Removing interference from signals received by detectors supported on a vehicle
阅读说明：本技术 从车辆上支持的检测器接收到的信号中移除干扰 (Removing interference from signals received by detectors supported on a vehicle ) 是由 孙顺桥 C·A·阿尔卡德 于 2020-03-04 设计创作，主要内容包括：说明性示例检测器设备(20)包括被配置成用于接收包括干扰的相应信号的多个接收器组件(24)。处理器(26)被配置成用于通过确定相应信号的相关性来标识主成分,并且将被标识的主成分从相应信号中移除以提供与相应信号对应的没有干扰的输出。(An illustrative example detector device (20) includes a plurality of receiver components (24) configured to receive respective signals including interference. A processor (26) is configured to identify the principal component by determining a correlation of the respective signals, and remove the identified principal component from the respective signals to provide an output corresponding to the respective signals without interference.)
1. A detector device (20) comprising:
a plurality of receiver components (24), the plurality of receiver components (24) configured to receive respective signals including interference; and
a processor (26), the processor (50) being configured for
Identifying a principal component of the correlation of the respective signals, an
Removing the identified principal component from the respective signal to provide an output corresponding to the respective signal free of the interference.
2. The apparatus (20) of claim 1, wherein the processor (26) is configured to determine the correlation by determining a covariance matrix of samples of the respective signals.
3. The apparatus (20) of claim 2, wherein the processor (26) is configured to identify the principal component of the covariance matrix.
4. The apparatus (20) of claim 3, wherein the processor (26) is configured to identify the principal component by performing a singular value decomposition of the covariance matrix.
5. The device (20) of claim 4, wherein the processor (26) is configured to remove the identified principal component by
Determining an orthogonal projection matrix according to the singular value decomposition of the covariance matrix; and
applying the orthogonal projection matrix to the matrix of the respective signal.
6. The apparatus (20) of claim 3, wherein the processor (26) is configured to identify the principal component by performing a linear regression or a diagonalization of the covariance matrix.
7. The apparatus (20) of claim 1, wherein the receiver assemblies (24) each include an antenna.
8. The device (20) of claim 1, wherein the received signal comprises a reflected radar signal and the interference comprises a transmission from at least one other detector device (20).
9. A method for processing signals respectively received by a plurality of receiver components (24), the received signals including interference, the method comprising:
identifying a principal component of a correlation of the received signal; and
removing the identified principal component from the received signal to provide an output corresponding to the signal without the interference.
10. The method of claim 9, comprising determining the correlation by determining a covariance matrix of samples of the respective signals.
11. The method of claim 10, wherein identifying the principal component comprises identifying a principal component of the covariance matrix.
12. The method of claim 11, wherein identifying the principal component comprises performing a singular value decomposition of the covariance matrix.
13. The method of claim 12, wherein removing the identified principal component comprises removing the identified principal component
Determining an orthogonal projection matrix according to the singular value decomposition of the covariance matrix; and
applying the orthogonal projection matrix to the matrix of the respectively received signals.
14. The method of claim 11, wherein identifying the principal component comprises performing linear regression or diagonalization of the covariance matrix.
15. The method of claim 9, wherein the receiver components (24) each comprise an antenna.
16. A detector device (20) comprising:
means (24), said means (24) for receiving a respective signal comprising interference; and
signal processing means (26), said signal processing means (26) being configured to identify a principal component from the correlation of the respective signal and remove the identified principal component from the respective signal to provide an output corresponding to the respective signal free of said interference.
17. The apparatus (20) of claim 16, wherein:
the signal processing means (26) is further for determining the correlation by determining a covariance matrix of samples of the respective signal; and
the principal component is identified from the covariance matrix.
18. The apparatus (20) of claim 17, wherein:
the signal processing means (26) identifying the principal components by performing a singular value decomposition of the covariance matrix; and
the signal processing means (26) removes the identified principal component by: an orthogonal projection matrix is determined from the singular value decomposition of the covariance matrix, and the orthogonal projection matrix is applied to a matrix of the respective signals.
19. The apparatus (20) of claim 17, wherein said signal processing means (26) identifies said principal components by performing a linear regression or diagonalization of said covariance matrix.
20. The apparatus (20) of claim 16, wherein:
the means (24) for receiving comprises a plurality of antennas; and is
The signal processing device (26) comprises a processor.
Background
Advances in electronics and technology have made it possible to incorporate various features into motor vehicles. Various sensing technologies, such as radar and lidar, have been developed for detecting objects near or in the path of a vehicle. Such systems are useful for object detection, parking assistance, and cruise control adjustment features, for example.
