System and method for detecting an object within a scene

Abstract

The present disclosure provides a multi-input multi-output (MIMO) radar system and method for detecting objects within a scene. The method includes transmitting frequency-modulated continuous wave (FMCW) in a radio frequency (RF) band and collecting radar measurements of the scene sampled in a time-frequency domain within an intermediate frequency (IF) bandwidth. The method further includes generating measurements of different segments of the scene for different range-Doppler bins formed by intersections of different range bins and different Doppler bins by converting the radar measurements into range-Doppler space, classifying the presence of hypothesized transmitters in different segments of the scene according to a signal model having internal classification, generating object parameters by combining the results of the classification, and outputting the object parameters.

Description

Technical Field

[0001] The present disclosure generally relates to radar systems, and more specifically to systems and methods for detecting objects in a scene in the presence of mutual interference in a radar system.

Background Art

[0002] Automotive radar has clarified its role from existing advanced driver assistance systems (ADAS) to emerging autonomous driving. Together with ultrasonic, camera, and light detection and ranging (LIDAR) sensors, automotive radar assists in the tasks of environmental perception and determination of parameters such as the distance, speed, and angle of nearby objects. In particular, automotive radar provides a direct measurement of line-of-sight speed, a long operating range, a small size in the millimeter or sub-terahertz frequency band, and high spatial resolution.

[0003] Automotive radar widely employs frequency modulated continuous wave (FMCW) because its transceiver architecture is simple and the requirements for the sampling rate to utilize the advantage of a wide frequency bandwidth are low. At the same time, to achieve high angular resolution, multiple-input multiple-output (MIMO) radar that synthesizes a virtual array using a limited number of transmit (Tx) and receive (Rx) antenna chains has been proposed. More specifically, a MIMO radar having M transmit antennas and N receive antennas can synthesize a virtual linear array (ULA) of size MN to detect an object. In the case of multiple FMCW radars operating in the same regulated frequency band, mutual radar interference is expected. Mutual radar interference degrades the accuracy of object detection. Therefore, systems and methods for mitigating mutual interference in MIMO radar are needed.

Summary of the Invention

[0004] The object of some embodiments is to provide a system and method for mitigating mutual interference in a MIMO radar system such as an FMCW-MIMO automotive radar. Additionally, or alternatively, the object of some embodiments is to provide a signal model that can integrate radar transmissions and interference of different natures in a manner that is easy to computationally process. Additionally, or alternatively, the object of some embodiments is to provide a signal model that can detect objects (pedestrians, vehicles, etc.) within a scene from radar measurements that are subject to unknown interference.

[0005] A MIMO radar system includes a set of transmitters and a set of receivers. Each transmitter in the set of transmitters transmits a reference signal, such as an FMCW signal, towards an object. The MIMO radar system may include a signal generator that generates the reference signal for the set of transmitters. Further, the object may be a moving object or a stationary object. To detect the object, the reference signals from different transmitters in the set of transmitters can be separated within several domains, such as the time domain, the frequency domain, or the code domain. To that end, the MIMO radar system encodes each reference signal with an orthogonal code (e.g., Hadamard code). The MIMO radar system may include an orthogonal code generator that generates the orthogonal code to encode each reference signal transmitted by each transmitter in the set of transmitters. Thus, when two transmitted signals encoded with orthogonal codes interfere, they ideally become nulls, and waveform separation for each reference waveform can be achieved. In this way, the MIMO radar system transmits several encoded pulses, and each encoded pulse is orthogonal to other encoded pulses. The orthogonal codes are also used at the receiver for waveform separation.

[0006] Furthermore, the MIMO radar system uses a set of receivers to receive the echoes or reflections of the transmitted signals (i.e., the encoded pulses / reference signals). The transmitted signals may be reflected from objects. Each receiver receives a signal that is a superposition of the reflections of a plurality of reference signals transmitted by the set of transmitters, i.e., each receiver receives a composite signal, and each signal in the composite signal corresponds to the reflections of all the transmitted signals. In some embodiments, the MIMO radar system is configured to separate each waveform of the reflection of the transmitted signal from the composite signal, which is a superposition of all the reflected transmitted signals, in order to detect an object.

[0007] Some embodiments are based on the recognition that the MIMO radar system can achieve waveform separation in the set of receivers by utilizing orthogonal codes used in the transmitters. To that end, each receiver is configured to multiply the received composite signal by the corresponding orthogonal code (i.e., the FMCW signal) used by the transmitter corresponding to that receiver. Due to the property of orthogonality of the codes, the result of multiplying two different codes is ideally a zero value, while the result of multiplying by the same code is a non-zero value.

[0008] Utilizing this property of orthogonality, each receiver can separate the reflection waveform corresponding to the reference signal transmitted by each transmitter from the composite signal. However, when interference waveforms arrive at the receiver along with different code sets, i.e., when some waveform separation residuals still remain in the separated reflection waveforms, complete waveform separation is unlikely to occur. The residuals may be due to interfering radars present in the scene. In particular, the residuals may be due to the signals transmitted by the set of transmitters of the interfering radar. In one embodiment, the interfering radar may be a MIMO radar. If the residuals due to the interfering radar are not considered in object detection, the accuracy of the MIMO radar system for object detection may be reduced.

[0009] Therefore, some embodiments are based on the recognition that a signal model for processing radar measurements needs to include (1) an object signal model for the reflection of the transmitted signal that forms the radar measurements and (2) an interference signal model for the interference caused by interfering radars.

[0010]

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[0011] In some embodiments, a t is a function of the relative angle between each transmitter of a set of transmitters and an object, the wavelength of the transmitted signal, and the relative distance between two consecutive transmitter elements of the set of transmitters. Similarly, a r is a function of the relative angle between each receiver of a set of receivers and an object, the wavelength of the received signal, and the relative distance between two consecutive receiver elements of the set of receivers. Such an object signal model can be realized as radar transmission from a hypothetical transmitter having the structure of the MIMO radar system and located at the same location as the object, by the representation of the reflection from the object in the scene.

