Linear prediction based dual static detector for automotive radar

Abstract

The present disclosure provides systems, apparatuses, and techniques for operating automotive MIMO radar in a crowded multi-path environment to obtain reliable detections by linearly predicting whether a double-static condition is present. To avoid saturation of computational resources processing double-static detections, the described techniques enable a radar system to quickly identify and discard radar detections from the field of view that can be the result of a double-static condition. By ignoring unusable radar returns that can be the result of a double-static condition, an example radar system can focus on processing radar returns from static conditions, e.g., providing radar-based detections as output to automotive systems driving a vehicle in autonomous or semi-autonomous mode. In doing so, an example radar system provides a highly accurate static object detector that is fast enough to detect double-static conditions for vehicle safety systems as well as autonomous and semi-autonomous control.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 148,978, filed February 12, 2021, pursuant to 35 USC119(e), the disclosure of which is incorporated herein by reference in its entirety. Background Technology

[0003] Many vehicles use radar systems to detect static objects. When driving along a road, such systems may encounter relatively congested environments, where numerous erroneous radar echoes can be detected, including what is known as "multipath detection." Multipath detection occurs when radar echoes encounter more than one object on the return path to the detector, for example, when the environment includes surfaces that highly reflect radar energy, such as walls, guardrails, or moving trucks. These multipath detections can appear in radar data similar to static detection; if these detections are relied upon for autonomous or semi-autonomous control, these erroneous detections can lead to unsafe or unstable driving behavior requiring human intervention. Summary of the Invention

[0004] This document describes techniques and systems related to bistatic detectors based on linear prediction for automotive radar. In some examples, a radar system for installation on a vehicle includes at least one processor. The at least one processor is configured to: acquire radar echoes reflected from one or more objects in the vehicle's environment, and determine, based on the radar echoes, a linear prediction regarding the presence of a bistatic condition in the one or more objects in the environment. The at least one processor is further configured to: avoid discarding radar echoes in response to determining that the linear prediction indicates the absence of a bistatic condition in the one or more objects in the environment. Further in response to determining that the linear prediction indicates the absence of a bistatic condition in the one or more objects, the at least one processor is configured to: output a range and a range rate associated with the one or more objects for locating the vehicle relative to the one or more objects in the environment.

[0005] This document also describes the methods performed by the technologies and components summarized above, as well as other configurations of the radar system described herein, and the apparatus for performing these methods.

[0006] This invention provides a simplified concept related to a linear prediction-based dual static detector for automotive radar, which is further described in the detailed description and accompanying drawings. This invention is not intended to identify essential features of the claimed subject matter, nor is it intended to define the scope of the claimed subject matter. Attached Figure Description

[0007] This document describes in detail one or more aspects of a linear prediction-based dual static detector for automotive radar with reference to the following figures. Throughout the figures, the same numbers are generally used to refer to similar features and components:

[0008] Figure 1-1 An example environment is shown where a radar system according to the technology of this disclosure can use dual static detection based on linear prediction;

[0009] Figure 1-2 The different detection conditions encountered by a vehicle with a radar system using dual static detection based on linear prediction, according to the technology of this disclosure, are shown.

[0010] Figure 1-3 The Fast Fourier Transform (FFT) spectrum of the invalid synthesis array formed under bistatic conditions is shown.

[0011] Figure 2 An example configuration of a vehicle with a radar system using dual static detection based on linear prediction is shown;

[0012] Figure 3-1 and Figure 3-2 An example conceptual diagram is shown of a portion of a synthetic array accessed by a radar system using dual static detection based on linear prediction; and

[0013] Figure 4 A flowchart is shown as an example method for using a radar system with bistatic detection based on linear prediction, according to the technology of this disclosure. Detailed Implementation

[0014] Overview

[0015] This disclosure provides systems, apparatus, and techniques for operating automotive multi-input multi-output (MIMO) radar in congested multi-path environments to achieve reliable detection by linearly predicting whether a bistatic condition occurs. In radar, a bistatic condition occurs when a reflection travels from an object to a receiver along multiple trajectories or paths. For example, an ideal radar reflection travels along a direct path between the reflecting object and the receiver; the direct path is the shortest distance between them. Two-way multipath reflections are also possible, in which the longest path between the object and the receiver is employed; both radar transmission and radar reflection each travel along multiple trajectories between the object and the transceiver. On the other hand, a bistatic condition represents an intermediate scenario where an intermediate-length path travels from the object to the receiver.

[0016] Under these bistatic conditions, radar detection is generally unreliable as an indicator, for example, for detecting static objects within the radar system's field of view. This is because, under bistatic conditions, radar reflections have a direction of arrival (DOA) that is not equal to the direction of origin (DOD) of the corresponding radar transmission. This mismatch violates a necessary condition for forming a synthetic array in some MIMO radar systems. Bistatic conditions also mask the position of the reflecting object, making it difficult to determine the radar range and rate of change of range.

[0017] MIMO-related technologies are widely used in automotive radar systems. In MIMO, the synthetic uniform linear array (ULA) (also simply called the "synthetic array") is typically formed with a larger aperture than the corresponding physical array. For example, the eight physical channels of a radar transceiver (e.g., two transmit channels and six receive channels) are configured using MIMO to provide twelve channels or any other number greater than the total number of physical channels. Utilizing the additional channels, MIMO can operate with improved angular resolution, relying on a flexible physical layout that is inexpensive and may require fewer hardware components than conventional non-MIMO radars. However, a synthetic array using MIMO can typically only be formed when the DOD (Direction of Detection) is equal to the DOA (Direction of Area). In other words, under bistatic conditions, the DOD is not equal to the DOA, which can lead to errors in radar detections observed using MIMO because these radar detections cannot be easily mapped to the corresponding synthetic array. This can result in computational resource saturation or overconsumption when attempting to interpret radar detections to locate vehicles. In the best-case scenario, this oversaturation merely delays the radar system's output; in the worst-case scenario, the output includes errors in mapping the surrounding environment, which can cause the car's systems or the vehicle's controllers to drive in an unsafe manner.

[0018] To avoid saturating computational resources for processing bistatic detection, the described technique enables radar systems to rapidly identify and discard radar detections from the field of view that may result from bistatic conditions. By ignoring unavailable radar echoes that may be a result of bistatic conditions, the example radar system can focus on processing radar echoes from static conditions, for example, providing radar-based detections as output to vehicle systems or controllers driving vehicles in autonomous or semi-autonomous modes. In doing so, the example radar system provides a highly accurate static object detector that is fast enough to detect bistatic conditions for use in vehicle safety systems as well as autonomous and semi-autonomous control.

[0019] Operating environment

[0020] Figure 1-1An example environment is shown where a radar system according to the present disclosure can use dual static detection based on linear prediction. In the depicted environment 100, a radar system 104 is mounted to or integrated into a vehicle 102 traveling on a road 106. Within a field of view 108, the radar system 104 can detect one or more static objects 110 near the vehicle 102. The radar system transmits a radar signal having a direction of departure (DOD) 124 and is expected to receive a corresponding radar reflection having a direction of arrival (DOA) 126 matching the DOD 124.

