Circuit system and method

Abstract

The present disclosure relates to circuitry for generating synthetic aperture radar images wherein the circuitry is configured to: obtain radar measurement data indicative of a received power and a received phase of a radar signal based on the radar signal detected by a radar sensor; mapping the received power of the radar signal to a measurement probability; generating a set of data tuples by associating each of a set of candidate phase values with a candidate measurement probability value of a set of candidate measurement probability values, the candidate measurement probability values of the set of candidate measurement probability values corresponding to a distribution of measurement probabilities according to phase uncertainty of the received phase; mapping the data tuple set to a phase candidate synthetic aperture radar PC-SAR image set; and determining a target phase value based on the PC-SAR image set.

Description

Technical Field

[0001] The present disclosure relates generally to circuit systems and methods, and more particularly, to circuit systems and methods for generating synthetic aperture radar images. Background Art

[0002] It is generally known to generate an image of the environment based on radar. For example, synthetic aperture radar (SAR) is based on transmitting radar signals into the environment and receiving reflected radar signals from the environment while moving through the environment. Based on the received reflected radar signals and the locations within the environment where the reflected radar signals have been received, a SAR image representing the environment can be generated.

[0003] While technology exists for generating SAR images, it is generally desirable to provide improved circuitry and methods for generating SAR images. Summary of the invention

[0004] According to a first aspect, the present disclosure provides a circuit system for generating a synthetic aperture radar image, wherein the circuit system is configured to obtain radar measurement data indicating received power and received phase of a radar signal based on a radar signal detected by a radar sensor; map the received power of the radar signal to a measurement probability; generate a set of data tuples by associating each candidate phase value in a candidate phase value set with a candidate measurement probability value in a candidate measurement probability value set, wherein the candidate measurement probability values ​​in the candidate measurement probability value set correspond to a distribution of measurement probabilities according to phase uncertainties of the received phase; map the set of data tuples to a phase candidate synthetic aperture radar (PC-SAR) image set; and determine a target phase value based on the PC-SAR image set.

[0005] According to a second aspect, the present disclosure provides a method for generating a synthetic aperture radar image, wherein the method includes: acquiring radar measurement data indicating received power and received phase of a radar signal based on a radar signal detected by a radar sensor; mapping the received power of the radar signal to a measurement probability; generating a data tuple set by associating each candidate phase value in a candidate phase value set with a candidate measurement probability value in a candidate measurement probability value set, wherein the candidate measurement probability values ​​in the candidate measurement probability value set correspond to a distribution of measurement probabilities according to phase uncertainties of received phases; mapping the data tuple set to a phase candidate synthetic aperture radar (PC-SAR) image set; and determining a target phase value based on the PC-SAR image set.

[0006] Additional aspects are set forth in the additional claims, drawings, and description below. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Embodiments are explained by way of example with respect to the accompanying drawings, in which:

[0008] Figure 1 A block diagram of a circuit system for generating a SAR image according to an embodiment is shown;

[0009] Figure 2 A flow chart of a method for generating a SAR image according to an embodiment is shown;

[0010] Figure 3 A diagram showing a probability distribution of a reception phase according to an embodiment;

[0011] Figure 4 illustrates a mapping of a set of data tuples to a set of PC-SAR images according to an embodiment;

[0012] Figure 5 is a block diagram depicting an example of a schematic configuration of a vehicle control system; and

[0013] Figure 6 1 is a diagram for assisting in explaining an example of the installation positions of the vehicle exterior information detection section and the imaging section. DETAILED DESCRIPTION

[0014] In giving reference Figure 1 Before describing in detail the embodiments of the present invention, an overall explanation is given.

[0015] As mentioned at the beginning, it is generally known to generate an image of the environment based on radar. For example, synthetic aperture radar (SAR) is based on transmitting radar signals into the environment and receiving reflected radar signals from the environment while moving through the environment. Based on the received reflected radar signals and the positions within the environment where the reflected radar signals have been received, a SAR image representing the environment can be generated.

[0016] Based on the received reflected radar signal, a SAR image can be generated in a variety of different ways. There are methods in the frequency domain and methods in the time domain. In some instances, these methods generate SAR images based on the amplitude and phase of the time signal or based on the frequency signal. In some instances, the received phase of each range cell in the grid map is corrected and then summed using various algorithms (such as backprojection algorithms).

[0017] In some instances, once a constructive superposition is obtained from this summation and thus a large amount of power is mapped into a cell, this cell represents a target, while the destructive superposition of all fingers brings a low-power cell that does not represent a target. In some instances, this method allows a simple and efficient way of generating a SAR map of the environment.

[0018] However, in some instances, this approach makes the resulting map directly dependent on the received power of each target. However, this does not indicate the actual occupancy state of the cells.

[0019] As mentioned, there are many different ways of developing a synthetic aperture for a SAR and generating a SAR image of the environment from the raw data already acquired by the SAR. However, in some instances, these different ways differ in the domain in which they are calculated. On the one hand, there are methods in the frequency domain and on the other hand, there are methods in the time domain. However, all these methods are amplitude-based, whereby only the complex numbers of the two measurements are summed. In this case, as described in Grebner, Timo et al.: "Radar-Based Mapping of the Environment: Occupancy Grid-Map Versus SAR" (IEEE Microwave and Wireless Components Letters, 2022, Digital Object Identifier (DOI): 10.1109 / LMWC.2022.3145661), according to equation (1), the currently existing map G contains for each cell m of the grid map G depending on a given sensor position x and sensor measurement value z. i The complex entry G(m i |z 1:t ,x 1:t ):

[0020]

[0021] Here, G(m i |z 1:t ,x 1:t ) describes the unit m at the current time frame t i The complex occupancy amplitude of describes the complex amplitude of the currently measured reflection signal as a function of the distance r and the K ramps with timestamp t emitted as radar signals into the environment in frame t. k The slope k corresponds to the complex amplitude pass Use unit m i With timestamp t k Sensor location at The distance between Perform phase correction, where f start is the starting frequency of the ramp and c0 is the speed of light. This process can be repeated for all received ramps and resulting cells of the map G.

[0022] In some instances, for real targets, all phases overlap constructively after correction, while targets representing noise overlap destructively, and thus all measured powers cancel each other.

[0023] Finally, there are many different ways to correct the receive phase, but all are based on the same principle: range-proportional phase correction.

[0024] In some instances, the disadvantage of this method is that only the amplitude information and phase information of the complex measurement values ​​are taken into account. Therefore, the dynamics of the resulting map will depend significantly on the strongest and weakest targets. These targets with a small radar cross section (RCS) but which bring about constructive overlap of complex pointers after applying the SAR algorithm due to the phase of the target may therefore not be shown in the map, although the measured phase may represent the target (object in the environment).

[0025] Additionally, in some instances, measurement inaccuracies (such as noise or position inaccuracies) are not taken into account, although they may have a significant impact on the measurement results.

