Method for signal analysis and evaluation in a positioning system, and positioning system for a motor vehicle

Abstract

The invention relates to a method for signal analysis and evaluation in a localization system having a plurality of radar sensors (10, 12), the localization areas (16, 18) of which overlap with one another, characterized in that the following steps are included: - analyzing and evaluating the signals of a first radar sensor (10) of the radar sensors and identifying non-empty distance cells, - for at least one of these distance cells: - selecting a second radar sensor (12) of the radar sensors and determining a distance area (46) in which an object (20, 22) located in the distance cell (D1) is definitely located from the perspective of the second radar sensor (12); - classifying the object configuration in the distance area (46) by means of signal identification from the second radar sensor (12). The invention also relates to a localization system for a motor vehicle and a software product having a machine-readable program code.

Description

Technical Field

[0001] The invention relates to a method for signal evaluation in a localization system having a plurality of radar sensors, the localization areas of which overlap with one another.

[0002] In particular, the present invention relates to a positioning system for a motor vehicle, which is able to sense the traffic environment as accurately as possible. Background Art

[0003] Radar sensors installed in motor vehicles usually sense the distance, relative speed and azimuth of the located objects, and also sense the elevation angle if necessary. Commonly used are frequency modulated continuous wave (FMCW) radar sensors, in which the frequency of the transmitted radar signal is modulated in a ramp shape. The received radar echo is mixed with a part of the currently transmitted signal so that a beat signal is obtained in the intermediate frequency band, the frequency of which depends on the frequency difference between the transmitted signal and the received signal. If the frequency ramp is very steep, the frequency difference is first determined by the signal propagation time and thus by the object distance. If the intermediate frequency signal is converted into a spectrum by a fast Fourier transform (FFT), each object is presented in the spectrum in the form of a peak at a certain frequency, and the determined frequency indicates the corresponding object distance. However, due to digital signal processing, the spacing space is divided into discrete spacing units. If no signal above the noise level is received at the relevant frequency, the unit should be called "empty".

[0004] In general, the influence of the relative speed of the object on the frequency position caused by the Doppler effect cannot be ignored. One possibility of separating the distance-related component and the speed-related component relative to each other is to use frequency ramps with different slopes. Since the slope only affects the distance-related component, by comparing the frequency positions of the peaks of different slopes, both the distance between the objects and the relative speed can be determined.

[0005] Another possibility is to transmit multiple frequency ramps with the same slope in rapid succession, the frequencies of which are offset relative to each other, so that these ramps themselves form a "ramp" with a slowly increasing frequency. By two-dimensional Fourier transformation of the fast and slow ramps, the amplitude distribution can be directly obtained in a two-dimensional distance-velocity diagram (vd diagram). In this diagram, there are multiple distance cells for each object distance, which represent different relative speeds.

[0006] Thus, it is also possible to measure the direction angle (in azimuth and / or in elevation) relative to the located object, each radar sensor having a plurality of transmitting antenna elements and / or a plurality of receiving antenna elements which are offset relative to one another on a straight line. If the radar echo does not strike at right angles to the straight line, there are propagation time differences between the antenna elements which result in a distribution of the (complex) amplitude of the received signal that characterizes the corresponding direction angle of the object. This direction angle can at least be estimated by searching in the antenna diagram for an amplitude distribution that best matches the measured distribution.

[0007] However, if there are a plurality of radar sensors whose localization areas overlap, known multilateration methods can also be used in order to localize the object in a two-dimensional coordinate system using different signal propagation times for the different radar sensors.

[0008] In known positioning systems, the accuracy with which the traffic environment can be sensed is limited because the radar sensor has only a limited ability to distinguish objects located in the same distance unit from one another. Summary of the invention

[0009] The object of the present invention is to specify a method which allows the environment to be sensed with increased accuracy and reliability.

[0010] According to the invention, this object is achieved by a method comprising the following steps:

[0011] - evaluating the signals of a first radar sensor of the radar sensors and identifying non-spatial distance cells,

[0012] -For at least one of these spacing units:

[0013] - selecting a second radar sensor of the radar sensors and determining a distance range in which an object located in the distance cell is definitely located from the perspective of the second radar sensor, and

[0014] - Classifying the object configuration in this distance range with the aid of the signal from the second radar sensor.

[0015] According to the invention, therefore, additional information about the environment is obtained by combining the signals obtained from a plurality of radar sensors, not in the sense of multilateration, but in such a way that the object configuration is classified in more detail and the differentiation capability and dynamic range are thereby improved.

