A method for indoor moving target detection based on Doppler frequency modulation profile and range-gated filtering

Abstract

The present invention discloses a method for detecting indoor moving targets using an unmanned aerial vehicle (UAV) through-the-wall radar. The method comprises: proposing a moving target detection method based on a Doppler frequency modulation profile and range-gated filtering. The imaging results of moving targets are defocused and easily submerged in strong stationary clutter, which reduces the moving target detection rate. To address this problem, the present invention analyzes the Doppler frequency modulation characteristics of stationary clutter and moving targets. Then, the range migration of the target echo is corrected in the range Doppler domain through sinc interpolation. Next, a fractional-order Fourier transform is applied to estimate the Doppler frequency modulation of each range gate of the echo. The estimated frequency modulation value is compared with the theoretical frequency modulation value of the stationary target. Based on the difference in Doppler frequency modulation, a range-gated filter is designed to suppress clutter generated by walls and stationary objects. Finally, an azimuth compression filter is constructed to achieve focused imaging of moving targets. Simulation and field experiments verify the feasibility and effectiveness of the proposed method.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing, and in particular to a method for detecting indoor moving targets by a through-wall radar carried by an unmanned aerial vehicle. Background Art

[0002] Through-the-wall radar uses electromagnetic waves in the L / S band to detect dangerous moving targets indoors. Drone-mounted through-the-wall radar leverages the advantages of drones' flexible deployment, rapid response, and wide-area coverage to achieve remote, precise, and non-contact detection and positioning of moving targets, making it particularly suitable for complex environments and inaccessible areas. However, the speed of moving targets causes the imaging results to defocus, making them easily submerged in strong stationary clutter, reducing the detection rate of moving targets. The method for indoor moving target detection using drone-mounted through-the-wall radar proposed in this invention aims to provide a technology that can suppress stationary clutter while focusing the image of moving targets.

[0003] Currently, through-the-wall radars for detecting indoor moving targets primarily rely on stationary platforms, and research on indoor moving target detection using through-the-wall radars mounted on unmanned aerial vehicles (UAVs) is relatively limited. However, airborne synthetic aperture radar (SAR) ground moving target indication (GMTI) technology has been a key research focus and hot topic in the SAR field, providing a technical reference for UAV-mounted through-the-wall radar moving target detection. Airborne SAR-GMTI technology can be categorized into multi-channel SAR moving target detection methods and single-channel SAR moving target detection methods, depending on the number of transceiver antennas used.

[0004] Multi-channel SAR moving target detection methods achieve better clutter suppression by increasing the number of along-track channels and leveraging spatial and temporal information. Typical methods include displaced phase center antenna (DPCA), along-track interferometry (ATI), velocity synthetic aperture radar (VSAR), subspace projection (SP), and robust principal component analysis (RPCA). However, these multi-channel SAR moving target detection methods require strict channel registration conditions to suppress stationary clutter. Furthermore, due to the small phase difference between channels for azimuthally moving targets, these methods result in significant energy loss, making it difficult to detect azimuthally moving targets.

[0005] Single-channel SAR has the advantages of low hardware cost and simple engineering implementation, and many related moving target detection methods have been proposed. G. Lv et al. proposed a moving target detection method based on symmetric defocusing. This method uses two symmetric matched filters with opposite phase signs to defocus the SAR image, generating two defocused SAR images. Moving targets are detected by comparing the sharpness of the two defocused SAR images. However, different moving targets require different defocus filter parameters, and when the signal-to-clutter ratio is low, defocusing will further weaken the target energy, which is not conducive to target detection. J. Wang halved the Doppler spectrum, generating a range Doppler (RD) image result for each half. The difference in the imaging results was calculated pixel by pixel, and then a threshold was set to detect moving targets. Z. Wang segmented the Doppler spectrum to generate a multi-view image sequence. The motion characteristics of the moving target response were described in the image sequence, and the optical flow field was introduced to achieve moving target detection. W. Pu proposed an unsupervised robust principal component analysis autoencoder network without constraining the low rank of stationary clutter, but this method has a long runtime. In addition, these methods do not consider the impact of significant target migration across distance units on target detection.

