WO2023129094A1 - Doppler shift based distributed drone detection system - Google Patents
Doppler shift based distributed drone detection system Download PDFInfo
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- WO2023129094A1 WO2023129094A1 PCT/TR2022/051689 TR2022051689W WO2023129094A1 WO 2023129094 A1 WO2023129094 A1 WO 2023129094A1 TR 2022051689 W TR2022051689 W TR 2022051689W WO 2023129094 A1 WO2023129094 A1 WO 2023129094A1
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- G—PHYSICS
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
Definitions
- the present invention relates to the detection and tracking of small aerial targets such as unmanned aerial vehicles (UAV), in particular rotary wing drones.
- UAV unmanned aerial vehicles
- Cyclic Spectral Density Transform is a method used in the literature for the classification of rotary wing UAVs in radar signals. The method is based on detecting the periodic structure in the auto correlation of the return signal from the target. The classification of the detected platform is carried out by the detection of this periodic structure.
- CFAR Constant false alarm rate
- CFAR methods are the methods that have been studied for a long time and applied in practice. CFAR methods refer to a common form of the adaptive algorithm used in radar systems to detect target returns against a background of noise, clutter, and interference. CFAR methods are used to detect radar targets in environments where the noise level is not constant. CFAR methods determine the threshold level independently of the noise level in the environment and attempt to provide the desired false alarm rate.
- Doppler Target localization is performed using the frequency shift (Doppler shift) information resulting from the movements of the source-target-receiver trio relative to each other.
- the area on which positioning can be performed is very dependent on the configuration in which the sensors are placed.
- target detection is performed first and then the classification of the target is made.
- multiple target detections can be performed prior to classification, resulting in multiple candidate targets for the classification function.
- This approach requires a lot of processing power in environments with a large number of radar detections.
- Cities are an example of environments with multiple targets.
- Another possible problem in the city environment is related to the coverage area. Due to the dense urbanization in the urban environment, the limiting factors of the radar coverage area also limit the applicability of classical radar solutions. Brief Description and Objects of the Invention
- UAVs unmanned aerial vehicles
- Detection of UAVs that can be used in the urban environment limits the use of radar due to a large number of blocking factors in the coverage area.
- radars are needed as a detecting system that can operate in various environmental conditions.
- Relevant system solutions in the sector are generally radar solutions derived by considering open areas. Cost-effective solutions for the building-dense urban environment are considered to be lacking in the market.
- the object of the invention is to filter non-UAV moving targets by classifying them before detection for detection and positioning of drones.
- the derived method can suppress target signals outside the drone in target-dense environments such as the urban environment.
- the distributed sensor structure used by the derived method makes the method advantageous in regions with limited coverage areas.
- Rotary wing UAV detection with the deinterleaving algorithm over the CSD (Cyclic Spectral Density) transform presented in the invention, and Doppler estimation over CSD are studies that are not available in the literature.
- the system solution approach presented for the urban environment is an approach that is not included in the literature.
- the system solution in the invention will not only describe a system that can operate alone but also serves as an auxiliary system to the systems currently in use.
- range data coming from a system that can only generate range information and the location-finding algorithm of the system solution in the invention can be operated together.
- Range data can assist in target discrimination of the inventive system in a multi- target environment. Frequency modulation can be added to the radar signal to extract range data.
- the method can work in any environment.
- urban environments the use of radar is limited due to a large number of blocking factors in the coverage area.
- radars are needed as a detecting system that can operate in various environmental conditions.
- One of the important information that radar systems provide for targets is the velocity of the target relative to the radar system. Thanks to this information, the radar system can distinguish the targets it is looking for from the clutter in the environment. Due to the large number of objects moving at different velocities in the urban environment, it is difficult for UAVs to separate from the background for detection. Another problem that will arise with classical radar approaches in the urban environment is that the radar coverage area will be blocked by buildings.
- the general structure in cities consists of the intersections of straight streets and avenues.