[0016] The invention further relates to a localization system for a motor vehicle, which has a plurality of radar sensors whose localization areas overlap with one another, and an evaluation unit which is configured to carry out the method according to the invention.

[0017] Furthermore, the invention relates to a software product having a machine-readable program code which, when loaded into an evaluation unit of a localization system according to the invention, arranges for the evaluation unit to carry out the method according to the invention.

[0018] In preferred embodiments, advantageous configurations and developments result.

[0019] According to one possible specific embodiment, the classification of the object configuration includes determining the number of objects in non-empty distance cells of the first radar sensor.

[0020] An important criterion in the classification of object configurations is the question of how many different radar targets are located in the same non-spatial distance cell. To date, this information has only been available with limited accuracy and reliability within the context of angle estimation using amplitude distributions. However, the difficulty is that, precisely when multiple radar targets are located in the same distance cell, signals from multiple objects and therefore arriving from different directions superimpose on one another, significantly reducing the angular quality and thus making it difficult to unambiguously interpret the amplitude distribution.

[0021] According to another possible specific embodiment, if a plurality of objects are located in the same distance cell for one of the radar sensors, an angle estimation is performed using a radar sensor that is located in separate distance cells for the one radar sensor to determine the direction angles relative to the objects.

[0022] The present invention now provides an additional possibility for distinguishing between single-target and multi-target scenarios that is independent of the angle estimation. That is, if two radar targets are located in the same distance cell for one of the radar sensors, then due to the spatial distance between these radar sensors, these two radar targets are located with high probability in two adjacent but mutually different radar cells for the other radar sensor, so that these targets can be distinguished by the other radar sensor. Using the second radar sensor, separate angle estimates can be carried out in the two distance cells, and the result is more precise and more reliable than the result that would be obtained using the first radar sensor.

[0023] According to another possible specific embodiment, the information of one radar sensor is used to carry out an improved dual-target angle estimation together with the other radar sensor if a plurality of objects are located in the same distance cell for one of the radar sensors.

[0024] If the direction angles relative to a plurality of targets are known, it is possible to calculate an amplitude distribution that can be expected when measuring with the first radar sensor, and by comparing the expected amplitude distribution with the amplitude distribution actually measured in the first radar sensor, information about the relative signal strengths of two radar targets seen from the perspective of the first radar sensor can be obtained.

[0025] According to another possible specific embodiment, the classification of the object configuration includes comparing radar cross sections of objects located by a plurality of radar sensors based on signal strengths received by the plurality of radar sensors.

[0026] Furthermore, the invention allows the localized objects to be characterized in more detail by means of the angular dependence of their radar cross section. The radar cross section, i.e. the signal strength of a radar signal which strikes an object from a certain direction and is then reflected back again in the same direction, depends on the direction of incidence of the radar signal, and the angular dependence of the radar cross section depends in a characteristic manner on the geometry of the object concerned, so that different object types, such as trucks, passenger cars and two-wheeled vehicles, can be distinguished from one another by means of the angular dependence of the radar cross section.

[0027] According to another possible specific embodiment, an assumption is made about the type of the located object using the comparison of the radar cross sections.

[0028] The present invention makes it possible to use two different radar sensors to observe the same object from different directions relative to one another, so that radar cross sections are obtained for two different angles and from this an indication about the type of object can be derived.

[0029] If at least one object is located in a certain distance cell using the first radar sensor, but no object is found in the associated distance range in the second radar sensor, this means that the radar echo of the object is outside the dynamic range of the second radar sensor, because the object is located at the boundary of the localization range for the second radar sensor or because the radar cross section of the object is too small in the direction from which the second radar sensor sees the object. Using the distance data and angle data obtained using the first radar sensor, it can be checked whether at least one of these assumptions is plausible or whether there is a functional disturbance in the localization system. If necessary, the noise threshold can also be temporarily lowered for the second radar sensor in order to check whether weak signals can also be found by the second radar sensor.

