DE102023114512B3 - System and method for improving the resolution of structures and objects detected by radar sensors - Google Patents

Abstract

The invention relates to a method for improving the resolution of structures and objects detected by radar sensors, wherein at least a first radar sensor module (200) with one or more radar sensors, at least a second radar sensor module (300) with one or more radar sensors and a data analysis module (400) are used, comprising:- detecting first radar data (270) in a first detection range (250) from the first radar sensor module (200) in a first detection mode;- detecting second radar data (370) in a second detection range (350) from the second radar sensor module (300) in a first detection mode;- determining at least one relevant region (region of interest, Rol) by means of an entropy, density and/or distance metric;- changing the first detection mode of the first radar sensor module (300) and the second radar sensor module (400) to a beamforming detection mode;- detecting third radar data (280) in a first modified detection area (290);- detecting fourth radar data (380) in a modified second detection area (390);- evaluating the third radar data (280) and the fourth radar data (380) in an overlap area (550) between the modified first detection area (290) and the second modified detection area (390).

Description

The invention relates to a system, method and computer program product for improving the resolution of structures and objects detected by radar sensors.

Modern vehicles are equipped with a variety of driver assistance systems (Advanced Driver Assistance Systems, ADAS) and highly automated driving functions (Automated Driving Systems, ADS) to support the driver while driving and increase safety. There are various types of driver assistance systems, such as adaptive cruise control, which automatically maintains a constant distance from the vehicle in front and adjusts the speed of the vehicle, lane departure warning, which warns when the vehicle is leaving the lane, brake assist, which automatically brakes the vehicle when a critical situation is detected, or parking assistant, which supports the driver when parking and leaving a parking space. Driver assistance systems are constantly being developed and are intended to help increase road safety and prevent accidents.

The development of highly automated driver assistance functions is associated with an increase in the complexity of perception requirements. This leads to an increase in the demands on vehicle sensor systems. For autonomous and highly automated driver functions for motor vehicles as well as in the field of industrial robotics, environmental perception plays a crucial role in generating an environmental model using various sensor technologies such as LiDAR, radar, image recording technologies using cameras and ultrasound in order to enable a motor vehicle or a robot to perceive the respective environment spatially precisely.

Radar has the advantage that the detection method is independent of light and weather conditions. Radar sensors generate a large number of point-like raw targets. However, a disadvantage of radar sensors compared to LiDAR sensors, for example, is the size of the range cells or angle cells, which reduces the resolution.

Within a range cell or angle cell, the raw targets cannot be separated from each other, so there is only one raw target in a cell at any one time. This usually results in a sparse point cloud. For a typical automotive radar resolution, the distance between the resolution points is about 10 cm to 1 m. This means that two objects that are less than 10 cm or more than 1 m apart are considered as one object.

The EP 3 457 173 A1 discloses a radar image processing method in which images are acquired from radar devices synchronized for beamforming, the acquired images are synthesized at least in an overlap region, and a high-resolution image is generated therefrom.

The EN 10 2018 113 922 A1 discloses a radar system for performing two-stage beamforming, wherein a first stage provides coarse beamforming and a second stage provides fine beamforming for a selected area.

The WO 2018 034754 A1 discloses a scanning device for performing a first scan over a target area, creating a detection map of the target area based on the signal intensities, identifying a distinct area within the target area based on the detection map, and performing a second scan over the distinct area. However, the identification of the distinct area is not very accurate and may even be incorrect.

The object underlying the invention is to create possibilities for improving the resolution of structures and objects detected by radar sensors, which are easy to implement, are characterized by high reliability, security and accuracy and require only low computing power.

The method according to the present invention can significantly increase the resolution of structures and objects detected by radar. For this purpose, a detection area is detected by at least one radar sensor in a first detection mode. In this detection area, a region of interest is then determined by a metric based on the entropy and/or the density of the radar signal strength in the set of distance cells or angle cells of the radar detection area and/or the distance between distance cells or angle cells with high radar signal strength. This region of interest is then detected again in a beamforming detection mode by at least two radar sensors. In an overlapping area, the size of the distance cells or angle cells can be reduced, which leads to a higher resolution.