[0021] Although shown as a passenger vehicle, vehicle 102 can represent other types of motorized vehicles (e.g., cars, motorcycles, buses, tractors, semi-trailers), non-motorized vehicles (e.g., bicycles), rail vehicles (e.g., trains), water vehicles (e.g., boats), aircraft (e.g., airplanes), or spacecraft (e.g., satellites). Typically, manufacturers can mount radar system 104 onto any mobile platform, including mobile machinery or robotic equipment.

[0022] In the depicted implementation, radar system 104 includes components mounted on the front of vehicle 102; radar system 104 radiates an object 110. Radar system 104 can detect object 110 from any external surface of vehicle 102. For example, components of radar system 104 may be arranged at the front, rear, top, bottom, or side of vehicle 102, within a bumper, integrated into a side mirror, formed as part of a headlight or taillight, or at any other internal or external location where object 110 needs to be detected. In some cases, vehicle 102 includes multiple radar systems 104, such as a first radar system 104 and a second radar system 104 providing a larger field of view 108. Generally, vehicle manufacturers may design the placement of one or more radar systems 104 to provide a specific field of view 108 encompassing a region of interest. Example fields of view 108 include 360-degree fields of view, one or more 180-degree fields of view, one or more 90-degree fields of view, etc., which may overlap to form a field of view 108 of a specific size.

[0023] Object 110 includes one or more materials that reflect radar signals. Depending on the application, object 110 may represent a target of interest from which vehicle 102 can safely navigate in road 106. For example, object 110 may be a parked vehicle, a roadside sign, a road obstacle, or debris on road 106.

[0024] Radar system 104 emits electromagnetic (EM) radiation by transmitting EM signals or waveforms via antenna elements. In environment 100, radar system 104 can detect and track object 110 by transmitting and receiving one or more radar signals. For example, radar system 104 may transmit EM signals between 100 and 400 GHz, between 4 and 100 GHz, or between approximately 70 and 80 GHz.

[0025] Radar system 104 is a MIMO radar system and relies on ULA to match radar echoes with corresponding signals; however, radar system 104 can operate as a conventional radar system that does not rely on dynamic MIMO technology. Radar system 104 may include a transmitter / receiver 112 and at least one antenna 114 for transmitting and receiving EM signals. Transmitter / receiver 112 includes one or more components for transmitting EM signals. Transmitter / receiver 112 also includes one or more components for detecting reflected EM signals. The manufacturer may integrate transmitter / receiver 112 onto a single integrated circuit (e.g., configured as a transceiver) or onto multiple integrated circuits.

[0026] The radar system 104 also includes one or more processors 116 (e.g., energy processing elements) and a computer-readable storage medium (CRM) 118. The processor 116 may be a microprocessor or a system-on-a-chip. The processor 116 can execute computer-executable instructions stored in the CRM 118. For example, the processor 116 can process EM energy received by the antenna 114 and use a static object detection module 120 dependent on the dual static detectors 122 to determine the position of an object 110 relative to the radar system 104. The static object detection module 120 can also detect various characteristics of the object 110 (e.g., range, target angle, rate of change of range, velocity).

[0027] Processor 116 can also generate radar data output by radar system 104 to at least one vehicle system of vehicle 102. For example, processor 116 can control an autonomous or semi-autonomous driving system of vehicle 102 based on processed EM energy from antenna 114. An autonomous driving system can control the operation of vehicle 102 to maneuver around object 110, or slow down or stop to avoid a collision with object 110. As another example, a semi-autonomous driving system can warn the operator of vehicle 102 that object 110 is in road 106.

[0028] The static object detection module 120 receives radar data, such as raw or time-series frames associated with EM energy received by antenna 114, and determines whether object 110 is in road 106 and various features associated with object 110. The static object detection module 120 may use a dual static detector 122 to assist the described operations and functions, for example, for linear prediction of dual static conditions. Radar system 104 may implement the static object detection module 120 and the dual static detector as computer-executable instructions in CRM 118, hardware executed by processor 116, software, or a combination thereof. The output of the static object detection module 120 may include: an identification of static object 110, and one or more features corresponding to static object 110 for subsequent classification, which may be taken into account when performing operations to control vehicle 102.

[0029] Using radar detection input including a range-time map, the bistatic detector 122 can perform a linear prediction regarding the presence of bistatic conditions in environment 100. The output of the bistatic detector 122 can include a flag indicating whether a portion of the input is associated with the bistatic conditions. This flag can trigger a filtering step performed as part of the static object detection module 120 and / or the bistatic detector 122 to remove or discard the portion of the input predicted to be associated with the bistatic conditions.

[0030] Considering environment 100, vehicle 102 is traveling on road 106. Radar system 104 detects object 110. In response to a linear prediction performed by dual static detector 122, radar system 104 may also discard radar echoes attributed to object 110 or track object 110 and extract features associated with it. As described above, vehicle 102 may also include at least one vehicle system that depends on data from radar system 104, such as a driver assistance system, autonomous driving system, or semi-autonomous driving system. Radar system 104 may include an interface for interfacing with the data-dependent vehicle system. For example, processor 116 outputs a signal based on EM energy received by antenna 114 via this interface.

[0031] Typically, automotive systems use radar data provided by radar system 104 to perform functions. For example, a driver assistance system can provide blind spot monitoring and generate warnings indicating a potential collision with object 110. Radar data can also indicate when changing lanes is safe or unsafe. An autonomous driving system can move vehicle 102 to a specific location on road 106 while avoiding a collision with object 110. The radar data provided by radar system 104 can also provide information about the distance to and position of object 110, enabling the autonomous driving system to perform emergency braking, lane changes, or adjust the speed of vehicle 102.

[0032] Figure 1-2 The illustration depicts different detection conditions 132 encountered by a vehicle using a radar system employing dual static detection based on linear prediction, according to the technology of this disclosure. One or more objects 110 are located at different positions relative to the vehicle 102; an obstacle 130 (e.g., a railing, a construction zone boundary) is present on either side of the vehicle 102. The radar system 104 detects one or more objects 110 by emitting radiation at DOD 124-1, DOD 124-2, DOD 124-3, or DOD 124-4 and receiving corresponding radar echoes from DOA 126-1, DOA 126-2, DOA 126-3, or DOA 126-4.

[0033] In one of the detection conditions 132, a static condition exists. The static condition is displayed in front of the vehicle 102. The emitted radiation is emitted as DOD 124-1, and the corresponding radar echo is reflected from the object 110 as DOA 126-1. The dual static detector 122 can determine that DOA 124-1 and DOA 126-1 are equal, therefore, there is no dual static condition in front of the vehicle 102.