- Tall buildings forming the boundaries of the streets limit the radar coverage areas placed at high points.
- a streetlength range requirement arises. This range requirement will cause a single radar system to be used to be large and costly.
- rotary wing drones perform classification before detecting and positioning. Therefore, it is ensured that many moving possible targets such as the urban environment are filtered before detection.
- the invention is aimed to detect rotary wing UAVs in the urban environment with low-power, low-cost radar systems.
- the CW signal will be used.
- a single radar system will not be able to provide range information due to the use of CW signals.
- MIMO multi-input multi-output
- Receivers and transmitters will use a single wide beamwidth antenna. Therefore, a single radar system will not produce direction information as well as range. Therefore, the Doppler-Only Localization method in the literature will be used to produce the target location information.
- UAV detection and positioning will be performed using multiple low-power transmitters and receivers in the urban environment.
- the radar signal model returning from the UAV is as stated in equation (1).
- This equation includes the following parameters
- N Blade number on the wing
- R Distance of the wing center to target t: Time v: The radial velocity of the target relative to the radar : wavelength of the RF signal
- the cyclic spectral density transform of radar signals returning from the UAV is periodic in both spectral frequency and cyclic frequency. Therefore, peaks occur in periodic spectral (f) and cyclic (a) frequencies in the cyclic spectral density transform.
- (3) of the radar signal returning from a wing it comprises the below wing-related information
- Deinterleaving algorithms are algorithms used in Electronic Support systems in the literature and detect periodic structures in complex time series.
- periodic signals in the cyclic spectral domain are expected in the case of Wing rotation. Therefore, the deinterleaving algorithm is used in the invention to detect the presence of a periodic signal and to determine the period if such a signal exists.
- a histogram based deinterleaving algorithm is used, but other deinterleaving methods in the literature are also suitable for the problem.
- Positioning with Doppler measurements obtained from different points is a method used especially in acoustic systems and various examples are found in the literature. Equations related to the method are given in (5)-(8). In these equations (xi, yi) indicates the position of the i-numbered sensor, (x, y) the position of the target, c indicates the velocity of light, and fi shows the target echo frequency measured in the i-numbered sensor. The target position is the lowest (x, y) value pair of the function presented in equation (8). The method of finding position with multiple radar systems using Doppler frequency shift is discussed in detail in the studies in the references [1], [2], and [3].
- CFK-024 5A radar transmit-receive module and moving wing were used to test the proposed method.
- the cyclic spectral density transform result of the obtained radar signal is shown in Figure 4.
- both signal components are rotating from the wing and noise components ( Figure 5).
- the deinterleaving algorithm was used to find out whether there is a periodic structure in the cyclic frequency in the CFAR result, and if there is such a structure, the frequency of this structure.
- This location data found by the invention can be used on its own or combined with other sensor data.
- the sensor data can be one or more of the range, azimuth, elevation angle, and velocity data.
- An example is shown in Figure 7.
- Data combining methods have been extensively discussed in the literature. As an example, the equation presented below can be used for data combining:
- the said invention is a drone (UAV) detection method that can be used in monostatic radars, bistatic radars, multistatic radars, MIMO radars, passive radars, radar networks, acoustic systems, characterized in that it comprises the following steps;
- cyclic stationary signals in the cyclic spectral density plane with the deinterleaving algorithm (e.g., histogram based deinterleaving, PRI transform deinterleaving) and determining the presence of drones (UAV) with wing rotation frequency detection,
- the deinterleaving algorithm e.g., histogram based deinterleaving, PRI transform deinterleaving
- UAV drones
- Drone (UAV) detection and positioning method characterized in that a large number of moving possible targets are filtered with the cyclic spectral density method before detection. • Elimination of Doppler ambiguity with spectral frequency information in possible Doppler ambiguities.
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Abstract
The present invention is intended to perform rotary wing drone (UAV) detection in the urban environment with low-power, low-cost radar systems. The system proposed in the invention, rotary wing drones (UAVs) perform classification before detection and positioning. Therefore, a large number of moving targets such as urban environments are filtered before detection.