This object is achieved with respect to a method by the features of patent claim 1, with regard to a system by the features of patent claim 8, and with regard to a computer program product by the features of patent claim 15. The further claims relate to preferred embodiments of the invention.

• - Erfassen von ersten Radardaten in einem ersten Erfassungsbereich von dem ersten Radarsensormodul in einem ersten Erfassungsmodus, wobei der erste Erfassungsbereich eine Mehrzahl von Entfernungszellen bzw. Winkelzellen umfasst;
• - Erfassen von zweiten Radardaten in einem zweiten Erfassungsbereich von dem zweiten Radarsensormodul in einem ersten Erfassungsmodus, wobei der zweite Erfassungsbereich eine Mehrzahl von Entfernungszellen bzw. Winkelzellen umfasst;

• - Analysieren der ersten Radardaten hinsichtlich der Radarsignalstärke in den Entfernungszellen bzw. Winkelzellen in Bezug auf eine Entropie und/oder Dichte und/oder Distanz;
• - Analysieren der zweiten Radardaten hinsichtlich der Radarsignalstärke in den Entfernungszellen bzw. Winkelzellen in Bezug auf eine Entropie und/oder Dichte und/oder Distanz;
• - Ermitteln zumindest einer relevanten Region (Region of Interest, Rol) mittels der Entropie-, Dichte- und/oder Distanzmetrik in dem ersten Erfassungsbereich und/oder zweiten Erfassungsbereich
• - Ändern des ersten Erfassungsmodus des ersten Radarsensormoduls und des zweiten Radarsensormoduls in einen Strahlformungs-Erfassungsmodus;
• - Erfassen von dritten Radardaten in einem ersten modifizierten Erfassungsbereich von dem ersten Radarsensormodul in dem Strahlformungs-Erfassungsmodus, wobei der modifizierte Erfassungsbereich eine Mehrzahl von Entfernungszellen bzw. Winkelzellen umfasst;
• - Erfassen von vierten Radardaten in einem modifizierten zweiten Erfassungsbereich von dem zweiten Radarsensormodul in einem Strahlformungs-Erfassungsmodus, wobei der modifizierte zweite Erfassungsbereich eine Mehrzahl von zweiten Entfernungszellen bzw. Winkelzellen umfasst,
• - Auswerten der dritten Radardaten und der vierten Radardaten in einem Überlappungsbereich zwischen dem modifizierten ersten Erfassungsbereich und dem zweiten modifizierten Erfassungsbereich.

According to a first aspect, the invention provides a method for improving the resolution of structures and objects detected by radar sensors. At least one first radar sensor module with one or more radar sensors, at least one second radar sensor module with one or more radar sensors and a data analysis module are used. In particular, the first radar sensor module and the second radar sensor module are arranged at a certain distance, preferably at the same height, on a vehicle. The method comprises the following method steps:

• - Acquiring first radar data in a first detection range from the first radar sensor module in a first detection mode, wherein the first detection range comprises a plurality of range cells or angle cells;
• - Acquiring second radar data in a second detection range from the second radar sensor module in a first detection mode, wherein the second detection range comprises a plurality of range cells or angle cells;

• - analyzing the first radar data with respect to the radar signal strength in the range cells or angle cells in relation to an entropy and/or density and/or distance;
• - analyzing the second radar data with respect to the radar signal strength in the range cells or angle cells in relation to an entropy and/or density and/or distance;
• - Determining at least one relevant region (Region of Interest, Rol) using the entropy, density and/or distance metric in the first detection area and/or second detection area
• - changing the first detection mode of the first radar sensor module and the second radar sensor module to a beamforming detection mode;
• - Acquiring third radar data in a first modified detection range from the first radar sensor module in the beamforming detection mode, the modified detection range comprising a plurality of range cells or angle cells;
• - Acquiring fourth radar data in a modified second detection range from the second radar sensor module in a beamforming detection mode, wherein the modified second detection range comprises a plurality of second range cells or angle cells,
• - Evaluating the third radar data and the fourth radar data in an overlap area between the modified first detection area and the second modified detection area.

In a further development, it is provided that in the overlapping area, by superimposing the first distance cells or angle cells and the second distance cells or angle cells, third distance cells or angle cells are created, the size of which is smaller than that of the first distance cells or angle cells and the second distance cells or angle cells.

