System and method for detecting objects in 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 relates generally to radar systems, and more particularly to a system and method for detecting objects in a scene in the presence of mutual interference in a radar system. [Background technology]

[0002] Automotive radar is emerging as a key player in a range of applications, 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 task of environmental detection and determining parameters such as the range, velocity, and angle of nearby objects. In particular, automotive radar offers direct measurement of radial velocity, long operating range, small size in the millimeter or sub-terahertz frequency band, and high spatial resolution.

[0003] Automotive radars widely adopt frequency modulated continuous wave (FMCW) radars due to their simple transceiver architecture and low sampling rate requirements to take advantage of wide frequency bandwidths. At the same time, to achieve high angular resolution, multiple-input multiple-output (MIMO) radars have been proposed, which synthesize a virtual array using limited transmit (Tx) and receive (Rx) antenna chains. More specifically, to detect objects, a MIMO radar with M transmit antennas and N receive antennas can synthesize a virtual linear array (ULA) of size MN. For multiple FMCW radars operating in the same regulated frequency band, mutual radar interference is expected. This reduces the accuracy of object detection. Therefore, a system and method for mitigating mutual interference in MIMO radars is needed. Summary of the Invention

[0004] An object of some embodiments is to provide a system and method for mutual interference mitigation in MIMO radar systems, such as FMCW-MIMO automotive radar. Additionally or alternatively, an object of some embodiments is to provide a signal model that can integrate disparate radar transmissions and interference in a computationally friendly manner. Additionally or alternatively, an object of some embodiments is to provide a signal model that can detect objects in a scene (pedestrians, cars, etc.) 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, toward an object. The MIMO radar system may include a signal generator that generates the reference signals for the set of transmitters. Furthermore, the object may be moving or stationary. To detect the object, reference signals from different transmitters in the set of transmitters can be separated in several domains, such as the time domain, the frequency domain, or the code domain. To do so, the MIMO radar system encodes each reference signal with an orthogonal code (e.g., a Hadamard code). The MIMO radar system may include an orthogonal code generator that generates an orthogonal code to encode each reference signal transmitted by each transmitter in the set of transmitters. Therefore, when two transmitted signals encoded with orthogonal codes interfere with each other, they ideally become null, enabling waveform separation for each reference waveform. In this way, the MIMO radar system transmits several coded pulses, each of which is orthogonal to the other coded pulses. The orthogonal codes are also used in the receiver for waveform separation.

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

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

[0008] Using this orthogonality, each receiver can separate the reflected waveforms corresponding to the reference signals transmitted by each transmitter from the composite signal. However, if the interfering waveforms arrive at the receiver with different code sets, i.e., if some waveform separation residuals still remain in the separated reflected waveforms, complete waveform separation is unlikely. The residuals may be due to interfering radars present in the scene. In particular, the residuals are due to signals transmitted by a set of interfering radar transmitters. In some embodiments, the interfering radar may be a MIMO radar. If the residuals due to the interfering radars are not taken into account in object detection, the accuracy of the MIMO radar system for detecting objects may be reduced.

[0009] As such, some embodiments are based on the recognition that a signal model for processing radar measurements must include (1) an object signal model for reflections of the transmitted signal that form the radar measurements, and (2) an interference signal model for interference caused by interferometric radar.

[0010]

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[0011] In some embodiments, a t 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. r 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. Such an object signal model can be realized by representing reflections from objects in the scene as radar transmissions from a hypothetical transmitter co-located with the object, having the structure of a MIMO radar system.

[0012]

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[0013] In particular, the receive steering vectors for MIMO radar systems and interferometric radars have the same structure but may modify the same or different angles. In other words, the receive steering vectors for MIMO radar systems and interferometric radars are functions of unknown angles, and different embodiments may impose constraints on having the same angles to describe radar measurements or relax this constraint. Such flexibility allows different embodiments to reduce the computational burden of evaluating radar measurements or introduce fewer assumptions for improved 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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[0015]

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[0016]

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

[0018]

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[0019] In this manner, some embodiments define a signal model with an internal classification that describes radar measurements of range-Doppler bins for unknown angles. For each angle, the signal model defines the corresponding radar measurements as: Any transmission of a known transmitter to a known receiver, for example, a waveform transmitted from a MIMO radar with known parameters, and an object reflection waveform; · Described as the mandatory transmission of an unknown transmitter to the same known receiver, for example in combination with an interference waveform transmitted from an interferometric radar with unknown parameters.