[0012]

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[0013] In particular, the receive steering vectors for the MIMO radar system and the interfering radar have the same structure but can be modified at the same angle or different angles. In other words, the receive steering vectors of the MIMO radar system and the interfering radar are functions of unknown angles, and different embodiments may impose constraints or relax this constraint on having the same angle for explaining the radar measurements. Such flexibility allows different embodiments to reduce the computational burden of evaluating the radar measurements or to introduce fewer assumptions for improving accuracy.

[0014] Considering the object signal model and the interference signal model, spatial domain object detection under mutual interference is formulated as a binary hypothesis problem.

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[0017] Therefore, solving the binary hypothesis problem at a certain quantized angle within the range-Doppler bin implies assuming the position of the interfering radar at each of the other quantized angles within the range-Doppler bin while determining the presence or absence of an object at a certain quantized angle. The presence or absence of an object can be represented as the result of a set of binary classifications performed over the range-Doppler bin and the quantized angles.

[0018]

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[0019] In this way, some embodiments define a signal model with an internal classification that explains the radar measurements of the range-Doppler bin for unknown angles. For each angle, the signal model explains the corresponding radar measurements as · a combination of any transmission from a known transmitter to a known receiver, e.g., an object reflection waveform by a waveform transmitted from an MIMO radar with known parameters, and · an essential transmission from an unknown transmitter to the same known receiver, e.g., an interference waveform transmitted from an interfering radar with unknown parameters.

[0020] Since any such transmission is determined by the result of a binary classification, such a classification is referred to herein as an internal classification. This is because it only explains a part of the signal model. However, the result of the binary classification is what the embodiments of the present disclosure attempt to determine in order to describe a scene. In this way, the processing of radar measurements can be reduced to a classification problem.

[0021] According to some embodiments, a generalized likelihood ratio test (GLRT) algorithm can be used to solve a binary hypothesis problem and determine the presence of an object in the spatial domain. The GLRT obtains a GLRT statistic value. The GLRT statistic value is compared with a predetermined threshold value. The predetermined threshold value is based on the number of transmitters and receivers. If the GLRT statistic value is greater than the predetermined threshold value, the second hypothesis is true. Conversely, if the GLRT statistic value is less than the predetermined threshold value, the first hypothesis is true.

[0022] Accordingly, one embodiment discloses a multiple-input multiple-output (MIMO) radar system for detecting objects within a scene. The MIMO radar system comprises a self-transmitter having a set of transmitters with a known mutual arrangement and a self-receiver having a set of receivers that form a virtual array of the MIMO radar system. A combination of different transmitters and different receivers of the virtual array paired together is configured to: (1) transmit frequency-modulated continuous wave (FMCW) in a radio frequency (RF) band over a series of pulse repetition intervals (PRIs); and (2) collect radar measurements of the scene sampled in a time-frequency domain within an intermediate frequency (IF) bandwidth where the reflection of the transmitted FMCW is shifted by mixing with a copy of the FMCW. The MIMO radar system further comprises a memory configured to store a signal model having an internal classification. The signal model having an internal classification describes measurements corresponding to a segment of the scene defined by relative distance and relative velocity to the self-transmitter as a combination of an optional transmission from a hypothesized transmitter having the structure of the self-transmitter and located within the segment of the scene to the self-receiver and an essential transmission from an interfering transmitter having an unknown structure and located within the segment of the scene to the self-receiver. The MIMO radar system further comprises a processor coupled with instructions that, when executed by the processor, cause the MIMO radar system to: generate measurements of different segments of the scene for different range-Doppler bins formed by intersections of different range bins and different Doppler bins by converting the radar measurements into range-Doppler space; classify the presence of hypothesized transmitters in different segments of the scene according to a signal model having an internal classification that independently explains the measurements of different segments of the scene from each other; generate object parameters indicated by the results of classification of the presence or absence of hypothesized transmitters in different segments of the scene by combining the results of classification; and output the object parameters.

[0023] Accordingly, another embodiment discloses a method for detecting an object within a scene. The method uses a processor coupled to a memory storing a signal model having internal classifications, the signal model describing measurements corresponding to a particular state within the scene as a combination of an optional transmission from a transmitter within the scene having the state to a receiver having the state of a multiple-input multiple-output (MIMO) radar system and an essential transmission from an interfering transmitter having an unknown structure and the state within the scene to the receiver having the state of the MIMO radar system, the processor being coupled to stored instructions that implement the method, the instructions, when executed by the processor, performing the steps of the method. The method includes transmitting frequency-modulated continuous wave (FMCW) in a radio frequency (RF) band over a series of pulse repetition intervals (PRIs), collecting radar measurements of the scene sampled in a time-frequency domain within an intermediate frequency (IF) bandwidth where reflections of the transmitted FMCW are shifted by mixing with a copy of the FMCW, generating measurements of different segments of the scene for different range bins and different Doppler bins formed by intersections of different range bins and different Doppler bins by converting the radar measurements to range-Doppler space, classifying the presence of hypothesized transmitters in different segments of the scene according to a signal model having internal classifications that independently explain the measurements of different segments of the scene from each other, generating object parameters indicated by the results of the classification of the presence or absence of hypothesized transmitters in different segments of the scene by combining the results of the classification, and outputting the object parameters.