In an advantageous embodiment, the entropy is a measure of the amount of information that can be obtained from the radar data, with a higher entropy the signals being more random and containing less useful information, while a lower entropy the signals being less random and containing more useful information.

In a further embodiment, it is provided that a radar sensor is designed as a short-range radar with a measuring range of up to approx. 30 m or as a medium-range radar with a measuring range of up to approx. 150 m or as a long-range radar with a measuring range of up to approx. 220 m.

In particular, a radar sensor is designed as a microwave radar.

Advantageously, the data analysis module comprises a software application that uses artificial intelligence algorithms.

In particular, neural networks are used as artificial intelligence algorithms.

According to a second aspect, the invention provides a system for improving the resolution of structures and objects detected by radar sensors. The system comprises at least a first radar sensor module with one or more radar sensors, at least a second radar sensor module with one or more radar sensors and a data analysis module. In particular, the first radar sensor module and the second radar sensor module are mounted on a vehicle at a certain distance, preferably at the same height. The first radar sensor module is designed to detect first radar data in a first detection range in a first detection mode, wherein the first detection range comprises a plurality of distance cells or angle cells. The second radar sensor module is designed to detect second radar data in a second detection range in a first detection mode, wherein the second detection range comprises a plurality of distance cells or angle cells. The data analysis module is designed to analyze the first radar data with respect to the radar signal strength in the range cells or angle cells in relation to an entropy and/or density and/or distance and the second radar data with respect to the radar signal strength in the range cells or angle cells in relation to an entropy and/or density and/or distance, to determine at least one relevant region (region of interest, Rol) in the first detection range and/or second detection range using the entropy, density and/or distance metric and to generate signals to change the first detection mode of the first radar sensor module and the second radar sensor module to a beamforming detection mode. The first radar sensor module is designed to detect third radar data in a first modified detection range from the first radar sensor module in the beamforming detection mode, wherein the modified detection range comprises a plurality of range cells or angle cells. The second radar sensor module is designed to acquire fourth radar data in a modified second detection range from the second radar sensor module in a beamforming detection mode, wherein the modified second detection range comprises a plurality of second range cells or angle cells. The data analysis module is designed to evaluate the third radar data and the fourth radar data in an overlap region between the modified first detection range and the second modified detection range.

In a further development, it is provided that in the overlapping area, by superimposing the first distance cells or angle cells and the second distance cells or angle cells, third distance cells or angle cells are created, the size of which is smaller than that of the first distance cells or angle cells and the second distance cells or angle cells.

Entropy is a measure of the amount of information that can be obtained from radar data, with higher entropy making signals more random and containing less useful information, while lower entropy making signals less random and containing more useful information.

In an advantageous embodiment, it is provided that a radar sensor is designed as a short-range radar with a measuring range of up to approx. 30 m or as a medium-range radar with a measuring range of up to approx. 150 m or as a long-range radar with a measuring range of up to approx. 220 m.

In a further embodiment, it is provided that a radar sensor is designed as a microwave radar.

In particular, the data analysis module includes a software application that uses artificial intelligence algorithms.

Advantageously, neural networks are used as artificial intelligence algorithms.

According to a third aspect, the invention provides a computer program product comprising executable program code configured to carry out the method according to the first aspect when executed.

The invention is explained in more detail below with reference to embodiments shown in the drawing.

• 1 ein Blockdiagramm zur Erläuterung eines Ausführungsbeispiels eines erfindungsgemäßen Systems;
• 2 eine schematische Darstellung eines Fahrzeugs mit zwei Radarsensormodulen in einem ersten Erfassungsmodus;
• 3 eine schematische Darstellung des Fahrzeugs mit zwei Radarsensoreinrichtungen in einem Strahlformungs-Erfassungsmodus;
• 4a eine schematische Darstellung von Entfernungszellen mit geringer Entropie;
• 4b eine schematische Darstellung von Entfernungszellen mit hoher Entropie;
• 5 eine schematische Darstellung der Überlagerung der Entfernungszellen eines ersten und zweiten Radarsensormoduls;
• 6 ein Flussdiagramm zur Erläuterung der einzelnen Verfahrensschritte des erfindungsgemäßen Verfahrens;
• 7 eine schematische Darstellung eines Computerprogrammprodukt