[0026] Therefore, some embodiments relate to a circuit system for generating a synthetic aperture radar (SAR) image, wherein the circuit system is configured to obtain radar measurement data indicating received power and received phase of the radar signal based on a radar signal detected by a radar sensor; map the received power of the radar signal to a measurement probability; generate a set of data tuples by associating each candidate phase value in a set of candidate phase values ​​with a candidate measurement probability value in a set of candidate measurement probability values, the candidate measurement probability values ​​in the set of candidate measurement probability values ​​corresponding to a distribution of measurement probabilities according to phase uncertainties of the received phase; map the set of data tuples to a set of phase candidate synthetic aperture radar (PC-SAR) images; and determine a target phase value based on the set of PC-SAR images.

[0027] For example, the radar sensor may include a chirp sequence radar sensor that may transmit a radar signal into the environment, wherein the frequency of the transmitted radar signal is modulated. For example, the frequency may vary according to a frequency ramp with a predetermined slope starting from a predetermined starting frequency. The slope may be constant (linear chirp) or may vary to achieve a nonlinear frequency variation (e.g., exponential chirp). The radar sensor may, for example, repeatedly transmit the radar signal at a predetermined repetition rate. The repeated radar signals may be modulated equally or differently.

[0028] The transmitted radar signal can be reflected by objects in the environment. For example, objects that reflect radar signals can include cars, trucks, bicycles, other vehicles, pedestrians, animals, walls, road signs, traffic barriers, bridges, trees, stones, buildings, fences, etc. Such objects can also be called targets.

[0029] The radar sensor may receive a radar signal reflected by an object in the environment, and may generate radar measurement data based on information extracted from the received reflected radar signal. For example, the radar sensor may determine the power of the received radar signal. The radar sensor may determine which transmitted radar signal the received radar signal corresponds to, for example, which slope corresponds to. The determination may be based on the timing of transmitting the radar signal into the environment, the timing of receiving the reflected radar signal, the starting frequency of the radar signal, the slope and / or shape (e.g., linear chirp, exponential chirp, etc.), etc. The radar sensor may also determine the phase of the received radar signal, for example, based on the delay between receiving the reflected radar signal and transmitting the corresponding radar signal.

[0030] Although a chirp sequence radar sensor has been mentioned as an example of a radar sensor that generates radar measurement data, the radar measurement data may be generated by any radar sensor that is capable of determining the received power and received phase of a radar signal reflected from the environment. For example, in some embodiments, the radar measurement data may be generated by a time division multiplexing (TDM) radar, a frequency division multiplexing (FDM) radar, a code division multiplexing (CDM) radar, etc.

[0031] The circuit system may include any entity capable of performing information processing (such as generating a SAR image). For example, the circuit system may include a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a microprocessor, etc. For example, the circuit system may obtain radar measurement data directly from a radar sensor, or indirectly via another information processing device or via a magnetic, optical, or semiconductor-based storage medium.

[0032] The circuit system may further include a storage unit, which may include, for example, a dynamic random access memory (DRAM), a flash memory, an electrically erasable programmable read-only memory (EEPROM), a hard disk drive, etc. The circuit system may further include an interface for receiving radar measurement data and outputting the generated SAR image, for example, via a serial peripheral interface (SPI), a peripheral component interconnect (PCI), a controller area network (CAN), a universal storage bus (USB), Ethernet, IEEE 802.11 (Wi-Fi), Bluetooth, etc. For example, the circuit system may receive radar measurement data from a radar sensor, from another information processing device, or from a storage medium via an interface. For example, the circuit system may output the generated SAR image to another information processing device or a storage medium via an interface.

[0033] The mapping of the received power of the radar signal to the measurement probability may, for example, map the received power to a value in the interval from zero to one, without limiting the disclosure to these values. For example, the mapping may depend on environmental conditions and / or the configuration of the radar sensor. For example, the mapping may depend on the radar cross section (RCS) of an object reflecting the radar signal (e.g., depending on the material, size, shape and / or distance of the object), on the power of the radar signal emitted by the radar sensor, on the expected received power of the radar signal, on the power of electromagnetic interference that degrades the quality of the radar signal, etc.

[0034] For example, the measurement probability may correspond to the probability that the radar signal is not measurement noise or an artifact but is reflected by a real object.

[0035] For example, the set of candidate phase values ​​may be predetermined or may be generated based on the received phase of the radar signal. For example, the candidate phase values ​​in the set of candidate phase values ​​may be uniformly distributed in an interval from 0° to 360° to cover the entire period without limiting the present disclosure to these values. For example, the candidate phase values ​​in the set of phase values ​​may be generated in an interval around the received phase of the radar signal and may be uniformly distributed in the interval, or may be distributed in a subinterval including the received phase of the radar signal with a higher density than in a subinterval not including the received phase.

[0036] The set of candidate measurement probability values ​​may be determined based on the set of candidate phase values. For example, for each candidate phase value in the set of candidate phase values, a candidate measurement probability value in the set of candidate measurement probability values ​​may be determined. For example, the candidate measurement probability value may be calculated for each candidate phase value in the set of candidate phase values ​​based on a probability density function, or the candidate measurement probability values ​​may be read from a predetermined table, wherein interpolation of candidate measurement probability values ​​located between entries of the table may be performed.

[0037] The distribution of the measurement probabilities may represent the phase uncertainty of the received phase. For example, the standard deviation of the distribution may be determined based on properties of the radar sensor and / or based on the quality of the radar signal.

[0038] The set of data tuples may be generated by associating, for each data tuple in the set of data tuples, a candidate phase value in the set of candidate phase values ​​with a candidate measurement probability value in the set of candidate measurement probability values.

[0039] For example, the PC-SAR image set may include an associated PC-SAR image for each candidate phase value in the candidate phase value set. The mapping of the data tuple set to the PC-SAR image set may assign corresponding candidate measurement probability values ​​of the data tuples in the data tuple set to corresponding PC-SAR images in the PC-SAR image set associated with the corresponding candidate phase value of the data tuple in the data tuple set.

[0040] For example, the determination of the target phase value may be based on selecting a candidate phase value in the set of candidate phase values ​​as the target phase value based on the candidate measurement probability values ​​assigned to the PC-SAR image to which the candidate phase value is mapped.

[0041] For example, the circuit system may then generate a SAR image based on the candidate probability values ​​assigned to the PC-SAR image associated with the target phase value. For example, the target phase value may be represented by an index of the PC-SAR image associated with the target phase value in the set of PC-SAR images.

[0042] In some embodiments, the mapping of the received power includes determining a signal-to-noise ratio of the radar signal based on the received power; and determining the measurement probability based on the signal-to-noise ratio of the radar signal.

[0043] For example, the signal-to-noise ratio of the radar signal may be determined as the quotient of the received power of the radar signal and the noise level of the radar sensor.