[0006] Based on the above research, we believe that existing airborne moving target detection methods struggle to simultaneously achieve strong stationary clutter suppression and focused imaging of moving targets in wall-penetrating scenarios. To address this issue, we propose a new method for indoor moving target detection. Our method first derives and analyzes the Doppler chirp frequency variation range of the target and the wall. Then, based on the Doppler chirp frequency differences, we design a moving target detection method that includes range migration correction, Doppler chirp frequency profile extraction, stationary clutter suppression, and focused imaging. Simulation and experimental results demonstrate that the proposed method improves the signal-to-clutter ratio and focusing effect of the imaging results.

[0007] Compared with the prior art, the present invention has significant innovations in the following aspects:

[0008] 1. When detecting through walls, the frequency modulation rates of moving targets, stationary objects, and walls are theoretically derived and analyzed, providing theoretical support for clutter suppression.

[0009] 2. A stationary clutter suppression method is proposed based on frequency modulation difference, which provides technical support for moving target detection;

[0010] 3. An azimuth compression filter is constructed based on the Doppler frequency estimation value to achieve focused imaging of indoor moving targets. Summary of the Invention

[0011] To address the technical problem of reduced detection rate of moving targets caused by the moving targets being submerged in strong stationary clutter during indoor moving target detection by a UAV-mounted through-wall radar, the present invention proposes a method for indoor moving target detection based on Doppler frequency modulation profile and range-gated filtering, comprising:

[0012] Step S1: obtaining the raw data of the moving target echo from the UAV-mounted through-wall radar, performing range compression on the raw data, and transforming the range-compressed echo data into the range Doppler domain;

[0013] Step S2: Calculate the precise range migration of the moving target based on the range Doppler domain expression of the target echo, and perform range migration correction through sinc interpolation;

[0014] Step S3: Obtain the echo data of each range gate by range indexing, and estimate the Doppler frequency modulation rate by fractional Fourier transform.

[0015] Step S4: construct a range-gating filter based on the Doppler frequency estimation value to suppress clutter from walls and stationary objects.

[0016] Step S5: constructing an azimuth compression filter according to the Doppler frequency modulation estimation value to achieve focused imaging of indoor moving targets.

[0017] Furthermore, the specific method of step S1 is:

[0018] UAV-mounted radar transmits linear frequency modulation signals

[0019]

[0020] where rect(·), t, T p 、f c , K r They are rectangular gate function, fast time variable, pulse width, signal center frequency and linear frequency modulation frequency. After receiving the radar echo data, it is compressed by distance to obtain

[0021] s r (t a ,t)=s w (t a ,t)+s sta (t a ,t)+s mov (t a ,t) (2)

[0022] where t a is the slow time variable, s w (t a ,t),s sta (t a ,t) and smov (t a ,t) are wall echo, indoor stationary target echo and moving target echo respectively. Moving target echo can be expressed as

[0023]

[0024] where w a (t a ) is the azimuth rectangular gate function, B is the signal bandwidth, λ is the signal wavelength corresponding to the signal center frequency, R(t a ) is the slant range between the target and the radar, which can be written as

[0025]

[0026] where x0, y0 are t a = The azimuth and range positions of the target relative to the radar at time 0, v x 、v y are the target’s azimuth velocity and range velocity, R w is the additional propagation distance of the signal generated during the wall penetration process, which is relative to R(t a ) is small and can be ignored.

[0027] Taking the derivative of equation (4), we can get the slow time when the slant distance is the smallest:

[0028]

[0029] The corresponding shortest slope distance is

[0030]

[0031] The static target echo is essentially the moving target echo in v x =0, v y = 0, it can be written as follows

[0032]

[0033] Ideally, the wall echo can be regarded as the result of the superposition of multiple closely adjacent static point target echoes. When the complex reflectivity of these point targets is the same, the wall echo after range compression can be expressed as

[0034]

[0035] where R wall Indicates the distance between the UAV-mounted wall-penetrating radar and the wall, R wall Can be considered as a constant.

