CN111123214B - Polynomial rotation-polynomial Fourier transform high-speed high-maneuvering target detection method - Google Patents

Abstract

The invention discloses a polynomial rotation-polynomial Fourier transformation high-speed high-mobility target detection method, which comprises the steps of respectively performing digital sampling and pulse compression processing on M radar pulse echoes in accumulation time to obtain a fast-slow two-dimensional radar echo data matrix; then determining the order and range of the search parameter set according to the motion characteristics of the target to be detected and initializing the search parameter set; performing coherent accumulation on the data matrix in the whole parameter search space by utilizing polynomial rotation-polynomial Fourier transformation to obtain a distance-Doppler distribution diagram; finally, whether the target exists or not is judged by utilizing the constant false alarm detection, and if the target exists, the distance and the motion state information of the target can be obtained. The polynomial rotation-polynomial Fourier transform can achieve the same theoretical optimal accumulation effect with the polynomial rotation-polynomial Fourier transform under the condition that the computational complexity is far less than Yu Anyi radon transform, and realize the coherent accumulation of high-speed and high-maneuvering targets in the near space under the environment of low signal-to-noise ratio.

Description

High-speed and high-maneuvering target detection method based on polynomial rotation-polynomial Fourier transform

technical field

The invention belongs to the technical field of radar signal processing and detection, and in particular relates to a polynomial rotation-polynomial Fourier transform high-speed and high-mobility target detection method.

Background technique

In recent years, with the development of science and technology, the emergence of high-speed and high-mobility targets near space has brought severe challenges to radar detection, such as super-sonic and high-mobility fighter jets and missiles in the air defense field, as well as orbits that need to be monitored in space Targets and space debris, etc. Some targets can fly at a speed of Mach 25, and can change their flight trajectory through many irregular ways such as flipping, jumping, and large corners, and have extremely strong maneuverability. In addition, due to the maturity of the stealth technology and the plasma generated by the high-speed movement of the aircraft in the atmosphere, the signal-to-noise ratio of the target echo is low, which reduces the detection performance of the radar.

Pulse coherent accumulation is an effective method to increase the probability of target detection, and prolonging the radar staring time is an effective means to increase the gain of coherent accumulation for low signal-to-noise ratio targets. However, the target's ultra-high speed and high maneuverability have brought certain limitations to the accumulation time. On the one hand, the high speed of the target leads to the span gate phenomenon in the distance dimension of the target in the accumulation time; on the other hand, the high maneuverability of the target leads to the Doppler frequency migration phenomenon in the Doppler frequency dimension of the target.

At present, coherent accumulation mainly revolves around moving target detection (MTD) technology, Keystone transform and Radon Fourier transform and their extensions. Most of the existing methods use the time-frequency analysis method to estimate the target motion parameters after correcting the distance walking. However, when the target's acceleration and other higher-order motion parameters lead to the target's nonlinear range walking and Doppler migration, most existing methods are difficult to achieve range walking correction and Doppler migration compensation at the same time, so they cannot achieve the theoretical goal. Detection performance.

Contents of the invention

The object of the present invention is to provide a polynomial rotation-polynomial Fourier transform high-speed and high-maneuvering target detection method, which is suitable for the detection of high-speed and high-maneuvering targets in adjacent spaces.

The technical solution that realizes the object of the present invention is: a kind of polynomial rotation-polynomial Fourier transform high-speed high-mobility target detection method, comprising:

Step 1. Perform digital sampling and pulse compression processing on the radar pulse echo within the accumulation time to obtain a two-dimensional echo data matrix;

Step 2, determine the number and range of parameters to be searched;

Step 3: Carry out polynomial rotation transformation on the two-dimensional echo data matrix from the initial search parameter group, and perform non-coherent accumulation on the transformed echo data matrix; if the maximum value of the superimposed one-dimensional data is greater than the threshold value, then transform Execute the polynomial Fourier transform in step 4 for the final echo data matrix; otherwise change the search parameter group and execute step 3;

Step 4, perform phase compensation and then accumulate the search parameter groups that meet the conditions in step 3, and then change the search parameter group to perform step 3 until all search parameter groups are traversed;

Step 5, performing constant false alarm detection on the obtained range-Doppler distribution map, and judging whether there is a target;

Step 6, determine the coordinates of the target position, and determine the target motion parameters.

Compared with the prior art, the remarkable advantage of the present invention is: the polynomial rotation-polynomial Fourier transform can achieve the same theoretical best accumulation effect and realize low-signal Coherent accumulation of high-speed and high-maneuvering targets in near space under noise ratio environment.

Description of drawings

FIG. 1 is a flow chart of the polynomial rotation-polynomial Fourier transform high-speed and high-mobility target detection method of the present invention.