Description
DESCRIPTION
DOPPLER SHIFT BASED DISTRIBUTED DRONE DETECTION SYSTEM
Technical Field
The present invention relates to the detection and tracking of small aerial targets such as unmanned aerial vehicles (UAV), in particular rotary wing drones.
Background
Cyclic Spectral Density Transform is a method used in the literature for the classification of rotary wing UAVs in radar signals. The method is based on detecting the periodic structure in the auto correlation of the return signal from the target. The classification of the detected platform is carried out by the detection of this periodic structure. CFAR (Constant false alarm rate) methods are the methods that have been studied for a long time and applied in practice. CFAR methods refer to a common form of the adaptive algorithm used in radar systems to detect target returns against a background of noise, clutter, and interference. CFAR methods are used to detect radar targets in environments where the noise level is not constant. CFAR methods determine the threshold level independently of the noise level in the environment and attempt to provide the desired false alarm rate. Doppler Target localization is performed using the frequency shift (Doppler shift) information resulting from the movements of the source-target-receiver trio relative to each other. The area on which positioning can be performed is very dependent on the configuration in which the sensors are placed. In the present drone detection approaches, target detection is performed first and then the classification of the target is made. In environments with multiple targets, multiple target detections can be performed prior to classification, resulting in multiple candidate targets for the classification function. This approach requires a lot of processing power in environments with a large number of radar detections. Cities are an example of environments with multiple targets. Another possible problem in the city environment is related to the coverage area. Due to the dense urbanization in the urban environment, the limiting factors of the radar coverage area also limit the applicability of classical radar solutions.
Brief Description and Objects of the Invention
As a result of technological developments in recent years, unmanned aerial vehicles (UAVs) have started to become a part of our daily lives. These vehicles are used for military purposes as well as for civilian purposes. Due to their low price, low probability of detection, and ability to carry pay loads, UAVs can also be classified as vehicles that can be used for terrorist activities.
Detection of UAVs that can be used in the urban environment limits the use of radar due to a large number of blocking factors in the coverage area. However, radars are needed as a detecting system that can operate in various environmental conditions.
Relevant system solutions in the sector are generally radar solutions derived by considering open areas. Cost-effective solutions for the building-dense urban environment are considered to be lacking in the market.
The object of the invention is to filter non-UAV moving targets by classifying them before detection for detection and positioning of drones. The derived method can suppress target signals outside the drone in target-dense environments such as the urban environment. The distributed sensor structure used by the derived method makes the method advantageous in regions with limited coverage areas.
Rotary wing UAV detection with the deinterleaving algorithm over the CSD (Cyclic Spectral Density) transform presented in the invention, and Doppler estimation over CSD are studies that are not available in the literature. In addition, the system solution approach presented for the urban environment is an approach that is not included in the literature.
The system solution in the invention will not only describe a system that can operate alone but also serves as an auxiliary system to the systems currently in use. As a result of combining the data created by an existing drone detection system (range, azimuth, elevation angle, velocity) with the data of the system in the invention, higher accuracy data will be produced. In addition, the range data coming from a system that can only generate range information and the location-finding algorithm of the system solution in the invention can be operated together. Range data can assist in target discrimination of the inventive system in a multi-
target environment. Frequency modulation can be added to the radar signal to extract range data.
Figures Describing the Invention
Figure 1. Algorithm flow
Figure 2. The cyclic spectral density transform of a wing's radar echo simulation
Figure 3. Projection
Figure 4 The cyclic spectral density transform of a wing's true radar echo
Figure 5 CFAR output of true spectral density transform
Figure 6. Cyclic spectral density transform of the experimental signal
Description of the References in the Figures
1. Radar Sign
2. Cyclic Spectral Density (CFAR)
3. Detections
4. Projection (Number of Detections in the Cyclic Spectral Density)
5. Deinterleaving (Wing Rotation Frequency Estimation)
6. Doppler (Doppler Estimation)
7. Positioning
8. UAV Position
Detailed Description of the Invention
Although the main motivation of the derived method is the detection of UAVs that can be used in the urban environment, the method can work in any environment. In urban environments, the use of radar is limited due to a large number of blocking factors in the coverage area. However, radars are needed as a detecting system that can operate in various environmental conditions.