[0036] Transforming the distance of equation (3) into Fourier transform, we can get

[0037]

[0038] where f r Represents the distance frequency, and the azimuth Fourier transform of equation (9) can be obtained

[0039]

[0040] where f a represents the Doppler frequency, f ac and D(f a ) represent the Doppler center frequency and range migration factor respectively, and their expressions are as follows

[0041]

[0042]

[0043] The square root of the phase in equation (10) is r / f c = 0 Taylor expansion

[0044]

[0045] The quadratic and cubic range frequencies in formula (13) will cause the distance of the two-dimensional time domain echo to be defocused upward. In order to solve this problem, a quadratic range compression filter is constructed as follows:

[0046]

[0047] Multiplying equation (13) and equation (14) yields

[0048]

[0049] The echo expression of the echo data in the range Doppler domain obtained by inverse Fourier transform of equation (15) is:

[0050]

[0051] Furthermore, the specific method of step S2 is:

[0052] According to formula (16), the distance migration of the moving target is calculated as follows:

[0053]

[0054] Similarly, for a stationary target, the range migration is

[0055]

[0056] From equations (17) and (18), we can see that the range migration of a stationary target is related to the radar speed, and the range migration of a moving target is related to both the radar speed and the speed of the moving target.

[0057] Range Cell Migration Correction (RCMC) is performed using sinc interpolation. Unlike traditional sinc interpolation methods, it can suppress the part of the echo with a large degree of range migration by setting the number of sampling points of the interpolation kernel function. This method can be expressed as

[0058]

[0059] Among them S rcmc (n a ,n r ) is the result after distance migration correction, S mov (n a ,n r +i) is the echo data before range migration correction, sinc(Δn r -i) is the interpolation kernel function, which can be regarded as the weight of the original sampling signal, Δn r is ΔR sta (f a ) corresponds to the distance migration, P is the number of sampling points of the interpolation function. The result of Sinc interpolation is

[0060]

[0061] Where W r (f a ) is the azimuth gate function after range migration correction.

[0062] Applying azimuth IFFT to equation (20) yields the RCMC result in the two-dimensional time domain:

[0063]

[0064] Furthermore, the specific method of step S3 is:

[0065] Taylor expansion of equation (12) yields

[0066]

[0067] Substituting equation (22) into equation (20) we get

[0068]

[0069] The one-dimensional azimuth signal can be obtained by indexing the range direction of formula (23):

[0070]

[0071] Transforming Equation (8) into the range Doppler domain yields

[0072]

[0073] Where δ(f a ) represents the impact signal. The one-dimensional azimuth signal of the range gate where formula (25) is located is

[0074]

[0075] From Equations (24) and (26), we can see that after range migration correction, the target echo can be directly extracted in the range-Doppler domain and the Doppler modulation rate can be estimated.

[0076] The fractional Fourier transform of equation (24) yields

[0077]

[0078] When the following relationship is satisfied, the peak value of formula (27) appears:

[0079]

[0080] That is, according to the angle α corresponding to the peak * Azimuth frequency modulation can be estimated

[0081]

[0082] Similarly, the frequency modulation of the wall is estimated to be

[0083]

[0084] Furthermore, the specific method of step S4 is:

[0085] The Doppler modulation frequency estimate Theoretical value of frequency modulation with stationary objects Subtract

[0086]

[0087] Where ΔK(t) measures the difference between the estimated frequency modulation rate and the theoretical value of the frequency modulation rate of a stationary object. The range-direction gating filter is constructed as

[0088]

[0089] Where α1 is a threshold. For any range gate's range-Doppler domain signal, if the difference between the estimated Doppler frequency modulation and the theoretical stationary object frequency modulation is less than α1, W1(t) is set to 0. After the range-Doppler domain echo data is weighted by W1(t), stationary object clutter can be suppressed.