[0024] Accordingly, yet other embodiments disclose a non-transitory computer-readable storage medium having a program executable by a processor to perform a method for detecting an object within a scene. The method includes transmitting frequency-modulated continuous wave (FMCW) in a radio frequency (RF) band over a series of pulse repetition intervals (PRIs), collecting radar measurements of the scene sampled in a time-frequency domain within an intermediate frequency (IF) bandwidth where reflections of the transmitted FMCW are shifted by mixing with a copy of the FMCW, generating measurements of different segments of the scene for different range bins and different Doppler bins formed by intersections of different range bins and different Doppler bins by converting the radar measurements into range-Doppler space, classifying the presence of hypothesized transmitters in different segments of the scene according to a signal model, wherein the signal model with internal classification describes measurements corresponding to segments of the scene defined by relative distance and relative velocity to the self-transmitter as a combination of any transmissions to the self-receiver from hypothesized transmitters having the structure of the self-transmitter and located within the segment of the scene and mandatory transmissions to the self-receiver from interfering transmitters having an unknown structure and located within the segment of the scene, the method further includes generating object parameters indicated by results of classification of the presence or absence of hypothesized transmitters in different segments of the scene by combining the classification results, and outputting the object parameters.

[0025] Embodiments of the present disclosure will be further described with reference to the accompanying drawings. The drawings shown are not necessarily to scale and generally focus on explaining the principles of the embodiments of the present disclosure.

Brief Description of the Drawings

[0026]

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DETAILED DESCRIPTION OF THE INVENTION

[0027] For the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent to one skilled in the art, however, that the present disclosure may be practiced without these specific details. In other instances, devices and methods are shown in block diagram form only to avoid obscuring the present disclosure.

[0028] As used in this specification and the claims, the terms “for example,” “for instance,” and “such as,” as well as the verbs “comprising,” “having,” “including,” and other forms of these verbs, when used in conjunction with a listing of one or more components or other items, each shall be construed as open-ended, meaning that the listing shall not be considered as excluding further components or items. The term “based on” means at least in part based on. Further, it should be understood that the style and terminology used herein are for the purpose of description and should not be considered limiting. Any headings used herein are for convenience only and have no legal or limiting effect.

[0029] FIG. 1A shows the architecture of a low-speed multiple-input multiple-output (MIMO) radar system 100 for detecting objects in a scene according to some embodiments of the present disclosure. The MIMO radar system 100 is configured to detect objects in a scene. The objects may be, for example, vehicles, pedestrians, trees, etc. The MIMO radar system 100 includes a self-transmitter having a set of M transmitters 101(a) - 101(m) and a self-receiver having a set of N receivers 103(a) - 103(n). Each transmitter transmits a sequence of encoded waveform pulses, and the same pulses are repeated over time and repeated K times for K pulses.

[0030] A set of M transmitters 101(a) - 101(m) and a set of N receivers 103(a) - 103(n) expand the dimensions of the MIMO radar system 100 to create a virtual array, which includes unique combinations of transmitter and receiver pairs (e.g., Tx#1 and Rx#1, Tx#2 and Rx#1, etc.) for measuring transmitted reflections. Further, the MIMO radar system 100 includes a memory (not shown). The memory may include random access memory (RAM), read only memory (ROM), flash memory, or any other suitable memory system. Additionally, in some embodiments, the memory may be implemented using a hard drive, optical drive, thumb drive, array of drives, or any combination thereof. The MIMO radar system 100 further includes a processor (not shown). The processor may be a single core processor, multi - core processor, computing cluster, or any number of other configurations.

[0031] The MIMO radar system 100 uses frequency-modulated continuous wave (FMCW). Therefore, the MIMO radar system 100 includes a signal generator (not shown) that generates a radar signal or an FMCW pulse provided to each of the M transmitters. Each pulse includes a plurality of frequencies that increase over time to generate a signal sweep as shown in FIG. 1A (as specified in the figure). Further, each transmitter transmits a set of FMCWs to illuminate the scene and form radar measurements. The pulses are transmitted in all directions. In an example of an embodiment, the pulse generated by the signal generator may be a chirp signal transmitted as a radar signal for object detection. Since the frequency of the transmitted signal is constantly changing, the echo has a slightly different frequency compared to the signal being transmitted at that instant. The difference between these frequencies is directly proportional to the echo delay (i.e., the distance from the transmitter to the object), which enables an accurate measurement of the level. Further, a set of N receivers 103(a) - 103(n) are configured to receive the reflected echo signals or reflections of the transmission. An advantage of the FMCW signaling scheme is that the reflected signal can be efficiently stored in the beat signal with respect to the object information by multiplying it with the source FMCW pulse at a low analog-to-digital (ADC) sampling rate.

[0032] Some embodiments are based on the recognition that it is necessary to jointly consider the reflections corresponding to all the signals transmitted by a set of M transmitters received at each of the N receivers to determine the angle of the object, and the angle of the object can be used to determine the spatial position of the object. However, the transmissions corresponding to all of the M transmitters may interfere with each other.

[0033] To address this problem, the MIMO radar system 100 uses a certain coding scheme (e.g., an orthogonal coding scheme) to minimize interference. In this case, the frequency-modulated pulses of different transmitters are coded pulse by pulse using orthogonal codes for each transmitter-receiver pair of the virtual array and decoded using the corresponding orthogonal codes. According to this coding scheme, the pulses transmitted by each transmitter are multiplied by K codes c(1) to c(K). As can be seen from this figure, the FMCW pulse transmitted by transmitter #1 can be represented as c1(1) to c1(K) when coded with K codes. Similarly, for the M-th transmitter, the coded FMCW pulse is represented by c M (1) to c M (K).