[0034] In each of the other conditions 132, multiple static conditions exist. For example, a dual static condition exists on the right front side of vehicle 102. The emitted radiation is emitted as DOD 124-2, and the corresponding radar echo is ultimately reflected from object 110 as DOA 126-1. However, the radar echoes are interrupted by obstacle 130 before reaching radar system 104. A radar echo traveling towards obstacle 130 in a central direction 128-1 is shown. Upon reaching obstacle 130, the radar echo is reflected back to dual static detector 122 as DOA 126-2. The dual static detector can determine that DOA 124-2 and DOA 126-2 are not equal, thus a dual static condition exists on the right front side of vehicle 102. Dual static detector 122 can set a flag indicating the dual static condition and discard or ignore radar echoes obtained from DOA 126-2 because they cannot be trusted to locate vehicle 102 relative to one or more objects 110.

[0035] Another of the turning conditions 132, where another dual static condition exists on the left front side of vehicle 102. The emitted radiation is emitted as DOD 124-3, and the corresponding radar echo is ultimately reflected from object 110 as DOA 126-3. However, before the radiation reaches object 110, it is interrupted by obstacle 130 and takes a middle direction 128-2 to reach object 110. Similarly, the radar echo reflected from object 110 takes another middle direction 128-3 back to obstacle 130 before reaching radar system 104 as DOD 126-3. The dual static detector can determine that DOA 124-3 and DOA 126-3 are not equal, or even if they are equal, the energy difference between them indicates the presence of a dual static condition on the left front side of vehicle 102. Dual static detector 122 can set a flag indicating the dual static condition and discard or ignore radar echoes obtained from DOA 126-3 because they cannot be trusted for positioning on road 106.

[0036] Finally, a third bistatic condition occurs at the right rear of vehicle 102. In this case, the emitted radiation is emitted at DOA 124-4, and the corresponding radar echo is ultimately reflected from obstacle 130 at DOA 126-4. However, the radar echoes are interrupted by object 110 before reaching radar system 104. The radar echoes are shown traveling toward object 110 in a central direction 128-4. Upon reaching object 110, the radar echoes are ultimately reflected back to bistatic detector 122 at DOA 126-4. The bistatic detector can determine that DOA 124-4 and DOA 126-4 are not equal, thus indicating a bistatic condition at the right rear of vehicle 102. The bistatic detector 122 can set a flag indicating the bistatic condition and discard or ignore radar echoes obtained from DOA 126-4, as they cannot be trusted to locate vehicle 102 relative to one or more objects 110.

[0037] Figure 1-3 The Fast Fourier Transform (FFT) spectrum 140 of the invalid synthesis array formed under bistatic conditions is shown. For example, the FFT spectrum 140 can correspond to... Figure 1-2 The multiple static conditions are shown. In Figure 1-3 In the FFT spectrum 140, the ground truth is depicted with approximately -20 degrees of DOD and approximately 30 degrees of DOA. However, the spectrum is distorted, peaks are formed elsewhere, and even zero at -20 degrees, making range and feature observation difficult. Eliminating radar echoes collected during multiple static conditions (including dual static conditions) prevents distortion of the FFT spectrum of the ULA generated and managed by the radar system 104 and the static object detection module 120.

[0038] By filtering radar detections in this way to remove those obtained under multi-static conditions, the dual-static detector 122 enables the static object detection module 120 to operate more efficiently when it comes to the storage capacity of the processor 116 and / or CRM 118. This allows the static object detection module 120 to avoid saturating the computational resources of the vehicle 102, enabling the radar system 104 to quickly identify and discard radar detections from the field of view 108 that might lead to errors in object classification or control of the vehicle 102. By ignoring, and in some cases even discarding, unusable radar echoes that may be a result of dual-static conditions, the radar system 104 can focus on processing radar echoes from static conditions, for example, providing radar-based detections as output to an automotive system or controller driving the vehicle 102 in autonomous or semi-autonomous mode. With the dual-static detector 122, the radar system 104 provides a highly accurate estimate of dual-static conditions, which is statically fast enough for use by vehicle safety systems and autonomous and semi-autonomous control.

[0039] Transportation configuration

[0040] Figure 2 An example configuration of a vehicle with a radar system using dual static detection based on linear prediction is shown. As described with respect to Figure 1, the vehicle 102 may include a radar system 104, a processor 116, a CRM 118, a static object detection module 120-1 (an example of a static object detection module 120), and a dual static detector 122-1 (an example of a dual static detector 122). Furthermore, a radar range and rate module 206 managing the synthetic array 208 forms part of the static object detection module 120-1. The vehicle 102 may also include one or more communication devices 202 and a control interface 210 to one or more vehicle-based systems.

[0041] The communication device 202 may include a sensor interface and a vehicle-based system interface. The sensor interface and the vehicle-based system interface may, for example, transmit data (e.g., radar data, distance calculations, other features of the object 110 mapped into the field of view 108) via the communication bus of the vehicle 102 when the various components of the static object detection module 120 are integrated within the vehicle 102.

[0042] Vehicle 102 also includes a control interface 210 to one or more vehicle-based systems, which individually or in combination provide a manner for receiving radar data to control vehicle 102. Some examples of vehicle-based systems to which control interface 210 provides radar data include driver assistance system 212 and autonomous driving system 214; each system may rely on information output from static object detection module 120-1. For example, they may rely on data transmitted via communication device 202 and obtained from radar system 104 to drive vehicle 102 (e.g., braking, lane changing). Typically, control interface 210 may use the data provided by static object detection module 120-1 to control the operation of vehicle 102 and perform certain functions that do not require control but are also used to issue warnings to passengers, pedestrians, and other vehicles. For example, driver assistance system 212 may warn the driver of one or more objects 110 and perform evasive maneuvers to avoid a collision with object 110. As another example, autonomous driving system 214 may navigate vehicle 102 to a specific location in road 106 to avoid a collision with one or more objects 110.

[0043] The radar range and rate module 206 receives radar data as input and subsequently outputs processed radar data, which may include the range, velocity, rate of change of range, and classification of object 110. The input processing function of the radar range and rate module 206 can maintain both feature extraction and post-processing / output functions. The input processing function enables the radar range and rate module 206 to receive radar data from the transmitter / receiver 112 and antenna 114 as input for constructing the synthetic array 208.

[0044] Typically, radar data is received as low-level time-series data from a MIMO antenna array of M elements to generate a synthetic array 208, which maps K radar echoes (e.g., narrowband signals) to its input and output channels. Using low-level time-series data enables the static object detection module 120 to provide better detection resolution and allows the radar range and rate module 206 to extract features associated with one or more objects 110 appearing in the field of view 108. As a ULA, the synthetic array 208 is formed using MIMO technology to map K narrowband signals obtained at the M elements to the input and output channels of the synthetic array 208 (where K is less than M) by impacting the synthetic array 208-1 from different directions in the field of view 108 (e.g., in the far field).

[0045] The input processing function of the radar range and rate module 206 can process the radar data maintained in the synthetic array 208 to generate interpolated range-angle maps, including interpolated range-azimuth maps and / or interpolated range-elevation maps. The interpolated range-azimuth format improves the accuracy of the static object detection module 120-1 by simplifying the labeling of static objects 110, for example, for use by a machine learning model configured to make further estimations or predictions based on the radar data received as input.

