first single-bit compressed sensing model is formed by a single-bit quantized value corresponding to a sampling signal and the sampling signal subjected to sparse representation of a coarse range gate indicating vector, the first single-bit compressed sensing model is solved by adopting a convex optimization algorithm, the coarse range gate indicating vector can be restored from the single-bit quantized value, and then after the restored coarse range gate indicating vector is subjected to modulus, the position corresponding to the peak value is the position of a coarse range gate where a target to be detected is located, and the coarse range gate corresponding to the target to be detected can be obtained corresponding to a range grid.

Specifically, for a single bit quantized value:

and

and the sampled signal after sparse representation of the coarse distance gate indication vector is as follows:

and forming a first single-bit compression perception model, and solving by using a convex optimization algorithm, wherein the convex optimization algorithm can be expressed as follows:

represents the real part;

representing the imaginary part.

S105, respectively discretizing the range of various parameters to be measured of the target to be measured into corresponding sub-grids aiming at the target coarse distance gate, and sparsely representing the sampling signal by adopting a target indication vector indicating actual parameter values of the parameters to be measured.

In this step, after the target coarse range gate where the target to be detected is located is determined, for the target to be detected in the same coarse range gate, the possible ranges of the multiple parameters to be detected of the target to be detected are discretized into corresponding sub grids, for example, for the three parameters to be detected of the distance, the movement speed and the direction angle of the target to be detected, the possible range of the distance parameter, the possible range of the movement speed parameter and the possible range of the direction angle parameter are discretized into triple grids of the distance, the movement speed and the direction angle. And sparsely representing the sampling signal by adopting a target indication vector which can indicate the actual parameter value corresponding to the target to be detected in the sub-grid.

In specific implementation, for sampling signals

Respectively to be separately provided with

Discretizing to form a triple grid with respective normalized distance, velocity and angle resolutions

Triple ofEach set of sub-grids in the grid may be represented as:

for representing a distance parameter sub-grid;

a sub-grid for representing a speed parameter;

in (1)

For representing the angular parameter sub-grid.

Further, assuming that L objects to be measured all fall within the treble grid, an object indication data block may be defined

The estimation of the actual parameter value corresponding to the parameter to be measured is assisted, wherein the elements are as follows:

further, the target is indicated to the data block

Vectorizing to obtain a target indication vector

Wherein

。

Specifically, step S105 can be implemented by steps S1051 to S1053 as follows:

s1051, determining a normalized reflection factor corresponding to the sampling signal and a target coarse distance frequency corresponding to the sampling signal at the target coarse distance gate aiming at each sub-grid.

S1052, constructing the target indication vector by the normalized reflection factor and the target coarse distance frequency.

And S1053, sparsely representing the sampling signal by adopting the target indication vector.

In a specific implementation, assuming that a target coarse range gate where a target to be measured is located is in a range grid, in a g-th coarse range gate, a sampling signal may be represented as:

here, the reflection factor is normalized by

And coarse range frequency of the target

Constructing the target indication vector:

since the multi-parameter estimation of the targets is processed in the same way in each target coarse range gate, only the parameter estimation in a single target coarse range gate needs to be analyzed, thereby being capable of being used

Instead of the former

Defining normalized distance frequency

The sampled signal sparsely represented by the target indication vector can be represented as:

at this point, the problem is further described as being estimated from the single-bit quantized value corresponding to the target coarse range gate

And (4) finishing.

S106, restoring the target indication vector from the single-bit quantization value corresponding to the target coarse range gate by adopting an iterative algorithm, and determining the actual parameter value according to the target indication vector.

In a specific implementation, similar to the estimation manner of the coarse range gate, the problem of recovering the target indication vector from the single-bit quantized value corresponding to the target coarse range gate may be understood as a single-bit compressed sensing model problem, but since the constructed network includes a plurality of sub-networks, the computational complexity is large, and an iterative algorithm may be used to solve the problem.