[0020] Because any such transmission is determined by the result of a binary classification, such a classification is referred to herein as an internal classification because it describes only a portion of the signal model. However, the result of the binary classification is what embodiments of the present disclosure seek to determine in order to describe the 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 determines a GLRT statistic. The GLRT statistic is compared to a predetermined threshold. The predetermined threshold is based on the number of transmitters and receivers. If the GLRT statistic is greater than the predetermined threshold, the second hypothesis is true. Conversely, if the GLRT statistic is less than the predetermined threshold, the first hypothesis is true.

[0022] Accordingly, one embodiment discloses a multiple-input multiple-output (MIMO) radar system for detecting objects in a scene, the MIMO radar system comprising: a transmitter having a set of transmitters and a receiver having a set of receivers with known co-locations forming a virtual array of the MIMO radar system, wherein paired combinations of different transmitters and receivers of the virtual array are configured to (1) transmit a frequency modulated continuous wave (FMCW) signal in a radio frequency (RF) band over a series of pulse repetition intervals (PRIs); and (2) collect radar measurements of the scene sampled in the time-frequency domain within an intermediate frequency (IF) bandwidth where reflections of the transmitted FMCW signal are shifted by mixing with copies of the FMCW signal. The MIMO radar system further comprises a memory configured to store a signal model with an internal classification, the signal model representing measurements corresponding to segments of the scene defined by relative range and velocity relative to the transmitter, in combination with optional transmissions to the receiver from hypothetical transmitters with the structure of the transmitter and located within the segment of the scene, and required transmissions to the receiver from interfering transmitters with unknown structure and located within the segment of the scene. The MIMO radar system further includes a processor coupled with instructions that, when executed by the processor, cause the MIMO radar system to: generate measurements of different segments of a scene for different range-Doppler bins formed by intersections of different range bins with different Doppler bins by transforming the radar measurements into range-Doppler space; classify the presence of hypothesized transmitters in the different segments of the scene according to a signal model having an internal classification that describes the measurements of the different segments of the scene independently of each other; combine results of the classification to generate object parameters indicated by the results of the classification of the presence or absence of the hypothesized transmitter in the different segments of the scene; and output the object parameters.

[0023] Accordingly, another embodiment discloses a method for detecting objects in a scene, using a processor coupled to a memory storing a signal model having an internal classification, the signal model having an internal classification describing measurements corresponding to a particular state in the scene as a combination of optional transmissions from a local transmitter having said state in the scene to a local receiver having a state of a multiple-input multiple-output (MIMO) radar system, and required transmissions from an interfering transmitter having an unknown structure and said state in the scene to the local receiver having the state of the MIMO radar system, the processor method and stored instructions implementing the method, which, when executed by a processor, perform the steps of the method, including transmitting a frequency modulated continuous wave (FMCW) in a radio frequency (RF) band over a series of pulse repetition intervals (PRI), collecting radar measurements of a scene sampled in the time-frequency domain within an intermediate frequency (IF) bandwidth in which reflections of the transmitted FMCW are shifted by mixing with copies of the FMCW, transforming the radar measurements into range-Doppler space to generate measurements of different segments of the scene for different range-Doppler bins formed by intersections of different range bins with different Doppler bins, classifying the presence of a hypothetical transmitter in the different segments of the scene according to a signal model having an internal classification that describes the measurements of the different segments of the scene independently of each other, combining results of the classification to generate parameters of an object indicated by the results of the classification of the presence or absence of the hypothetical transmitter in the different segments of the scene, and outputting the parameters of the object.