[0030] Overall, the probability that an actually present object is actually also located by at least one of the radar sensors can be increased by evaluating and comparing the signals of a plurality of radar sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] An exemplary embodiment is explained in detail below with reference to the accompanying drawings. The accompanying drawings show:

[0032] Figure 1 Positioning diagram of a positioning system with two radar sensors;

[0033] Figure 2 Depend on Figure 1 Illustration of the signals received by the two radar sensors in the corresponding distance-speed diagram;

[0034] Figure 3 Schematic diagram of a radar sensor that performs angle resolution;

[0035] Figure 4 A simplified diagram illustrating the rear radar cross section of a truck;

[0036] Figure 5 A simplified diagram for illustrating a radar cross section of a two-wheeled vehicle oriented obliquely to the propagation direction of the radar waves;

[0037] Figure 6 Front view of a truck with a positioning system configured for the method according to the invention. DETAILED DESCRIPTION

[0038] Figure 1 A positioning system is shown which, in this simplified example, has two radar sensors 10, 12 and a central analysis and evaluation unit 14 in which the signals of the radar sensors are combined and evaluated. The two radar sensors 10, 12 have positioning areas 16, 18 which overlap with each other. In the overlapping area there are two objects 20, 22 which can be located by the two radar sensors. The two objects 20, 22 are located on a circle 24 centered on the radar sensor 10, so that the radar sensor 10 measures the same distance d1 for the two objects. It should also be assumed that the two objects 20, 22 also have the same relative speed v relative to the radar sensors 10, 12. Therefore, in the distance-speed diagram 26 ( Figure 2 ), the two objects are represented by a single signal peak 28, which is located in a single distance cell D1. Therefore, the two objects cannot be distinguished from each other by simply measuring the distance and the relative speed using radar sensor 10.

[0039] Radar sensor 12 is spatially offset relative to radar sensor 10 and therefore measures two different distances d2 and d3 relative to one another for two objects 20, 22. In the distance-velocity diagram 30 of the radar sensor, the objects are therefore represented by signal peaks 32 and 34 in different distance bins D2 and D3.

[0040] By evaluating the signal of radar sensor 12 , it can therefore be determined simply and reliably that peak 28 is a superposition of radar echoes from two different objects.

[0041] For example, if the orientation angles of the objects 20 and 22 are to be determined by angle estimation, this information is very valuable.

[0042] like Figure 3 As shown, the radar sensor 10 has an antenna array with four antenna elements 36, which are arranged in a horizontal straight line. The radar beam 38 emitted by the antenna element 36 is reflected on the object 20 and then received by the same antenna element that emitted these radar beams, so these radar beams differ in their travel by the amount of 2*delta1. These travel differences lead to differences in the composite amplitude, especially the differences in the signal phases received by different antenna elements 36 and then mapped to the distance-velocity diagram 26 by Fourier transformation. The distribution of the composite amplitude of the four antenna elements 36 depends on this azimuth angle α20 in a characteristic way: the object 20 is seen by the radar sensor 10 at this azimuth angle. This angular dependence of the amplitude distribution can be shown in the antenna curve diagram, and the azimuth angle α20 can be estimated in principle by comparing the measured amplitude distribution with the antenna curve diagram.

[0043] The same applies in this sense to radar beam 40 reflected at object 22 viewed by the radar sensor at azimuth angle α22. In this case, the distance difference is 2*delta2.

[0044] However, since the signals received from objects 20 and 22 in all four receiving channels of radar sensor 10 are located in the same distance unit D1, these signals cannot be distinguished from each other based on their frequencies, and the amplitude distribution finally measured is the result of the superposition of radar beams 38 and 40. As a result, the correlation of the amplitude distribution with the antenna curve diagram deteriorates (the angular quality deteriorates), and the azimuth angle at which the amplitude distribution still best correlates with the measured distribution is a misidentified angle that is consistent with neither α20 nor α22.

[0045] Therefore, radar sensor 10 does not locate two objects 20 and 22, but rather a false object somewhere else on circle 24. Although the reliability of the angle estimate can be roughly estimated when calculating the resulting angle quality, the true azimuth angles α20 and α22 cannot be determined from this.

[0046] However, the evaluation of the signals of radar sensor 12 offers a simpler and more reliable possibility for detecting and correcting errors of the type described above.

[0047] If radar sensor 10 determines a non-empty distance cell, for example distance cell D1, it is possible to determine the distance D1 of the object in this cell and, using the spatial arrangement of radar sensors 10, 12, it is possible to calculate in which distance range the other radar sensor 12 can definitely see the object or objects. Figure 1 ) is known, the measured distance d1 provides the position of a circle 24 in which the object must be located. The intersection of this circle 24 with the boundaries of the localization areas 16 and 18 then provides the upper limit 42 and the lower limit 44 of a distance area 46 in which the objects 20 and 22 are definitely located from the perspective of the radar sensor 12.

