CN109100687B - Radar equipment LFM pulse signal PSLR parameter determination method - Google Patents
Radar equipment LFM pulse signal PSLR parameter determination method Download PDFInfo
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- 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
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Abstract
The invention discloses a method for determining parameters of LFM pulse signals PSLR of radar equipment, which comprises the following steps: building a PSLR parameter determination system; carrying out data caching on the LFM pulse signal to form a data sample; respectively carrying out time-frequency transformation on the data sample and the matched filter; carrying out time-frequency inverse transformation on the data subjected to windowing processing on the frequency domain data product result; and performing polynomial curve fitting on the time domain data, and obtaining a PSLR parameter according to the fitting data. The method solves the problem that the traditional pulse signal PSLR parameter determining method is low in processing speed, determines the PSLR parameter accurately, and better meets the use requirement of a system.
Description
Technical Field
The invention relates to a pulse signal PSLR parameter determination method, in particular to a radar device LFM pulse signal PSLR parameter determination method.
Background
When the radar equipment works, a partial least squares regression (PSLR) parameter of an input Linear Frequency Modulation (LFM) pulse signal needs to be determined, and the PSLR parameter is used for judging the waveform quality of the input LFM pulse signal. The frequency of the linear frequency modulation LFM pulse signal is linearly scanned upwards or downwards in the pulse width, and the narrow pulse signal can be obtained after the LFM pulse signal is processed by adopting a pulse compression method. The peak sidelobe ratio PSLR parameter is the power ratio of the main lobe peak to the maximum sidelobe within 10 times the main lobe width, typically expressed in decibels.
The traditional pulse signal PSLR parameter determination method is usually carried out in a time domain video, a I, Q orthogonal dual-channel processing scheme is adopted, signals are restored into baseband video signals through orthogonal phase detection, digital signals are formed through analog-to-digital conversion, I, Q dual-channel digital signals formed through orthogonal digital frequency mixing are subjected to complex correlation operation, namely matched filtering processing, the output of the dual-channel correlation operation is subjected to modulus taking processing to obtain a compressed pulse envelope, and the compressed pulse envelope is analyzed and determined to obtain PSLR parameters. Because the traditional pulse signal PSLR parameter determination method uses complex correlation operation in the time domain, the processing speed is low.
Disclosure of Invention
In view of the above technical problems, an object of the present invention is to provide a method for determining a PSLR parameter of an LFM pulse signal of a radar device, so as to solve the problem of a slow processing speed of a conventional method for determining a PSLR parameter of a pulse signal.
A method for determining parameters of an LFM pulse signal PSLR of radar equipment comprises the following steps:
s1, building a PSLR parameter determination system;
s2, carrying out data caching on the LFM pulse signal to form a data sample;
s3, performing time-frequency transformation on the data sample and the matched filter respectively;
s4, carrying out time-frequency inverse transformation on the data subjected to windowing processing on the frequency domain data product result;
and S5, performing polynomial curve fitting on the time domain data, and obtaining PSLR parameters according to fitting data.
Further, the PSLR parameter determination system specifically includes a data caching module, a time-frequency transform module, a time-frequency inverse transform module, and a PSLR parameter determination module.
Further, the step S2 is executed by a data caching module,
the data caching module performs data caching on the LFM pulse signal x (t) within the time range of the pulse width tau to form a data sample x (n), wherein x (t) = Aexp [ j2 pi (f) 0 t+μt 2 /2)]，
Wherein A is amplitude and signal sampling frequency is f S Sampling interval
t is a time parameter, N is a time domain data point index, N =1,2, …, N is the length of a data sample, exp [ ·]Denotes e [·] ；
The center frequency of LFM pulse signal x (t) is f 0 Low end frequency of f 1 Bandwidth B, chirp rate μ = B/τ.