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

[0025] Embodiments of the present disclosure are further described with reference to the accompanying drawings, in which the drawings are not necessarily to scale, with emphasis generally being placed upon illustrating the principles of embodiments of the present disclosure. [Brief explanation of the drawings]

[0026] [Figure 1A] FIG. 1 illustrates a multiple-input multiple-output (MIMO) radar system architecture using codes to detect objects in a scene, according to some embodiments of the present disclosure. [Figure 1B]FIG. 1 illustrates a vehicle equipped with a MIMO radar system, according to some embodiments of the present disclosure. [Figure 1C] FIG. 2 illustrates various components present in a separated waveform in a MIMO radar system, according to some embodiments of the present disclosure. [Figure 2A] FIG. 1 illustrates a binary hypothesis problem, according to some embodiments of the present disclosure. [Figure 2B] 2 is a block diagram illustrating steps performed by a spatial MIMO detector according to some embodiments of the present disclosure. FIG. [Figure 2C] 1 shows a schematic diagram of binary hypothesis testing according to some embodiments of the present disclosure. [Figure 2D] FIG. 1 illustrates a block diagram for the calculation of a generalized likelihood ratio test (GLRT) statistic, according to some embodiments of the present disclosure. [Figure 3] FIG. 1 shows a block diagram of a MIMO radar system according to some embodiments of the present disclosure. [Figure 4A] 1 illustrates a schematic diagram of a vehicle communicatively coupled to a MIMO radar system, according to an embodiment of the present disclosure. [Figure 4B] FIG. 1 illustrates parking a vehicle in a parking space according to an embodiment of the present disclosure. [Figure 5A] FIG. 1 illustrates a performance evaluation of receiver operating characteristic (ROC) curves according to an embodiment of the present disclosure. [Figure 5B] FIG. 1 illustrates a performance evaluation of receiver operating characteristic (ROC) curves according to an embodiment of the present disclosure. [Figure 5C] FIG. 1 illustrates a performance evaluation of receiver operating characteristic (ROC) curves according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0030] The set of M transmitters 101(a)-101(m) and the 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 reflections of transmissions. Furthermore, the MIMO radar system 100 includes 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, an optical drive, a thumb drive, an 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, a multi-core processor, a computing cluster, or any number of other configurations.

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

[0032] Some embodiments are based on the recognition that the reflections corresponding to all signals transmitted by a set of M transmitters received at each of the N receivers must be considered together to determine the angle of the object, which can be used to determine the spatial location of the object. However, the transmissions corresponding to all M transmitters may interfere with each other.

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

[0034] These pulses are then reflected from the object. A receiver is configured to receive the reflected signals. The received reflected signals are referred to as radar measurements. Upon receiving the reflected signals, each receiver can decode the received signals using a coding scheme to obtain a signal corresponding to each unique transmission per pair. After decoding, the receiver can determine parameters of the object, such as the radial velocity, spatial angle, and distance to the object. In one embodiment, the reflected signals are processed through a 1D fast-time FFT 105, a slow-time MIMO decoder 107, a 1D slow-time FFT 109, and a spatial MIMO detector 111 to determine the object parameters.

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

[0036] 1B illustrates a vehicle 113 equipped with a MIMO radar system 100 according to some embodiments of the present disclosure. The MIMO radar system 100 equipped on the vehicle 113 is referred to as the vehicle radar. Here, the MIMO radar system 100 is configured to detect objects in a scene, such as a pedestrian 115 and a car 117. The objects 115 and 117 may be stationary or moving. A set of receivers 103(a)-103(n) of the MIMO radar system 100 receives reflections of the transmitted signals (i.e., coded pulse / reference signals) from the objects 115 and 117.

[0037] Each receiver receives a signal that is a superposition of reflections of multiple signals transmitted by the set of transmitters, i.e., each receiver receives a composite signal, with each signal in the composite signal corresponding to a reflection of all the transmitted signals. To detect objects 115 and / or 117, in some embodiments, MIMO radar system 100 (or its own radar) 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.

[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)-103(n) by utilizing the orthogonal codes used in the set of transmitters 101(a)-101(m). To that end, each receiver is configured to multiply the received composite signal by the corresponding orthogonal code used by the transmitter associated with that receiver. Due to the orthogonality property of codes, the result of multiplication by two different codes is ideally zero, while the result of multiplication by the same code is non-zero.

[0039] This orthogonality allows each receiver to separate the reflected waveforms corresponding to the reference signals transmitted by each transmitter from the composite signal. However, if the interfering waveforms arrive at the receiver with different code sets, i.e., if some waveform separation residuals still remain in the separated reflected waveforms, perfect waveform separation is unlikely. The residuals may be due to an interfering radar 119 present in the scene. In particular, the residuals are due to signals transmitted by a set of transmitters of the interfering radar 119. In one embodiment, the interfering radar may be a MIMO radar.