[0048] If it is now apparent that in this distance region exactly two peaks 32, 34 occur in distance-velocity diagram 30 of radar sensor 12, it is known that these two peaks definitely represent an object located in distance cell D1 in distance-velocity diagram 26 of radar sensor 10. It is therefore clear that peak 28 in distance cell D1 is a superposition that represents exactly two objects.

[0049] Since peaks 32 and 34 in distance-velocity space 30 of radar sensor 12 are located in separate distance cells D2 , D3 , their amplitude distributions can be evaluated separately, allowing high-quality angle estimation to be performed to determine the azimuths of objects 20 and 22 from the perspective of radar sensor 12 .

[0050] Therefore, in order to obtain a picture of the environment of the positioning system that is as accurate and applicable as possible, the angle estimation performed by radar sensor 10 is not used in this case, and instead the angle estimation performed using radar sensor 12 is relied upon.

[0051] Based on the azimuth angles measured by radar sensor 12 for objects 20 and 22, azimuth angles α20 and α22 from the perspective of radar sensor 10 can also be calculated by a simple geometric transformation. Alternatively, information from radar sensor 12 can also be used to perform an improved dual target angle estimation together with radar sensor 10.

[0052] The absolute values ​​of the amplitudes at the apex of peaks 32 and 34 also provide a basis for the signal strength ratio of radar beams 38 and 40. From this, the amplitude distribution that can be expected in radar sensor 10 can be calculated. In order to check for consistency, this expected amplitude distribution can now be compared with the amplitude distribution actually measured by radar sensor 10. If a deviation is obtained, this can be corrected by changing the assumed signal strength ratio. In this way, it can be determined whether the reflectivity of objects 20 and 22 for the signal of radar sensor 10 is different from the reflectivity for the signal of radar sensor 12. Such differences in reflectivity can be expected if the objects are not point-shaped or spherical, but have an angle-dependent radar cross section.

[0053] Obviously, the reverse process can also be performed, first searching for non-empty distance cells in the distance-speed map 30 of the radar sensor 12, and then searching for the corresponding distance area in the distance-speed map of the radar sensor 10. Depending on the object situation, it is also possible that two objects can be distinguished using only the radar sensor 10 but not the radar sensor 12.

[0054] A situation is also conceivable in which radar sensor 10 determines a non-empty distance cell D1, but radar sensor 12 locates only a single object in the corresponding distance region 46. In this case, an angle estimate with high angular quality can be performed in both radar sensors 10, 12, and the results must be consistent. Such a consistency check can be used, for example, to detect and, if necessary, correct a possible misorientation of one of the two radar sensors 10, 12.

[0055] Since the reflectivity of a single located object can be angle-dependent and the two radar sensors observe the object from slightly different directions, the signal strengths measured in the two radar sensors can be different despite approximately the same distance between the objects. Since the distances to the object measured using the two sensors are known, the distance dependency of the signal strength can be corrected by computer, so that the radar cross sections of the object for the viewing directions of the two radar sensors can be compared with one another.

[0056] As an example, Figure 4 The following situation is shown: radar sensor 10 sees the rear of truck 48 at an azimuth angle α10 of approximately 0°. The largely flat rear of the truck, oriented at right angles to radar beam 38, results in a large radar cross section and a correspondingly high reflectivity in the 0° direction. Radar sensor 12, on the other hand, sees truck 48 at an azimuth angle α12 different from 0°. The radar beam of this sensor (at Figure 4The radar beams (not shown) are irradiated obliquely to the rear of the truck and are reflected in such a way that these radar beams (apart from diffraction effects) no longer impinge on the radar sensor 12. The angular dependence of the reflectivity to the rear of the truck 48 is Figure 4 This is shown by curve 50. Radar sensor 10 measures a high reflectivity, whereas radar sensor 12 measures a significantly lower reflectivity.

[0057] For comparison, Figure 5 The situation in which radar sensor 10 locates a two-wheeled vehicle 54 at an azimuth angle α10 of approximately 0° is shown. Radar sensor 12 locates the two-wheeled vehicle at an azimuth angle α12 different from 0 and therefore looks at two-wheeled vehicle 54 obliquely, which is symbolized here by the inclined position of the two-wheeled vehicle relative to radar beam 56 of radar sensor 12. The angular dependence of the radar cross section is illustrated here by curve 58. When viewed from the side, the radar cross section of two-wheeled vehicle 54 is greater than when viewed directly from behind, so that in this case, radar sensor 12 measures a stronger signal than radar sensor 10.