[0090] Next, in order to suppress wall clutter, based on Construct the range-gated filter as

[0091]

[0092] Where α2 is a threshold. Since the theoretical Doppler frequency modulation of a wall is 0, α2 is also a threshold that measures the difference between the estimated frequency modulation and the theoretical wall frequency modulation. For any range gate's range-Doppler domain signal, if the estimated Doppler frequency modulation is less than α2, the filter is set to 0.

[0093] At this point, the stationary target and wall clutter are suppressed in the range Doppler domain, and the moving target is retained. The echo expression in the range Doppler domain after the stationary clutter is suppressed is:

[0094] S a (f a ,t)=S rcmc (f a ,t)×W1(t)×W2(t) (34)

[0095] Furthermore, the specific method of step S5 is:

[0096] according to Construct the azimuth matched filter as

[0097]

[0098] in is the estimated frequency modulation rate of each distance unit. Multiplying equation (34) and equation (35) yields

[0099] S a (f a ,t)=S a (f a ,t)H ac (f a ,t) (36)

[0100] Transform the equation (36) in the inverse Fourier transform direction to obtain

[0101]

[0102] It can be seen from formula (37) that the moving target is focused in the azimuth and range directions.

[0103] Beneficial effects:

[0104] (1) This invention proposes a method for indoor moving target detection based on Doppler frequency modulation profile and range-gated filtering, which is different from traditional moving target detection technology. This technology does not require complex calculation processes and large amounts of data processing, has higher processing efficiency, and can quickly achieve stationary clutter suppression and focused imaging of moving targets;

[0105] (2) The present invention proposes a method for indoor moving target detection based on Doppler frequency modulation profile and range gating filtering, which is different from traditional moving target detection technology. The drone used in this technology is equipped with radar and can perform large-scale search and detection. This makes the technology not restricted by the ground environment, floor height, etc., and has a very large effective search range;

[0106] (3) This invention proposes a method for indoor moving target detection based on Doppler frequency modulation profiles and range-gated filtering, which differs from traditional moving target detection techniques. This technique does not require channel alignment and echo cancellation, greatly preserves the energy of the moving target, and is more conducive to the detection of azimuthally moving targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 A schematic flow chart of a method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering provided by the present invention;

[0108] Figure 2 A schematic diagram of a modeling method for indoor moving target detection based on Doppler frequency modulation profile and range gating filtering provided by the present invention;

[0109] Figure 3 A schematic diagram of the radar antenna array structure of an indoor moving target detection method based on Doppler frequency modulation profile and range gating filtering provided by the present invention;

[0110] Figure 4 A schematic diagram of a simulation scenario of an indoor moving target detection method based on Doppler frequency modulation profile and range gating filtering provided by the present invention;

[0111] Figure 5 The frequency modulation rate estimation and stationary clutter suppression results of an indoor moving target detection method based on Doppler frequency modulation rate profile and range-gated filtering provided by the present invention;

[0112] Figure 6 This is the imaging result of an indoor moving target detection method based on Doppler frequency modulation profile and range gating filtering provided by the present invention. DETAILED DESCRIPTION

[0113] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0114] like Figure 1The method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering includes:

[0115] Step S1: Obtain the raw data of the moving target echo from the UAV-mounted through-wall radar, perform range compression on the raw data, and transform the range-compressed echo data into the range Doppler domain. The specific method is as follows:

[0116] Consider a closed space surrounded by four walls, such as Figure 2 As shown in Figure 1. The UAV flies parallel to the front wall at a speed of V. The radar antenna array is as follows: Figure 3 As shown in Figure 1, the antenna array consists of a transmitting antenna and a receiving antenna, and the antenna spacing d is very small and can be ignored. The UAV-mounted radar transmits a linear frequency modulation signal

[0117]

[0118] where rect(·), t, T p 、f c , K r They are rectangular gate function, fast time variable, pulse width, signal center frequency and linear frequency modulation frequency. After receiving the radar echo data, it is compressed by distance to obtain

[0119] s r (t a ,t)=s w (t a ,t)+s sta (t a ,t)+s mov (t a ,t) (39)

[0120] where t a is the slow time variable, s w (t a ,t),s sta (t a ,t) and s mov (t a ,t) are wall echo, indoor stationary target echo and moving target echo respectively. Moving target echo can be expressed as

[0121]

[0122] where w a (t a ) is the azimuth rectangular gate function, B is the signal bandwidth, λ is the signal wavelength corresponding to the signal center frequency, R(t a ) is the slant range between the target and the radar, which can be written as

[0123]

[0124] where x0, y0 are t a = The azimuth and range positions of the target relative to the radar at time 0, v x 、v y are the target’s azimuth velocity and range velocity, R w is the additional propagation distance of the signal generated during the wall penetration process, which is relative to R(t a ) is small and can be ignored.