[0034] Furthermore, these pulses are reflected from the object. The receiver is configured to receive the reflected signal. The received reflected signal is called a radar measurement. When receiving the reflected signal, each receiver can decode the received signal using the coding scheme and obtain the signal corresponding to each unique transmission for each pair. After decoding, the receiver can determine the parameters of the object, such as the line-of-sight velocity, spatial angle, and distance to the object. In one embodiment, to determine the parameters of the object, the reflected signal is processed through a 1D high-speed time FFT 105, a low-speed time MIMO decoder 107, a 1D low-speed time FFT 109, and a spatial MIMO detector 111.

[0035] The MIMO radar system 100 may be mounted on a vehicle to detect objects around the vehicle. Hereinafter, such an embodiment will be described with reference to FIG. 1B.

[0036] Figure 1B shows a vehicle 113 equipped with a MIMO radar system 100 according to some embodiments of the present disclosure. The MIMO radar system 100 mounted on the vehicle 113 is called a self-radar. Here, the MIMO radar system 100 is configured to detect objects in a scene, such as a pedestrian 115 and a vehicle 117. The objects 115 and 117 may be stationary or moving. The set of receivers 103(a) to 103(n) of the MIMO radar system 100 receives the reflections of the transmission signals (i.e., encoded pulses / reference signals) from the objects 115 and 117.

[0037] Each receiver receives a signal that is a superposition of the reflections of a plurality of signals transmitted by a set of transmitters. That is, each receiver receives a composite signal, and each signal in the composite signal corresponds to the reflection of all the transmission signals. In order to detect the object 115 and / or 117, in some embodiments, the MIMO radar system 100 (or self-radar) is configured to separate each waveform of the reflection of the transmission signal from the composite signal that is a superposition of all the reflected transmission signals.

[0038] Some embodiments are based on the recognition that the MIMO radar system 100 can achieve waveform separation in the set of receivers 103(a) to 103(n) by utilizing orthogonal codes used in the set of transmitters 101(a) to 101(m). For this purpose, each receiver is configured to multiply the received composite signal by the corresponding orthogonal code used by the transmitter corresponding to that receiver. Due to the property of orthogonality of the codes, the result of multiplying two different codes is ideally a zero value, while the result of multiplying by the same code is a non-zero value.

[0039] By utilizing this property of orthogonality, each receiver can separate the reflected waveform corresponding to the reference signal transmitted by each transmitter from the composite signal. However, when interference waveforms arrive at the receiver along with different code sets, that is, when some waveform separation residuals still remain in the separated reflected waveforms, complete waveform separation is unlikely to occur. The residuals may be due to the interfering radar 119 present in the scene. In particular, the residuals may be due to the signals transmitted by the set of transmitters of the interfering radar 119. In one embodiment, the interfering radar may be a MIMO radar.

[0040] FIG. 1C shows the various components present in the separated waveforms according to some embodiments of the present disclosure. Each of the separated waveforms 121, 123, and 125 includes reflections from objects, interference components (i.e., residuals due to the interfering radar 119), and noise. If the interference components, i.e., the residuals due to the interfering radar 119, are not considered in the detection of objects 115 and 117, the detection accuracy of objects 115 and 117 may decrease.

[0041] Therefore, some embodiments are based on the recognition that the signal model for processing radar measurements needs to include (1) an object signal model for the reflection of the transmitted signal that forms the radar measurement, and (2) an interference signal model for the interference due to the interfering radar 119.

[0042]

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[0043] In some embodiments a t is a function of the relative angle between each transmitter of the set of transmitters and an object (e.g., vehicle 117), the wavelength of the transmitted signal, and the relative distance between two consecutive transmitter elements of the set of transmitters 101(a) - 101(m). Similarly, a ris a function of the relative angle between each receiver in a set of receivers and an object, the wavelength of the received signal, and the relative distance between two consecutive receiver elements in the set of receivers 103(a) to 103(n). Such an object signal model can be realized as radar transmission from a hypothetical transmitter having the structure of the MIMO radar system and located at the same location as the object by the representation of reflections from objects in the scene.

[0044]

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[0045] In particular, the receive steering vectors for the MIMO radar system 100 and the interference radar 119 have the same structure but can be modified to the same or different angles. In other words, the receive steering vectors of the MIMO radar system 100 and the interference radar 119 are functions of unknown angles, and different embodiments may impose constraints on having the same angle to explain the radar measurements or may relax this constraint. Such flexibility allows different embodiments to reduce the computational burden of evaluating radar measurements or to introduce fewer assumptions for improving accuracy.

[0046] Taking into account the object signal model and the interference signal model, spatial domain object detection under mutual interference is formulated as a binary hypothesis problem. FIG. 2A shows a binary hypothesis problem 201 according to some embodiments of the present disclosure. In one embodiment, the binary hypothesis problem 201 is given as follows.

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[0048] Therefore, solving the binary hypothesis problem 201 at a certain quantized angle in the range Doppler bin suggests determining the presence or absence of an object at a certain quantized angle while assuming the position of the interfering radar at each of the other quantized angles in the range Doppler bin. The presence or absence of an object can be represented as the result of binary classification.

[0049]

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[0050] In this way, some embodiments define a signal model with an internal classification that explains the radar measurements of the range Doppler bin for unknown angles. For each angle, the signal model describes the corresponding radar measurement as · Any transmission from a known transmitter to a known receiver, e.g., an object reflection waveform by a waveform transmitted from a MIMO radar with known parameters, and · A combination with an essential transmission from an unknown transmitter to the same known receiver, e.g., an interference waveform transmitted from an interfering radar with unknown parameters.

[0051] Since any transmission is determined by the result of binary classification, such classification is referred to herein as internal classification. Because this only explains a part of the signal model. However, the result of binary classification is what the embodiments of the present disclosure attempt to determine to explain the scene. In this way, the processing of radar measurements can be reduced to a classification problem.

[0052] The binary hypothesis problem 201 is formulated and solved by the spatial MIMO detector 111.