It shows:

• 1 a block diagram for explaining an embodiment of a system according to the invention;
• 2 a schematic representation of a vehicle with two radar sensor modules in a first detection mode;
• 3 a schematic representation of the vehicle with two radar sensor devices in a beamforming detection mode;
• 4a a schematic representation of low entropy distance cells;
• 4b a schematic representation of high entropy distance cells;
• 5 a schematic representation of the superposition of the range cells of a first and second radar sensor module;
• 6 a flow chart explaining the individual process steps of the process according to the invention;
• 7 a schematic representation of a computer program product

Additional features, aspects and advantages of the invention or embodiments thereof will become apparent from the detailed description taken in conjunction with the claims.

1 shows a system 100 according to the invention for improving the resolution of structures detected with radar sensors. The system 100 comprises at least a first radar sensor module 200, a second radar sensor module 300 and a data analysis module 400. In particular, the first radar sensor module 200 and the second radar sensor module 300 are arranged at a certain distance, preferably at the same height, on a vehicle 500. The vehicle can be designed as a motor vehicle, an automated vehicle, an agricultural vehicle such as a combine harvester, a robot in production or in service facilities, a watercraft or a flying object such as a drone. The first radar sensor module 200, the second radar sensor module 300 and the data analysis module 400 can each be provided with a processor and/or a storage unit. The data analysis module can be integrated in the vehicle 500 or designed as a cloud-based solution that is connected to the vehicle 500 via a cellular connection. For this purpose, for example, the first radar sensor module 200 and/or the second radar sensor module 300 or another communication module can be equipped with radio modules for data transmission to the cloud. In particular, 5G radio modules can be used.

In the context of the invention, a "processor" can be, for example, a machine or an electronic circuit. A processor can in particular be a central processing unit (CPU), a microprocessor or a microcontroller, e.g. an application-specific integrated circuit or a digital signal processor, possibly in combination with a memory unit for storing program instructions. A processor can also be a virtualized processor, a virtual machine or a soft CPU. It can also be, for example, a programmable processor that is equipped with configuration steps for carrying out the above-mentioned method according to the invention or is configured with configuration steps such that the programmable processor implements the inventive features of the method, the component, the modules or other aspects and/or partial aspects of the invention. In particular, the processor can contain highly parallel computing units and powerful graphics modules.

In the context of the invention, a "memory unit" or "memory module" and the like can be understood as, for example, a volatile memory in the form of a random access memory (RAM) or a permanent memory such as a hard disk or a data carrier or, for example, a removable memory module. The memory module can also be a cloud-based storage solution.

In the context of the invention, a "module" can be understood as, for example, a processor and/or a memory unit for storing program instructions. For example, the processor is specially designed to execute the program instructions in such a way that the processor and/or the control unit executes functions in order to carry out or implement the method according to the invention or a step of the method according to the invention.

The first radar sensor module 200 and the second radar sensor module 300 each contain at least one radar sensor, which is designed in particular as a microwave radar. Microwave radars work with electromagnetic waves in the frequency range from 1 to 100 GHz and are used for navigation, for detecting objects such as aircraft, ships or vehicles, and in industry for monitoring production environments and for automating work processes. The radar sensor can be designed as a short-range radar (SRR) with a measuring range of up to approx. 30 m, as a mid-range radar (MRR) with a measuring range of up to approx. 150 m or as a long-range radar (LRR) with a measuring range of up to approx. 220 m. Short-range radar is mainly used to detect obstacles in the immediate vicinity of a vehicle, e.g. when parking or changing lanes. Medium-range radar is used to detect vehicles in adjacent lanes and to support driver assistance systems such as adaptive cruise control. Long-range radar is mainly used to detect obstacles at a greater distance, e.g. other vehicles on the highway.