One of the important information that radar systems provide for targets is the velocity of the target relative to the radar system. Thanks to this information, the radar system can distinguish the targets it is looking for from the clutter in the environment.
Due to the large number of objects moving at different velocities in the urban environment, it is difficult for UAVs to separate from the background for detection. Another problem that will arise with classical radar approaches in the urban environment is that the radar coverage area will be blocked by buildings.
The general structure in cities consists of the intersections of straight streets and avenues. Tall buildings forming the boundaries of the streets limit the radar coverage areas placed at high points. In order for the radars to be placed on the streets to cover the whole street, a streetlength range requirement arises. This range requirement will cause a single radar system to be used to be large and costly.
In the system proposed in our invention, rotary wing drones (UAV) perform classification before detecting and positioning. Therefore, it is ensured that many moving possible targets such as the urban environment are filtered before detection.
With the invention, it is aimed to detect rotary wing UAVs in the urban environment with low-power, low-cost radar systems. In order to make the system as simple as possible, the CW signal will be used. A single radar system will not be able to provide range information due to the use of CW signals. However, multi-input multi-output (MIMO) will eliminate the need for synchronization in the radar system structure. Receivers and transmitters will use a single wide beamwidth antenna. Therefore, a single radar system will not produce direction information as well as range. Therefore, the Doppler-Only Localization method in the literature will be used to produce the target location information. UAV detection and positioning will be performed using multiple low-power transmitters and receivers in the urban environment.
The basic algorithm flow to be used is as shown in Figure 1.
The radar signal model returning from the UAV is as stated in equation (1).
This equation includes the following parameters;
N: Blade number on the wing
LI: Distance from the beginning of the blade to the center of the wing
L2: Distance from the end of the blade to the center of the wing
R: Distance of the wing center to target t: Time v: The radial velocity of the target relative to the radar : wavelength of the RF signal
0: Angle between the axis of rotation of the wing and the radar-target line fc: Frequency of the radar signal fr: The rotation frequency of the wing.
According to equation 2, the cyclic spectral density transform of radar signals returning from the UAV is periodic in both spectral frequency and cyclic frequency. Therefore, peaks occur in periodic spectral (f) and cyclic (a) frequencies in the cyclic spectral density transform.
The locations of the respective peaks are as given in (3) for the integers m and n.
Considering the cyclic spectral density transform, (3) of the radar signal returning from a wing, it comprises the below wing-related information;
• Rotation frequency of the wing
• Doppler frequency due to the radial velocity of the wing relative to the radar.
Projection
In order to detect whether there is a periodic structure in the cyclic frequency plane, the number of detections in the 2D CSD plane is summed up for each cyclic frequency value. This process is called Projection. As a result of this process, the detection numbers in the cyclic frequency are subtracted. In the case of rotary wing UAVs, peaks occur in a periodic structure at cyclic frequency. An example projection result is shown in Figure 3.
Deinterleaving
Deinterleaving algorithms are algorithms used in Electronic Support systems in the literature and detect periodic structures in complex time series. In the invention, periodic signals in the cyclic spectral domain are expected in the case of Wing rotation. Therefore, the deinterleaving algorithm is used in the invention to detect the presence of a periodic signal and to determine the period if such a signal exists. For example, a histogram based deinterleaving algorithm is used, but other deinterleaving methods in the literature are also suitable for the problem.
Doppler Frequency Calculation
The peaks formed in CSD shift along the spectral axis as much as the Doppler frequency. Since each peak undergoes a Doppler shift, it is possible to find the Doppler shift by averaging the amount of shift in the peaks. It is possible to find the relevant Doppler shift with the equation presented in (4). P denotes the number of peaks.