[0044] The noise level may be determined in any suitable manner. For example, the noise level may be determined based on a calibration measurement of a known environment, which may, for example, not include an object (target) in a predetermined portion. For example, the noise level may be determined based on radar measurement data, for example, based on an average, median, or another suitable quantile of the received power of a plurality of radar signals, or based on clustering (such as hierarchical clustering or density-based spatial clustering application with noise (DBSCAN)), where the noise level may be determined based on the largest or densest cluster of received power values, for example, based on a maximum, average, quantile, or density distribution of the cluster.

[0045] For example, the measurement probability can be determined based on an exponential function, where the exponent includes the signal-to-noise ratio. The exponent can also include a scaling factor that is multiplied by the signal-to-noise ratio to determine a finite increase in the measurement probability. For example, the scaling factor can be determined empirically based on the configuration of the radar sensor and / or expected environmental conditions.

[0046] For example, the measurement probability can be selected from the interval between the minimum probability and the maximum probability based on the received power of the radar signal. For example, the minimum probability can be 0, 0.01, 0.1 or 0.5, and if the received power is equal to or drops below the noise level, the minimum probability can be selected. For example, the maximum probability can be 1, 0.99 or 0.9, and if the received power is equal to the expected maximum received power, the maximum probability can be selected, wherein the expected maximum received power can correspond to the power of the transmitted radar signal reduced according to the inverse square law relative to the distance (range) determined based on the radar signal. The maximum probability can also be reduced according to the difference between the expected maximum received power and the noise level. If the received power is at least a specific (e.g., predetermined) amount lower than the noise level, the minimum probability can also be selected to take into account the overlap between the distribution of the received power of the noise signal and the distribution of the received power of the radar signal reflected from the object. However, the minimum probability and the maximum probability are not limited to the values ​​described herein, and those skilled in the art can find other values ​​of the minimum probability and the maximum probability suitable for determining the measurement probability.

[0047] In some embodiments, the distribution of candidate measurement probability values ​​for the set of data tuples corresponds to a probability distribution, wherein at least one of a mean and a mode of the probability distribution corresponds to a received phase of the radar signal and has a probability value corresponding to a received power of the radar signal.

[0048] For example, the probability distribution may correspond to a Gaussian distribution (normal distribution). The standard deviation of the probability distribution may correspond to the phase uncertainty of the received phase, for example, corresponding to the finite precision of the radar sensor used to measure the received phase. The candidate measurement probability values ​​in the set of candidate measurement probability values ​​may be based on a probability density function that evaluates the probability distribution for the candidate phase values ​​in the set of candidate phase values. The candidate measurement probability values ​​may also be scaled relative to the probability density function corresponding to the measurement probability.

[0049] In some embodiments, the determination of the target phase value includes selecting as the target phase value the candidate phase value of the PC-SAR image mapped to the PC-SAR image in the set of PC-SAR images that is associated with the highest candidate measurement probability value.

[0050] For example, the circuitry may generate a SAR image based on a PC-SAR image associated with a target phase value (eg, based on a highest candidate measurement probability value associated with the PC-SAR image).

[0051] In some embodiments, the circuit system is further configured to obtain additional radar measurement data indicating received power and received phase of at least one additional radar signal detected by the radar sensor; perform mapping of received power, generation of a set of data tuples, and mapping of the set of data tuples respectively for the at least one additional radar signal; update candidate measurement probability values ​​associated with the corresponding PC-SAR image for the at least one additional radar signal based on the set of data tuples generated for the at least one additional radar signal; and determine the target phase value based on the updated candidate measurement probability values.

[0052] For example, the at least one additional radar signal may include one additional radar signal, 100 additional radar signals, or 10,000 additional radar signals, without limiting the present disclosure to these numbers or these orders of magnitude.

[0053] For example, as described above, the circuit system may map the received power of each additional radar signal to a corresponding measurement probability. As described above, the circuit system may also generate a set of data tuples by associating candidate phase values ​​in the set of candidate phase values ​​with candidate measurement probability values ​​in the set of candidate measurement probability values, wherein the candidate measurement probability values ​​may be generated, for example, by evaluating a distribution of measurement probabilities according to phase uncertainty of the received phase at the candidate phase values ​​in the set of candidate phase values, as described above.

[0054] The circuitry may then map the generated set of data tuples to the set of PC-SAR images for each additional radar signal. For example, each PC-SAR image in the set of PC-SAR images may be associated with a candidate phase value in the set of candidate phase values. The circuitry may map each data tuple in the set of data tuples generated for the additional radar signal to a PC-SAR image in the set of PC-SAR images that is associated with the candidate phase value for the corresponding data tuple.

[0055] For example, the number of PC-SAR images in the set of PC-SAR images may correspond to the number of candidate phase values ​​in the set of candidate phase values, and for the radar signal and for each additional radar signal, a candidate measurement probability value may be generated for the set of candidate measurement probability values ​​for each candidate phase value in the set of candidate phase values. Thus, each PC-SAR image in the set of PC-SAR images may be associated with one data tuple (which may associate a candidate phase value with a candidate measurement probability value with one another), and for the radar signal and for each additional radar signal one data tuple is associated with one data tuple.

[0056] However, the present disclosure is not limited to providing the same number of elements in a candidate phase value set, in a candidate measurement probability value set, in a data tuple set, and / or in a PC-SAR image set. For example, the circuit system may generate fewer candidate measurement probability values ​​than the candidate phase values ​​present in the candidate phase value set, for example, the circuit system may generate candidate measurement probability values ​​only for candidate phase values ​​whose distribution of measurement probabilities produces values ​​exceeding a predetermined threshold (e.g., for candidate phase values ​​within a predetermined number of standard deviations around the received phase). For example, the circuit system may generate fewer data tuples than the candidate measurement probability values ​​present in the candidate measurement probability value set, for example, the circuit system may generate data tuples only for candidate measurement probability values ​​exceeding a predetermined threshold. For example, if, for example, the circuit system generates candidate measurement probability values ​​or corresponding data tuples only for candidate measurement probability values ​​exceeding a predetermined threshold, the circuit system may generate fewer data tuples than the PC-SAR images present in the PC-SAR image set, which data tuples associate candidate phase values ​​with candidate measurement probability values. Therefore, the PC-SAR images in the PC-SAR image set may be associated with fewer data tuples than the reflected radar signals received by the radar sensor.

[0057] The updating of the candidate measurement probability values ​​associated with the respective PC-SAR image for the at least one further radar signal may include: for each data tuple generated for the at least one further radar signal, updating the candidate measurement probability value assigned to the respective PC-SAR image associated with the candidate phase value of the respective data tuple, wherein the circuitry updates the candidate measurement probability value assigned to the respective PC-SAR image such that the updated candidate measurement probability value assigned by the circuitry to the respective PC-SAR image depends on the candidate measurement probability value of the respective data tuple. For example, the circuitry may cause the candidate measurement probability value associated with the respective PC-SAR image to depend on the candidate measurement probability value associated with the respective candidate phase value by any data tuple generated for the radar signal or for the at least one further radar signal.