[0046] The bistatic detector 122-1 includes a bistatic condition flag 204. The bistatic detector 122 can be configured to set the bistatic condition flag 204 (which represents a parameter indicating whether a bistatic condition occurs) in the CRM 118, and based on this parameter, determine whether to discard radar data stored in the synthetic array 208 during that time period. For example, if the bistatic condition flag 204 indicates that a bistatic condition may exist, the bistatic detector 122 may remove or cause the radar range and rate module 206 to remove any inaccurate radar data acquired during that time period from the synthetic array 208. Conversely, the bistatic detector 122 can be configured to avoid discarding radar echoes captured in the synthetic array 208 in response to setting the bistatic condition flag 204 to indicate that a bistatic condition has not occurred.

[0047] The dual-static detector 122 can reuse the dual-static condition flag 204 to indicate whether a subsequent radar echo was received during the dual-static condition. The dual-static detector 122 can be configured to set another parameter or dual-static condition flag in the CRM 118 to indicate the state of another linear prediction. In this way, the dual-static detector 122 can maintain the dual-static condition across multiple frames or time periods. The recurring dual-static condition can serve as a validation of an earlier linear prediction to further improve the accuracy of each linear prediction. The dual-static detector 122 can be configured to discard radar echoes in response to setting the dual-static condition flag 204 or any other parameter indicating further linear prediction.

[0048] To discard unusable radar echoes acquired during bistatic conditions, the bistatic detector 122 and / or the radar range and rate module 206 may clear (e.g., erase, delete, invalidate, or deprecate) all or a portion of the synthetic array 208 containing the unusable radar echoes. The synthetic array 208 or a portion thereof may be cleared so that the unusable radar echoes are no longer accessible from the CRM 118. In other examples, instead of actively erasing portions of the synthetic array 208 that can be used by other systems (which may utilize radar echoes acquired under bistatic conditions), the bistatic detector 122 and / or the radar range and rate module 206 may ignore, rather than erase, the unusable radar echoes when performing radar range and range change rate calculations.

[0049] The bistatic detector 122-1 is configured to closely manage the synthetic array 208 to detect anomalies occurring under bistatic conditions. This bistatic detection capability enables the radar system 104 to process radar data quickly and efficiently to accurately locate the vehicle 102 in and around one or more objects 110 as the vehicle travels on the road 106. In this way, the radar system 104, through linear prediction of bistatic conditions, enables the control interface 210 to safely operate the vehicle 102 in autonomous or semi-autonomous mode.

[0050] Linear prediction theory

[0051] Figure 3-1 and Figure 3-2 An example concept diagram of a portion of the synthetic array 208-1, accessed by a radar system using dual static detection based on linear prediction, is shown. Figures 3-1 to 3-2 The image shows snapshots of composite array 208-1 at different times: time 1, time 2, time 3, time 4, time 5, and time 6. Composite array 208-1 is... Figure 2 An example of the synthetic array 208 is shown. The state (e.g., true or false) of the bistatic conditional flag 204-1 (a bistatic conditional flag 204-1 is an example of bistatic conditional flag 204) is also shown, as well as how the state of the bistatic conditional flag 204-1 changes during each time period: time 1, time 2, time 3, time 4, time 5, and time 6. Several bistatic detection methods based on linear prediction theory are now described in more detail.

[0052] As previously described, the synthetic array 208-1 is a ULA formed using MIMO technology to map K narrowband signals (where K is less than M) received at M antenna elements onto the input and output channels of the synthetic array 208. K radar echoes are mapped onto the synthetic array 208-1 by impacting it from different directions within the field of view 108 (e.g., in the far field).

[0053] This collision leads to observations of M elements sampled at time t, which can be represented as x1(t), x2(t), ..., x M (t). According to linear prediction theory, there exists a unique set of P complex coefficients {c1,c2,…,c P}, where K is less than or equal to P, and P is less than M, such that equation 1 is satisfied, where m equals P+1, P+2, ..., M:

[0054]

[0055] Without loss of generality, by ignoring timestamps, Equation 1 can be written as the following system of linear Equation 2:

[0056] g-Fc=0 Equation 2

[0057] In Equation 2, the variables are defined in Equations 3, 4, and 5:

[0058]

[0059]

[0060]

[0061] In practice, observable noise may always exist, so Equation 1 can be rewritten to account for this noise δ (where the noise δ is close to a zero vector), as shown in Equation 6.

[0062] g-Fc=δ Equation 6

[0063] Next, the coefficient vector c can be obtained by using the least squares function defined by equations 7 and 8:

[0064]

[0065]

[0066] Equation 8 can be used to form a polynomial from which the roots of the polynomial can be calculated, and finally, an estimate of the direction of the source signal K. Using the total least squares function is another option for estimating c. Compared to traditional least squares, total least squares will provide higher accuracy, but it also increases computational complexity. Although the following discussion is based on least squares estimation, equivalent results can be derived using total least squares estimation. When using least squares, the diagonal loading technique can even handle ill-conditioned F from Equation 8. H Case F.

[0067] Binary hypothesis testing

[0068] To detect whether bistatic reflection has occurred, the bistatic detector 122 can be configured to predict whether H0 and H1 occur using a binary hypothesis test, which can be expressed as Equation 9:

[0069]

[0070] Based on the previous discussion of MIMO technology, especially in automotive radar applications, this binary hypothesis H test can be rewritten as H', as shown in Equation 10:

[0071]

[0072] Under real-world driving conditions, obtaining radar echoes with a DOD exactly equal to the corresponding DOA can be difficult. Therefore, a small tolerance ∈ can be used to configure the dual static detector 122 to make the testing of the binary hypothesis H' feasible. This is expressed as H” in Equation 11:

[0073]

[0074] For example, the synthetic array 208 generated for radar system 104 may include a MIMO subarray configuration. Radar system 104 may include a MIMO subarray having six receiving elements spaced 0.5λ apart, and another MIMO subarray having three transmitting elements spaced 3.0λ apart.

[0075] In this example, the synthetic array 208 generated by the static object detection module 120 and / or the radar range and rate module 206 can be a twelve-channel ULA with a spacing of 0.5λ when the bistatic condition H0 does not occur. Based on the linear prediction theory outlined above, and by setting M equal to 12 and P to 3, the bistatic detector 122 can be solved for the following:

[0076]

[0077] In equation 12, Equal to 1-P F This is equal to =IF(F H F) -1 F H .