[0058] Therefore, the type of the located object can be inferred using this difference in the signal strength or reflectivity measured by the radar sensor.

[0059] The radar sensors 10 and 12 are Figure 1 In practice, the localization areas 16, 18 shown in the form of sectors do not have clear boundaries at the left and right edges. More precisely, not only the illumination with the emitted radar beam but also the sensitivity to the received radar echo gradually decreases with increasing azimuth. Therefore, for objects close to the localization area boundary of the radar sensor, the radar echo may become so weak that it can no longer be detected by the sensor or it can no longer be reliably distinguished from the noise background. Therefore, the following situation may also occur: for example, the radar sensor 10 locates at least one object in the distance unit D1, but the radar sensor 12 does not determine the object in the corresponding distance area 46. However, such a situation is only allowed to occur when the azimuth measured by the radar sensor 10 indicates that the object position is close to the boundary of the localization area 18 of the radar sensor 12 in the case of error-free operation of the localization system. Therefore, in these situations, the method according to the invention also allows a self-check of the localization system.

[0060] Of course, the method is not limited to a positioning system with only two radar sensors. The positioning system preferably comprises at least three radar sensors, of which one is preferably also offset in the vertical direction relative to the other two sensors, so that the direction angles at which the objects are positioned by the different radar sensors vary not only in azimuth but also in elevation. Such a positioning system can also achieve a high degree of differentiation in the case of two radar targets or two reflection centers located at the same distance and with the same azimuth but different altitudes. By sensing the difference in radar cross sections at different elevation angles, a more differentiated object classification can also be achieved, in particular a clearer distinction between passenger cars and trucks.

[0061] As an example, Figure 6 A positioning system is shown having three radar sensors 10, 12, 60, which are mounted on the front of a truck 62. In this case, the radar sensors form an isosceles right triangle with a horizontal base in a view from the front.

Claims

1. A method for signal analysis and evaluation in a positioning system having a plurality of radar sensors (10, 12, 60), the positioning areas (16, 18) of the plurality of radar sensors overlapping each other, characterized in that: The following steps are involved: - evaluating a signal of a first radar sensor (10) of the plurality of radar sensors and identifying a non-empty distance cell (D1), - for at least one of such spacing units: - selecting a second radar sensor (12) from the plurality of radar sensors and determining a distance range (46) in which an object (20, 22) located in the distance cell (D1) is definitely located from the perspective of the second radar sensor (12); and - classifying the object configuration in the distance region (46) with the aid of a signal from the second radar sensor (12), wherein the classification of the object configuration comprises: determining the number of objects (20, 22) in a non-empty distance cell (D1) of the first radar sensor, wherein if a plurality of objects are located in the same distance cell (D1) for the first radar sensor (10), the second radar sensor (12) is used to distinguish between the plurality of objects, which are located in separate distance cells (D2, D3) for the second radar sensor, wherein the second radar sensor (12) is spatially offset relative to the first radar sensor (10) and thus measures different distances relative to one another for the plurality of objects.

2. The method according to claim 1, wherein: If a plurality of objects are located in the same distance cell (D1) for the first radar sensor (10), an angle estimation is performed using a second radar sensor (12) to determine the direction angles relative to these objects, which are located in separate distance cells (D2, D3) for the second radar sensor.

3. The method according to claim 1, wherein: If a plurality of objects are located in the same distance cell (D1) for the first radar sensor (10), information from the second radar sensor (12) is used to carry out an improved dual-target angle estimation together with the first radar sensor (10).

4. A method according to any one of the preceding claims, wherein: Classifying the object configuration includes comparing radar cross sections of objects located by a plurality of radar sensors (10, 12) based on signal strengths received by the plurality of radar sensors.

5. The method according to claim 4, wherein: With the aid of the comparison of the radar cross sections, an assumption is made about the type of the located object.

6. A positioning system for a motor vehicle, comprising a plurality of radar sensors (10, 12, 60), the positioning areas (16, 18) of which overlap with one another, and comprising an analysis and evaluation unit (14) configured to carry out a method according to any one of claims 1 to 5.

7. A software product having a machine-readable program code, which, when loaded into an evaluation unit (14) of a positioning system according to claim 6, arranges for the evaluation unit to carry out a method according to any one of claims 1 to 5.

Images