[0058] For example, the circuit system may determine the candidate phase value associated with the PC-SAR image associated with the highest candidate measurement probability value in the PC-SAR image set as the target phase value. The circuit system may then set the highest candidate measurement probability value assigned to the PC-SAR image associated with the target phase value as the measurement probability value, or as a target probability indicating the probability of presence of the target in the SAR image generated by the circuit system.

[0059] In some embodiments, the updating is based on a product of a first factor and a second factor, wherein the first factor is based on a candidate measurement probability value associated with the corresponding PC-SAR image and the second factor is based on a corresponding candidate measurement probability value of the at least one additional radar signal.

[0060] For example, the updating of the candidate measurement probability values ​​associated with the respective PC-SAR images may be based on odds ratios, and may be based, for example, on the candidate measurement probability values ​​of the current measurement (e.g., the radar signal currently being processed), on one or more candidate probability values ​​on which the candidate PC-SAR image already depends (“previous candidate probability values”), and on prior information. For example, the circuit system may iteratively update the candidate measurement probability values ​​associated with the respective PC-SAR images for each respective data tuple.

[0061] For example, the update can be based on an update formula for an occupancy grid map (OGM), such as Equation (2), based on Thrun, Sebastian: "Learning occupancy grids with forward models", Proceedings 2001 IEEE / RSJ International Conference on Intelligent Robots and System, 2001, Digital Object Identifier (DOI): 10.1109 / IROS.2001.977219:

[0062]

[0063] In equation (2), p(m i |z 1:t ,x 1:t ) represents the cell m of the grid map (e.g., PC-SAR image) i The probability associated with z is based on the sensor measurements z of time frames 1 to t (i.e., the current processing time frame t and all previous processing time frames). 1:t and the corresponding sensor position x 1:t Therefore, p(m i |z t ,x t ) represents the sensor measurement value z based on the current processing time frame t t and the corresponding sensor position x t The unit m i The probability associated with this, and p(m i |z 1:t-1 ,x1:t-1 ) represents the sensor measurement value z based on the time frames from 1 to t-1 (i.e., there is no current processing time frame t) 1:t-1 and the corresponding sensor position x 1:t-1 The unit m i For time frame 1, i.e., when the first probability is inserted into the cell, the recursive term can be omitted (or set to 1). In addition, p(m i ) represents unit m i The occupancy prior of and may be initialized to, for example, 0.5, without limiting the present disclosure thereto.

[0064] The terms labeled "current measurement" in equation (2) may correspond to currently processed measurements, e.g., candidate measurement probabilities for data tuples based on which the circuitry updates candidate measurement probabilities assigned to the PC-SAR image. The terms labeled "recursive terms" in equation (2) may correspond to previously processed measurements, e.g., candidate measurement probability values ​​previously assigned to the PC-SAR image. The terms labeled "prior information" in equation (2) may correspond to predetermined a priori probabilities. Thus, the terms p(m i |z 1:t ,x 1:t ) is assigned to the PC-SAR image as the updated candidate measurement probability value.

[0065] The circuitry may evaluate the product in a logarithmic domain, where a product of the first factor and the second factor may be represented as a sum of a logarithm of the first factor and a logarithm of the second factor.

[0066] In some embodiments, the radar signal and the at least one further radar signal are detected by the radar sensor from different locations.

[0067] For example, a radar sensor may be disposed on a mobile platform, and radar measurements are performed while the mobile platform (including the radar sensor) is moving through an environment. The performance of the radar measurements may include transmitting radar signals into the environment and receiving radar signals from the environment that have been reflected by objects in the environment. The mobile platform may include, for example, a car, a truck, a motorcycle, a bicycle, a tractor, an excavator, a train, a boat, a ship, a helicopter, an airplane, a drone, etc.

[0068] While moving through the environment, the radar sensor may transmit radar signals into the environment from different locations and may receive radar signals from the environment from different locations. Thus, the radar signals received by the radar sensor may correspond to different views of the environment, wherein objects in the environment may be sensed from different directions and / or from different distances.

[0069] Based on the position of the radar sensor in the environment when the radar signal reflected from the object in the environment is received, the circuit system can determine the position of the object in the environment. The circuit system can determine the position of the radar sensor in the environment based on the movement of the mobile platform, for example, based on a velocity sensor, an acceleration sensor, a rotation sensor, a global navigation satellite system (GNSS) (such as a global positioning system (GPS), Galileo, Michibiki, Beidou or GLONASS, etc.) and / or based on simultaneous positioning and mapping (SLAM).

[0070] Thus, the circuit system may determine the location of an object in the environment even though only the distance to the object but not the angle may be determined based on the reflected radar signals received from the object.

[0071] In some embodiments, the radar measurement data further indicates a range of the radar signal; wherein the mapping of the set of data tuples includes mapping the set of data tuples to a portion of the corresponding PC-SAR image corresponding to the radar signal.

[0072] For example, the radar sensor and / or circuit system can determine the range of the radar signal based on a received phase of the radar signal. The received phase of the radar signal can indicate a delay between transmitting the radar signal and receiving the corresponding reflected radar signal. For example, the range of the radar signal can be determined based on a Fourier transform (e.g., a fast Fourier transform (FFT)) of the radar signal. The range of the radar signal can correspond to the distance between the radar sensor and an object in the environment that reflected the radar signal.

[0073] For example, the circuit system may map the set of data tuples to a portion of the corresponding PC-SAR image corresponding to the range of the radar signal, for example, to a portion corresponding to the distance from the radar sensor, based on the range of the radar signal when the radar signal is received.

[0074] For example, a portion of the PC-SAR image corresponding to the range of the radar signal may be arranged on an arc, wherein the radius of the arc may correspond to the range of the radar signal, and the center associated with the arc may correspond to the position of the radar signal in the environment when the radar signal was received. By updating the candidate measurement probability values ​​assigned to the PC-SAR image for one or more additional radar signals that have been received by the radar sensor at different locations, the corresponding candidate measurement probability values ​​may be mapped to portions of the PC-SAR image that may be arranged in arcs that are offset relative to each other. Thus, after updating the candidate measurement probability values ​​associated with the set of PC-SAR images for a sufficient number of additional radar signals received from different locations within the environment, the corresponding arcs of the updated portions of the PC-SAR image may overlap at portions corresponding to the positions of corresponding objects (targets) in the environment, so that the updated candidate measurement probability values ​​of the corresponding portions may be added or accumulated (positive coherence) at portions corresponding to the positions of the corresponding objects (targets) in the environment, and may cancel or average each other (negative coherence) at portions that do not correspond to the positions of the corresponding objects (targets) in the environment.