[0040] 1C illustrates 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., residual errors due to the interferometric radar 119), and noise. If the interference components, i.e., residual errors due to the interferometric radar 119, are not taken into account in detecting the objects 115 and 117, the accuracy of detecting the objects 115 and 117 may be reduced.

[0041] As such, some embodiments are based on the recognition that a signal model for processing radar measurements must include (1) an object signal model for reflections of the transmitted signal that form the radar measurements, and (2) an interference signal model for interference caused by the interferometric radar 119.

[0042]

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

[0044]

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[0045] In particular, the receive steering vectors for MIMO radar system 100 and interferometric radar 119 have the same structure but may correct for the same or different angles. In other words, the receive steering vectors for MIMO radar system 100 and interferometric radar 119 are functions of unknown angles, and different embodiments may impose a constraint on having the same angles to describe radar measurements or relax this constraint. Such flexibility allows different embodiments to reduce the computational burden of evaluating radar measurements or introduce fewer assumptions for improved 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. Figure 2A shows the 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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[0047]

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[0048] Thus, solving a binary hypothesis problem 201 at a quantized angle within a range-Doppler bin implies determining the presence or absence of an object at the quantized angle while assuming the position of the interferometric radar at each of the other quantized angles within the range-Doppler bin. The presence or absence of an object can be expressed as a binary classification result.

[0049]

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[0050] In this manner, some embodiments define a signal model with an internal classification that describes radar measurements of range-Doppler bins for unknown angles. For each angle, the signal model defines the corresponding radar measurements as: Any transmission of a known transmitter to a known receiver, for example, a waveform transmitted from a MIMO radar with known parameters, and an object reflection waveform; · Described as the mandatory transmission of an unknown transmitter to the same known receiver, for example in combination with an interference waveform transmitted from an interferometric radar with unknown parameters.

[0051] Because any transmission is defined by the result of a binary classification, such a classification is referred to herein as an internal classification because it describes only a portion of the signal model. However, the result of the binary classification is what embodiments of the present disclosure seek to determine in order to describe the scene. In this way, the processing of radar measurements can be reduced to a classification problem.

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

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

[0054]

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[0055]

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[0056]

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[0057]

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[0058] 3 shows a block diagram of a MIMO radar system 100 according to some embodiments. The MIMO radar system 100 can have several interfaces connecting 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 through a bus 303 to a network 305 that connects the MIMO radar system 100 to sensing devices. The MIMO radar system 100 includes a transmitter interface 307 configured to instruct a set of transmitters 101 to transmit FMCW pulses in the radio frequency (RF) band over a series of pulse repetition intervals (PRI). The transmitter interface 307 communicates with a signal generator 309 that generates the FMCW pulses.

[0059] Additionally, an orthogonal code generator 311 is used to generate different orthogonal codes that are multiplied by the 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 a scene through a network 305. The radar measurements 315 of a scene are sampled in the time-frequency domain within an intermediate frequency (IF) bandwidth where reflections of the transmitted FMCW are shifted by mixing with copies of the FMCW.

[0060] Additionally, 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 may be connected to one or more input / output (I / O) devices via a bus 303.

[0061] The storage medium 319 is configured to store a signal model 319a with internal classification that describes measurements corresponding to a segment of a scene defined by a relative range and velocity relative to the own transmitter as a combination of optional transmissions to the own receiver from a hypothetical transmitter having the structure of the own transmitter and located within the segment of the scene, and required transmissions to the own receiver from an interfering transmitter having an unknown structure and located within the segment of the scene. In one embodiment, the signal model 319a with internal classification describes measurements for different quantized angles in a range-Doppler bin as the sum of a binary-classified Kronecker product of the own receiver steering vector and the own transmitter steering vector that corrects for the unknown angle, and a Kronecker product of the own receiver steering vector and the interfering transmitter steering vector that corrects for the unknown angle. Instead, in some embodiments, the signal model with internal classification describes measurements of a segment of a scene for different angles in a range-Doppler bin as the sum of a binary-classified Kronecker product of the own receiver steering vector and the own transmitter steering vector that corrects a first unknown angle and a Kronecker product of the own receiver steering vector and the interfering transmitter steering vector that corrects a second unknown angle.