Further, said step S3 is performed by a time-frequency transform module,
the time-frequency transformation module carries out time-frequency transformation on the data sample X (n), and transforms the data sample X (n) from a time domain to a frequency domain to obtain frequency domain data X (k);
constructing a matched filter h (n) for the data sample x (n):
and performing time-frequency transformation on the matched filter H (N), and transforming the matched filter H (N) from a time domain to a frequency domain to obtain frequency domain data H (k), wherein k is a frequency domain data point index, k =1,2, …, and N is the length of a data sample.
Further, in the step S3, the time-frequency transformation is implemented by using a radix-2 FFT algorithm.
Further, said step S4 is performed by an inverse time-frequency transform module,
the time-frequency inverse transformation module multiplies the frequency domain data X (k) by the frequency domain data H (k), and windowing is carried out on the product result to obtain windowed data G (k):
G(k)＝X(k)·H(k)·W(k)，
wherein W (k) is Hamming window frequency domain data,
and performing time-frequency inverse transformation on the data G (k), and transforming the data G (k) from a frequency domain to a time domain to obtain time domain data G (n).
Further, the time domain data g (n) in step S4 is obtained by the following formula:
wherein G * (k) Is the conjugate data of data G (k), W N In order to be a factor of rotation,
j is an imaginary unit, e -j2πnk/N In complex representation.
Further, said step S5 is performed by a PSLR parameter determination module,
the PSLR parameter determination module performs polynomial curve fitting on time domain data g (n) to obtain fitting data f (n), and the fitting data f (n) can be obtained through an equation
Solving for f (n), wherein i is a data index;
taking a module value of the fitting data f (n), converting the module value into a decibel form, and then carrying out normalization processing to obtain a normalization processing result eta (n);
determining the index value n of the time domain point number corresponding to eta (n) = -3dB 1 And n 2 Then the main lobe width L = (n) 2 -n 1 )/f s ；
Determining the index n corresponding to the first zero point 3 And n 4 Remove the index n 3 To n 4 The maximum value eta of eta (n) is determined within the width range of 10L max ，-η max I.e. the PSLR parameter to be determined.
Further, the normalizing specifically includes:
η(n)＝20×lg(|x'(n)|/A)，
where a is the maximum value of the modulus | x' (n) |, i.e., the main lobe peak.
Further, when the polynomial curve fitting is performed on the time domain data g (n) in step S5, the degree of the polynomial is 10.
The method solves the problem that the traditional pulse signal PSLR parameter determination method is low in processing speed, determines the PSLR parameter accurately, and meets the use requirement of the system better.
Drawings
Fig. 1 is a flow chart of a method for determining parameters of LFM pulse signals PSLR of a radar apparatus according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A method for determining parameters of LFM pulse signals PSLR of radar equipment comprises the following specific steps:
firstly, establishing a PSLR parameter determination system
A PSLR parameter determination system comprising: the device comprises a data caching module, a time-frequency transformation module, a time-frequency inverse transformation module and a PSLR parameter determination module.
The data caching module is used for caching data of the LFM pulse signal to form a data sample;
the time-frequency transformation module is used for respectively carrying out time-frequency transformation on the data sample and the matched filter;
the time-frequency inverse transformation module is used for carrying out time-frequency inverse transformation on the data subjected to windowing processing on the frequency domain data product result;
the PSLR parameter determining module is used for carrying out polynomial curve fitting on the time domain data and obtaining PSLR parameters according to fitting data.
Secondly, the data caching module caches the LFM pulse signal to form a data sample
The data caching module performs data caching on the LFM pulse signal x (t) within the time range of the pulse width tau to form a data sample x (n), wherein x (t) = Aexp [ j2 pi (f) 0 t+μt 2 /2)]，
Where A is amplitude and the signal sampling frequency f S =2.5GHz, sampling interval
t is a time parameter, N is a time domain data point index, N =1,2, …, N is the length of a data sample, exp [ ·]Denotes e [·] ；
The center frequency of LFM pulse signal x (t) is f 0 Low end frequency of f 1 Bandwidth B, chirp rate μ = B/τ.