[0075] In some embodiments, the PC-SAR images in the set of PC-SAR images include a grid map, and the portions of the PC-SAR images correspond to cells of the grid map.

[0076] For example, the cells of the grid map may have a rectangular or quadratic shape. However, the present disclosure is not limited thereto. For example, the cells may alternatively have a triangular or hexagonal shape or any other suitable shape.

[0077] For example, each cell may have an assigned candidate measurement probability value that may be updated independently of the candidate measurement probability values ​​assigned to other cells of the grid map.

[0078] Each cell of the grid map may correspond to a portion of the environment. The size of the cell (e.g., the side length of a rectangular or quadratic cell, the diameter of a hexagonal cell, the height of a triangular cell, or the diameter of a circular cell) may correspond, for example, to 1 cm, 1 decimeter, or 1 meter in the environment, without limiting the present disclosure to these values ​​or these orders of magnitude.

[0079] All cells of the grid map may have the same size and shape. However, the present disclosure is not limited thereto, and the grid map may include cells of different sizes and / or different shapes.

[0080] For example, all PC-SAR images in the PC-SAR image set may include a grid map, wherein the grid maps of all PC-SAR images in the PC-SAR image set may be identically configured, for example, the grid maps of all PC-SAR images may have the same number of cells, the same number of cell rows and columns, the same cell shape, the same cell size, and the same mapping of cells to the environment. Likewise, the grid maps of the SAR images to be generated by the circuit system may be identically configured as the grid maps of the PC-SAR images.

[0081] However, the present disclosure is not limited to identically configured grid maps. For example, the grid maps in the PC-SAR image set and / or the grid maps of the SAR images to be generated may be configured differently from each other. However, in some embodiments, the grid map of each PC-SAR image in the PC-SAR image set and the grid map of the SAR images to be generated by the circuit system may have a defined mapping from its cells to locations in the environment, and each grid map of the PC-SAR image in the PC-SAR image set may have a defined mapping to the grid map of the SAR images to be generated by the circuit system.

[0082] For example, the cells of the grid map of the SAR image to be generated by the circuit system may correspond to the pixels of the SAR image. When the SAR image is displayed, the pixels of the SAR image corresponding to the cells of the grid map of the SAR image may be displayed with a color corresponding to the probability (e.g., measurement probability or target probability) assigned to the corresponding cell.

[0083] In some implementations, the circuit system is further configured to correct a received phase of the radar signal based on a range of the radar signal; and perform the generating and mapping of the set of data tuples based on the corrected phase.

[0084] For example, the range of the radar signal may represent the distance between the radar sensor and an object in the environment that has reflected the radar signal, and the circuit system may correct the reception phase based on the range of the radar signal. For example, the circuit system may obtain the distance based on the radar signal (e.g., based on the phase between the reception phase of the radar signal and the phase of a previously transmitted radar signal corresponding to the radar signal received from the environment).

[0085] For example, the correction of the received phase can be based on multiplying the phase correction factor by a complex pointer, which represents the received power (or the measurement probability based on the received power) in its absolute value and represents the received phase of the radar signal in its argument. For example, the phase correction factor can be based on a complex exponential function, as shown in equation (3):

[0086]

[0087] In equation (3), j is an imaginary unit, f start is the starting frequency of the frequency ramp of the radar signal emitted into the environment, c0 is the speed of light, and r is the determined distance between the radar sensor and the object that reflected the radar signal. The factor of 2 is inserted to account for the round trip from the sensor to the reflecting object and back to the sensor.

[0088] Thus, the corrected phase may correspond to the phase at the location of the radar sensor. In some embodiments, correcting the receive phase in this manner allows comparison of the receive phases of radar signals received at different sensor locations in the environment (eg, at different distances from a reflecting object).

[0089] The circuit system may correct the receive phase before generating the set of data tuples and before mapping the set of data tuples to the set of PC-SAR images. The circuit system may correct the receive phase before generating the set of candidate phase values. The circuit system may generate the set of candidate phase values ​​based on the corrected phase, and may perform the generation of the set of data tuples and the mapping of the set of data tuples to the set of PC-SAR images based on the candidate phase values ​​based on the corrected phase.

[0090] Some embodiments relate to a method for generating a SAR image, wherein the method includes acquiring radar measurement data indicating received power and received phase of a radar signal based on a radar signal detected by a radar sensor; mapping the received power of the radar signal to a measurement probability; generating a set of data tuples by associating each candidate phase value in a set of candidate phase values ​​with a candidate measurement probability value in a set of candidate measurement probability values, wherein the candidate measurement probability values ​​in the set of candidate measurement probability values ​​correspond to a distribution of measurement probabilities according to phase uncertainties of the received phase; mapping the set of data tuples to a set of PC-SAR images; and determining a target phase value based on the set of PC-SAR images.

[0091] The method may be configured as described above with respect to the circuit system. Hence, all features of the circuit system may correspond to features of the method.

[0092] When executed on a computer and / or processor, in some embodiments, the methods described herein are also implemented as a computer program that causes a computer and / or processor to perform the method. In some embodiments, a non-transitory computer-readable recording medium storing a computer program product is also provided, which, when executed by a processor (such as the above-mentioned processor), causes the methods described herein to be performed.

[0093] return Figure 1 , Figure 1A block diagram of a circuit system 1 for generating a SAR image according to an embodiment is shown. The circuit system 1 includes a processor 2, a storage unit 3 and a communication unit 4.

[0094] The processor 2 controls the operation of the circuit system 1 based on the instructions stored in the storage unit 3. The storage unit 3 stores the instructions for the processor 2 and the additional data for generating the SAR image, including the radar measurement data, the temporary data (including the PC-SAR image) and the generated SAR image. The communication unit 4 includes a universal storage bus (USB) interface, receives the radar measurement data via the USB interface, and outputs the SAR image generated based on the received radar measurement data via the USB interface.

[0095] The circuit system 1 further includes a measurement data acquisition unit 5 , a phase correction unit 6 , a power mapping unit 7 , a data tuple generation unit 8 , a data tuple mapping unit 9 , an updating unit 10 and a target phase determination unit 11 .

[0096] The measurement data acquisition unit 5 acquires radar measurement data indicating the received power of the radar signal, the received phase of the radar signal, and the range of the radar signal based on the radar signal detected by the radar sensor. The radar signal is detected by the radar sensor from different positions within the environment of the radar sensor. The radar signal corresponds to the radar signal emitted by the radar sensor into the environment and is reflected by an object (target) in the environment. The radar sensor includes a chirp sequence radar sensor capable of measuring the received power and received phase of the received radar signal. The range of the radar signal is determined based on the received phase of the radar signal. The measurement data acquisition unit 5 acquires the radar measurement data via the communication unit 4 and stores the radar measurement data in the storage unit 3.