[0078] On the other hand, when the bistatic condition H1 occurs, the portion of the radar echo acquired during that condition contained in the synthetic array 208 is invalid; therefore, the synthetic array 208 is not used as a ULA. By setting M equal to 12 and P to 3, the error e under H1 will not approach zero. Therefore, the bistatic detection problem can be formulated as H”’, as expressed in Equation 13:

[0079]

[0080] The generalized dual static detector 122 can be configured to operate correctly under a wide range of application and environmental conditions. The dual static detector 122 can be configured as a mean square error (MSE) detector following Equation 14:

[0081]

[0082] When the observed noise power is known, setting ρ to the noise power is an intuitive option. Training data can also be used to find the threshold ρ. Specifically, a large amount of non-bistatic data can be simulated and fed into the bistatic detector 122 to generate H0 statistics, from which the threshold ρ can be derived. This allows the static object detection module 120 to achieve a specific false alarm rate. Alternatively, real road data can be collected and tested as input to the bistatic detector 122 to further refine the threshold ρ. The coefficient size P should be greater than or equal to K; K is the number of sources. When K is known prior, a typical value of P is equal to K. Otherwise, P can be set to be greater than the number of sources K in most cases, while ensuring that the coefficients provide a unique solution.

[0083] exist Figure 3-1 and Figure 3-2 In the example scenario shown, the dual static detector 122-1 can process the radar echo according to the detector logic γ from Equation 14. At each of the different time intervals time 1, time 2, time 3, time 4, time 5, and time 6, the dual static condition flag 204-1 is one of two values, such as true, false, zero, and one. When the dual static condition flag 204-1 is true, the dual static detector 122-1 has determined that the radar echo within at least a portion of the synthetic array 208-1 is invalid due to the dual static condition. The dual static condition flag 204-1 may otherwise be false; since the vehicle 102 does not always encounter the dual static condition, it may occur less frequently than the static condition. However, in the case of the dual static condition, the dual static detector 122-1 changes the dual static condition flag 204-1 from a false state to a true state, which invalidates the synthetic array 208-1, or invalidates the portion of the synthetic array 208-1 containing suspicious radar data.

[0084] At time 1, radar echo 302-1 is obtained using radar system 104. Radar range and rate module 206 can create synthetic array 208-1 upon receiving radar echo 302-1. Bistatic detector 122-1 can access synthetic array 208-1 to determine a linear prediction of whether bistatic conditions exist for object 110 in environment 100.

[0085] The dual static detector 122-1 can evaluate γ according to Equation 14 to determine whether H0 (static condition) or H1 (dual static condition) satisfies the threshold ρ. Based on whether the radar echo 302-1 is received under static or dual static conditions, the dual static detector 122-1 sets the dual static condition flag 204-1 to false or true, respectively. Figure 3-1 In the example shown, the bistatic detector 122-1 sets the bistatic condition flag 204-1 to false in response to determining from γ that one or more objects 110 in the environment 100 do not have a bistatic condition. To this end, the bistatic detector 122-1 performs the following logic: it actively avoids discarding radar echo 302-1 and / or synthetic array 208-1, while outputting radar echo 302-1 and / or synthetic array 208-1 to the control interface 210 for locating or manipulating vehicle 102.

[0086] At time 2, radar echo 302-2 is obtained using radar system 104. To determine whether a linear prediction of bistatic conditions exists for object 110 in environment 100 during time 2, bistatic detector 122-1 can revisit synthetic array 208-1. Bistatic detector 122-1 can re-evaluate γ according to equation 14 to determine whether H0 (static condition) or H1 (bistatic condition) satisfies threshold ρ. Based on whether radar echo 302-2 was received under static or bistatic conditions, bistatic detector 122-1 sets the bistatic condition flag 204-1 to false or true, respectively. Figure 3-1 In the example shown, the bistatic detector 122-1 sets the bistatic condition flag 204-1 to true in response to determining from γ that one or more objects 110 in environment 100 may indeed have a bistatic condition. The bistatic detector 122-1 performs the following logic: this logic causes the bistatic detector 122-1 to actively discard radar echoes 302-2 and / or synthetic array 208-1 and prevent them from being used by the control interface 210 for vehicle control.

[0087] Time 3 is a repetition of Time 1; however, radar echo 302-3 is received and stored in synthetic array 208-1. Radar echo 302-3 is determined to have not occurred during the bistatic condition, and the bistatic condition flag 204-1 is set to false.

[0088] Conversely at time 3, at time 4, radar echo 302-4 is received and stored in synthetic array 208-1. Radar echo 302-4 is determined to have occurred during bistatic conditions, and bistatic condition flag 204-1 is set to true to prevent them from being ultimately used by static object detection module 120-1 and / or control interface 210.

[0089] Next, times 5 and 6 are instances where the bistatic detector 122-1 determines that the bistatic condition does not exist. Radar echoes 302-5 and 302-6 are determined to have not occurred during the bistatic condition, and the bistatic condition flag 204-1 is set to false so that they can ultimately be used by the static object detection module 210-1 and / or the control interface 210.

[0090] Variations of hypothesis testing

[0091] The following is a further variation of the MSE detector γ described with reference to Equation 14.

[0092] In the first variant γ', it is considered that M and P can remain constant throughout the entire statistical calculation performed by the dual static detector 122. Therefore, Equation 15 can be derived as:

[0093]

[0094] Therefore, the dual static detector 122 can follow the first variation of Equation 14, known as the statistical gamma γ', which is equal to And the threshold ρ' equals

[0095] Next, through some standard mathematical operations, Equation 15 can be decomposed into Equations 16 and 18 to produce another variant of the binary hypothesis test, Gamma γ.

[0096]

[0097]

[0098] As mentioned earlier, the threshold ρ is equal to the noise power and is measured asymptotically. Therefore, when the sample length is sufficiently long or the signal-to-noise ratio (SNR) is sufficiently high, Equation 18 can be derived as follows:

[0099]

[0100] The detector statistic gamma γ″ is equal to And the threshold ρ″ equals Where SNR is the SNR of the observed value. It can be seen that, in the case of high SNR, ρ″ is very close to the value 1.

[0101] Another example is an interpolation-based detector. The linear prediction theory, neglecting noise, is given by Equation 19:

[0102]

[0103] In equation 19, f i Let be the i-th column of matrix F. After some manipulation, the linear equation 19 above can be written as equation 20:

[0104] f p -Gh=0 Equation 20

[0105] Equations 21 and 22 below are derived from Equation 21 and illustrate the interpolation-based detector γ”':

[0106] G = [g, f1, f2, ..., f p-1 ,f p+1 ,…,f P Equation 21

[0107]

[0108] In these equations, p is greater than or equal to 1 and less than or equal to P, and c p Not equal to zero. Because c has a unique solution, h is uniquely existent, and total least squares or least squares functions can be used based on f even in the presence of noise. p And G is estimated. Other variations of these detectors can be derived.

[0109] The last variation of the MSE detector γ may be useful when a uniform linear array actually exists in both the transmitter and receiver subarrays. The detectors γ, γ', γ” and γ”’ proposed above, along with the error e syn Calculated as equal to The correlation is shown, where g and F are observations from the synthetic array 208.

