CN111580082A - Radar moving and static target distinguishing method based on clutter spectrum estimation - Google Patents

Abstract

The invention relates to a radar moving and static target distinguishing method based on clutter spectrum estimation, aiming at the phenomena of broadening of clutter spectrum and shifting of spectrum center of ground clutter when a radar platform moves, and accurately estimating the spectrum center and the spectrum width so as to accurately distinguish moving and static targets. The invention adopts a method of jointly estimating the clutter spectrum by using inertial navigation data and echo data to calculate the center and the spectrum width of the clutter spectrum, and then distinguishes moving and static targets by taking the center and the spectrum width as the reference. The method adopts a joint estimation method to estimate the spectrum center, so that the problem of inaccurate estimation caused by low precision of inertial navigation data can be avoided, and the problem of large estimation deviation caused by poor quality of echo data can be avoided; the clutter spectrum width is estimated by adopting the real-time inertial navigation information, the problem of overlarge redundancy caused by fixed spectrum width can be avoided, and the detection distinguishing capability of a near clutter region, namely a low-speed target, is improved.

Description

Radar moving and static target distinguishing method based on clutter spectrum estimation

Technical Field

The invention belongs to the technical field of radar signal processing, and particularly relates to a radar moving and static target distinguishing method based on clutter spectrum estimation. The method aims at the phenomenon that ground clutter has spread spectrum and spectrum center shift when a radar platform moves, and the spectrum center and the spectrum width need to be accurately estimated, so that moving and static targets are accurately distinguished.

Background

Moving object detection is one of the important functions that radar must have. In general, when the radar platform is in a stationary state, the ground clutter is concentrated at the 0 Doppler frequency (i.e., f) on the range-Doppler plane_d0Hz), the clutter spectrum is also narrow. In general, in signal processing, the center frequency is often 0Hz and a fixed spectral width (i.e. f) is set_dwC (constant)), the target doppler frequency is located at [ -f [_dw/2,f_dw/2]The ones within the interval are considered static targets, while those outside the interval are considered dynamic targets.

When the radar platform moves, the ground clutter has a certain speed relative to the radar, the center of a clutter spectrum is shifted, and the speeds of ground clutter scatterers in different directions relative to the radar are different, so that the clutter spectrum is widened. At this time, the clutter spectrum center and the spectrum width are not fixed, and the spectrum center and the spectrum width need to be accurately estimated so as to accurately distinguish the moving target and the static target.

In signal processing, clutter spectrum estimation usually adopts two methods, namely inertial navigation information estimation and echo data estimation. The two methods have respective advantages and disadvantages: the inertial navigation information is generally stable and is not easy to generate larger deviation, but the accuracy of clutter spectrum estimation is directly influenced by data accuracy and update rate; although echo data is more fit with an actual clutter spectrum, the risk of generating poor quality data under the influence of a system exists, and clutter spectrum estimation deviation is large.

Disclosure of Invention

Technical problem to be solved

In the motion of a radar platform, ground clutter has the phenomena of broadening of a clutter spectrum and shifting of a spectrum center, and the clutter spectrum center and the spectrum width need to be accurately estimated so as to accurately distinguish a moving target and a static target. In order to avoid the defects of the prior art, the invention provides a radar moving and static target distinguishing method based on clutter spectrum estimation.

Technical scheme

A radar dynamic and static target distinguishing method based on clutter spectrum estimation is characterized by comprising the following steps:

step 1: obtaining real-time inertial navigation information and range-Doppler domain echo data under an NED coordinate system;

step 2: with the speed information: eastern speed v_eastNorth velocity v_northVelocity v_up(ii) a Angle information: azimuth angle θ, pitch angle Φ, and system information: azimuth beam width θ_3dBPulse repetition frequency PRF and coherent accumulation pulse number M; calculating the center f of the spectrum_{dc_ins_index}And spectral width f_{dw_ins_num}：