[0053] FIG. 2B is a block diagram showing the steps performed by the spatial MIMO detector 111 according to some embodiments of the present disclosure.

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[0058] FIG. 3 shows a block diagram of a MIMO radar system 100 according to some embodiments. The MIMO radar system 100 can have several interfaces that connect the MIMO radar system 100 to other systems and devices. For example, a network interface controller (NIC) 301 is adapted to connect the MIMO radar system 100 to a network 305 that connects the MIMO radar system 100 to a sensing device through a bus 303. The MIMO radar system 100 includes a transmitter interface 307 configured to command a set of transmitters 101 to transmit FMCW pulses in a radio frequency (RF) band over a series of pulse repetition intervals (PRIs). The transmitter interface 307 communicates with a signal generator 309 that generates the FMCW pulses.

[0059] Furthermore, the orthogonal code generator 311 is used to generate different orthogonal codes that are multiplied by FMCW pulses associated with each transmitter in the set of transmitters 101. The MIMO radar system 100 is connected to the set of receivers 103 via a receiver interface 313. The set of receivers is configured to collect radar measurements 315 of the scene through a network 305. The radar measurements 315 of the scene are sampled in the time-frequency domain within an intermediate frequency (IF) bandwidth where the reflection of the transmitted FMCW is shifted by mixing with a copy of the FMCW.

[0060] Furthermore, the MIMO radar system 100 includes a processor 317 configured to execute instructions stored in a storage medium 319 and a memory 321. The processor 317 may be a single-core processor, a multi-core processor, a computing cluster, or any number of other configurations. The memory 321 may include random access memory (RAM), read-only memory (ROM), flash memory, or any other suitable memory system. The storage medium 319 may include random access memory (RAM), read-only memory (ROM), flash memory, or any other suitable memory system. The processor 317 can be connected to one or more input / output (I / O) devices through a bus 303.

[0061] The memory medium 319 is configured to store a signal model 319a having internal classifications, and this signal model describes measurements corresponding to segments of a scene defined by the relative distance and relative velocity to the self-transmitter, as a combination of an arbitrary transmission to the self-receiver from a hypothesized transmitter having the structure of the self-transmitter and located within the segment of the scene, and an essential transmission to the self-receiver from an interfering transmitter having an unknown structure and located within the segment of the scene. In certain embodiments, the signal model 319a having internal classifications describes measurements for different quantized angles in a range-Doppler bin as a sum of a binary classification Kronecker product of a self-receiver steering vector that corrects for an unknown angle and a self-transmitter steering vector, and a Kronecker product of a self-receiver steering vector that corrects for an unknown angle and an interfering transmitter steering vector. Alternatively, in some embodiments, the signal model having internal classifications describes measurements of segments of a scene for different angles in a range-Doppler bin as a sum of a binary classification Kronecker product of a self-receiver steering vector that corrects for a first unknown angle and a self-transmitter steering vector, and a Kronecker product of a self-receiver steering vector that corrects for a second unknown angle and an interfering transmitter steering vector.

[0062] The processor 317 is configured to generate measurements of different segments of a scene for different range-Doppler bins formed by the intersection of different range bins and different Doppler bins by converting radar measurements into range-Doppler space. The processor 317 is further configured to classify the presence of hypothesized transmitters in different segments of the scene according to a signal model, where the internal classifications explain the measurements for different segments of the scene independently of each other. The processor 317 is further configured to generate object parameters indicated by the results of the classification of the presence or absence of hypothesized transmitters in different segments of the scene by combining the results of the classification, and output the object parameters.

[0063] Furthermore, in some embodiments, processor 317 is further configured to evaluate measurements of different segments of a scene independently of each other. To evaluate the measurements of the segments, processor 317 is further configured to test for the presence of a hypothesized transmitter for each of different values of a first unknown angle, so as to account for the measurements of the segments for different values of a second unknown angle. In some other embodiments, processor 317 is further configured to statistically evaluate the measurements of the segments over multiple pulse repetition intervals using a generalized likelihood ratio test (GLRT).

[0064] MIMO radar system 100 includes an output interface 323 configured to output parameters associated with an object. The parameters include at least one of a line-of-sight velocity, a spatial angle, and a distance to the object. Output interface 323 may output the parameters onto a display device 325, store the parameters in a storage medium, and / or transmit the parameters via network 305. For example, MIMO radar system 100 can be coupled to a display interface adapted to connect MIMO radar system 100 to a display device 325 such as, among other things, a computer monitor, a camera, a television, a projector, or a mobile device, through bus 303. Additionally, in some embodiments, MIMO radar system 100 is connected to an application interface adapted to connect MIMO radar system 100 to a device for performing various tasks.

[0065] In an example of some embodiments, an automobile may include MIMO radar system 100. The automobile may be an autonomous vehicle. While the automobile is traveling on a highway or parked in a parking space, MIMO radar system 100 detects objects (such as vehicles, pedestrians, etc.). Based on the detected objects, the autonomous vehicle may control its navigation. Such embodiments are described below with reference to FIGS. 4A and 4B.

[0066] FIG. 4A shows a schematic diagram of a vehicle 401 communicatively coupled to a MIMO radar system 100 according to an embodiment of the present disclosure. The vehicle 401 may be any type of wheeled vehicle such as a passenger car, a bus, or a rover. Also, the vehicle 401 may be an autonomous vehicle or a semi-autonomous vehicle. In one embodiment, the steering system 405 is controlled by a controller 403. Additionally or alternatively, the steering system 405 may be controlled by a driver of the vehicle 401.

