Patent ID: 12222435

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

InFIG.1, a radar system10which includes three sensor units12and a central evaluation unit14is shown as a block diagram. Each sensor unit12is formed by a SoC (system-on-chip), on which the functions of the high-frequency part of a radar sensor including multiple receiver channels and the function of a digital pre-evaluation of the received signals are integrated. Central evaluation unit14may be formed by a processor which takes over the further evaluation of the signals pre-evaluated in the sensor units.

A communication network which connects the components of radar system10to one another has a star-shaped master/slave architecture including evaluation unit14as the master.

As an example, it is to be assumed that sensor units12are FMCW sensor units. The method provided here may also be carried out, however, if chirp sequences or digital modulation forms are used.

Within each measuring cycle, each sensor unit12calculates a two-dimensional spectrum for each of its receiver channels, in which one dimension represents the distances and the other dimension represents the relative velocity of the located objects. Each located object stands out in this spectrum as a peak which rises more or less clearly above the noise background and whose position in the spectrum indicates the distance and the relative velocity of the relevant object.

In a radar system for a motor vehicle, sensor units12may be installed at various locations in the vehicle or also situated jointly on a shared circuit board, preferably so that the antenna elements of all sensor units together form a one-dimensional or two-dimensional array including a large aperture, which enables an object detection with a high angle resolution in the azimuth and/or in elevation. The detection areas, thus the areas in the surroundings of the vehicle in which objects may be detected, are to be identical for all three sensor units12in this example, so that an object which is located inside this detection area theoretically has to be “seen” by each sensor unit12. In evaluation unit14, the angle positions of the object may be calculated with high resolution on the basis of the complex amplitudes of the signals which were received together for a given object in all receiver channels of all three sensor units12.

In the case of objects which only generate a relatively weak radar echo, the peaks which are associated with these objects in the spectrum often stand out only little or not at all from the noise background, so that these objects are not detectable with equal clarity by different sensor units12and possibly are only detectable at all in one or two of the sensor units. If a certain signal deflection is established in a single sensor unit12at a specific position in the spectrum, it therefore may not be reliably decided whether this deflection is a noise signal or a real object. A certain uncertainty remains even upon evaluation of all receiver channels of the relevant sensor unit. Real objects may only be distinguished with higher reliability from the noise background when the detection results of all sensor units12are considered in combination in evaluation unit14.

In the method provided here in accordance with an example embodiment of the present invention, sensor units12do not transfer their complete detection result, i.e., the complete two-dimensional spectrum per receiver channel, to evaluation unit14, but rather in a first method step, each sensor unit12only sends a short message16to evaluation unit14, which only represents a greatly shortened (compressed) outline of the detection result. In particular, it may be sufficient to carry out a single entry in short message16per object, even if sensor unit12possesses multiple receiver channels. For example, short message16includes a distance index and a velocity index for each object located or supposedly located by the sensor unit, which together indicate the position of the relevant peak in the spectrum, and a scalar quality measure, which indicates the probability that the detected peak is a real object. The pieces of information from multiple receiver channels may be incorporated in the calculation of the quality measure. Methods for calculating the quality measures are conventional. For example, the quality measure may be calculated on the basis of the vertex height of the peak above the noise background (preferably averaged over all receiver channels) and/or on the basis of the power integrated via the peak in relation to the noise power and/or on the basis of the quality (width) of the peak. The supposed or real object, identified by its distance index and its velocity index, is only incorporated as a detected object in short message16if the quality measure exceeds a certain threshold value.

Evaluation unit14calculates, on the basis of short messages16from all three sensor units, an existence probability for each object which was detected by at least one of sensor units12. For example, this existence probability may be proportional to the sum of the quality measures which the three sensor units have reported.

In a further step, evaluation unit14compares the existence probability of each real or supposed object to a threshold value which is greater than the sum of the threshold values which were used in sensor units12for the decision as to whether or not the object is to be reported at all. Thus, for example, an object which was only just above the threshold value in all three sensor units12will be discarded by evaluation unit14as nonexistent.

With this strategy, it is possible to use a very low detection threshold in individual sensor units12to ensure that no relevant objects are overlooked. The number of the objects considered to be real is reduced to a realistic measure by the higher threshold value in evaluation unit14.

Via a feedback channel18, evaluation unit14sends a request to each of sensor units12for each object which was assessed as real to transfer a detail from the two-dimensional spectrum which contains the peak associated with this object. On the basis of these details from the spectra, evaluation unit14may carry out a more accurate angle estimation for each object and optionally also improve the accuracy of the measured object distances and relative velocities in that statistical variations are suppressed by averaging over the measuring results of all three sensor units. Those parts of the spectra recorded in the individual sensor units in which no real objects are located are not transferred to evaluation unit14, so that the data volume and thus the load of the communication network are reduced without the accuracy and reliability of the detection result suffering from this.