[0125] Taking the derivative of equation (41), we can get the slow time when the slant distance is the smallest:

[0126]

[0127] The corresponding shortest slope distance is

[0128]

[0129] The static target echo is essentially the moving target echo in v x =0, v y = 0, it can be written as follows

[0130]

[0131] Ideally, the wall echo can be regarded as the result of the superposition of multiple closely adjacent static point target echoes. When the complex reflectivity of these point targets is the same, the wall echo after range compression can be expressed as

[0132]

[0133] where R wall Indicates the distance between the UAV-mounted wall-penetrating radar and the wall, R wall Can be considered as a constant.

[0134] Transforming the distance of equation (40) to Fourier transform yields

[0135]

[0136] where f r represents the distance frequency, and the azimuth Fourier transform of equation (46) can be obtained

[0137]

[0138] where f a represents the Doppler frequency, f ac and D(f a ) represent the Doppler center frequency and range migration factor respectively, and their expressions are as follows

[0139]

[0140]

[0141] The square root of the phase in equation (47) is r / f c = 0 Taylor expansion

[0142]

[0143] The quadratic and cubic range frequencies in Equation (50) will cause the distance of the two-dimensional time domain echo to be defocused upward. To solve this problem, a quadratic range compression filter is constructed as

[0144]

[0145] Multiplying equation (50) and equation (51) yields

[0146]

[0147] The echo expression of the echo data in the range Doppler domain obtained by inverse Fourier transform of Equation (52) is:

[0148]

[0149] Step S2: Calculate the precise range migration of the moving target based on the range Doppler domain expression of the target echo, and perform range migration correction through sinc interpolation. The specific method is:

[0150] According to formula (53), the distance migration of the moving target is calculated as

[0151]

[0152] Similarly, for a stationary target, the range migration is

[0153]

[0154] From equations (54) and (55), we can see that the range migration of a stationary target is related to the radar speed, and the range migration of a moving target is related to both the radar speed and the speed of the moving target.

[0155] Range Cell Migration Correction (RCMC) is performed using sinc interpolation. Unlike traditional sinc interpolation methods, it can suppress the part of the echo with a large degree of range migration by setting the number of sampling points of the interpolation kernel function. This method can be expressed as

[0156]

[0157] Among them Srcmc (n a ,n r ) is the result after distance migration correction, S mov (n a ,n r +i) is the echo data before range migration correction, sinc(Δn r -i) is the interpolation kernel function, which can be regarded as the weight of the original sampling signal, Δn r is ΔR sta (f a ) corresponds to the distance migration, P is the number of sampling points of the interpolation function. The result of Sinc interpolation is

[0158]

[0159] Where W r (f a ) is the azimuth gate function after range migration correction.

[0160] Applying azimuth IFFT to Equation (57) yields the RCMC result in the two-dimensional time domain:

[0161]

[0162] Step S3: Obtain the echo data of each range gate by range indexing, and estimate the Doppler frequency modulation rate by fractional Fourier transform. The specific method is as follows:

[0163] Taylor expansion of equation (49) yields

[0164]

[0165] Substituting equation (59) into equation (57), we get

[0166]

[0167] The one-dimensional azimuth signal can be obtained by indexing the range direction of formula (60):

[0168]

[0169] Transforming Equation (45) to the range Doppler domain yields

[0170]

[0171] Where δ(f a ) represents the impact signal. The one-dimensional azimuth signal of the range gate where Equation (62) is

[0172]

[0173] From Equations (61) and (63), we can see that after range migration correction, the target echo can be directly extracted in the range-Doppler domain and the Doppler modulation rate can be estimated.