[0110] When the transmitter and receiver subarrays contain a ULA structure for binary hypothesis testing H””, H”” can be formed as follows:

[0111]

[0112] In the above equation, e sub This represents the error calculated based on the characteristics of the composite array 208 of the transmit and receive subarrays, from which equation 24 is derived.

[0113]

[0114] Therefore, another detector Equation 24 gives us that it is equivalent to the detector γ from Equation 13, because Equation 25:

[0115]

[0116] Error e sub This can be considered a rough estimate of the noise power, and τ is an additional adjustment to it. The above-mentioned variant of detector γ... It can be easily extracted, as in Equation 26:

[0117]

[0118] Equation 26 forms a detector that is also equivalent to detector γ, that is, e sub It is an estimate of the noise power, and τ' is a scaling factor.

[0119] Figure 4 A flowchart of an example method 400 using a radar system employing linear prediction-based dual static detection according to the technology of this disclosure is shown. Method 400 is shown as a set of operations (or actions) 402 to 414, which may be performed in the order or combination shown, but are not necessarily limited to, the order or combination shown. Furthermore, any one or more of operations 402 to 414 may be repeated, combined, or rearranged to provide variations of method 400. In the various sections discussed below, reference may be made to the environment 100 of Figure 1 and the... Figure 1-1 , Figure 1-2 , Figure 1-3 , Figure 2 The entities described in detail in Figure 3 are referenced for illustrative purposes only. This technique is not limited to being performed by one or more entities.

[0120] At 402, radar echoes reflected from one or more objects in the vehicle environment are obtained. For example, radar system 104 receives radiated signals reflected from the location of one or more objects 110.

[0121] At position 404, based on the radar echo, a linear prediction regarding the existence of bistatic conditions for one or more objects is determined. For example, radar system 104 determines γ, γ', γ”, γ”', and / or

[0122] At position 406, based on linear prediction, it is determined whether one or more objects in the environment exhibit bistatic conditions. For example, radar system 104 evaluates γ, γ', γ”, γ”', and / or This is to determine whether the radar echo was received under static or bistatic conditions.

[0123] At 406, "Yes" indicates that a linear prediction may indicate the presence of bistatic conditions for one or more objects in the environment: at 408, radar echoes are discarded, and at 410, the outputs for locating vehicles in the environment, including distance and rate of change of distance, are avoided. For example, radar system 104 discards synthetic array 208 or portions thereof to prevent radar signals acquired under bistatic conditions from being used to interpret the relative positions of one or more objects 110.

[0124] Conversely, at 406, "No," in response to determining that a linear prediction indicates the absence of a bistatic condition for one or more objects in the environment: at 412, radar echoes are avoided from being discarded, and at 414, the distance and rate of change of distance relative to one or more objects are output to the vehicle's control interface for positioning the vehicle relative to one or more objects in the environment. For example, radar system 104 uses synthetic array 208 to interpret the relative position of one or more objects 110 based on the position of vehicle 102, and the relative position of one or more objects 110 is then output to control interface 210, for example, to safely maneuver vehicle 102 in and around one or more objects 110.

[0125] Several bistatic detectors based on linear prediction theory have been proposed, and they are particularly suitable for automotive MIMO radars. Detectors operating according to the described techniques can be used before the angle measurement function of radar system 104 to determine if there is a large difference between DOD and DOA. If DOD and DOA are significantly different, the function performed under the assumption that the synthetic array 208 includes accurate radar data will likely output incorrect range, range change rate, and angle estimates. Adjusting the input of the angle estimation function based on the output of a bistatic detector (such as bistatic detector 122) can help save computational resources of radar system 104 and / or trigger other special processing functions designed to better interpret radar data in bistatic cases.

[0126] Example

[0127] Examples are provided in the following sections.

[0128] Example 1. A method comprising: obtaining radar echoes reflected from one or more objects in the environment of a vehicle using a radar system mounted on a vehicle; determining, based on the radar echoes, a linear prediction by the radar system regarding the presence of a bistatic condition in the one or more objects in the environment; and in response to determining that the linear prediction indicates the absence of a bistatic condition in the one or more objects in the environment: avoiding discarding the radar echoes; and outputting a distance and a rate of change of distance associated with the one or more objects to a control interface of the vehicle for positioning the vehicle relative to the one or more objects in the environment.

[0129] Example 2. The method of Example 1, wherein the radar echo includes a first radar echo reflected from one or more first objects in the environment of the vehicle, the method further comprising: obtaining a second radar echo reflected from one or more second objects in the environment of the vehicle using a radar system; determining, based on the second radar echo, another linear prediction by the radar system regarding the existence of a bistatic condition for one or more second objects in the environment; and in response to determining that the other linear prediction indicates the existence of a bistatic condition for one or more second objects in the environment: discarding the second radar echo; and avoiding outputting another distance and a rate of change of distance associated with one or more second objects to prevent the vehicle from being positioned relative to one or more second objects in the environment.

[0130] Example 3. A method of any of the preceding examples, further comprising, in response to determining that another linear prediction indicates the presence of a bistatic condition in the environment, the method further comprising: immediately acquiring a third radar echo reflected from one or more third objects in the environment of the vehicle using a radar system; determining a third linear prediction, based on the third radar echo, regarding the presence of a bistatic condition for one or more third objects in the environment; and, in response to determining that the third linear prediction indicates that a bistatic condition does not exist for one or more third objects in the environment: avoiding discarding the third radar echo; and outputting a third distance and a distance change rate associated with one or more third objects to a control interface of the vehicle for positioning the vehicle relative to one or more third objects in the environment and preventing the vehicle from being positioned relative to one or more second objects in the environment.

[0131] Example 4. The method of any of the previous examples further includes: setting parameters in the memory of the radar system to indicate linear prediction; and avoiding discarding the first radar echo in response to setting the parameters to indicate that one or more objects in the environment do not have bistatic conditions.

[0132] Example 5. The method of any of the previous examples further includes: setting another parameter in memory to indicate another linear prediction; and discarding a second radar echo in response to setting the other parameter to indicate that one or more objects in the environment have bistatic conditions.

[0133] Example 6. A method of any of the previous examples, wherein discarding the second radar echo includes: clearing a portion of the synthetic array that includes the second radar echo.

[0134] Example 7. The method of any of the previous examples, where clearing a portion of the synthesized array includes clearing the entire synthesized array.

[0135] Example 8. A method of any of the previous examples, wherein discarding the second radar echo includes: ignoring the second radar echo used for calculating the radar range and range change rate performed by the radar system.

[0136] Example 9. A method of any of the previous examples, wherein one or more objects comprise one or more static objects having a speed less than a movement threshold.