FIG.2shows radar system10during the execution of the second step, in that sensor units12, on request by evaluation unit14, transfer a detail20from the spectrum to evaluation unit14for each located object assessed as real and the evaluation unit calculates and outputs on the basis of these data a consolidated detection result22, which contains the distance, relative velocity, and angle data of those objects which were assessed to be real.

In a modified specific embodiment of the present invention, the method may be supplemented by at least one step in that evaluation unit14instructs sensor units12which have not seen a certain object to repeat the evaluation of the spectrum at this point once again using a lower threshold value and to initially transfer the result in the form of a modified short message. The calculation of the existence probability is carried out for this object on the basis of the modified short messages.

FIG.3shows a further example of a radar system24including three sensor units12, which communicate with one another via a ring bus26. The sensor units are additionally identified here using labels S1, S2, and S3for better differentiation. In radar system24, the processors of sensor units12also take over at least a part of the functions for which evaluation unit14is provided inFIG.1.FIG.3illustrates the first three steps of the evaluation method. In a first step, sensor unit S1sends a short message16to sensor unit S2. This short message has content K1. This content includes the distance indices and relative velocity indices and the quality measures of all objects which were detected by sensor unit S1. In sensor unit S2, these data may be compared to the intrinsic detection result of sensor unit S2. During the comparison, sensor unit S2supplements all intrinsic detections which were not included in short messages K1up to this point.

Furthermore, the quality measures of all detections which were detected by both S1and S2are combined.

In the following, the comparison of multiple short messages is represented by the character “&”. Sensor unit S2sends a short message including content K1& K2to sensor unit S3. This content includes the distance and angular velocity indices of all objects which were detected by at least one of sensor units S1and S2, and the cumulative quality measures which the sensor units have associated with these objects. In the following description, the quality measures are combined with the aid of unweighted addition, however, arbitrary mathematical operations, for example, a weighted sum, a product, or a sum of logarithmic values are also possible.

Sensor unit S3in turn compares content K1& K2to its intrinsic detection result and sends a short message including content K1& K2& K3back to sensor unit S1. This short message K1& K2& K3contains the distance and relative velocity indices of all objects which were detected by at least one of the three sensor units, and the sum of all three quality measures which the sensor units have associated with these objects. Insofar as this relates to the determination of the existence probabilities of the objects, the short message including content K1& K2& K3already represents a consolidated detection result. Sensor unit S1uses this result to compare the sums of the quality measures to a higher threshold value and to discard as nonexistent objects for which the sum is below the threshold value.

FIG.4shows two further steps of the method, in which detection result K1and K2and K3is passed on from sensor unit S1to sensor unit S2and finally to sensor unit S3, so that all three sensor units have the same information level with respect to the existence probability of the objects and may each carry out the further signal evaluation on the basis of their intrinsic spectra. Ring bus24may optionally also be used for transferring details of spectra from one sensor unit to the next, so that at least one of the sensor units may carry out the complete evaluation which is carried out inFIG.1by evaluation unit14. These evaluation functions may optionally also be distributed to the processors of different sensor units S1, S2, and S3, however, for example, in that the objects considered to be real, for which the further evaluation is to take place, are allocated between the three sensor units so that a uniform utilization is achieved.

In another specific embodiment (not shown), however, certain evaluation functions, for example, the angle estimation, may also be delegated to a central evaluation unit.

InFIG.5, possible contents of the short messages exchanged in radar system24are shown in table form. Each table contains, in first column D, the distance index of the located objects, in second column V, the relative velocity index, and in the third column, quality measure Q. Sensor unit S1has located six objects A through F in this example, so that short message K1is made up of six lines.

Sensor unit S2, in contrast, has only detected four objects, so that short message K2only includes four lines. In two lines, namely the second and the fourth, the distance and relative velocity indices are the same as in the case of objects C and E in short message K1. These lines or the associated objects may therefore be identified with objects C and E. In contrast, there is no correspondent in short message K1for the distance and relative velocity indices in the two remaining lines, so that these are “new” objects here, which were only seen by sensor unit S2.

Short message K1& K2combines short messages K1and K2. Two lines were appended to short message K1for the two new objects G and H. In addition, the quality measures were added in column Q in the case of objects C and E, which were seen by both sensor units.

Sensor unit S3has detected the four objects A, B, E, and F, which were also detected by sensor unit S1, and three further objects I, J, and K, which were detected by neither of the other two sensor units.

Short message K1& K2& K3combines the contents of short messages K1, K2, and K3. Accordingly, three further lines for objects I, J, and K were appended to short messages K1and K2, and the quality measures from K3and K1& K2were added in column Q for the objects A, B, E, and F.

To form the consolidated detection result, in this example, the threshold value for the quality measure was set to the value 10 in sensor unit S3. Objects D, G, H, I, and K, which do not reach this threshold value 10 in K1& K2& K3, are therefore discarded as mock objects. In this way, a consolidated short message K0is obtained, which only contains the data of the objects considered to be real and forms the basis for the calculation of the consolidated detection result.