[0174] The fractional Fourier transform of equation (61) yields

[0175]

[0176] When the following relationship is satisfied, the peak value of equation (64) appears:

[0177]

[0178] That is, according to the angle α corresponding to the peak * Azimuth frequency modulation can be estimated

[0179]

[0180] Similarly, the frequency modulation of the wall is estimated to be

[0181]

[0182] Step S4: construct a range-gated filter based on the Doppler frequency modulation estimate to suppress clutter from walls and stationary objects. The specific method is as follows:

[0183] The Doppler modulation frequency estimate Theoretical value of frequency modulation with stationary objects Subtract

[0184]

[0185] Where ΔK(t) measures the difference between the estimated frequency modulation rate and the theoretical value of the frequency modulation rate of a stationary object. The range-direction gating filter is constructed as

[0186]

[0187] Where α1 is a threshold. For any range gate's range-Doppler domain signal, if the difference between the estimated Doppler frequency modulation and the theoretical stationary object frequency modulation is less than α1, W1(t) is set to 0. After the range-Doppler domain echo data is weighted by W1(t), stationary object clutter can be suppressed.

[0188] Next, in order to suppress wall clutter, based on Construct the range-gated filter as

[0189]

[0190] Where α2 is a threshold. Since the theoretical Doppler frequency modulation of a wall is 0, α2 is also a threshold that measures the difference between the estimated frequency modulation and the theoretical wall frequency modulation. For any range gate's range-Doppler domain signal, if the estimated Doppler frequency modulation is less than α2, the filter is set to 0.

[0191] At this point, the stationary target and wall clutter are suppressed in the range Doppler domain, and the moving target is retained. The echo expression in the range Doppler domain after the stationary clutter is suppressed is:

[0192] S a (f a ,t)=S rcmc (f a ,t)×W1(t)×W2(t) (71)

[0193] Step S5: constructing an azimuth compression filter based on the Doppler frequency modulation estimate to achieve focused imaging of indoor moving targets. The specific method is as follows:

[0194] according to Construct the azimuth matched filter as

[0195]

[0196] in is the estimated frequency modulation rate of each distance unit. Multiplying equation (71) and equation (72) yields

[0197] S a (f a ,t)=S a (f a ,t)H ac (f a ,t) (73)

[0198] Transform the equation (73) in the inverse Fourier transform direction to obtain

[0199]

[0200] It can be seen from formula (74) that the moving target is focused in the azimuth and range directions.

[0201] In order to verify the effectiveness of the proposed method, a real experiment was designed for analysis. The experimental photos and radar detection schematics are shown in Figure 2. Figure 4 (a) and Figure 4As shown in (b), the drone's onboard radar is traveling at a speed of 4 m / s, flying from (-8, 0) m to (8, 0) m. The range coordinate of the front wall is 3 m, the azimuth length of the room is 16 m, and the range length is 12 m. There are two stationary people and one moving person in the room. The stationary person's coordinates are (-4, 9) m. During the drone's flight, the moving person moves from (-2, 7) m to (4, 7) m. The system parameters of the drone's onboard radar are shown in Table 1.

[0202] Table 1 Radar system parameter settings

[0203]

[0204] Execute step S1 to transform the range-compressed echo data into the range-Doppler domain.

[0205] Execute step S2 to calculate the range migration of the target echo and perform range migration correction through sinc interpolation.

[0206] Execute step S3 to extract the echo data of each range gate and estimate its Doppler frequency modulation rate using fractional Fourier transform.

[0207] Execute step S4 to construct a range-gated filter based on the estimated value of the modulation rate to suppress stationary clutter.

[0208] Execute step S5 to construct an azimuth compression filter according to the estimated value of the modulation rate to achieve focused imaging of the moving target.