[0097] The phase correction unit 6 corrects the reception phase of the radar signal based on the distance of the radar signal. The correction of the reception phase is based on equation (3). The phase correction unit 6 acquires radar measurement data from the storage unit 3 and stores the corrected phase in the storage unit 3.

[0098] The power mapping unit 7 maps the received power of the radar signal to the measurement probability. The power mapping unit 7 receives the received power from the storage unit 3 and stores the measurement probability to which the received power is mapped in the storage unit 3.

[0099] The power mapping unit 7 includes a signal-to-noise ratio (SNR) determination unit 12 and a probability determination unit 13. The SNR determination unit 12 determines the SNR of the radar signal based on a quotient of the received power and a predetermined noise level. The probability determination unit 13 determines the measurement probability based on an exponential function, wherein the exponent of the exponential function includes the product of the SNR determined by the SNR determination unit 12 and a predetermined scale factor. The power mapping unit 7 outputs the measurement probability determined by the probability determination unit 13 to the storage unit 3.

[0100] The data tuple generation unit 8 generates a data tuple set by associating each candidate phase value in the candidate phase value set with a candidate measurement probability value in the candidate measurement probability value set for each radar signal of the radar measurement data, wherein the candidate measurement probability values ​​in the candidate measurement probability value set correspond to the distribution of the measurement probability according to the phase uncertainty of the reception phase. The data tuple generation unit 8 receives the measurement probability and the reception phase (represented by the corrected phase corrected by the phase correction unit 6) from the storage unit 3, and stores the generated data tuple set in the storage unit 3.

[0101] The distribution of the candidate measurement probability values ​​of the set of data tuples corresponds to a probability distribution, wherein the mean and mode of the probability distribution correspond to the received phase of the radar signal and have probability values ​​corresponding to the received power of the radar signal. Figure 1 In the embodiment of , the distribution of the measurement probability corresponds to a Gaussian distribution (normal distribution). The mean and mode of the distribution are at the corrected phase of the corresponding radar signal (corrected by the phase correction unit 6 and representing the received phase of the radar signal), and the probability values ​​of the distribution at the mean and the mode correspond to the measurement probability determined by the power mapping unit 7 based on the received power of the corresponding radar signal. The standard deviation of the distribution corresponds to the phase uncertainty of the received phase.

[0102] The data tuple generation unit 8 evaluates the probability density function of the distribution of the measurement probability at the candidate phase values ​​in the candidate phase value set. The candidate phase values ​​in the candidate phase value set are predetermined and uniformly distributed in the interval of 0° to 360°, that is, the candidate phase value set covers one cycle. The value obtained by evaluating the probability density function at the candidate phase value in the candidate phase value set is the candidate measurement probability value in the measurement probability value set.

[0103] To generate a set of data tuples, the data tuple generation unit 8 associates each candidate phase value in the candidate phase value set with a corresponding candidate measurement probability value in the candidate measurement probability value set, wherein each pair of associated candidate phase value and candidate measurement probability value is a data tuple in the set of data tuples.

[0104] The data tuple generation unit 8 generates a data tuple set for each radar signal indicated by the radar measurement data, and stores each generated data tuple set in the storage unit 3 .

[0105] The data tuple mapping unit 9 maps the data tuple set generated by the data tuple generating unit 8 to the PC-SAR image set. The data tuple mapping unit 9 receives the data tuple set from the storage unit 3 and stores the generated mapping in the storage unit 3.

[0106] Each PC-SAR image in the PC-SAR image set is associated with a candidate phase value in the candidate phase value set. The data tuple mapping unit 9 maps each data tuple generated by the data tuple generating unit 8 to a PC-SAR image in the PC-SAR image set that is associated with the candidate phase value of the data tuple.

[0107] In addition, each PC-SAR image in the set of PC-SAR images includes a grid map, wherein each cell of the grid map is quadratic and corresponds to a one centimeter by one centimeter area in the environment. The grid maps of the PC-SAR images have the same number of cell rows and the same number of cell columns, wherein cells at the same position in the grid maps correspond to the same area in the environment for all grid maps. The mapping of the set of data tuples includes mapping the set of data tuples to a portion of the corresponding PC-SAR image corresponding to the corresponding radar signal, wherein the portion of the corresponding PC-SAR image corresponds to a cell of the grid map of the PC-SAR image.

[0108] The data tuple mapping unit 9 determines which cells correspond to the radar signal based on the range of the radar signal and based on the position of the radar sensor in the environment when the radar signal was received. The data tuple mapping unit 9 then maps each corresponding data tuple to a determined cell in the PC-SAR image associated with the data tuple that corresponds to the range of the radar signal around the position of the radar sensor in the environment when the radar signal was received.

[0109] The data tuple mapping unit 9 performs mapping for each set of data tuples generated by the data tuple generating unit 8 for each radar signal indicated by the radar measurement data.

[0110] The updating unit 10 updates the candidate measurement probability values ​​associated with the corresponding PC-SAR image for the radar signal based on the data tuple set generated for the radar signal. The updating unit 10 receives the mapping generated by the data tuple mapping unit 9 from the storage unit 3 and stores the updated candidate measurement probability values ​​in the storage unit 3.

[0111] The updating of the candidate measurement probability values ​​is based on the product of a first factor and a second factor, wherein the first factor is based on the candidate measurement probability values ​​associated with the corresponding PC-SAR image and the second factor is based on the corresponding candidate measurement probability values ​​of the corresponding radar signal. More specifically, the updating is based on equation (2) and is performed iteratively for each data tuple.

[0112] The target phase determination unit 11 determines a target phase value based on the PC-SAR image set, wherein the determination of the target phase value includes selecting a candidate phase value of the PC-SAR image associated with the highest updated candidate measurement probability value mapped to the PC-SAR image set as the target phase value. The target phase determination unit 11 receives the PC-SAR image set and the updated candidate measurement probability value determined by the updating unit 10 from the storage unit 3, and stores the determined target phase value in the storage unit 3.

[0113] More specifically, the target phase determination unit 11 determines, for each cell in the grid map of the PC-SAR image set, a target phase value indicating a candidate phase value associated with a PC-SAR image from which the candidate measurement probability value of the corresponding cell should be used for a SAR image to be generated by the circuit system 1. The target phase determination unit 11 selects, as the target phase value, a candidate phase value associated with a PC-SAR image in which the maximum candidate measurement probability value in the PC-SAR image set is assigned to the corresponding cell.

[0114] The circuit system 1 also generates a SAR image, the SAR image including a grid map having the same number of cell columns and the same number of cell rows as the grid map of the PC-SAR image set, and wherein the cells of the grid map of the SAR image correspond to the same corresponding areas in the environment as the corresponding cells in the grid map of the PC-SAR image set. The circuit system 1 assigns to each cell of the grid map of the SAR image a value corresponding to a candidate measurement probability value assigned to the corresponding cell of the PC-SAR image associated with the target phase value determined by the target determination unit 11. The circuit system 1 then outputs the generated SAR image via the communication unit 4.