Figure 2 is the MTD accumulation distance-Doppler distribution map.

Figure 3 is the PRPFT cumulative range-Doppler distribution map.

Figure 4 is a graph of the non-coherent accumulation results of a single PRT.

Detailed ways

As shown in Figure 1, the high-speed and high-maneuvering target detection method of the polynomial rotation-polynomial Fourier transform (PRPFT) of the present invention first carries out digital sampling and pulse compression processing to the M radar pulse echoes in the accumulation time respectively, and obtains Fast time-slow time two-dimensional radar echo data matrix; then determine the order and range of the search parameter group and initialize the search parameter group according to the motion characteristics of the target to be detected; then use polynomial rotation-polynomial Fourier transform in the entire parameter search space The internal data matrix is coherently accumulated to obtain the range-Doppler distribution map; finally, the constant false alarm detection is used to judge the presence or absence of the target, and if there is a target, the distance and motion state information of the target can be obtained. Specific steps are as follows:

Step 1: Echo signal sampling and pulse compression

Sampling the coherently accumulated echo signals of M cycles and performing pulse compression processing to obtain a two-dimensional echo data matrix s(n,m), where n represents the label of the sampling point in the distance dimension, n=1,2,.. ., N, N is the total number of single-cycle echo sampling points, m represents the number label of echo signals, m=1,2,...,M, M is the total number of coherent accumulated echoes;

Step 2: Determine the parameter search scope

According to the actual situation of the radar and the target to be detected, determine the number and range of parameters to be searched;

Set the search speed range as [v _min , v _max ], where v _min is the lower bound of the speed search range, and v _max is the upper bound of the speed search range; set its speed search interval as Δv, Δv=λ/2T, where λ is the carrier wavelength, T is the accumulation time; there are N _v speed search points, N _v = round((v _max -v _min )/Δv), where round(·) is a rounding function; the search acceleration range is set as [a _min , a _max ], wherein a _min is the lower bound of the acceleration search range, a _max is the upper bound of the acceleration search range; the acceleration search interval is set to be Δa, there are N _a acceleration search points, Na ₌ round( (a _max -a _min )/Δa); if there are higher-order motion parameters, the parameter search range is set in turn. Determine the search parameter group (α ₁ ,α ₂ ,...,α _k ), where α _i ,i=1,2,...,k corresponds to the i-th order motion parameter of the target and α _i contains N _i search points .

Step 3: Polynomial Rotation Transformation

Starting from the initial search parameter set (α ₁ ,α ₂ ,...,α _k ), perform polynomial rotation transformation on the two-dimensional echo data matrix s(n,m) to obtain the rotated data matrix s(n′,m′ ), the transformation relationship is as follows:

Among them, B is the bandwidth of the chirp signal, T _r is the pulse repetition interval, and c is the speed of light. Non-coherent accumulation is performed on the transformed echo data matrix, that is, M echo signal envelopes are superimposed. If the maximum value of the superimposed one-dimensional data is greater than the threshold A _th , perform the polynomial Fourier transform in step 4 on s(n′,m′), and the threshold A _th is

Among them, abs(·) is the modulo function, N is the total number of single-period echo sampling points, and M is the total number of coherent accumulated echoes; otherwise, change the search parameter group and execute step 3.

Step 4: Polynomial Fourier Transform

Perform phase compensation on the search parameter groups that meet the conditions in step 3 and then accumulate, the method is as follows

Among them, s(n′,m′) is the data matrix after rotation transformation, (α ₁ ,α ₂ ,...,α _k ) is the search parameter group, λ is the carrier wavelength, and T _r is the pulse repetition interval. Then change the search parameter group and perform step 3 until all search parameter groups are traversed.

Step 5: Constant False Alarm Detection

Constant false alarm detection is performed on the obtained range-Doppler distribution map to judge whether there is a target or not.

Step 6: Motion Parameter Estimation

Determine the coordinates of the existing target position, and determine the target motion parameters.

The present invention will be described in detail below through embodiments and accompanying drawings.

Example

A polynomial rotation-polynomial Fourier transform high-speed and high-mobility target detection method, comprising:

Step 1: Digitally sample M periodic radar signal echoes within the radar coherent accumulation time T at the sampling frequency f _s , and then perform pulse compression processing on the sampled data to obtain fast time-slow time two-dimensional radar echo data Matrix s(n,m). Among them, n represents the number of sampling points in the distance dimension, n=1,2,...,N, N is the total number of sampling points of single-cycle echoes, m represents the number of echo signals, m=1,2,..., M, M is the total number of coherent accumulated echoes.