[0067] In some embodiments, the vehicle 401 may include an engine 411 that can be controlled by a tracker 403 or other components of the vehicle 401. In some embodiments, the vehicle 401 may include an electric motor instead of the engine 411, which can be controlled by a tracker 403 or other components of the vehicle 401. The vehicle 401 may also include one or more sensors 407 for detecting the surrounding environment. In some embodiments, the vehicle 401 includes one or more sensors 409 for detecting its current motion parameters and internal state. Examples of one or more sensors 409 include a global positioning system (GPS), an accelerometer, an inertial measurement unit, a gyroscope, a shaft rotation sensor, a torque sensor, a deflection sensor, a pressure sensor, and a flow sensor. The vehicle 401 may be provided with a transceiver 413 that enables the communication function of the controller 403 with the controller 109 via a wired or wireless communication channel. For example, the controller 403 receives object parameters from the MIMO radar system 100 through the transceiver 413.

[0068] Figure 4B shows the parking of vehicle 401 in parking space 415 according to an embodiment of the present disclosure. The parking space 415 includes parking spots such as spot 417 for parking a vehicle. The range of the parking space 415 is defined by boundaries 419a and 419b. The parking space 415 further includes one or more objects such as vehicles 421, 423, 425 that vehicle 401 must avoid colliding with and pedestrian 427.

[0069] Vehicle 401 is at starting point 429 and needs to park in target parking spot 431 without colliding with vehicles 421, 423, 425, and 427, and boundaries 419a and 419b. According to some embodiments, controller 403 is configured to track a motion path 433 for controlling the motion of vehicle 401 from starting point 429 to target parking spot 431. The parking space 415 also includes an interference radar associated with vehicle 435 that causes mutual interference. The MIMO radar system 100 associated with vehicle 401 accurately determines the parameters of an object, such as pedestrian 427, under mutual interface.

[0070] Furthermore, the MIMO radar system 100 transmits the parameters of pedestrian 427 to controller 403. The parameters may include the position of pedestrian 427 or the distance to pedestrian 427. Based on the parameters of pedestrian 427, controller 403 generates a control input for safely tracking the motion path 433. For example, controller 403 determines whether pedestrian 427 intersects the motion path 433 based on the parameters of pedestrian 427. If pedestrian 427 intersects the motion path 433, controller 403 generates a control input for stopping or decelerating vehicle 401. The control input includes, for example, a control command specifying a value of one or a combination of the steering angle of the wheels of vehicle 401, the rotational speed of the wheels of the vehicle, and the acceleration of vehicle 401.

[0071] Similarly, while the vehicle 401 is traveling on a road / highway, the MIMO radar system 100 detects one or more objects around the vehicle 401 based on the signal model 319a. The controller 403 can control the movement of the vehicle 401 on the road based on the one or more detected objects.

[0072] The formulation of the signal model 319a will be mathematically explained below.

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[0086]

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[0087] Figures 5A to 5C show the performance evaluation of the ROC curve according to an embodiment of the present disclosure. Figure 5A uses Monte Carlo simulation over 10 6 runs to verify the theoretical ROC performance of the fluoroscopy detector and the GLRT detector. Figure 5B shows that the ROC performance of the GLRT detector generally functions better as the Rx array size N increases and approaches the performance of the fluoroscopy detector when N is of medium size. Figure 5C shows that the average performance of the GLRT detector is between the fluoroscopy detector and the mismatched / normalized filter.

[0088] This specification provides only embodiments as examples and is not intended to limit the scope of disclosure, applicability, or configuration. Rather, the following description of embodiments as examples will provide those skilled in the art with an explanation that enables implementation of one or more embodiments as examples. Various changes are intended to be made to the functions and configurations of elements without departing from the spirit and scope of the disclosed subject matter recited in the appended claims.

[0089] Specific details are given in the following description for a thorough understanding of the embodiments. However, those skilled in the art will understand that the embodiments can be practiced without these specific details. For example, systems, processes, and other elements in the disclosed subject matter may sometimes be shown as components in block diagram form to not obscure the embodiments with unnecessary detail. In other instances, well-known processes, structures, and techniques may be shown without unnecessary detail so as not to obscure the embodiments. Further, like reference numerals and designations in the various drawings denote like elements.

[0090] Also, individual embodiments may be described as a process shown as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. A flowchart can describe the operations as a sequential process, but many of the operations can be performed in parallel or simultaneously. Further, the order of the operations can be rearranged. A process may end when its operations are completed, but may have additional steps that are not discussed or are not included in the figure. Further, all operations in any specifically described process may not occur in all embodiments. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, the end of the function may correspond to returning the function to the calling function or the main function.

[0091] Furthermore, embodiments of the disclosed subject matter can be implemented, at least in part, either manually or automatically. Manual or automatic implementation may be performed or at least assisted through the use of a machine, hardware, software, firmware, middleware, microcode, a hardware description language, or any combination thereof. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks may be stored on a machine-readable medium. A processor can execute the necessary tasks.

[0092] The various methods or processes outlined herein may be encoded as software executable on one or more processors employing any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and / or programming or scripting tools, and may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine. Typically, the functionality of program modules may be combined or distributed as desired in various embodiments.

[0093] Embodiments of the present disclosure may be implemented as a method, and an example is provided. The order of operations performed as part of this method may be determined in any suitable manner. Accordingly, embodiments may be configured so that operations are performed in an order different from that illustrated, which may include performing some operations simultaneously, even though in the illustrated embodiments some operations are shown as a series of operations.

[0094] Furthermore, the embodiments of the present disclosure and the functional operations described herein can be implemented in digital electronic circuits, in computer software or firmware tangibly embodied therein, in computer hardware including the structures disclosed herein and their structural equivalents, or in combinations of one or more of them. Additionally, some embodiments of the present disclosure can be implemented as one or more computer programs, i.e., as one or more modules of computer program instructions encoded on a tangible non-transitory program carrier for execution by, or to control the operation of, a data processing apparatus. Still further, the program instructions can be encoded on an artificially generated propagated signal, for example, on an electrical, optical, or electromagnetic signal generated by a machine. The propagated signal is generated to encode information that is transmitted to an appropriate receiver apparatus for execution by a data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random access memory device, or a serial access memory device, or a combination of one or more of them.