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

[0063] Additionally, in some embodiments, the processor 317 is further configured to evaluate measurements of different segments of the scene independently of one another, and to evaluate the measurements of the segments, the processor 317 is further configured to account for measurements of the segments for different values ​​of the second unknown angle by testing for the presence of a hypothetical transmitter for each of different values ​​of the first unknown angle. In some other embodiments, the processor 317 is further configured to statistically evaluate measurements of the segments over multiple pulse repetition intervals using a generalized likelihood ratio test (GLRT).

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

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

[0066] 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 car, a bus, or a rover. The vehicle 401 may also be an autonomous or semi-autonomous vehicle. In one embodiment, the steering system 405 is controlled by the 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 the 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 the tracker 403 or other components of the vehicle 401. The vehicle 401 may also include one or more sensors 407 for sensing the surrounding environment. In some embodiments, the vehicle 401 includes one or more sensors 409 that sense its current motion parameters and internal conditions. Examples of the 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 also include a transceiver 413 that enables the controller 403 to communicate 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] 4B illustrates parking of vehicle 401 in parking space 415, according to an embodiment of the present disclosure. Parking space 415 includes a parking spot, such as spot 417, for parking the vehicle. Parking space 415 is bounded by boundaries 419a and 419b. Parking space 415 further includes one or more objects, such as vehicles 421, 423, 425 and pedestrian 427, with which vehicle 401 must avoid colliding.

[0069] Vehicle 401 is at a starting point 429 and needs to park at a 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 movement of vehicle 401 from starting point 429 to target parking spot 431. Parking space 415 also includes an interferometric radar associated with vehicle 435, which causes mutual interference. MIMO radar system 100 associated with vehicle 401 accurately determines parameters of an object, e.g., pedestrian 427, under the mutual interference.

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

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[0087] 5A-5C show performance evaluation of ROC curves according to an embodiment of the present disclosure. 6 We validate the theoretical ROC performance of the fluoroscopic and GLRT detectors using Monte Carlo simulations over runs. Figure 5B shows that the ROC performance of the GLRT detector generally performs better with increasing Rx array size N, approaching that of the fluoroscopic detector when N is moderately large. Figure 5C shows that the average performance of the GLRT detector is between that of the fluoroscopic detector and the mismatched / regular filter.

[0088] This specification provides only exemplary embodiments and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the following description of exemplary embodiments will provide those skilled in the art with an enabling description for implementing one or more exemplary embodiments. Various changes are contemplated that may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosed subject matter, as set forth in the appended claims.

[0089] Specific details are provided in the following description to provide a thorough understanding of the embodiments. However, those skilled in the art will understand that the embodiments may be practiced without these specific details. For example, systems, processes, and other elements of the disclosed subject matter may be shown as components in block diagram form in order to avoid obscuring the embodiments in unnecessary detail. In other instances, well-known processes, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. Furthermore, like reference numbers and names in the various drawings indicate like elements.

[0090] Also, particular embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. While a flowchart may describe operations as a sequential process, many of the operations may be performed in parallel or simultaneously. Moreover, the order of operations may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not discussed or included in the diagram. Moreover, not all operations in any specifically described process may 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 termination of the function may correspond to a return of 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. The manual or automatic implementation may be performed or at least assisted through the use of machines, hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or microcode, the program code or code segments that perform the necessary tasks may be stored on a machine-readable medium. A processor can perform the necessary tasks.

[0092] The various methods or processes outlined herein may be coded 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 runs on a framework or virtual machine. Typically, the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0093]

[0013] Embodiments of the present disclosure may be implemented as a method, an example of which is provided. The order of operations performed as part of the method may be determined in any suitable manner. Thus, embodiments may be configured to perform operations in an order different from that illustrated, which may include performing some operations simultaneously, even though they are shown as a sequence in the illustrated embodiment.

[0094] Furthermore, embodiments of the present disclosure and the functional operations described herein can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware containing the structures disclosed herein and their structural equivalents, or in one or more combinations thereof. Furthermore, 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, an electrical, optical, or electromagnetic signal generated by a machine. The propagated signal is generated to encode information that is transmitted to a suitable receiving device 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 one or more combinations thereof.