Thirdly, the time frequency conversion module respectively carries out time frequency conversion on the data sample and the matched filter
The time-frequency transformation module carries out time-frequency transformation on the data sample X (n) by using a radix-2 FFT algorithm, and transforms the data sample X (n) from a time domain to a frequency domain to obtain frequency domain data X (k); constructing a matched filter h (n) for the data sample x (n):
and performing time-frequency transformation on the matched filter H (N) by using a radix-2 FFT algorithm, and transforming the matched filter H (N) from a time domain to a frequency domain to obtain frequency domain data H (k), wherein k is a frequency domain data point index, and k =1,2, …, N.
Fourthly, the time-frequency inverse transformation module carries out time-frequency inverse transformation on the data after the windowing of the frequency domain data product result
The inverse time-frequency transform module multiplies the frequency domain data X (k) by the frequency domain data H (k), and the product result is subjected to windowing: g (k) = X (k) · H (k) · W (k), resulting in windowed data G (k),
wherein W (k) is Hamming window frequency domain data, and can be represented by formula
Obtaining; and performing time-frequency inverse transformation on the data G (k):
transforming the data G (k) from the frequency domain to the time domain to obtain time domain data G (n), wherein G * (k) Is the conjugate data of data G (k), W N In order to be a factor of rotation,
j is an imaginary unit, representing
e -j2πnk/N In complex representation.
Fifthly, the PSLR parameter determination module performs polynomial curve fitting on the time domain data and obtains PSLR parameters according to the fitting data
The PSLR parameter determination module performs polynomial curve fitting on time domain data g (n) to obtain fitting data f (n), and the fitting data f (n) can be obtained through an equation
Solving for f (n), wherein i is a data index; and (3) taking a module value of the fitting data f (n), converting the fitting data f (n) into a decibel form, and then carrying out normalization processing: η (n) =20 × lg (| x '(n) |/a), obtaining a normalization processing result η (n), wherein | · | represents a modular operation, and a is a maximum value in a module value | x' (n) |, namely a main lobe peak value;
determining the index value n of the time domain point number corresponding to eta (n) = -3dB 1 =1970 and n 2 =2052, the main lobe width L = (n) 2 -n 1 )/f s =32.8ns; determine the first zeroIndex n corresponding to point 3 =830 and n 4 =3082, remove index n 3 To n 4 The maximum value eta of eta (n) is determined within the width range of 10L max ＝-37.9dB，-η max The PSLR parameter to be determined is =37.9dB, and the time required for determining the PSLR parameter is 808ms, which is 1.2S better than the time required for calculating by the traditional method.
Therefore, the parameter of the LFM pulse signal PSLR of the radar equipment is determined.
It should be understood that the above embodiments are only examples for clarity of description, and are not limiting. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are intended to be within the scope of the invention.
Claims (10)
1. A method for determining parameters of an LFM pulse signal PSLR of radar equipment is characterized by comprising the following steps:
s1, building a PSLR parameter determination system;
s2, carrying out data caching on the LFM pulse signal to form a data sample;
s3, performing time-frequency transformation on the data sample and the matched filter respectively;
s4, carrying out time-frequency inverse transformation on the data subjected to windowing processing on the frequency domain data product result to obtain time domain data g (n);
s5, performing polynomial curve fitting on the time domain data g (n) to obtain fitting data f (n), and performing equation
Solving for f (N), wherein i is a data index, N is a time domain data point index, N =1,2, …, and N is the length of a data sample; taking a module value of the fitting data f (n), converting the module value into a decibel form, then carrying out normalization processing, and obtaining a normalization processing result eta (n) through an equation eta (n) =20 × lg (| x '(n) |/A), wherein the normalization processing result eta (n) is obtained through the equation eta (n) =20 × lg (| x' (n) |/A)Wherein a is the maximum value of the module values | x' (n) |, i.e. the main lobe peak value; determining the index value n of the time domain point number corresponding to eta (n) = -3dB 1 And n 2 Then the main lobe width L = (n) 2 -n 1 )/f s Wherein f is s A signal sampling frequency; determining the index n corresponding to the first zero point 3 And n 4 Remove the index n 3 To n 4 The maximum value eta of eta (n) is determined within the width range of 10L max ，-η max I.e. the PSLR parameter to be determined.