Specifically, the method for recovering the target indication vector from the single-bit quantized value corresponding to the target coarse range gate by using an iterative algorithm may be implemented by steps S1061 to S1066 shown in fig. 2, and as shown in fig. 2, the method is a flowchart of another method for detecting a parameter of a radar target according to an embodiment of the present disclosure, where the method includes steps S1061 to S1066, where:

and S1061, combining the real part and the imaginary part of the single-bit quantization value to generate a single-bit quantization vector.

S1062, constructing an observation matrix corresponding to the sampling signal according to a vector corresponding to the sampling signal at the target coarse range gate, and constructing a noise matrix corresponding to the sampling signal according to a noise component in the sampling signal.

And S1063, constructing a reconstruction indication vector matrix taking the target indication vector as an element.

S1064, constructing a second single-bit compressed sensing model aiming at the reconstruction indication vector in the reconstruction indication vector matrix based on the single-bit quantization vector, the reconstruction indication vector matrix, the observation matrix and the noise matrix.

S1065, solving the second single-bit compressed sensing model by adopting a binary soft threshold algorithm, and determining an optimal reconstruction indication vector corresponding to the sampling signal at the target coarse range gate;

s1066, representing the real part and the imaginary part of the target indication vector by the optimal reconstruction indication vector, and determining the target indication vector.

In a specific implementation, a single bit is first quantized to the real part of the value

And imaginary part

Merging into a single-bit quantized vector:

building an observation matrix

Here, elements in the observation matrix

As a sub-observation matrix, the sub-observation matrix A satisfies

Wherein, in the step (A),

represents the vector corresponding to the g-th range gate in the NPQ subband signal, namely the position of the sampling signal at the target coarse range gate;

representing white gaussian noise in the sampled signal.

Here, the sub-observation matrix

The elements in (1) can be represented as:

wherein the content of the first and second substances,

。

further, the noise matrix

The target indication vector is a reconstruction indication vector matrix of elements

Also following the noise matrix

And an observation matrix

Is constructed in a structural form of

Using twoThe binary weighted soft threshold iterative algorithm is used for solving, and at this time, the second single-bit compressed sensing model can be constructed as follows:

wherein the function

，

Representing positive weight values, the expression above can be simplified using a weight matrix.

Further, the second single-bit compressed sensing model may be described as:

wherein the content of the first and second substances,

is a diagonal weight matrix, and diagonal elements are weight values.

Here, since the problem of solving the second single-bit compressed sensing model is a non-convex problem, the problem needs to be solved by a threshold algorithm, and a soft threshold operator is defined as:

representing a soft threshold operator;

the representative regularization parameter may be determined by a user according to an application scenario in a specific implementation, and is not limited in this respect.

Further, by soft threshold operator

The optimality condition for solving the problem of the second single-bit compressed perceptual model may be written:

and deducing the whole algorithm flow according to the optimality condition as follows:

inputting:

，

，

，

initialization:

，

While

and

do

computing

Updating

Updating

Computing

；

End while

Return

Specifically, the following algorithm parameters are specified:

represents the observation matrix constructed in step S1066;

representing a single bit quantized vector;

representing the total number of iterations of the algorithm;

the representative regularization parameter may be determined by a user according to an application scenario in a specific implementation, which is not specifically limited herein;

representing the error threshold when the error is less than or equal to

Stopping iteration;

representing the number of iterations;

representing the error per iteration;

is a normal number to ensure

The denominator is not zero.

Further, determining a target parameter index corresponding to a maximum element in the target indication vector; and aiming at each sub-grid, taking the parameter value corresponding to the target parameter index in the sub-grid as the parameter value of the to-be-measured parameter type corresponding to the sub-grid.

Here, a binary soft threshold algorithm is used to obtain the optimal reconstruction indication vector

Then, the target indication vector

Can be expressed as:

then find out

The elements with the same number of the maximum number as the number of the targets to be measuredAnd calculating to obtain the actual parameter value corresponding to the target parameter to be measured.