[0095] According to embodiments of the present disclosure, the term "data processing apparatus" may encompass all types of apparatus, devices, and machines that process data, including, by way of example, a programmable processor, computer, or multiple processors or computers. The apparatus may include special-purpose logic circuitry, such as an FPGA (field-programmable gate array) or an ASIC (application-specific integrated circuit). In addition to hardware, the apparatus may also include code that creates an execution environment for the computer program, such as code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or one or more combinations thereof.

[0096] A computer program (which may also be called or described as a program, software, software application, module, software module, script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and can be deployed in any form, such 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, but need not, correspond to a file in a file system. A program can be stored in part of a file that holds other programs or data, for example, in one or more scripts stored in a markup language document, in a single file dedicated to the program, or in multiple coordinated files, for example, files that store one or more modules, subprograms, or portions of code.

[0097] A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communications network. Computers suitable for running computer programs may, by way of example, be based on general-purpose or special-purpose microprocessors or both, or any other type of central processing unit. Typically, a central processing unit receives instructions and data from a read-only memory or a random-access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data.

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

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

[0100] Embodiments of the subject matter described herein can be implemented in a computing system that includes a back-end component, e.g., a data server, or includes a middleware component, e.g., an application server, or includes a front-end component, e.g., a client computer having a graphical user interface or web browser that allows a user to interact with an implementation of the subject matter described herein, or includes any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communications network. Examples of communications networks include local area networks ("LANs") and wide area networks ("WANs"), e.g., the Internet.

[0101] A computing system may 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 by virtue of computer programs running on the respective computers and having a mutual client-server relationship.

[0102] While the present disclosure has been described in terms of certain preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the disclosure. It is, therefore, the object of the following claims to cover all such variations and modifications that fall within the true spirit and scope of the disclosure.

Claims

1. 1. A multiple-input multiple-output (MIMO) radar system for detecting objects in a scene, the MIMO radar system comprising: a transmitter having a set of transmitters and a receiver having a set of receivers with known mutual locations forming a virtual array of the MIMO radar system, wherein paired combinations of different transmitters and different receivers of the virtual array are configured to (1) transmit a 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 the time-frequency domain within an intermediate frequency (IF) bandwidth in which reflections of the transmitted FMCW are shifted by mixing with copies of the FMCW, and the MIMO radar system further comprises: a memory configured to store a signal model having an internal classification, the signal model describing measurements corresponding to a segment of the scene defined by a relative distance and a relative velocity to the own transmitter as a combination of optional transmissions to the own receiver from a hypothetical transmitter having the structure of the own transmitter and located within the segment of the scene, and mandatory transmissions to the own receiver from an interfering transmitter having an unknown structure and located within the segment of the scene; a processor coupled to instructions that, when executed by the processor, cause the MIMO radar system to: transforming the radar measurements into range-Doppler space to generate measurements of different segments of the scene for different range-Doppler bins formed by intersections of different range bins with different Doppler bins; classifying the presence of the hypothesized transmitter in different segments of the scene according to a signal model having an internal classification that describes the measurements in different segments of the scene independently of each other; combining the results of the classifications to generate parameters of the object as indicated by the results of the classification of the presence or absence of the hypothetical transmitter in different segments of the scene; and outputting parameters of the object.

2. 2. The MIMO radar system of claim 1, wherein the signal model with internal classification describes the scene measurements for different quantized angles in range-Doppler bins as a sum of a binary-classified Kronecker product of a local receiver steering vector and a local transmitter steering vector that corrects an unknown angle and a Kronecker product of a local receiver steering vector and an interfering transmitter steering vector that corrects the unknown angle.

3. 2. The MIMO radar system of claim 1, wherein the signal model with internal classification describes measurements of the segment of the scene for different angles in range-Doppler bins as a sum of a binary-classified Kronecker product of a local receiver steering vector and a local transmitter steering vector that corrects a first unknown angle and a Kronecker product of the local receiver steering vector and an interfering transmitter steering vector that corrects a second unknown angle.

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

5. 4. The MIMO radar system of claim 3, wherein the processor is further configured to statistically evaluate measurements of the segments over multiple pulse repetition intervals using a generalized likelihood ratio test (GLRT).

6. 4. The MIMO radar system of claim 3, wherein the own 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.

7. 4. The MIMO radar system of claim 3, wherein the own 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.

8. The MIMO radar system of claim 1 , wherein the object parameters include at least one of a radial velocity, a spatial angle, and a range to the object.