Positioning
Positioning with Doppler measurements obtained from different points is a method used especially in acoustic systems and various examples are found in the literature. Equations related to the method are given in (5)-(8). In these equations (xi, yi) indicates the position of the i-numbered sensor, (x, y) the position of the target, c indicates the velocity of light, and fi shows the target echo frequency measured in the i-numbered sensor. The target position is the lowest (x, y) value pair of the function presented in equation (8).
The method of finding position with multiple radar systems using Doppler frequency shift is discussed in detail in the studies in the references [1], [2], and [3].
CFK-024 5A radar transmit-receive module and moving wing were used to test the proposed method. The cyclic spectral density transform result of the obtained radar signal is shown in Figure 4. As a result of the CFAR performed on the transform in Figure 4, both signal components are rotating from the wing and noise components (Figure 5). The deinterleaving algorithm was used to find out whether there is a periodic structure in the cyclic frequency in the CFAR result, and if there is such a structure, the frequency of this structure.
According to the deinterleaving output shown in Figure 6, a signal with a cyclic frequency of 113 Hz was detected. When the previously mentioned Doppler frequency estimation method is used, a value of -15 Hz is obtained.
It has been shown that by using the specified method with this experimental setup, the presence of a propeller can be detected, and the Doppler frequency of the propeller motion can be estimated. By making similar measurements from at least 5 points, the position of the rotary wing UAV can be found by using the location-finding algorithm from the Doppler frequencies discussed in the previous section.
This location data found by the invention can be used on its own or combined with other sensor data. The sensor data can be one or more of the range, azimuth, elevation angle, and velocity data. An example is shown in Figure 7. Data combining methods have been extensively discussed in the literature. As an example, the equation presented below can be used for data combining:
In this equation; p: Combined target location pb'- Target location data generated by the system solution of the invention p . Target position data generated by other sensor(s)
• Cb'- The covariance of the target location data produced by the system solution of the invention
• C . Covariance of target position data produced by other sensor(s)
• Cbi'- The cross-covariance of the target position data produced by the system solution in the invention and the target position data produced by other sensor(s)
• Cib'. Cross covariance of target position data produced by other sensor(s) and target position data produced by the system solution of the invention
In line with the above information, the said invention is a drone (UAV) detection method that can be used in monostatic radars, bistatic radars, multistatic radars, MIMO radars, passive radars, radar networks, acoustic systems, characterized in that it comprises the following steps;
- Using transmit-receive modules for the detection of drones (UAVs),
- Applying cyclic spectral density transform to radar signals,
- Detecting on CFAR (Constant False Alarm Rate) method Cyclic Spectral Density,
- Determining the number of detections by taking projections from the CFAR result,
- Detecting cyclic stationary signals in the cyclic spectral density plane with the deinterleaving algorithm (e.g., histogram based deinterleaving, PRI transform deinterleaving) and determining the presence of drones (UAV) with wing rotation frequency detection,
- Determining Doppler frequencies due to the radial velocity of the wing relative to the radar, using the cyclic spectral density analysis from the drone (UAV) wing and the rotation frequency data of the wing,
- Detecting Drone (UAV) position using a positioning algorithm from points different from Doppler frequencies.
Other preferred embodiments of the invention are as follows;
Drone (UAV) detection and positioning method, characterized in that a large number of moving possible targets are filtered with the cyclic spectral density method before detection.
• Elimination of Doppler ambiguity with spectral frequency information in possible Doppler ambiguities.
• Performing the detection of the position of the Drone (UAV) by using the locationfinding algorithm from multiple points from Doppler frequencies. • The step of combining the resulting data by working together with other sensor systems (RF-based sensor systems, image-based sensor systems, acoustic -based sensor systems) that can detect and produce one or more of the range, azimuth, elevation angle, and velocity data of the system.
• Range detection using different waveforms.