[0095] According to an embodiment of the present disclosure, the term "data processing apparatus" can include, by way of example, all kinds of devices, apparatuses, and machines that process data, including a programmable processor, a computer, or multiple processors or computers. The apparatus can include dedicated logic circuitry, such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The apparatus can also include, in addition to the hardware, code that creates an execution environment for the computer program, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

[0096] A computer program (also referred to as or described as a program, software, software application, module, software module, script, or code) can be written in any form of programming language including a compiled or interpreted language, or a declarative or procedural language, and can be deployed in any form as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may or may not correspond to a file in a file system. The program can be stored as part of a file that holds other programs or data, such as one or more scripts stored in a markup language document, a single file dedicated to the target program, or multiple coordinated files, such as files that hold one or more modules, subprograms, or portions of code.

[0097] A computer program can be deployed to execute on one computer or on multiple computers located in one place or distributed across multiple places and interconnected by a communication network. A computer suitable for the execution of a computer program may, by way of example, be based on a general purpose microprocessor or a special purpose microprocessor or both, or any other kind of central processing unit. Generally, the central processing unit receives instructions and data from a read only memory or a random access memory or both. Essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing the instructions and data.

[0098] Generally, a computer also includes one or more mass storage devices for storing data, such as magnetic, magneto-optical disks, or optical disks, or is operatively coupled to such disks to receive data from, transfer data to, or both. However, a computer may not have such a device. Further, a computer can be embedded in another device. For example, it can be embedded in a cellular phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable memory device, such as a universal serial bus (USB) flash drive.

[0099] To provide interaction with a user, embodiments of the subject matter described herein may be implemented on a computer having a display device, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user, and a keyboard and a pointing device, such as a mouse or trackball, by which the user can provide input to the computer. Other kinds of devices may be used to provide interaction with the user. For example, feedback provided to the user may be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input received from the user may be in any form including acoustic input, voice input, or tactile input. Additionally, a computer can provide interaction with the user by sending documents to and receiving documents from the device used by the user, for example, by sending a web page in response to a request received from a web browser on the user's client device.

[0100] Embodiments of the subject matter described in this specification can be implemented in a computing system that includes, for example, backend components as a data server, or includes middleware components such as an application server, or includes a frontend component such as a client computer having a graphical user interface or a web browser through which a user can interact with an implementation of the subject matter described in this specification, or includes any combination of one or more of such backend, middleware, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication, such as a communication network. Examples of communication networks include local area networks ("LAN") and wide area networks ("WAN"), such as the Internet.

[0101] A computing system can include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises from computer programs that run on each computer and have a mutual relationship of client and server.

[0102] Although the present disclosure has been described with some preferred embodiments, it should be understood that various other adaptations and modifications can be made within the spirit and scope of the present disclosure. Accordingly, it is the aspect of the following claims to cover all such variations and modifications that are included within the true spirit and scope of the present disclosure.

Claims

1. A multiple-input multiple-output (MIMO) radar system for detecting objects within a scene, the MIMO radar system comprising: a self-transmitter having a set of transmitters and a self-receiver having a set of receivers with known mutual arrangements that form a virtual array of the MIMO radar system, and combinations of different transmitters and different receivers of the virtual array paired together that are configured to: (1) transmit frequency-modulated continuous wave (FMCW) in a radio frequency (RF) band over a series of pulse repetition intervals, and (2) collect radar measurements of the scene sampled in a time-frequency domain within an intermediate frequency (IF) bandwidth where reflections of the transmitted FMCW are shifted by mixing with a copy of the FMCW; the MIMO radar system further comprising: a memory configured to store a signal model having internal classifications, the signal model having internal classifications that describe measurements corresponding to segments of the scene determined by relative distance and relative velocity to the self-transmitter as a combination of any transmissions from a hypothesized transmitter having the structure of the self-transmitter and located within the segment of the scene to the self-receiver and mandatory transmissions from an interfering transmitter having an unknown structure and located within the segment of the scene to the self-receiver; the MIMO radar system further comprising: a processor coupled with instructions that, when executed by the processor, cause the MIMO radar system to: generate measurements of different segments of the scene for different range bins and different Doppler bins formed by intersections of different range bins and different Doppler bins by converting the radar measurements into a range-Doppler space; classify the presence of the hypothesized transmitters in different segments of the scene according to the signal model having internal classifications that independently explain the measurements of different segments of the scene from one another; generate parameters of the object indicated by the results of classification of the presence or absence of the hypothesized transmitters in different segments of the scene by combining the results of the classification; and output the parameters of the object. A MIMO radar system.

2. The signal model having the internal classification describes the measurements of the scene for different quantized angles in the range Doppler bin as the sum of the binary classification Kronecker product of the self-receiver steering vector and the self-transmitter steering vector that corrects the unknown angle and the Kronecker product of the self-receiver steering vector that corrects the unknown angle and the interference transmitter steering vector. The MIMO radar system according to claim 1.

3. The signal model having the internal classification describes the measurements of the segment of the scene for different angles in the range Doppler bin as the sum of the binary classification Kronecker product of the self-receiver steering vector and the self-transmitter steering vector that corrects the first unknown angle and the Kronecker product of the self-receiver steering vector that corrects the second unknown angle and the interference transmitter steering vector. The MIMO radar system according to claim 1.

4. The processor is further configured to evaluate the measurements of different segments of the scene independently of each other. To evaluate the measurements of the segment, the processor is further configured to explain the measurements of the segment for different values of the second unknown angle by testing the presence of the hypothetical transmitter for each different value of the first unknown angle. The MIMO radar system according to claim 3.