Step 2.1: calculating the speed v of the central sight line direction of the wave beam_losAnd beam width two boundary direction velocity

v_los＝v_east×cos(φ)×sin(θ)+v_north×cos(φ)×cos(θ)+v_up×sin(φ)

Step 2.2: calculating the center f of the spectrum_{dc_ins}And spectral width f_{dw_ins}And deblurring the spectral center:

f_{dc_ins}＝mod(f_{dc_ins},PRF)

step 2.3: quantifying the Doppler frequency, and calculating the Doppler channel number f corresponding to the spectrum center_{dc_ins_index}Number of Doppler channels f corresponding to the spectral width_{dw_ins_num}：

f_{dc_ins_index}＝round(f_{dc_ins}/Δf_d)

f_{dw_ins_num}＝round(f_{dw_ins}/Δf_d)

Δf_d＝PRF/M

Wherein M is the number of coherent accumulation points;

and step 3: the range-Doppler domain echo data is segmented along a range dimension, the number of range units is N, the range units are evenly divided into K sections, and a spectrum center f is calculated_{dc_data_index}：

Step 3.1: for each section of power spectrum P_f(m, n) are added along the distance dimension to calculate the sum sigma of the power spectra of each segment_f(m,k)：

Step 3.2: for sigma_f(m, k) searching the maximum value coordinate along the Doppler dimension, and calculating the center f of each section of spectrum_{dc_seg_index}(k)：

Step 3.3: for each segment of spectrum center f_{dc_seg_index}(k) Calculating the center of mass to calculate the average spectrum center f_{dc_data_index}：

And 4, step 4: combined inertial navigation estimated value f_{dc_ins_index}And the data estimation value f_{dc_data_index}Calculating the final estimated value f of the center and width of the spectrum_{dc_index}、f_{dw_num}：

f_{dc_index}＝(|f_{dc_data_index}-f_{dc_ins_index}|<)？f_{dc_data_index}:f_{dc_ins_index}

f_{dw_num}＝k×f_{dw_ins_num}

And 5: selecting a Target (f) to be detected_index) And judging whether the target is a moving target:

step 6: and outputting the dynamic and static characteristic information of the target.

The number in step 4 is 5.

Advantageous effects

The invention provides a radar dynamic and static target distinguishing method based on clutter spectrum estimation, which comprises the following steps: (a) obtaining real-time inertial navigation information and range-Doppler domain echo data; (b) calculating the center and the width of an inertial navigation spectrum by using the speed information, the angle information and the system information; (c) carrying out segmentation processing on the range-Doppler domain echo data along a range dimension, calculating the center of a power spectrum of each segment, solving the center of mass of the power spectrum, and calculating the center of a data spectrum; (d) calculating a spectrum center and a final spectrum width estimated value by combining the inertial navigation estimated value and the data estimated value; (e) selecting a detection target, if the absolute value of the difference value between the Doppler of the target and the spectrum center is less than half of the spectrum width (if the absolute value is quantized into a Doppler channel number, frequency folding needs to be considered), judging the target to be a static target, and otherwise, judging the target to be a moving target; (f) and outputting the dynamic and static characteristic information of the target. The clutter spectrum center is estimated by combining the inertial navigation information and the echo data, so that the problem of inaccurate estimation caused by low accuracy of the inertial navigation information is solved, and the problem of large estimation deviation caused by poor quality of the echo data is solved; the clutter spectrum width is estimated by adopting the real-time inertial navigation information, the problem of overlarge redundancy caused by fixed spectrum width can be avoided, and the detection distinguishing capability of a near clutter region, namely a low-speed target, is improved.