The first radar sensor module 200 and the second radar sensor module 300 also contain beamforming antennas. Radar beamforming is a technique used in radar systems to direct radar energy in a specific direction while suppressing it in other directions. This is achieved by combining the signals from multiple antenna elements into a beam that can be electronically steered. This improves radar performance. To do this, the incoming radar signal is first received by each antenna element. Then the signals are combined so that the waves from each element constructively add in the desired direction while canceling each other out in the other directions. To do this, the phase and amplitude of the individual signals are adjusted before combining. Once the signals are combined, the resulting beam can be controlled by adjusting the phase and amplitude of the signals in each antenna element. This allows the radar system to be aimed at a specific target or area of interest while minimizing interference from other sources.

Beamforming allows the radar to create a narrow beam that can detect and track targets with high accuracy even when noise, clutter, and interference are present. It also allows the radar to electronically steer the beam in real time without physically moving the antennas. There are two main types of beamforming: analog and digital. In analog beamforming, the phase and amplitude of the signals from each antenna are adjusted using analog circuits. In digital beamforming, the signals from the antennas are combined and manipulated using digital signal processing. Radar beamforming is used in various applications such as weather forecasting, surveillance, navigation, and communications. The benefits include improved range, resolution, sensitivity, and target detection.

In the 2 the vehicle 500 is shown with the radar sensor module 200 and the second radar sensor module 300. The first radar sensor module 200 detects a first detection area 250 and generates first radar data 270. The second radar sensor module 300 detects a second detection area 350 and generates second radar data 370. The first detection area 250 and the second detection area 350 do not overlap. An object 700 located in this intermediate area cannot be detected. Other objects 700 located in the first detection area 250 or the second detection area 350 can only be detected with a limited resolution, which is determined by the size of the distance cells 220, 320 or angle cells. The result of radar detection of the detection range 250, 350 is a point cloud with a certain signal strength in the various distance cells 220, 320 or angle cells. In radar technology, the signal strength is usually given in dBm (decibel milliwatts) or dBW (decibel watts). A higher signal strength usually means that an object is easier to detect.

The first radar data 270 with a point cloud in the first detection area 250 and the second radar data 370 with a point cloud in the second detection area 350 are fed to the data analysis module 400. The data analysis module 400 comprises a software application 450 with algorithms for analyzing the first radar data 270 and the second radar data 370. The software application 450 is designed to localize relevant regions (Regions of Interest, Rols). A relevant region, Rol, is understood to be an area within a radar detection area 250, 350 in which two objects 700 can be located, but which cannot be separated from one another due to the low radar resolution and are therefore recognized as one object.

According to the invention, the localization of the Rols is based on an analysis of the distribution of the measured radar signal strength in the distance cells 220, 320 or angle cells in the radar detection range 250,350.

In a radar system, a range cell refers to a section of distance along the radar's line of sight, typically measured in meters. The range cell allows the radar to distinguish between objects at different distances from the radar. By dividing the detection area into small cells, the radar can detect and locate targets at specific distances with high accuracy. The size of the range cell, and hence the minimum achievable range resolution, is determined in particular by the radar's pulse duration, but also by factors such as antenna radiation, waveform, and signal processing technique. A shorter pulse length results in a smaller range cell, allowing the radar to achieve a higher range resolution. However, a shorter pulse length also reduces the radar's maximum range. The range cell is a fundamental concept in radar systems to determine the radar's minimum range resolution and to detect and locate targets at specific distances.

An angle cell refers to a segment of angle measurement along the radar's line of sight, usually measured in degrees or radians. The angle cell allows the radar to distinguish between objects that are at different angles to the radar's line of sight. By dividing the angle into small cells, the radar can detect and locate targets at specific angles with high accuracy. The size of the angle cell depends on the width of the radar beam, which determines the minimum achievable angular resolution. A smaller beam width results in a smaller angle cell and thus a higher angular resolution. However, this also reduces the radar's detection range and the radar must scan more frequently to cover a certain area. The angle cell thus determines the minimum angular resolution of the Radars to detect and locate targets at specific angles to the radar's line of sight.

Within a range cell or angle cell, the raw targets cannot be separated from each other, so there is only one raw target in a cell at any one time. This usually results in a sparse point cloud. For a typical automotive radar resolution, the distance between the resolution points is about 10 cm to 1 m. This means that two objects that are less than 10 cm or more than 1 m apart are considered as one object.