Step 2: For the high-speed and high-maneuvering target to be detected has a higher-order motion state above acceleration, set the search speed range to [v _min , v _max ], where v _min is the lower bound of the speed search range, and v _max is the speed search The upper bound of the range. Set the speed search interval as Δv, Δv=λ/2T, where λ is the carrier wavelength, and T is the accumulation time. There are N _v speed search points, N _v = round((v _max -v _min )/Δv), where round( ) is a rounding function; set the search acceleration range to [a _min ,a _max ], where is a _min is the lower bound of the acceleration search range, and a _max is the upper bound of the acceleration search range. Set its acceleration search interval as Δa, Δa=λ/4T ² . There are N _a acceleration search points, N _a =round((a _max -a _min )/Δa); if there are higher-order motion parameters, the parameter search ranges are set sequentially. Determine the search parameter group (α ₁ ,α ₂ ,...,α _k ), where α _i ,i=1,2,...,k corresponds to the i-th order motion parameter of the target and α _i contains N _i search points .

For the convenience of explanation, the third-order and above motion parameters of the radar target are ignored in the following detailed description, that is, the target moves in a uniformly accelerated linear motion relative to the radar, then the search parameter group can be determined as (v,a), and the initialization parameter group is (v _min , a _min ).

Step 3: The polynomial rotation transformation relationship can be obtained by using the search parameter group

Where (v _i , a _j ) is the current search parameter set, B is the bandwidth of the chirp signal, T _r is the pulse repetition interval, and c is the speed of light. Use the polynomial rotation transformation relationship to transform s(n,m) into s(n',m'), obtain the fast time-slow time two-dimensional echo data matrix after rotation, and perform non-phased radar echo in the slow time dimension Parameter accumulation, the method is as follows

Among them, abs(·) is a modulo function. Set the threshold A _th as

When max(R(n′))>A _th , perform polynomial Fourier transform in step 3, otherwise update the search parameter set and perform polynomial rotation transformation again until all search parameter sets are traversed.

Step 4: Perform polynomial Fourier transform on the current search parameter set (v _i , a _j ) to realize coherent accumulation. The polynomial rotation transformation screens out possible search parameter groups for polynomial Fourier transformation, which reduces the computational complexity of the algorithm. The Fourier transform is as follows

Among them, s(n′,m′) is the data matrix after rotation transformation, (v _i , a _j ) is the search parameter group, λ is the carrier wavelength, and T _r is the pulse repetition interval. Then update the search parameter group and execute step 3 until all search parameter groups are traversed.

The method of traversing all search parameter groups (v, a) is: the total number of cycles is N _v N _a , the current number of cycles is k, k=1, 2,..., N _v N _a , assuming the kth group of search parameters The group is (v _i , a _j ), where i=floor(k/N _v ), floor(·) is the rounding down function, j=mod(k,N _v ), mod(·) is the remainder function . k=N _{v Na} represents that all search parameter spaces have been traversed. At this time, the range-Doppler distribution map after coherent accumulation can be obtained.

Step 5: Carry out constant false alarm detection on the range-Doppler distribution map, and judge whether there is a target. The judgment method is as follows

In the formula, (vi, aj) is the search parameter group, η is the detection threshold, and N is the total number of single-cycle echo sampling points. If the amplitude value of the detection unit is higher than the threshold, it is determined that the detection unit has a target, otherwise it is determined that there is no target, and the detection process of the subsequent unit is continued.

Step 6: If there is a target, extract the target position information as n′, velocity information as v _i , and acceleration information as a _j .

Effect of the present invention is verified as follows by Matlab:

The simulation parameters are set as follows: carrier frequency f ₀ = 1GHz, pulse width T = 60us, bandwidth B = 2MHz, pulse repetition frequency T _r = 600us, accumulated pulse number N _pulse = 64, sampling frequency f _s = 4MHz, initial target distance R ₀ =4000km, radial initial velocity V ₀ =4000m/s, acceleration a=100m/s ² .

Simulation results and analysis: As can be seen from Figure 2, the traditional MTD algorithm has poor cumulative performance when facing high-speed and high-maneuvering targets. The distance movement of the target leads to a large deviation in the range unit of the target estimate, and the Doppler migration makes it impossible to gather the target energy well through FFT. Therefore, the final cumulative performance cannot meet the requirements of constant false alarm detection. It can be seen from Figure 3 that after the polynomial rotation-polynomial Fourier transform, the energy of the target accumulation is well gathered, which is obviously better than the MTD algorithm. Figure 4 shows the results of PRT non-coherent accumulation. It can be seen that the peak energy of the target is obvious but the noise floor is too high. It is not suitable for target detection and can only be used as a reference for the existence of the target.