REFERENCES
[1] Y. Kalkan and B. Baykal, "Frequency-based target localization methods for widely separated MIMO radar," in Radio Science, vol. 49, no. 1, pp. 53-67, Jan. 2014
[2] Y. Kalkan and B. Baykal, "Target localization and velocity estimation methods for frequency-only MIMO Radars," 2011 IEEE RadarCon (RADAR), 2011, pp. 458- 463
[3] Y. Kalkan and B. Baykal, "Frequency based target localization methods for MIMO radar," 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU), 2011, pp. 74-77, doi: 10.1109/SIU.2011
Claims
CLAIMS . Drone (UAV) detection method that can be used in monostatic radars, bistatic radars, multistatic radars, MIMO radars, passive radars, radar networks, and acoustic systems, characterized in that it comprises the following steps;
- Using transmit-receive modules for the detection of drones (UAVs),
- Applying cyclic spectral density transform to radar signals,
- Detecting on CFAR (Constant False Alarm Rate) method Cyclic Spectral Density,
- Determining the number of detections by taking projections from the CFAR result,
- Detecting cyclic stationary signals in the cyclic spectral density plane with the deinterleaving algorithm and determining the presence of drones (UAV) with wing rotation frequency detection,
- Determining Doppler frequencies due to the radial velocity of the wing relative to the radar, using the cyclic spectral density analysis from the drone (UAV) wing and the rotation frequency data of the wing,
- Detecting Drone (UAV) position using a location-finding algorithm from points different from Doppler frequencies. . The drone (UAV) detection and positioning method according to Claim 1, characterized in that a large number of moving possible targets are filtered with the cyclic spectral density method before detection. . The drone (UAV) detection and positioning method according to Claim 1, characterized in that Doppler ambiguity is eliminated by spectral frequency information in case of possible Doppler ambiguities. . The drone (UAV) detection and positioning method according to Claim 1 or 2, characterized in that the detection of the position of the Drone (UAV) is performed by using the location-finding algorithm from multiple points from Doppler frequencies. . The drone (UAV) detection and positioning method according to any one of the preceding claims, characterized in that it comprises the step of combining the resulting
data by working together with other sensor systems that can detect and produce one or more of the range, azimuth, elevation angle, velocity data. The drone (UAV) detection and positioning method according to Claim 1, characterized in that it provides range detection using different waveforms. The drone (UAV) detection and positioning method according to Claim 1, characterized in that the said deinterleaving mentioned in the method is a histogram based deinterleaving or a PRI transform deinterleaving. The drone (UAV) detection and positioning method according to Claim 5, characterized in that the said sensor systems are RF-based sensor systems, image-based sensor systems or acoustic -based sensor systems.
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| TR2021022245 | 2021-12-31 | ||
| TR2021/022245 TR2021022245A2 (en) | 2021-12-31 | DOPPLER SHIFT BASED DISTRIBUTED DRONE DETECTION SYSTEM |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017205874A1 (en) * | 2016-05-27 | 2017-11-30 | Rhombus Systems Group, Inc. | Radar system to track low flying unmanned aerial vehicles and objects |
| US11061114B2 (en) * | 2016-06-02 | 2021-07-13 | Qinetiq Limited | Radar system for the detection of drones |
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- 2022-12-29 WO PCT/TR2022/051689 patent/WO2023129094A1/en unknown
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017205874A1 (en) * | 2016-05-27 | 2017-11-30 | Rhombus Systems Group, Inc. | Radar system to track low flying unmanned aerial vehicles and objects |
| US11061114B2 (en) * | 2016-06-02 | 2021-07-13 | Qinetiq Limited | Radar system for the detection of drones |
Non-Patent Citations (1)
| Title |
|---|
| BJORKLUND, S.: "Target detection and classification of small drones by boosting on radar micro-Doppler", IN 2018 15TH EUROPEAN RADAR CONFERENCE (EURAD, September 2018 (2018-09-01), pages 182 - 185, XP033453422, DOI: 10.23919/EuRAD.2018.8546569 * |
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