One difficulty associated with the emergence of such automotive sensing technologies is that more signals from more vehicles increases the likelihood that sensors of one vehicle interfere with sensors on another vehicle. In the case of radar, for example, one sensor has a transmitter and a receiver. The emitted signal or radiation has a higher energy than the reflected signal detected at the receiver. If a transmitter on one vehicle is facing generally toward a receiver on another vehicle, the signal transmitted from the one vehicle will cause interference with any reflections from nearby objects received by the receiver.
Such interference may hinder the ability of the radar sensor to accurately detect the one or more target objects, as the interfering signal will typically have a much higher amplitude than any reflected signal detected by the receiver. It is difficult to handle such interference in a computationally efficient manner. The processing costs associated with previously proposed methods are prohibitive for the type of computing equipment typically used for vehicle radar. Furthermore, changing the reflected signal as a result of processing disturbances can distort the result of target identification or localization, which is undesirable.
Disclosure of Invention
An illustrative example detector device includes a plurality of receiver components configured to receive respective signals including interference. The processor is configured to identify a principal component according to the correlation of the respective signal, and remove the identified principal component from the respective signal to provide an output corresponding to the respective signal without interference.
In an example embodiment having one or more features of the apparatus of the preceding paragraph, the processor is configured to determine the correlation by determining a covariance matrix of samples of the respective signals.
In an example embodiment having one or more features of the apparatus of any of the preceding paragraphs, the processor is configured to identify a principal component of the covariance matrix.
In an example embodiment having one or more features of the apparatus of any of the preceding paragraphs, the processor is configured to identify the principal components by performing a singular value decomposition of a covariance matrix.
In an example embodiment having one or more features of the apparatus of any of the preceding paragraphs, the processor is configured to remove the identified principal components by determining an orthogonal projection matrix from a singular value decomposition of the covariance matrix and applying the orthogonal projection matrix to a matrix of the respective signal.
In an example embodiment having one or more features of the apparatus of any of the preceding paragraphs, the processor is configured to identify the principal components by performing a linear regression or diagonalization of a covariance matrix.
In an example embodiment having one or more features of the apparatus of any of the preceding paragraphs, the receiver components each include an antenna.
In an example embodiment having one or more features of the device of any of the preceding paragraphs, the received signal comprises a reflected radar signal and the interference comprises a transmission from at least one other detector device.
An illustrative example embodiment of a method of processing signals that include interference and are respectively received by a plurality of receiver components includes: the method includes identifying a principal component of a correlation of the received signal, and removing the identified principal component from the received signal to provide an output corresponding to the signal without interference.
An example embodiment having one or more features of the method of the preceding paragraph includes determining the correlation by determining a covariance matrix of samples of the respective signals.
In an example embodiment having one or more features of the method of any of the preceding paragraphs, identifying the principal components includes identifying principal components of a covariance matrix.
In an example embodiment having one or more features of the method of any one of the preceding paragraphs, identifying the principal components includes performing a singular value decomposition of a covariance matrix.
In an example embodiment having one or more features of the method of any of the preceding paragraphs, removing the identified principal component comprises: an orthogonal projection matrix is determined from a singular value decomposition of the covariance matrix, and the orthogonal projection matrix is applied to a matrix of the respectively received signals.
In an example embodiment having one or more features of the method of any of the preceding paragraphs, identifying the principal components includes performing a linear regression and diagonalization of a covariance matrix.
In example embodiments having one or more features of the method of any of the preceding paragraphs, the receiver components each include an antenna.
An illustrative example embodiment of a detector apparatus comprises: means for receiving respective signals including interference; and signal processing means for identifying the principal component in dependence on the correlation of the respective signal and removing the identified principal component from the respective signal to provide an output corresponding to the respective signal without interference.
In an example embodiment having one or more features of the apparatus of the preceding paragraph, the signal processing device is further configured to determine the correlation by determining a covariance matrix of the samples of the respective signals, and the principal component is identified according to the covariance matrix.
In an example embodiment having one or more features of the apparatus of any of the preceding paragraphs, the signal processing device identifies the principal component by performing a singular value decomposition of a covariance matrix, and the signal processing device removes the identified principal component by determining an orthogonal projection matrix from the singular value decomposition of the covariance matrix and applying the orthogonal projection matrix to a matrix of the respective signal.
In an example embodiment having one or more features of the apparatus of any of the preceding paragraphs, the signal processing device identifies the principal components by performing a linear regression or diagonalization of a covariance matrix.
In an example embodiment having one or more features of the apparatus of any of the preceding paragraphs, the means for receiving comprises a plurality of antennas and the signal processing means comprises a processor.
The various features and advantages of at least one disclosed example embodiment will become apparent to those skilled in the art from the following detailed description. The drawings that accompany the detailed description can be briefly described as follows.
Drawings
Figure 1 schematically illustrates a vehicle including a detector apparatus designed according to an embodiment of this invention.