Claims (6)

1. a kind of polynomial rotation-polynomial Fourier transform high-speed high maneuvering target detection method, it is characterized in that, comprising: Step 1. Perform digital sampling and pulse compression processing on the radar pulse echo within the accumulation time to obtain a two-dimensional echo data matrix; Step 2, determine the number and range of parameters to be searched; Step 3: Perform polynomial rotation transformation on the two-dimensional echo data matrix starting from the initial search parameter group, and perform non-coherent accumulation on the transformed echo data matrix; if the maximum value of the superimposed one-dimensional data is greater than the threshold value, then transform Execute the polynomial Fourier transform in step 4 for the final echo data matrix; otherwise change the search parameter group and execute step 3; the specific method is: Starting from the initial search parameter set (α ₁ ,α ₂ ,...,α _k ), perform polynomial rotation transformation on the two-dimensional echo data matrix s(n,m) to obtain the rotated data matrix s(n′,m′ ), where α _i corresponds to the i-th order motion parameter of the target and α _i includes N _i search points, n represents the label of the sampling point in the distance dimension, n=1,2,...,N, and N is the sampling point of the single-cycle echo The total number, m represents the label of the number of echo signals, m=1,2,...,M, M is the total number of coherent accumulated echoes; The conversion relationship is as follows: Among them, k is the order of the polynomial, B is the bandwidth of the chirp signal, T _r is the pulse repetition interval, and c is the speed of light; non-coherent accumulation is performed on the transformed echo data matrix, that is, M echo signal envelopes are Superposition; if the maximum value of the superimposed one-dimensional data is greater than the threshold A _th , then perform the polynomial Fourier transform in step 4 on s(n′,m′), and the threshold A _th is Among them, abs( ) is a modulo function; Otherwise, change the search parameter group and execute step 3; Step 4, perform phase compensation and then accumulate the search parameter groups that meet the conditions in step 3, and then change the search parameter group to perform step 3 until all search parameter groups are traversed; Step 5, performing constant false alarm detection on the obtained range-Doppler distribution map, and judging whether there is a target; Step 6, determine the coordinates of the target position, and determine the target motion parameters. 2. the high-speed high maneuvering target detection method of polynomial rotation-polynomial Fourier transform according to claim 1, it is characterized in that, step 1 is specially: The coherently accumulated echo signals of M cycles are sampled and processed by pulse compression to obtain a two-dimensional echo data matrix s(n,m). 3. the method for detecting high-speed and high-maneuvering targets of polynomial rotation-polynomial Fourier transform according to claim 1, characterized in that, step 2 is specifically: setting the search speed range as [v _min , v _max ], where v _min is the lower limit of the speed search range, v _max is the upper limit of the speed search range; set the speed search interval to Δv, Δv=λ/2T, where λ is the carrier wavelength, T is the accumulation time; there are N _v speed search points , N _v = round((v _max -v _min )/Δv), where round( ) is a rounding function; set the search acceleration range to [a _min , a _max ], where a _min is the lower bound of the acceleration search range , a _max is the upper bound of the acceleration search range; set the acceleration search interval to Δa, there are N _a acceleration search points, N _a = round((a _max -a _min )/Δa); if there are higher-order motion parameters , and its parameter search range is set sequentially; determine the search parameter group (α ₁ ,α ₂ ,...,α _k ). 4. the high-speed high maneuvering target detection method of polynomial rotation-polynomial Fourier transform according to claim 1, it is characterized in that, step 4 is specially: Perform phase compensation on the search parameter groups that meet the conditions in step 3 and then accumulate, the method is as follows Among them, s(n′,m′) is the data matrix after rotation transformation, (α ₁ ,α ₂ ,...,α _k ) is the search parameter group, λ is the carrier wavelength, T _r is the pulse repetition interval; then To change the search parameter group, perform step 3 until all search parameter groups have been traversed. 5. the high-speed high maneuvering target detection method of polynomial rotation-polynomial Fourier transform according to claim 1, it is characterized in that, step 5 is specially: Perform constant false alarm detection on the range-Doppler distribution map to judge whether there is a target. The judgment method is as follows: In the formula, (v _i , a _j ) is the search parameter group, η is the detection threshold, and N is the total number of single-cycle echo sampling points; if the amplitude value of the detection unit is higher than the threshold, it is determined that the detection unit has a target, otherwise it is determined that there is no target. If there is a target, the detection process of the subsequent unit is continued. 6. The high-speed and high-maneuvering target detection method of polynomial rotation-polynomial Fourier transform according to claim 1, characterized in that, in step 6, target motion parameters include target velocity and acceleration information.