[0137] Example 10. A method of any of the previous examples, further comprising: determining, based on detector γ, a linear prediction about the existence of a bistatic condition for one or more objects in the environment, where: γ equals H0 is the assumption that the bistatic conditions do not occur; H1 is the assumption that the bistatic conditions do occur; the radar echo is sampled at time t and represented as x1(t), x2(t), ..., x M (t), where M is the number of elements in the MIMO array of the radar system, P is greater than zero and less than M and includes a set of complex coefficients {c1,c2,…,c P}, g equals F multiplied by c; g equals F equals And c equals

[0138] Example 11. A method of any of the previous examples, further comprising: determining, based on detector γ', a linear prediction about the existence of a bistatic condition for one or more objects in the environment, where: γ' equals

[0139] Example 12. A method of any of the previous examples, further comprising: determining, based on detector γ”, a linear prediction about the existence of a bistatic condition for one or more objects in the environment, wherein: γ” equals Furthermore, SNR is equal to the signal-to-noise ratio of the radar system.

[0140] Example 13. A system comprising: a radar system for mounting on a vehicle and having at least one processor configured to: acquire radar echoes reflected from one or more objects in the environment of the vehicle; determine, based on the radar echoes, a linear prediction regarding the presence of bistatic conditions in the one or more objects in the environment; and, in response to determining that the linear prediction indicates the absence of bistatic conditions in the one or more objects in the environment: avoid discarding radar echoes; and output distances and distance change rates associated with the one or more objects for locating the vehicle relative to the one or more objects in the environment.

[0141] Example 14. The system of Example 13 further includes: a vehicle system for controlling a vehicle, the vehicle system being configured to operate the vehicle in an autonomous or semi-autonomous mode, partially based on distance and a rate of change of distance.

[0142] Example 15. The system of Example 13 further includes a vehicle that includes a radar system.

[0143] Example 16. The system of Example 15, wherein at least one processor is configured to: obtain radar echoes by forming a synthetic array based on a multi-input multi-output channel of a phased antenna array.

[0144] Example 17. A computer-readable storage medium including instructions that, when executed, configure a processor of a radar system to: acquire radar echoes reflected from one or more objects in the environment of a vehicle; determine, based on the radar echoes, a linear prediction regarding the presence of a bistatic condition for one or more objects in the environment; and, in response to determining that the linear prediction indicates the absence of a bistatic condition for one or more objects in the environment: avoid discarding the radar echoes; and output a distance and a rate of change of distance associated with one or more objects for locating the vehicle relative to one or more objects in the environment.

[0145] Example 18. A computer-readable storage medium of Example 17, wherein the radar echo includes a first radar echo reflected from one or more first objects in the environment of a vehicle, the instructions, when executed, further configuring the processor to: obtain a second radar echo reflected from one or more second objects in the environment of the vehicle; determine, based on the second radar echo, another linear prediction regarding the existence of a bistatic condition for one or more second objects in the environment; and in response to determining that the other linear prediction indicates the existence of a bistatic condition for one or more second objects in the environment: discard the second radar echo; and avoid outputting another distance and distance change rate associated with one or more second objects to prevent the vehicle from positioning relative to one or more second objects in the environment.

[0146] Example 19. A computer-readable storage medium of Example 18, wherein the instructions, when executed, further configure the processor to: in response to determining that another linear prediction indicates the presence of a bistatic condition in one or more objects in the environment: immediately obtain a third radar echo reflected from one or more third objects in the environment of the vehicle; based on the third radar echo, determine a third linear prediction regarding the presence of a bistatic condition in one or more third objects in the environment; and in response to determining that the third linear prediction indicates the absence of a bistatic condition in one or more third objects in the environment: avoid discarding the third radar echo; and output a third distance and a rate of change of distance associated with one or more third objects to a control interface of the vehicle for positioning the vehicle relative to one or more third objects in the environment and preventing the vehicle from being positioned relative to one or more second objects in the environment.

[0147] Example 20. A computer-readable storage medium of Example 18, wherein the instructions, when executed, further configure a processor to: set parameters to indicate that one or more objects in a linear prediction indication environment do not have bistatic conditions; and avoid discarding a first radar echo in response to setting the parameters to indicate that one or more objects in a linear prediction indication environment do not have bistatic conditions.

[0148] Example 21. A system comprising a radar system for mounting on a vehicle, the radar system having at least one processor configured to perform a method of any of the preceding examples.

[0149] Example 22. A system including a radar system for mounting on a vehicle, the radar system including means for performing the method of any of the preceding examples.

[0150] Example 23. A computer-readable storage medium including instructions that, when executed by at least one processor of a radar system, cause the at least one processor to perform any of the methods in the preceding examples.

[0151] Conclusion

[0152] While various embodiments of the present disclosure have been described in the foregoing description and illustrated in the accompanying drawings, it should be understood that the present disclosure is not limited thereto, but can be practiced in various ways within the scope of the following claims. It will be apparent from the foregoing description that various modifications can be made without departing from the scope of the present disclosure as defined by the appended claims. In addition to radar systems, problems associated with bistatic conditions can arise in other systems (e.g., imaging systems, lidar systems, ultrasonic systems) that identify and process trajectories from various sensors. Therefore, although described as one way to improve radar detection of static objects, the techniques described above can be applied to other problems to effectively detect bistatic conditions and take appropriate action.

[0153] Unless the context explicitly states otherwise, the use of terms grammatically related to "or" indicates an unrestricted, non-exclusive alternative. As used herein, the phrase referring to "at least one" of a list of items means any combination of those items, including a single member. As an example, "at least one of a, b, or c" is intended to cover: a, b, c, ab, ac, bc, and abc, as well as any combination with multiple identical elements (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbb, cc, and ccc, or any other ordering of a, b, and c).

Claims

1. A method for a means of transport, the method comprising: Use a radar system mounted on a vehicle to obtain radar echoes reflected from one or more objects in the environment of the vehicle; The radar system uses a detector γ to determine, based on the radar echo, whether a linear prediction exists regarding the existence of a bistatic condition for one or more objects in the environment, wherein: γ equals H0 is the assumption that the bistatic condition has not occurred; H1 is the assumption that the bistatic conditions actually occur; The radar echo is sampled at time t and represented as x1(t), x2(t), ..., x M (t), M is the number of elements in the MIMO array of the radar system. P is greater than zero and less than M, and includes a set of complex coefficients {c1, c2, ..., c}. P }, g equals F multiplied by c; g equals F equals c equals and equals IP F This is equivalent to IF(F H F) -1 F H ; as well as In response to determining that the linear prediction indicates that one or more objects in the environment do not have bistatic conditions: Avoid discarding the radar echo; and Output the distance and distance change rate associated with the one or more objects to locate the vehicle relative to the one or more objects in the environment.

2. The method as described in claim 1, characterized in that, The radar echo includes a first radar echo reflected from one or more first objects in the environment of the vehicle, and the method further includes: The radar system is used to obtain a second radar echo reflected from one or more second objects in the environment of the vehicle; The radar system determines, based on the second radar echo, another linear prediction regarding the existence of a bistatic condition for the one or more second objects in the environment; and In response to determining that the other linear prediction indicates the existence of a bistatic condition for the one or more second objects in the environment: Discard the second radar echo; and Avoid outputting another distance and distance change rate associated with the one or more second objects to prevent the vehicle from positioning relative to the one or more second objects in the environment.