Drawings

FIG. 1 flow chart of joint estimation method

FIG. 2 Radar platform geometry map

FIG. 3 distance segmentation

FIG. 4 distance segmentation estimation schematic diagram

FIG. 5 is a flow chart of the present invention

Detailed Description

The invention will now be further described with reference to the following examples and drawings:

the method for estimating the clutter spectrum by combining inertial navigation information and echo data (hereinafter referred to as a combined estimation method) is adopted to calculate the center and the spectrum width of the clutter spectrum, and then the dynamic and static targets are distinguished by taking the center and the spectrum width as references.

FIG. 1 is a flow chart of a joint estimation method, which includes two parts, inertial navigation estimation and data estimation. The inertial navigation estimation is to calculate an inertial navigation estimation value of a spectrum center and a spectrum width by utilizing speed information and angle information in the inertial navigation information; and the data estimation is to perform distance segmentation processing by using the power spectrum of the echo data and calculate the data estimation value of the spectrum center.

The final estimated value of the spectrum center takes an inertial navigation estimated value as a reference, if the absolute value of the difference between the data estimated value and the data estimated value is smaller than a threshold (usually a counted value), the data estimated value is taken as a final result, and if the absolute value of the difference is larger, the inertial navigation estimated value is taken as a final result; the spectral width is multiplied by a factor k (k is usually related to the beam-sharpening ratio) based on the inertial navigation estimate.

The dynamic and static target discrimination takes the spectrum center and the spectrum width as the reference: if the absolute value of the difference between the target Doppler and the spectrum center is less than half the spectrum width (if the difference is quantized into the Doppler channel number, frequency folding needs to be considered), the target is considered to be a static target, otherwise, the target is considered to be a moving target.

The inertial navigation estimation mainly depends on a formula to describe the specific implementation in detail, and the angle geometric relationship refers to fig. 2; the data estimation will be described in detail with reference to fig. 3 and 4.

Step 1: obtaining real-time inertial navigation information and range-Doppler domain echo data under an NED coordinate system;

step 2: using velocity information (east velocity v)_eastNorth velocity v_northVelocity v_up) Angle information (azimuth angle θ, depression)Elevation angle phi) and system information (azimuth beam width theta)_3dBPulse repetition frequency PRF, number of coherent integration pulses M), and calculating center f of spectrum_{dc_ins_index}And spectral width f_{dw_ins_num}：

Step 2.1: calculating the speed v of the central sight line direction of the wave beam_losAnd beam width two boundary direction velocity

v_los＝v_east×cos(φ)×sin(θ)+v_north×cos(φ)×cos(θ)+v_up×sin(φ)

Step 2.2: calculating the center f of the spectrum_{dc_ins}And spectral width f_{dw_ins}And deblurring the spectral center:

f_{dc_ins}＝mod(f_{dc_ins},PRF)

step 2.3: the doppler frequency is quantized (quantization interval Δ f)_dPRF/M), calculating the Doppler channel number f corresponding to the center of the spectrum_{dc_ins_index}Number of Doppler channels f corresponding to the spectral width_{dw_ins_num}：

f_{dc_ins_index}＝round(f_{dc_ins}/Δf_d)

f_{dw_ins_num}＝round(f_{dw_ins}/Δf_d)

And step 3: the range-Doppler domain echo data is segmented along the range dimension (N range units are provided, and the range units are divided into K sections on average), and the spectrum center f is calculated_{dc_data_index}：

Step 3.1: for each section of power spectrum P_f(m, n) are added along the distance dimension to calculate the sum sigma of the power spectra of each segment_f(m,k)：

Step 3.2: for sigma_f(m, k) searching the maximum value coordinate along the Doppler dimension, and calculating the center f of each section of spectrum_{dc_seg_index}(k)：

Step 3.3: for each segment of spectrum center f_{dc_seg_index}(k) Calculating the center of mass to calculate the average spectrum center f_{dc_data_index}：

And 4, step 4: combined inertial navigation estimated value f_{dc_ins_index}And the data estimation value f_{dc_data_index}Calculating the final estimated value f of the center and width of the spectrum_{dc_index}、f_{dw_num}：

f_{dc_index}＝(|f_{dc_data_index}-f_{dc_ins_index}|<)？f_{dc_data_index}:f_{dc_ins_index}

f_{dw_num}＝k×f_{dw_ins_num}

And 5: selecting a Target (f) to be detected_index) And judging whether the target is a moving target:

step 6: and outputting the dynamic and static characteristic information of the target.