[0115] Figure 2 FIG. 2 is a flow chart of a method 20 for generating a SAR image according to an embodiment. The method 20 comprises Figure 1 The circuit system 1 is executed.

[0116] At S21, the method 20 acquires radar measurement data. The acquisition of the radar measurement data at S21 is performed by Figure 1The measurement data acquisition unit 5 is executed. The acquisition of measurement data at S21 is based on the radar signal detected by the radar sensor to acquire radar measurement data indicating the received power of the radar signal, the received phase of the radar signal and the range of the radar signal. The radar signal is detected by the radar sensor from different positions within the environment of the radar sensor. The radar signal corresponds to the radar signal emitted by the radar sensor into the environment and is reflected by the object (target) in the environment. The radar sensor includes a chirp sequence radar sensor capable of measuring the received power and received phase of the received radar signal. The range of the radar signal is determined based on the received phase of the radar signal. The acquisition of measurement data is performed via Figure 1 The communication unit 4 acquires the radar measurement data and stores the radar measurement data in Figure 1 In storage unit 3.

[0117] At S22, the method 20 corrects the received phase of the radar signal. The correction of the received phase at S22 is calculated by Figure 1 The phase correction unit 6 performs the correction of the received phase at S22. The received phase of the radar signal is corrected based on the distance of the radar signal. The correction of the received phase is based on equation (3). The correction of the received phase is from Figure 1 The storage unit 3 acquires the radar measurement data and stores the corrected phase in Figure 1 In storage unit 3.

[0118] At S23, method 20 maps the received power of the radar signal to the measurement probability. The mapping of the received power at S23 is performed by Figure 1 The power mapping unit 7 performs the mapping of the received power from Figure 1 The storage unit 3 receives the received power and stores the measurement probability mapped to the received power in Figure 1 In storage unit 3.

[0119] The mapping of the received power at S23 includes determining a signal-to-noise ratio (SNR) at S24 and determining a measurement probability at S25. The determination of the SNR at S24 is determined by Figure 1 The SNR determination unit 12 is executed, and the SNR of the radar signal is determined based on the quotient of the received power and the predetermined noise level. The determination of the measurement probability at S25 is performed by Figure 1 The probability determination unit 13 is executed, and the measurement probability is determined based on an exponential function, wherein the exponent of the exponential function includes the product of the SNR determined at S24 and a predetermined proportional factor. The mapping of the received power at S23 outputs the measurement probability determined at S25 to Figure 1 Storage unit 3.

[0120] At S26, method 20 generates a data tuple set. The data tuple set at S26 is generated by Figure 1The data tuple generation unit 8 is executed, and for each radar signal of the radar measurement data, a data tuple set is generated by associating each candidate phase value in the candidate phase value set with a candidate measurement probability value in the candidate measurement probability value set, wherein the candidate measurement probability values ​​in the candidate measurement probability value set correspond to the distribution of the measurement probability according to the phase uncertainty of the received phase. The generation of the data tuple set at S26 is from Figure 1 The storage unit 3 receives the measurement probability and the received phase (represented by the corrected phase corrected at S22), and stores the generated data tuple set in Figure 1 In storage unit 3.

[0121] The distribution of the candidate measurement probability values ​​of the set of data tuples corresponds to a probability distribution, wherein the mean and mode of the probability distribution correspond to the received phase of the radar signal and have probability values ​​corresponding to the received power of the radar signal. Figure 2 In an embodiment of the invention, the distribution of the measurement probability corresponds to a Gaussian distribution (normal distribution). The mean and mode of the distribution are at the corrected phase of the corresponding radar signal (corrected at S22 and representing the received phase of the radar signal), and the probability values ​​of the distribution at the mean and mode correspond to the measurement probability determined at S23 based on the received power of the corresponding radar signal. The standard deviation of the distribution corresponds to the phase uncertainty of the received phase.

[0122] The generation of the data tuple set at S26 evaluates the probability density function of the distribution of the measurement probability at the candidate phase values ​​in the candidate phase value set. The candidate phase values ​​in the candidate phase value set are predetermined and uniformly distributed in the interval of 0° to 360°, that is, the candidate phase value set covers one cycle. The value obtained by evaluating the probability density function at the candidate phase value in the candidate phase value set is the candidate measurement probability value in the measurement probability value set.

[0123] In order to generate a set of data tuples, the generation of the set of data tuples at S26 associates each candidate phase value in the set of candidate phase values ​​with a corresponding candidate measurement probability value in the set of candidate measurement probability values, wherein each pair of mutually associated candidate phase values ​​and candidate measurement probability values ​​is a data tuple in the set of data tuples.

[0124] The generation of the data tuple set at S26 generates a data tuple set for each radar signal indicated by the radar measurement data, and stores each generated data tuple set in Figure 1 In storage unit 3.

[0125] At S27, the method 20 maps the data tuple set generated at S26 to the PC-SAR image set. The mapping of the data tuple set at S27 is performed by the data tuple mapping unit 9. The mapping of the data tuple set at S27 is performed from Figure 1 Storage unit 3 receives a set of data tuples and stores the generated mapping in Figure 1 In storage unit 3.

[0126] Each PC-SAR image in the PC-SAR image set is associated with a candidate phase value in the candidate phase value set. The mapping of the data tuple set at S27 maps each data tuple generated at S26 to a PC-SAR image in the PC-SAR image set that is associated with the candidate phase value of the data tuple.

[0127] In addition, each PC-SAR image in the set of PC-SAR images includes a grid map, wherein each cell of the grid map is quadratic and corresponds to a one centimeter by one centimeter area in the environment. The grid maps of the PC-SAR images have the same number of cell rows and the same number of cell columns, wherein cells at the same position in the grid maps correspond to the same area in the environment for all grid maps. The mapping of the set of data tuples includes mapping the set of data tuples to a portion of the corresponding PC-SAR image corresponding to the corresponding radar signal, wherein the portion of the corresponding PC-SAR image corresponds to a cell of the grid map of the PC-SAR image.

[0128] The mapping of the set of data tuples at S27 determines which cells correspond to the radar signal based on the range of the radar signal and based on the position of the radar sensor in the environment when the radar signal was received. Then, the mapping of the set of data tuples at S27 maps each corresponding data tuple to a determined cell in the PC-SAR image associated with the data tuple that corresponds to the range of the radar signal around the position of the radar sensor in the environment when the radar signal was received.

[0129] Mapping of the set of data tuples at S27 Mapping is performed for each set of data tuples generated at S26 for each radar signal indicated by the radar measurement data.