[0209] The experimental results of frequency modulation estimation and clutter suppression are as follows: Figure 5 As shown, Figure 5 (a) is the curve of the estimated value of the frequency modulation and the theoretical value of the frequency modulation of the stationary target along the distance direction. Figure 5 (b) is the result of incoherent accumulation of echo data along the azimuth frequency before and after suppressing stationary clutter based on the frequency modulation. As can be seen from the figure, the estimated frequency modulation of the wall echo is approximately 0, the estimated frequency modulation of the stationary target is approximately equal to the theoretical value, and the estimated frequency modulation of the moving target is lower than the theoretical value of the frequency modulation of the stationary target. This is because the moving target moves in the same direction as the radar azimuth in the experiment. Figure 5 (b) shows that the wall and stationary targets are suppressed, while the energy of the moving targets is preserved.

[0210] In order to highlight the superiority of the algorithm, the imaging results of this method are compared with the original imaging results, such as Figure 6 The original imaging results are shown as Figure 6 (a), we can see that there are walls, stationary targets, and moving targets at the same time, and the target is almost submerged in the wall clutter; Figure 6 (b) shows that the clutter generated by walls and stationary objects is suppressed, and the imaging effect of moving targets is good.

[0211] The processing results of the present invention basically suppress the stationary clutter of the wall and have a good focusing effect on moving targets, providing technical support for the detection of moving targets by UAV-mounted wall-penetrating radar.

[0212] The above specific embodiments merely illustrate the design principles of the present invention. The shapes and names of the components described herein may vary and are not limiting. Therefore, those skilled in the art may modify or substitute equivalents for the technical solutions described in the above embodiments. Such modifications and substitutions, without departing from the inventive spirit and technical solutions of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering, characterized in that: include: Step S1: obtaining the raw data of the moving target echo from the UAV-mounted through-wall radar, performing range compression on the raw data, and transforming the range-compressed echo data into the range Doppler domain; The moving target echo is expressed as (3) in is the azimuthal rectangular gate function, is the signal bandwidth, is the signal wavelength corresponding to the signal center frequency, is the speed of light, is the slant range between the target and the radar, which can be written as (4) in is the speed of the drone-mounted wall-penetrating radar, 、 yes The azimuth and range positions of the target relative to the radar at all times, 、 are the target’s azimuth velocity and range velocity, It is the additional propagation distance of the signal generated during the wall penetration process. The value is small and can be ignored; Transforming the distance of equation (3) into Fourier transform, we can get (9) in Represents the distance frequency, and the azimuth Fourier transform of equation (9) can be obtained (10) in represents the Doppler frequency, and They represent the Doppler center frequency and range migration factor respectively, and their expressions are as follows (11) (12) The square root of the phase in equation (10) is Taylor expansion (13) The quadratic and cubic range frequencies in formula (13) will cause the distance of the two-dimensional time domain echo to be defocused upward. In order to solve this problem, a quadratic range compression filter is constructed as follows: (14) Multiplying equation (13) and equation (14) yields (15) The echo expression of the echo data in the range Doppler domain obtained by inverse Fourier transform of equation (15) is: (16); Step S2: Calculate the precise range migration of the moving target based on the range Doppler domain expression of the target echo, and perform range migration correction through sinc interpolation; According to formula (16), the distance migration of the moving target is calculated as follows: (17) Similarly, for a stationary target, the range migration is (18) From equations (17) and (18), we can see that the range migration of a stationary target is related to the radar speed, and the range migration of a moving target is related to both the radar speed and the speed of the moving target. Step S3: obtaining echo data of each range gate by range indexing, and estimating Doppler frequency modulation rate by fractional Fourier transform; Step S4: constructing a range-gated filter based on the Doppler frequency modulation estimate to suppress clutter from walls and stationary objects; Step S5: constructing an azimuth compression filter according to the Doppler frequency modulation estimation value to achieve focused imaging of indoor moving targets.

2. The method according to claim 1, wherein In step S1, the linear frequency modulation signal transmitted by the drone-borne radar is expressed as (1) in 、 、 、 、 They are rectangular gate function, fast time variable, pulse width, signal center frequency and modulation frequency of linear frequency modulation signal respectively; after receiving the radar echo data, it is compressed by distance to obtain (2) in is a slow-time variable, 、 and They are wall echo, indoor stationary target echo and moving target echo.