According to the parameter detection method, the parameter detection device and the electronic equipment for the radar target, a radar is controlled to transmit a carrier frequency signal and receive a corresponding echo signal; separating out sub-band signals after frequency modulation from the echo signals, acquiring sampling signals from the single-bit sampling sub-band signals, and determining single-bit quantization values corresponding to the sampling signals; discretizing the detection range of the radar into a distance grid, and sparsely representing a sampling signal by adopting a coarse range gate indication vector; according to the single-bit quantized value, solving a coarse distance gate indication vector by adopting a convex optimization algorithm, and determining a target coarse distance gate where a target to be detected is located; respectively discretizing the range of various parameters to be measured of the target to be measured into corresponding sub-grids aiming at the target coarse range gate, and sparsely representing a sampling signal by adopting a target indication vector for indicating the actual parameter value of the parameter to be measured; and restoring a target indication vector from the single-bit quantized value corresponding to the target coarse range gate by adopting an iterative algorithm, and determining an actual parameter value according to the target indication vector. The accuracy of monitoring the radar target parameters can be improved, the power consumption in the radar signal processing process is reduced, and the cost of a radar system is saved.

It will be understood by those of skill in the art that in the above method of the present embodiment, the order of writing the steps does not imply a strict order of execution and does not impose any limitations on the implementation, as the order of execution of the steps should be determined by their function and possibly inherent logic.

Based on the same inventive concept, the embodiment of the present disclosure further provides a radar target parameter detection apparatus corresponding to the radar target parameter detection method, and as the principle of the apparatus in the embodiment of the present disclosure for solving the problem is similar to the radar target parameter detection method in the embodiment of the present disclosure, the implementation of the apparatus may refer to the implementation of the method, and repeated details are not repeated.

Referring to fig. 3, fig. 3 is a schematic diagram of a parameter detection apparatus for a radar target according to an embodiment of the present disclosure. As shown in fig. 3, a parameter detection apparatus 300 for a radar target according to an embodiment of the present disclosure includes:

a transmitting and receiving module 310, configured to control a radar to transmit a carrier frequency signal and receive a corresponding echo signal;

the single-bit sampling module 320 is configured to separate out a sub-band signal after frequency modulation from the echo signal, sample the sub-band signal with a single bit to obtain a sampled signal, and determine a single-bit quantization value corresponding to the sampled signal;

a distance grid division module 330, configured to discretize a detection range of the radar into distance grids, and sparsely represent the sampling signal by using a coarse range gate indication vector;

the coarse distance gate determining module 340 is configured to solve the coarse distance gate indication vector by using a convex optimization algorithm according to the single-bit quantized value, and determine a target coarse distance gate where the target to be detected is located;

a sub-grid dividing module 350, configured to, for the target coarse distance gate, respectively discretize ranges of multiple parameters to be measured of the target to be measured into corresponding sub-grids, and sparsely represent the sampling signal by using a target indication vector indicating actual parameter values of the parameters to be measured;

a parameter determining module 360, configured to recover the target indication vector from the single-bit quantized value corresponding to the target coarse range gate by using an iterative algorithm, and determine the actual parameter value according to the target indication vector.

The description of the processing flow of each module in the device and the interaction flow between the modules may refer to the related description in the above method embodiments, and will not be described in detail here.

The parameter detection device for the radar target provided by the embodiment of the disclosure controls a radar to transmit a carrier frequency signal and receive a corresponding echo signal; separating out sub-band signals after frequency modulation from the echo signals, acquiring sampling signals from the single-bit sampling sub-band signals, and determining single-bit quantization values corresponding to the sampling signals; discretizing the detection range of the radar into a distance grid, and sparsely representing a sampling signal by adopting a coarse range gate indication vector; according to the single-bit quantized value, solving a coarse distance gate indication vector by adopting a convex optimization algorithm, and determining a target coarse distance gate where a target to be detected is located; respectively discretizing the range of various parameters to be measured of the target to be measured into corresponding sub-grids aiming at the target coarse range gate, and sparsely representing a sampling signal by adopting a target indication vector for indicating the actual parameter value of the parameter to be measured; and restoring a target indication vector from the single-bit quantized value corresponding to the target coarse range gate by adopting an iterative algorithm, and determining an actual parameter value according to the target indication vector. The accuracy of monitoring the radar target parameters can be improved, the power consumption in the radar signal processing process is reduced, and the cost of a radar system is saved.