2. The method of claim 1, wherein the PSLR parameter determination system comprises a data buffer module, a time-frequency transform module, an inverse time-frequency transform module, and a PSLR parameter determination module.
3. The determination method according to claim 2, wherein said step S2 is performed by a data caching module,
the data caching module performs data caching on the LFM pulse signal x (t) within the time range of the pulse width tau to form a data sample x (n), wherein x (t) = Aexp [ j2 pi (f) 0 t+μt 2 /2)]，
Wherein A is amplitude and signal sampling frequency is f s Sampling interval
t is a time parameter, N is a time domain data point index, N =1,2, …, and N is the length of a data sample;
center frequency f of LFM pulse signal x (t) 0 Low end frequency of f 1 Bandwidth is B, chirp rate μ = B/τ.
4. The determination method according to claim 3, characterized in that said step S3 is performed by a time-frequency transform module,
the time-frequency transformation module carries out time-frequency transformation on the data sample X (n), and transforms the data sample X (n) from a time domain to a frequency domain to obtain frequency domain data X (k);
constructing a matched filter h (n) for the data sample x (n):
and performing time-frequency transformation on the matched filter H (N), and transforming the matched filter H (N) from a time domain to a frequency domain to obtain frequency domain data H (k), wherein k is a frequency domain data point index, k =1,2, …, and N is the length of a data sample.
5. The method of claim 4, wherein the time-frequency transform in step S3 is implemented by a radix-2 FFT algorithm.
6. The determination method according to claim 4, characterized in that said step S4 is performed by an inverse time-frequency transform module,
the time-frequency inverse transformation module multiplies the frequency domain data X (k) by the frequency domain data H (k), and windowing is carried out on a product result to obtain windowed data G (k):
G(k)＝X(k)·H(k)·W(k)，
wherein W (k) is Hamming window frequency domain data,
and performing time-frequency inverse transformation on the data G (k), and transforming the data G (k) from a frequency domain to a time domain to obtain time domain data G (n).
7. The determination method according to claim 6, wherein the time domain data g (n) in step S4 is obtained by the following equation:
wherein G is * (k) Is the conjugate number of the data G (k)According to W N In order to be a factor of rotation,
j is an imaginary unit, e -j2πnk/N In complex representation.
8. The determination method according to claim 2, wherein said step S5 is performed by a PSLR parameter determination module,
the PSLR parameter determination module performs polynomial curve fitting on time domain data g (n) to obtain fitting data f (n), and the fitting data f (n) is obtained through an equation
Solving for f (n), wherein i is a data index;
taking a module value of the fitting data f (n), converting the module value into a decibel form, and then carrying out normalization processing to obtain a normalization processing result eta (n);
determining the index value n of the time domain point number corresponding to eta (n) = -3dB 1 And n 2 Then the main lobe width L = (n) 2 -n 1 )/f s ；
Determining the index n corresponding to the first zero point 3 And n 4 Remove the index n 3 To n 4 The maximum value eta of eta (n) is determined within the width range of 10L max ，-η max I.e. the PSLR parameter to be determined.
9. The determination method according to claim 8, wherein the performing normalization processing specifically includes:
η(n)＝20×lg(|x'(n)|/A)，
where a is the maximum value of the modulus | x' (n) |, i.e., the main lobe peak.
10. The determination method according to claim 8, wherein, when the polynomial curve fitting is performed on the time-domain data g (n) in the step S5, the degree of the polynomial is 10.
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