Note 1: NED (North East Down) coordinate system, the northeast coordinate system, also called the navigation coordinate system, with the axes defined as follows:

n-the north axis points to the north of the earth;

e-the east axis points to the east of the Earth;

d-the earth's axis is directed vertically downward from the earth's surface.

Note 2: in the geometrical relationship of the radar platform, an azimuth angle theta is an included angle between the projection of the radar sight direction on an NE plane and the N direction, and a pitch angle phi is an included angle between the radar sight direction and the NE plane. When the pitch angle phi is smaller, the component v of the antenna speed in the radar sight line direction_up× sin (phi) is negligible.

Claims (2)

1. A radar dynamic and static target distinguishing method based on clutter spectrum estimation is characterized by comprising the following steps:

step 1: obtaining real-time inertial navigation information and range-Doppler domain echo data under an NED coordinate system;

step 2: with the speed information: eastern speed v_eastNorth velocity v_northVelocity v_up(ii) a Angle information: azimuth angle θ, pitch angle Φ, and system information: azimuth beam width θ_3dBPulse repetition frequency PRF and coherent accumulation pulse number M; calculating the center f of the spectrum_{dc_ins_index}And spectral width f_{dw_ins_num}：

Step 2.1: calculating the speed v of the central sight line direction of the wave beam_losAnd beam width two boundary direction velocity

v_los＝v_east×cos(φ)×sin(θ)+v_north×cos(φ)×cos(θ)+v_up×sin(φ)

Step 2.2: calculating the center f of the spectrum_{dc_ins}And spectral width f_{dw_ins}And deblurring the spectral center:

f_{dc_ins}＝mod(f_{dc_ins},PRF)

step 2.3: quantifying the Doppler frequency, and calculating the Doppler channel number f corresponding to the spectrum center_{dc_ins_index}Number of Doppler channels f corresponding to the spectral width_{dw_ins_num}：

f_{dc_ins_index}＝round(f_{dc_ins}/Δf_d)

f_{dw_ins_num}＝round(f_{dw_ins}/Δf_d)

Δf_d＝PRF/M

Wherein M is the number of coherent accumulation points;

and step 3: the range-Doppler domain echo data is segmented along a range dimension, the number of range units is N, the range units are evenly divided into K sections, and a spectrum center f is calculated_{dc_data_index}：

Step 3.1: for each section of power spectrum P_f(m, n) are added along the distance dimension to calculate the sum sigma of the power spectra of each segment_f(m,k)：

Step 3.2: for sigma_f(m, k) searching the maximum value coordinate along the Doppler dimension, and calculating the center f of each section of spectrum_{dc_seg_index}(k)：

Step 3.3: for each segment of spectrum center f_{dc_seg_index}(k) Calculating the center of mass to calculate the average spectrum center f_{dc_data_index}：

And 4, step 4: combined inertial navigation estimated value f_{dc_ins_index}And the data estimation value f_{dc_data_index}Calculating the final estimated value f of the center and width of the spectrum_{dc_index}、f_{dw_num}：

f_{dc_index}＝(f_{dc_data_index}-f_{dc_ins_index}|<)？f_{dc_data_index}:f_{dc_ins_index}

f_{dw_num}＝k×f_{dw_ins_num}

And 5: selecting a Target (f) to be detected_index) And judging whether the target is a moving target:

step 6: and outputting the dynamic and static characteristic information of the target.

2. The method for discriminating between moving and stationary radar targets based on clutter spectrum estimation according to claim 1, wherein said step 4 is 5.

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