5. The processor is further configured to statistically evaluate the measurements of the segment over a plurality of pulse repetition intervals using a generalized likelihood ratio test (GLRT). The MIMO radar system according to claim 3.

6. The self-transmitter steering vector is a function of the relative angle between each transmitter in the set of transmitters and the object, the wavelength of the transmitted signal, and the relative distance between two consecutive transmitter elements in the set of transmitters. The MIMO radar system according to claim 3.

7. The self-receiver steering vector is a function of the relative angle between each receiver in the set of receivers and the object, the wavelength of the received signal, and the relative distance between two consecutive receiver elements in the set of receivers. The MIMO radar system according to claim 3.

8. The MIMO radar system according to claim 1, wherein the parameters of the object include at least one of a line-of-sight velocity, a spatial angle, and a distance to the object.

9. The MIMO radar system according to claim 1, wherein the processor is further configured to execute a Generalized Likelihood Ratio Test (GLRT) algorithm to generate the parameters of the object.

10. The signal model having the internal classification is formulated as either a first hypothesis or a second hypothesis. The first hypothesis defines that the radar measurement values include a residual due to interference and noise, and the second hypothesis defines that the radar measurement values include a reflected signal from the object, a residual due to the interference, and the noise. The MIMO radar system according to claim 1.

11. The MIMO radar system according to claim 9, wherein the processor is further configured to compare the GLRT statistic with a predetermined threshold value. The GLRT statistic is obtained by the GLRT algorithm, and the predetermined threshold value is based on the number of transmitters and receivers.

12. The MIMO radar system according to claim 11, wherein when the GLRT statistic is less than the predetermined threshold value, the first hypothesis is true.

13. The MIMO radar system according to claim 11, wherein when the GLRT statistic is greater than the predetermined threshold value, the second hypothesis is true.

14. A method for detecting an object in a scene, the method using a processor coupled to a memory storing a signal model having an internal classification. The signal model having the internal classification describes measurements corresponding to a segment of the scene defined by a relative distance and a relative velocity to a self-transmitter as a combination of an optional transmission from a hypothesized transmitter having the structure of the self-transmitter to a self-receiver and an essential transmission from an interfering transmitter having an unknown structure to the self-receiver within the segment of the scene. The processor is coupled to stored instructions that, when executed by the processor, perform the steps of the method. The steps of the method are: Transmitting frequency-modulated continuous wave (FMCW) in a radio frequency (RF) band over a series of pulse repetition intervals (PRIs); Collecting radar measurements of the scene sampled in the time-frequency domain within an intermediate frequency (IF) bandwidth in which the reflection of the transmitted FMCW is shifted by mixing with a copy of the FMCW; Generating measurements of different segments of the scene for different range Doppler bins formed by the intersection of different range bins and different Doppler bins by converting the radar measurements into range Doppler space; Classifying the presence of the hypothesized transmitter in different segments of the scene according to a signal model having the internal classification that independently explains the measurements of different segments of the scene from each other; Generating parameters of the object indicated by the result of classification of the presence or absence of the hypothesized transmitter in different segments of the scene by combining the results of the classification; Outputting the parameters of the object, a method comprising.

15. The method according to claim 14, wherein the signal model having the internal classification explains the measurements of the scene for different quantized angles in the range Doppler bin as the sum of a binary classification Kronecker product of a self-receiver steering vector and a self-transmitter steering vector that corrects an unknown angle and a Kronecker product of the self-receiver steering vector that corrects the unknown angle and an interfering transmitter steering vector.

16. The method according to claim 15, wherein the self-transmitter steering vector is a function of the relative angle between each transmitter in the set of transmitters and the object, the wavelength of the transmitted signal, and the relative distance between two consecutive transmitter elements in the set of transmitters.

17. The method according to claim 15, wherein the self-receiver steering vector is a function of the relative angle between each receiver in the set of receivers and the object, the wavelength of the received signal, and the relative distance between two consecutive receiver elements in the set of receivers.

18. The method according to claim 14, wherein the parameters of the object include at least one of a line-of-sight velocity, a spatial angle, and a distance to the object.

19. The signal model having the internal classification is formulated as either a first hypothesis or a second hypothesis, the first hypothesis defining that the radar measurement values include a residual due to interference and noise, and the second hypothesis defining that the radar measurement values include a reflected signal from the object, the residual due to interference, and the noise, the method according to claim 14.

20. A non-transitory computer-readable storage medium having a program executable by a processor to perform a method for detecting an object in a scene, the method comprising: Transmitting frequency-modulated continuous wave (FMCW) in a radio frequency (RF) band over a series of pulse repetition intervals (PRIs); Collecting radar measurement values of the scene sampled in a time-frequency domain within an intermediate frequency (IF) bandwidth in which the reflection of the transmitted FMCW is shifted by mixing with a copy of the FMCW; Generating measurements of different segments of the scene for different range bins and different Doppler bins formed by intersections of different range bins and different Doppler bins by converting the radar measurement values into a range-Doppler space; Classifying the presence of hypothesized transmitters in different segments of the scene according to a signal model having an internal classification, the signal model having an internal classification explaining measurements corresponding to segments of the scene determined by relative distance and relative speed to a self-transmitter as a combination of any transmission from a hypothesized transmitter having the structure of the self-transmitter and located within the segment of the scene to a self-receiver and an essential transmission from an interfering transmitter having an unknown structure and located within the segment of the scene to the self-receiver, the method further comprising: Generating parameters of the object indicated by results of classification of the presence or absence of the hypothesized transmitters in different segments of the scene by combining the results of the classification; Outputting the parameters of the object, a non-transitory computer-readable storage medium.

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