3. The method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering according to claim 2, wherein: In step S1, the derivative of equation (4) yields the slow time moment when the slant distance is the smallest: (5) The corresponding shortest slope distance is (6) The static target echo is actually the moving target echo. 、 The special case can be written as follows (7) Ideally, the wall echo can be regarded as the result of the superposition of multiple closely adjacent static point target echoes. When the complex reflectivity of these point targets is the same, the wall echo after range compression can be expressed as (8) in Indicates the distance between the drone-mounted wall-penetrating radar and the wall. Can be considered as a constant.

4. The method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering according to claim 3, wherein: In step S2, range cell migration correction (RCMC) is performed by sinc interpolation; this method is expressed as (19) in is the result after distance migration correction, is the echo data before range migration correction, is the interpolation kernel function, which can be regarded as the weight of the original sampling signal. is the azimuth index of the echo data, is the range index of the echo data, yes The corresponding distance migration is is the number of sampling points of the interpolation function; the result of Sinc interpolation is (20) in is the azimuth gate function after range migration correction; the azimuth IFFT of (20) can be used to obtain the RCMC result in the two-dimensional time domain: (21)。 5. The method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering according to claim 4, wherein: In step S3, Taylor expansion of equation (12) is performed to obtain (22) Substituting equation (22) into equation (20) we get (23) in is the Doppler modulation frequency of the moving target; the one-dimensional azimuth signal can be obtained by indexing the range direction of formula (23): (24) Transforming Equation (8) into the range Doppler domain yields (25) in represents the impact signal; the one-dimensional azimuth signal of the range gate where formula (25) is (26) From Equations (24) and (26), we can see that after range migration correction, the target echo can be directly extracted in the range-Doppler domain and the Doppler modulation rate can be estimated.

6. The method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering according to claim 5, wherein: In step S3, the fractional Fourier transform of equation (24) yields (27) in is the rotation angle, represents the fractional domain frequency; when the following relationship is satisfied, a peak value appears in equation (27): (28) That is, according to the angle corresponding to the peak Azimuth frequency modulation can be estimated (29) Similarly, the frequency modulation of the wall is estimated to be (30)。 7. The method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering according to claim 6, wherein: In step S4, the Doppler modulation frequency estimation value Theoretical value of frequency modulation with stationary objects Subtract (31) in Measures the difference between the estimated frequency modulation rate and the theoretical value of the frequency modulation rate of a stationary object.

8. The method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering according to claim 7, wherein: In step S4, the distance gating filter is constructed as follows: (32) in Is a threshold; for the range Doppler domain signal of any range gate, if the difference between the Doppler modulation frequency estimate and the theoretical value of the modulation frequency of a stationary object is less than ,but Set to 0; the echo data in the range Doppler domain is After weighting, the clutter of stationary objects can be suppressed.

9. The method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering according to claim 8, wherein: In step S4, in order to suppress wall clutter, based on the Doppler frequency modulation estimation value Construct the range-gated filter as (33) in is a threshold value; since the theoretical value of the wall Doppler modulation frequency is 0, It is also the threshold for measuring the difference between the estimated frequency modulation rate and the theoretical value of the wall frequency modulation rate. For the range Doppler domain signal of any range gate, if the estimated Doppler frequency modulation rate is less than , the filter is set to 0.

10. The indoor moving target detection method based on Doppler frequency modulation profile and range gating filtering according to claim 9, characterized in that: In step S4, the echo expression in the range Doppler domain after stationary clutter suppression is: (34)。 11. The method for detecting indoor moving targets based on Doppler frequency modulation profile and range gating filtering according to claim 10, wherein: The specific method of step S5 is: According to the Doppler frequency estimation Construct the azimuth matched filter as (35) Multiplying (34) and (35) yields (36) Transform the equation (36) in the inverse Fourier transform direction to obtain (37) It can be seen from formula (37) that the moving target is focused in the azimuth and range directions.

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