9. The MIMO radar system of claim 1 , wherein the processor is further configured to execute a generalized likelihood ratio test (GLRT) algorithm to generate the object parameters.

10. 10. The MIMO radar system of claim 9, wherein 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 includes a residual due to interference and noise, and the second hypothesis defining that the radar measurement includes a reflected signal from the object, the residual due to the interference, and the noise.

11. 11. The MIMO radar system of claim 10, wherein the processor is further configured to compare a GLRT statistic to a predetermined threshold, the GLRT statistic determined by the GLRT algorithm, and the predetermined threshold based on a number of transmitters and receivers.

12. The MIMO radar system of claim 11 , wherein the first hypothesis is true if the GLRT statistic is less than the predetermined threshold.

13. The MIMO radar system of claim 11 , wherein the second hypothesis is true if the GLRT statistic is greater than the predetermined threshold.

14. 1. A method for detecting objects in a scene, the method using a processor coupled to a memory storing a signal model having an internal classification that describes measurements corresponding to a segment of the scene defined by a relative distance and a relative velocity to an own transmitter as a combination of optional transmissions to the own receiver from a hypothetical transmitter having a structure of the own transmitter and located in the segment of the scene, and required transmissions to the own receiver from an interfering transmitter having an unknown structure and located in the segment of the scene, the processor being coupled to stored instructions that, when executed by the processor, perform steps of the method, the steps of the method comprising: transmitting a frequency modulated continuous wave (FMCW) signal in a radio frequency (RF) band over a series of pulse repetition intervals (PRI); collecting radar measurements of the scene sampled in the time-frequency domain within an intermediate frequency (IF) bandwidth in which reflections of the transmitted FMCW are shifted by mixing with copies of the FMCW; transforming the radar measurements into range-Doppler space to generate measurements of different segments of the scene for different range-Doppler bins formed by intersections of different range bins with different Doppler bins; classifying the presence of the hypothesized transmitter in different segments of the scene according to a signal model having an internal classification that describes the measurements in different segments of the scene independently of each other; combining the results of the classifications to generate parameters of the object as indicated by the results of the classification of the presence or absence of the hypothetical transmitter in different segments of the scene; and outputting a parameter of the object.

15. 15. The method of claim 14, wherein the signal model with internal classification describes the scene measurements for different quantized angles in range Doppler bins as a sum of a binary-classified Kronecker product of an own receiver steering vector and an own transmitter steering vector that corrects an unknown angle and a Kronecker product of an own receiver steering vector and an interfering transmitter steering vector that corrects the unknown angle.

16. 16. The method of claim 15, wherein the own 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. 16. The method of claim 15, wherein the own 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 of 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. 15. The method of claim 14, wherein the signal model having the internal classification is formulated as one of a first hypothesis and a second hypothesis, the first hypothesis defining that the radar measurement includes a residual due to interference and noise, and the second hypothesis defining that the radar measurement includes a reflected signal from the object, the residual due to the interference, and the noise.

20. 1. A non-transitory computer-readable storage medium having embedded thereon a program executable by a processor to perform a method for detecting an object in a scene, the method comprising: transmitting a frequency modulated continuous wave (FMCW) signal in a radio frequency (RF) band over a series of pulse repetition intervals (PRI); collecting radar measurements of the scene sampled in the time-frequency domain within an intermediate frequency (IF) bandwidth in which reflections of the transmitted FMCW are shifted by mixing with copies of the FMCW; transforming the radar measurements into range-Doppler space to generate measurements of different segments of the scene for different range-Doppler bins formed by intersections of different range bins with different Doppler bins; classifying the presence of hypothetical transmitters in different segments of the scene according to a signal model having an internal classification, the signal model having an internal classification describing measurements corresponding to segments of the scene defined by their relative distance and velocity to the own transmitter as a combination of optional transmissions to the own receiver from a hypothetical transmitter having the structure of the own transmitter and located in the segment of the scene, and mandatory transmissions to the own receiver from an interfering transmitter having an unknown structure and located in the segment of the scene, the method further comprising: combining the results of the classifications to generate parameters of the object as indicated by the results of the classification of the presence or absence of the hypothetical transmitter in different segments of the scene; and outputting a parameter of the object.

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