Corresponding to the parameter detection method of the radar target in fig. 1 and fig. 2, an embodiment of the present disclosure further provides an electronic device 400, as shown in fig. 4, a schematic structural diagram of the electronic device 400 provided in the embodiment of the present disclosure includes:

a processor 41, a memory 42, and a bus 43; the memory 42 is used for storing execution instructions and includes a memory 421 and an external memory 422; the memory 421 is also referred to as an internal memory, and is configured to temporarily store operation data in the processor 41 and data exchanged with the external memory 422 such as a hard disk, the processor 41 exchanges data with the external memory 422 through the internal memory 421, and when the electronic device 400 operates, the processor 41 and the memory 42 communicate with each other through the bus 43, so that the processor 41 executes steps of the method for detecting a parameter of a radar target in fig. 1 and 2.

The disclosed embodiments also provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method for detecting parameters of a radar target described in the above method embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.

The embodiments of the present disclosure also provide a computer program product, where the computer program product includes computer instructions, and when the computer instructions are executed by a processor, the steps of the method for detecting a parameter of a radar target in the foregoing method embodiments may be executed.

The computer program product may be implemented by hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK) or the like.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again. In the several embodiments provided in the present disclosure, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in software functional units and sold or used as a stand-alone product, may be stored in a non-transitory computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present disclosure. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

Finally, it should be noted that: the above-mentioned embodiments are merely specific embodiments of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, but not to limit the technical solutions, and the scope of the present disclosure is not limited thereto, and although the present disclosure is described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art can modify or easily conceive of the technical solutions described in the foregoing embodiments or equivalent technical features thereof within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present disclosure, and should be construed as being included therein. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A method for detecting parameters of a radar target is characterized by comprising the following steps:

2. The method according to claim 1, wherein the discretizing a detection range of the radar into a range grid, and sparsely representing the sampled signals with coarse range gate indication vectors comprises:

discretizing a detection range of the radar into a range grid, and constructing the coarse range gate indication vector indicating the position of the target coarse range gate according to the sampling signals;

3. The method according to claim 1, wherein the determining the target coarse range gate where the target to be measured is located by solving the coarse range gate indication vector by using a convex optimization algorithm according to the single-bit quantization value specifically includes:

performing modulus extraction on the coarse range gate indicating vector, and determining a target position indicated by a peak value after modulus extraction of the coarse range gate indicating vector, wherein the target position is a position of a coarse range gate corresponding to the target coarse range gate;

4. The method according to claim 1, wherein the discretizing, for the target coarse range gate, ranges of various parameters to be measured of the target to be measured into corresponding sub-grids respectively, and sparsely representing the sampling signal by using a target indication vector indicating actual parameter values of the parameters to be measured, specifically includes:

5. The method according to claim 1, wherein the recovering the target indication vector from the single-bit quantized value corresponding to the target coarse range gate by using an iterative algorithm comprises:

6. The method according to claim 1, wherein the determining the actual parameter value according to the target indication vector specifically comprises:

and aiming at each sub-grid, taking the parameter value corresponding to the target parameter index in the sub-grid as the actual parameter value of the parameter type to be measured corresponding to the sub-grid.

7. A parameter detection apparatus for a radar target, comprising:

the single-bit sampling module is used for separating out sub-band signals after frequency modulation from the echo signals, sampling the sub-band signals by single bits to obtain sampling signals, and determining single-bit quantization values corresponding to the sampling signals;

the distance grid division module is used for discretizing the detection range of the radar into distance grids and sparsely representing the sampling signals by adopting coarse distance gate indication vectors;

8. The apparatus of claim 7, wherein the distance meshing module is specifically configured to:

9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the method of parameter detection of a radar target according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when being executed by a processor, performs the steps of the method for parameter detection of a radar target according to one of claims 1 to 6.