Fig. 2 schematically shows an interference signal.
Fig. 3 schematically shows received signal characteristics including an example of interference.
Fig. 4 is a flow chart summarizing an example method for removing interference from a received signal.
Detailed Description
Fig. 1 schematically illustrates a detector device 20 supported on a vehicle 22. The example detector devices may be used to detect objects in the path or proximity of the vehicle 22 for one or more purposes. Example uses of detector devices include adaptive cruise control, autonomous vehicle control, and driver assistance. In some embodiments, the detector apparatus 20 uses radar technology, and in other embodiments, the detector apparatus 20 uses lidar technology.
The detector device 20 includes a plurality of receiver assemblies 24 that detect radiation directed at the receiver assemblies 24. In some examples, the
As shown schematically in fig. 1, a transmitter 30 of another device (which may be supported, for example, on another vehicle) transmits a signal or wave shown schematically at 32. The transmission from the transmitter 30 is generally directed to the
Fig. 2 and 3 show an example scenario in which an interference wave or
The processor 26 is configured or suitably programmed for effectively removing interference from the received signals so that the received signals can be processed for identifying or detecting one or more target objects. Fig. 4 is a flowchart diagram 40 summarizing an example method for removing interference from a received signal. At 42, a signal or wave is received at each of the receiver assemblies 24. Each of those received signals includes interference. In some embodiments, four antennas receive one or more signals, while in other embodiments, there are eight antennas that receive signals that include interference.
The example of fig. 4 includes establishing correlation of the received signals at 44. In an example embodiment, the correlation is determined or established by determining a covariance matrix of the received signal including the interference. In some embodiments, time series samples are taken from each of the receiver component antennas, and those samples are considered as a received signal for purposes of establishing correlation.
In an example embodiment using radar signals, during one pulse or chirp (chirp), received samples including interference may be represented asWhere N is the number of corrupted samples including interference, and MrIs the number of receive antennas. X ═ XR+XIWherein X isRIs a signal reflected from a target object, and XIIs an interfering signal. At 44The determined covariance matrix for establishing correlation of the received signals may be represented as R ═ XXH. Such covariance matrices represent all possible correlations of data corresponding to the received signals.
At 46, the principal components of the correlation are determined, for example, by performing a singular value decomposition of the covariance matrix R. Other example embodiments include using linear regression or diagonalization of covariance matrices for identifying principal components that correspond to interference. The singular value decomposition SVD (R) may be represented as [ U, Σ, V ]. The principal component identifying the correlation of the received signal identifies or separates the interfering signal from the remaining data of the received signal (which is the radiation reflected from one or more target objects). Whereas the interference typically has a much larger magnitude as shown at 38 in fig. 4, the principal component identification method separates the interference from the remainder of the signal.
At 48, the identified principal component is removed from the signal. An output corresponding to the signal without interference is provided at 50. The removal of the identified principal component is accomplished in one example embodiment using an orthogonal projection matrix to effectively replace the interference with the underlying data of the received signal. The output corresponding to the signal without interference can be represented asWherein P ⊥ is the orthogonal projection matrix P ⊥ ═ I-U (: 1) ═ U (: 1)HWhere I is the identity matrix. Using this method, the interference of the received signal is represented by the singular vector corresponding to the largest singular value.
The output provided at 50 may then be used for known radar range and doppler signal processing for, for example, angle measurement, target detection or target identification.
One feature of the example technique is that it requires only a relatively small computational budget, whereby the processing is fast and can be done by a variety of inexpensive processors. There is no need for heavy or complex calculations for the purpose of separating and removing interference from the received signal. For example, determining covariance matrices and singular value matrix decompositions involves relatively lightweight computations.
One aspect of the example technique is that it exploits the fact that: an interfering signal, such as the signal or wave 32 shown in fig. 1, will be received by all antennas from the same angle, which allows the principal component analysis to accurately separate the interference from the remainder of the signal data.
The disclosed example techniques effectively use array samples to characterize an interferer (interference) in time series, such as the transmitter 30 shown in fig. 1, without the need for array calibration. The orthogonal projection based approach is effective for reducing interference in the time series of signal samples. There is no need to estimate the amplitude or phase of the interference. Another feature of the disclosed example techniques is that the output corresponding to the signal without interference will not include artifacts that would otherwise have an effect on the two-dimensional Fast Fourier Transform (FFT) spectrum. Accordingly, interference may be efficiently and effectively removed from the received signal, allowing known signal processing for object detection or identification to continue.
The preceding description is exemplary rather than limiting in nature. Variations and modifications to the disclosed examples may become apparent to those skilled in the art that do not necessarily depart from the essence of this invention. The scope of legal protection given to this invention can only be determined by studying the following claims.