3. The method as described in claim 2, characterized in that, In a further response to determining that the other linear prediction indicates the presence of bistatic conditions for the one or more objects in the environment, the method further includes: The radar system is used to immediately obtain third radar echoes reflected from one or more third objects in the environment of the vehicle; Based on the third radar echo, determine whether a third linear prediction exists regarding the one or more third objects in the environment under bistatic conditions; and In response to determining that the third linear prediction indicates that the one or more third objects in the environment do not have bistatic conditions: Avoid discarding the third radar echo; and Output a third distance and distance change rate associated with the one or more third objects to the control interface of the vehicle for positioning the vehicle relative to the one or more third objects in the environment and preventing the vehicle from being positioned relative to the one or more second objects in the environment.

4. The method as described in claim 2, characterized in that, Further includes: Parameters are set in the memory of the radar system to indicate the linear prediction; as well as In response to setting the parameter to indicate that the linear prediction indicates that one or more objects in the environment do not have bistatic conditions, the first radar echo is avoided from being discarded.

5. The method as described in claim 4, characterized in that, Further includes: Another parameter is set in the memory to indicate the other linear prediction; as well as In response to setting the other parameter to indicate that the other linear prediction indicates that one or more objects in the environment have bistatic conditions, the second radar echo is discarded.

6. The method as described in claim 2, characterized in that, Discarding the second radar echo includes: The synthetic array includes a portion of the second radar echo.

7. The method as described in claim 6, characterized in that, Clearing a portion of the synthesized array includes clearing the entire synthesized array.

8. The method as described in claim 2, characterized in that, Discarding the second radar echo includes: The second radar echo used for calculating radar range and range change rate performed by the radar system is ignored.

9. The method as described in claim 1, characterized in that, The one or more objects include one or more static objects having a speed less than a movement threshold.

10. The method as described in claim 1, characterized in that, The dual static condition indicates that the radar reflection has an arrival direction DOA that is not equal to the departure direction DOD of the corresponding radar emission.

11. The method as described in claim 1, characterized in that, Further includes: The linear prediction regarding the existence of bistatic conditions for the one or more objects in the environment is determined based on detector γ, wherein: γ” equals and SNR is equal to the signal-to-noise ratio of the radar system, P F Equal to F(F) H F) -1 F H .

12. A system for a vehicle, the system comprising: A radar system for installation on a vehicle and having at least one processor configured to: Obtain radar echoes reflected from one or more objects in the environment of the vehicle; The detector γ is used to determine, based on the radar echo, whether a linear prediction exists regarding the existence of a bistatic condition for the one or more objects in the environment, wherein: γ equals H0 is the assumption that the bistatic condition has not occurred; H1 is the assumption that the bistatic conditions actually occur; The radar echo is sampled at time t and represented as x1(t), x2(t), ..., x M (t), M is the number of elements in the MIMO array of the radar system. P is greater than zero and less than M, and includes a set of complex coefficients {c1, c2, ..., c}. P }, g equals F multiplied by c; g equals F equals c equals and equals IP F This is equivalent to IF(F H F) -1 F H ;and In response to determining that the linear prediction indicates that one or more objects in the environment do not have bistatic conditions: Avoid discarding the radar echo; and Output the distance and distance change rate associated with the one or more objects to locate the vehicle relative to the one or more objects in the environment.

13. The system as described in claim 12, characterized in that, Further includes: A vehicle system for controlling the vehicle and configured to operate the vehicle in an autonomous or semi-autonomous mode, in part based on the distance and the rate of change of distance.

14. The system as described in claim 12, characterized in that, It further includes vehicles, which include the radar system.

15. The system as described in claim 14, characterized in that, The at least one processor is configured to obtain the radar echo by forming a synthetic array through multiple input multiple output channels based on a phased antenna array.

16. The system as claimed in claim 12, characterized in that, The dual static condition indicates that the radar reflection has an arrival direction DOA that is not equal to the departure direction DOD of the corresponding radar emission.

17. A computer-readable storage medium comprising instructions, which, when executed, configure a processor of a radar system mounted on a vehicle to: Obtain radar echoes reflected from one or more objects in the environment of the vehicle; The detector γ is used to determine, based on the radar echo, whether a linear prediction exists regarding the existence of a bistatic condition for the one or more objects in the environment, wherein: γ equals H0 is the assumption that the bistatic condition has not occurred; H1 is the assumption that the bistatic conditions actually occur; The radar echo is sampled at time t and represented as x1(t), x2(t), ..., x M (t), M is the number of elements in the MIMO array of the radar system. P is greater than zero and less than M, and includes a set of complex coefficients {c1, c2, ..., c}. P }, g equals F multiplied by c; g equals F equals c equals and equals IP F This is equivalent to IF(F H F) -1 F H ;and In response to determining that the linear prediction indicates that one or more objects in the environment do not have bistatic conditions: Avoid discarding the radar echo; and Output the distance and distance change rate associated with the one or more objects to locate the vehicle relative to the one or more objects in the environment.

18. The computer-readable storage medium as claimed in claim 17, characterized in that, The radar echo includes a first radar echo reflected from one or more first objects in the environment of the vehicle, and the instructions, when executed, further configure the processor to: Obtain a second radar echo reflected from one or more second objects in the environment of the vehicle; Based on the second radar echo, determine whether another linear prediction exists regarding the existence of bistatic conditions for the one or more second objects in the environment; and In response to determining that the other linear prediction indicates the existence of a bistatic condition for the one or more second objects in the environment: Discard the second radar echo; and Avoid outputting another distance and distance change rate associated with the one or more second objects to prevent the vehicle from positioning relative to the one or more second objects in the environment.

19. The computer-readable storage medium as claimed in claim 18, characterized in that, When executed, the instructions further configure the processor to: Further in response to determining that the other linear prediction indicates the presence of bistatic conditions for the one or more objects in the environment: Immediately obtain third radar echoes reflected from one or more third objects in the environment of the vehicle; Based on the third radar echo, determine whether there is a third linear prediction with bistatic conditions for the one or more third objects in the environment; and In response to determining that the third linear prediction indicates that the one or more third objects in the environment do not have bistatic conditions: Avoid discarding the third radar echo; and Output a third distance and distance change rate associated with the one or more third objects to the control interface of the vehicle for positioning the vehicle relative to the one or more third objects in the environment and preventing the vehicle from being positioned relative to the one or more second objects in the environment.

20. The computer-readable storage medium as claimed in claim 19, characterized in that, When executed, the instructions further configure the processor to: Parameters are set to indicate that the linear prediction indicates that one or more objects in the environment do not have bistatic conditions; and In response to setting the parameter to indicate that the linear prediction indicates that one or more objects in the environment do not have bistatic conditions, the first radar echo is avoided from being discarded.

21. The computer-readable storage medium as claimed in claim 17, characterized in that, The dual static condition indicates that the radar reflection has an arrival direction DOA that is not equal to the departure direction DOD of the corresponding radar emission.

Images