According to the invention, the entropy of the distance cells 220, 320 or angle cells is used as a metric for analysis. 4a shows a series of range cells 220 which have an overall low entropy, since a radar signal strength of 20 dBm and 24 dBm was measured for two range cells. 4b shows a series of range cells 320 that have a high overall entropy, as here a radar signal strength was measured in five range cells, which also varies from 1 dBm to 18 dBm. Entropy is a measure of the amount of information that can be extracted from the radar signals. Higher entropy means that the signals are more random and contain less useful information, while lower entropy means that the signals are less random and contain more useful information. The entropy of a range cell can be affected by various factors, including the reflectivity of objects within the range cell, the presence of noise or interference in the signals, and the sensitivity and resolution of the radar.

A density metric and/or a distance metric can be used as an additional metric for locating a relevant region. With a density metric, the density distribution of the radar signal strengths in the set of distance cells 220, 320 is analyzed, and with a distance metric, the distance between distance cells 220, 320 with high or low radar signal strength is analyzed. The radar signal strengths in the individual distance cells 220, 320 or angle cells are thus compared with one another as a function of a density distribution or a distance. With the help of such an entropy-, density- and/or distance-based metric, a relevant region (Rol) can be identified quickly and efficiently without requiring high computing power. In particular, the various metrics can be combined with one another, which can increase the performance and efficiency of the analysis.

In order to increase the efficiency of the role determination, the software application 450 can also use artificial intelligence algorithms to increase the accuracy of pattern recognition within the framework of an entropy and/or density and/or distance analysis. In particular, neural networks are used for this purpose.

Neural networks are often cited as the prototype of AI algorithms. Neural networks are also interesting because they can be combined to increase learning success. Such coupled neural networks are also known as deep learning. A neural network consists of neurons that are arranged in several layers and are connected to each other in different ways. A neuron is able to receive information from outside or from another neuron at its input, evaluate the information in a certain way and pass it on in a modified form at the neuron output to another neuron or output it as the final result. Hidden neurons are arranged between the input neurons and output neurons. Depending on the type of network, there can be several layers of hidden neurons. They ensure that the information is forwarded and processed. Output neurons finally provide a result and output it to the outside world. The arrangement and connection of neurons creates different types of neural networks such as feedforward neural networks (FNN), recurrent neural networks (RNN) or convolutional neural networks (CNN).

The convolutional neural network is a special form of an artificial neural network. It has several convolutional layers and is very well suited for machine learning and applications with artificial intelligence (AI) in the field of pattern recognition. The individual layers of the CNN are the convolutional layer, the pooling layer and the fully connected layer. The convolutional layer is the actual convolutional layer and is able to recognize and extract individual features in the input data.

After a role has been located, this information is transmitted to the first radar sensor module 300 and the second radar sensor module 400. As in 3 As shown, the first radar sensor module 300 and the second radar sensor module 400 then switch to a second detection mode with beam forming. The extent and direction of the detected radar area changes. The first radar sensor module 300 now detects a first modified detection area area 290 and the second radar sensor module 400 a second modified detection area 390. An overlap area 550 is now created between the first modified detection area 290 and the second modified detection area 390.

This overlap area 550 is in 5 in relation to the distance cells 220 of the first radar sensor module 200 and the distance cells 320 of the second radar sensor module 300. The overlap now results in smaller distance cells 520, the size of which is reduced compared to the distance cells 220 of the first radar sensor module 200 and the distance cells 320 of the second radar sensor module 300. This allows the resolution of the system 100 to be significantly increased, since the radar signals of objects 700 located in the various smaller distance cells 520 can now be differentiated from one another.

In 6 The process steps for improving the resolution of structures and objects detected by radar sensors are shown.

In a step S10, first radar data 270 are acquired in a first detection range 250 by the first radar sensor module 200 in a first detection mode, wherein the first detection range 250 comprises a plurality of distance cells 210 or angle cells.

In a step S20, second radar data 370 is acquired in a second detection range 350 by the second radar sensor module 300 in a first detection mode, wherein the second detection range 350 comprises a plurality of distance cells 390 or angle cells.

In a step S30, the first radar data 270 are analyzed with respect to the radar signal strength in the range cells 290 or angle cells in relation to an entropy and/or density and/or distance.

In a step S40, the second radar data 270 are analyzed with respect to the radar signal strength in the range cells 290 or angle cells in relation to an entropy and/or density and/or distance.

In a step S50, at least one relevant region (region of interest, Rol) is determined by means of the entropy, density and/or distance metric in the first detection area 250 and/or second detection area 350.

In a step S60, the first detection mode of the first radar sensor module 300 and the second radar sensor module 400 is changed to a beamforming detection mode.

In a step S70, third radar data 280 is acquired in a first modified detection range 290 by the first radar sensor module 200 in the beamforming detection mode, wherein the modified detection range 290 comprises a plurality of range cells 220 or angle cells.

In a step S80, fourth radar data 380 is acquired in a modified second detection range 390 by the second radar sensor module 300 in a beamforming detection mode, wherein the modified second detection range 390 comprises a plurality of second range cells 320 or angle cells.

In a step S90, the third radar data 280 and the fourth radar data 380 are evaluated in an overlap region 550 between the modified first detection region 290 and the second modified detection region 390.

7 schematically illustrates a computer program product 900 comprising an executable program code 950 configured to perform the method according to the first aspect of the present invention when executed.

The method according to the present invention can significantly increase the resolution of structures and objects detected by radar. For this purpose, a detection area is detected by at least one radar sensor in a first detection mode. In this detection area, a region of interest is then determined by a metric based on the entropy and/or the density of the radar signal strength in the set of distance cells or angle cells of the radar detection area and/or the distance between distance cells or angle cells with high radar signal strength. This region of interest is then detected again in a beamforming detection mode by at least two radar sensors. In an overlapping area, the size of the distance cells or angle cells can be reduced, which leads to a higher resolution.

Reference symbol

100

system

200

first radar sensor module

220

Distance cell

250

first detection area

270

first radar data

280

second radar data

290

modified first detection area

300

second radar sensor module

320

Distance cell

350

second detection area

370

second radar data

380

fourth radar data

390

modified second detection area

400

Data analysis module

450

Software application

500

vehicle

550

Overlap area

520

Distance cell

700

object

900

Computer program product

950

Program code

Claims (15)

Method for improving the resolution of structures and objects detected by radar sensors, wherein at least a first radar sensor module (200) with one or more radar sensors, at least a second radar sensor module (300) with one or more radar sensors and a data analysis module (400) are used, and wherein in particular the first radar sensor module (200) and the second radar sensor module (300) are arranged at a certain distance, preferably at the same height, on a vehicle (500), comprising: - detecting (S10) first radar data (270) in a first detection range (250) from the first radar sensor module (200) in a first detection mode, wherein the first detection range (250) comprises a plurality of distance cells (210) or angle cells; - Acquiring (S20) second radar data (370) in a second detection range (350) from the second radar sensor module (300) in a first detection mode, wherein the second detection range (350) comprises a plurality of distance cells (390) or angle cells; - Analyzing (S30) the first radar data (270) with regard to the radar signal strength in the distance cells (290) or angle cells in relation to an entropy and/or density and/or distance; - Analyzing (S40) the second radar data (270) with regard to the radar signal strength in the distance cells (290) or angle cells in relation to an entropy and/or density and/or distance; - Determining (S50) at least one relevant region (region of interest, Rol) using the entropy, density and/or distance metric in the first detection range (250) and/or second detection range (350); - Changing (S60) the first detection mode of the first radar sensor module (300) and the second radar sensor module (400) to a beamforming detection mode; - Acquiring (S70) third radar data (280) in a first modified detection range (290) from the first radar sensor module (200) in the beamforming detection mode, wherein the modified detection range (290) comprises a plurality of distance cells (220) or angle cells; - Acquiring (S80) fourth radar data (380) in a modified second detection range (390) from the second radar sensor module (300) in a beamforming detection mode, wherein the modified second detection range (390) comprises a plurality of second distance cells (320) or angle cells, - Evaluating (S90) the third radar data (280) and the fourth radar data (380) in an overlap region (550) between the modified first detection range (290) and the second modified detection range (390). Procedure according to Claim 1 , wherein in the overlap region (550) by the superposition of the first distance cells (220) or angle cells and the second distance cells (320) or angle cells, third distance cells (520) or angle cells are created, the size of which is smaller than that of the first distance cells (220) or angle cells and the second distance cells (320) or angle cells. Procedure according to Claim 1 or 2 , where the entropy is a measure of the amount of information that can be obtained from the radar data (270, 280; 370, 380), with a higher entropy the signals are more random and contain less useful information, while a lower entropy the signals are less random and contain more useful information. Procedure according to one of the Claims 1 until 3 , whereby a radar sensor is designed as a short-range radar with a measuring range of up to approx. 30 m or as a medium-range radar with a measuring range of up to approx. 150 m or as a long-range radar with a measuring range of up to approx. 220 m. Procedure according to one of the Claims 1 until 4 , wherein a radar sensor is designed as a microwave radar. Procedure according to one of the Claims 1 until 5 , wherein the data analysis module (400) comprises a software application (450) that uses artificial intelligence algorithms. Procedure according to Claim 6 , where neural networks are used as artificial intelligence algorithms. System (100) for improving the resolution of structures and objects detected with radar sensors, comprising at least a first radar sensor module (200) with one or more radar sensors, at least a second radar sensor module (300) with one or more radar sensors and a data analysis module (400), wherein in particular the first radar sensor module (200) and the second radar sensor module (300) are arranged at a certain distance, preferably at the same height, on a vehicle (500), wherein the first radar sensor module (200) is designed to detect first radar data (270) in a first detection range (250) in a first detection mode, wherein the first detection range (250) comprises a plurality of distance cells (210) or angle cells; wherein the second radar sensor module (200) is configured to acquire second radar data (370) in a second detection range (350) in a first detection mode, wherein the second detection range (350) comprises a plurality of distance cells (390) or angle cells; wherein the data analysis module (400) is designed to analyze the first radar data (270) with regard to the radar signal strength in the range cells (290) or angle cells in relation to an entropy and/or density and/or distance and the second radar data (270) with regard to the radar signal strength in the range cells (290) or angle cells in relation to an entropy and/or density and/or distance, to determine at least one relevant region (region of interest, Rol) in the first detection area (250) and/or second detection area (350) by means of the entropy, density and/or distance metric and to generate signals in order to change the first detection mode of the first radar sensor module (300) and the second radar sensor module (400) to a beamforming detection mode; wherein the first radar sensor module (200) is configured to acquire third radar data (280) in a first modified detection range (290) from the first radar sensor module (200) in the beamforming detection mode, wherein the modified detection range (290) comprises a plurality of range cells (220) or angle cells; wherein the second radar sensor module (300) is configured to acquire fourth radar data (380) in a modified second detection range (390) from the second radar sensor module (300) in a beamforming detection mode, wherein the modified second detection range (390) comprises a plurality of second range cells (320) or angle cells; and wherein the data analysis module (400) is designed to evaluate the third radar data (280) and the fourth radar data (380) in an overlap region (550) between the modified first detection region (290) and the second modified detection region (390). System (100) after Claim 8 , wherein in the overlap region (550) by the superposition of the first distance cells (220) or angle cells and the second distance cells (320) or angle cells, third distance cells (520) or angle cells are created, the size of which is smaller than that of the first distance cells (220) or angle cells and the second distance cells (320) or angle cells. System (100) after Claim 8 or 9 , where the entropy is a measure of the amount of information that can be obtained from the radar data (270, 280; 370, 380), with a higher entropy the signals are more random and contain less useful information, while a lower entropy the signals are less random and contain more useful information. System (100) according to one of the Claims 8 until 10 , whereby a radar sensor is designed as a short-range radar with a measuring range of up to approx. 30 m or as a medium-range radar with a measuring range of up to approx. 150 m or as a long-range radar with a measuring range of up to approx. 220 m. System (100) according to one of the Claims 8 until 11 , wherein a radar sensor is designed as a microwave radar. System (100) according to one of the Claims 8 until 12 , wherein the data analysis module (400) comprises a software application (450) that uses artificial intelligence algorithms. System (100) after Claim 13 , where neural networks are used as artificial intelligence algorithms. Computer program product (900) comprising an executable program code (950) configured to perform the method according to one of the Claims 1 until 7 executes.

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