[0130] At S28, the method 20 updates the candidate measurement probability values ​​associated with the PC-SAR image. The update at S28 is performed by the update unit 10, and based on the data tuple set generated for the radar signal, the candidate measurement probability values ​​associated with the corresponding PC-SAR image are updated for the radar signal. The update at S28 is from Figure 1 The storage unit 3 receives the mapping generated at S27 and stores the updated candidate measurement probability value in Figure 1 In storage unit 3.

[0131] The updating of the candidate measurement probability values ​​is based on the product of a first factor and a second factor, wherein the first factor is based on the candidate measurement probability values ​​associated with the corresponding PC-SAR image and the second factor is based on the corresponding candidate measurement probability values ​​of the corresponding radar signal. More specifically, the updating is based on equation (2) and is performed iteratively for each data tuple.

[0132] At S29, method 20 determines a target phase value. The target phase value at S29 is determined by Figure 1 The target phase determination unit 11 is executed, and the target phase value is determined based on the PC-SAR image set, wherein the determination of the target phase value includes selecting the candidate phase value of the PC-SAR image associated with the highest updated candidate measurement probability value mapped to the PC-SAR image set as the target phase value. The determination of the target phase value at S29 is performed from Figure 1 The storage unit 3 receives the PC-SAR image set and the updated candidate measurement probability value determined at S28, and stores the determined target phase value in Figure 1 In storage unit 3.

[0133] More specifically, the determination of the target phase value at S29 determines, for each cell in the grid map of the PC-SAR image set, a target phase value indicating a candidate phase value associated with a PC-SAR image from which the candidate measurement probability value of the corresponding cell should be used for the SAR image to be generated by the method 20. The determination of the target phase value at S29 selects, as the target phase value, a candidate phase value associated with the PC-SAR image in which the maximum candidate measurement probability value in the PC-SAR image set is assigned to the corresponding cell.

[0134] The method 20 also generates a SAR image, the SAR image comprising a grid map having the same number of cell columns and the same number of cell rows as the grid map of the PC-SAR image set, and wherein the cells of the grid map of the SAR image correspond to the same corresponding areas in the environment as the corresponding cells in the grid map of the PC-SAR image set. The method 20 assigns to each cell of the grid map of the SAR image a value corresponding to the candidate measurement probability value assigned to the corresponding cell of the PC-SAR image associated with the target phase value determined at S29. Then, the method 20 performs the following steps via Figure 1 The communication unit 4 outputs the generated SAR image.

[0135] Thus, in some embodiments, the present disclosure enables synthetic aperture radar (SAR) processing using probability. Compared to conventional SAR processing, in some embodiments, not only complex numbers are added (e.g., with an absolute value corresponding to the received power of the radar signal and with an argument corresponding to the received phase of the radar signal), but also probabilistic assumptions are made based on the radar measurement data and a model is created, which in some embodiments results in a more robust SAR image of the environment.

[0136] As a result, in some embodiments, the resulting image is no longer power-dependent, but probability-dependent. This may allow for a direct state description for each cell of the grid map of the SAR image.

[0137] Some embodiments differ from conventional SAR algorithms in the range estimation and the actual SAR processing, which is based on a common backprojection algorithm.

[0138] In the following, an embodiment is described in which a radar signal representing a frequency ramp is processed in two steps to generate a SAR image.

[0139] In the first step, for each ramp k received, a one-dimensional Fast Fourier Transform (FFT) is calculated to transform the time samples acquired by the SAR into the frequency domain to determine the range data. Since this is amplitude dependent, it is calculated based on the noise level according to equation (4):

[0140] FFT Range,prob =(1-0.5·exp(l·SNR))·exp(j·∠(FFT Range,amp ))(4)

[0141] Here, FFT Range,prob Represents the Fourier transform of the radar signal after mapping the received power of the radar signal to the measurement probability. ∠(FFT Range,amp ) represents the complex amplitude angle of the Fourier transform of the radar signal and corresponds to the received phase of the radar signal. The measured angle remains unchanged, but the length of the complex pointer is only between 0 and 1.

[0142] l marks a factor that can determine the limited growth of the measurement probability. Since this step does not significantly depend on the noise level, but is only guided by the noise level, the noise level is determined sufficiently accurately by a median calculation, i.e. the noise level corresponds to the median value of the received power of the radar signals received from the environment. The signal-to-noise ratio SNR of the radar signal is determined as the quotient of the received power of the radar signal and the noise level.

[0143] In the second step, the probability range FFT is used Range,probApply back-projection. Since there is no back-projection for probabilistic complex pointers, the corresponding model presented below is used.

[0144] According to equation (5), the measured complex pointer A r Apply phase correction.

[0145]

[0146] The phase correction is based on multiplying the phase correction factor of equation (3). Therefore, for the description of the phase correction factor, refer to the above description of equation (3). r Similarly, the complex pointer A′ after phase correction r It includes the absolute value corresponding to the received power of the corresponding radar signal and the argument corresponding to the received phase of the corresponding radar signal, wherein A′ r The received phase value indicated is relative to that indicated by A r The indicated value is corrected.

[0147] Because A′ r corresponds to a complex number, so it is divided into two components A′ r and ∠A′ r , where the absolute value |A′ r | corresponds to the received power of the radar signal and is represented by the measurement probability determined in the first step.

[0148] Due to measurement uncertainty, the distribution of the received phase of the radar signal is based on the argument ∠A′ according to equation (6): r Use a Gaussian distribution to approximate it.

[0149]

[0150] Here, the expected value μ corresponds to the (corrected) received phase of the radar signal, and thus μ=∠A′ r Applicable. In addition, the standard deviation σ corresponds to the phase uncertainty of the radar sensor and is selected accordingly. According to the Gaussian distribution, based on the (corrected) received phase ∠A′ of the radar signal r Different candidate phase values ​​generated have different probabilities, such as Figure 3 Indicated in.

[0151] Figure 3 A diagram 30 of a probability distribution of a reception phase according to an embodiment is shown. The diagram 30 indicates one period of the reception phase of a radar signal in the horizontal direction and indicates the probability of the corresponding phase value in the vertical direction. The diagram 30 shows a probability density function 31 of a Gaussian distribution.

[0152] The maximum value of the probability density function 31 indicated by arrow 32 corresponds to the mean and mode of the probability density function and is located at the (corrected) reception phase ∠A′ r The corresponding phase value ( Figure 3 The maximum value and FFT Range,prob The amplitude corresponds to |A′ r |Correspondingly, this means that the stronger the target, the higher the probability. Figure 3 The probability distribution of has a standard deviation σ corresponding to the phase uncertainty of the radar sensor.

[0153] To obtain a set of candidate measurement probability values, a probability density function is evaluated 31 at the candidate phase values ​​in the set of candidate phase values. Figure 3 In an embodiment, the candidate phase value set includes 18 candidate phase values ​​uniformly distributed in one period, such that the candidate phase values ​​are 20° apart from each other (the disclosure is not limited to these values). The candidate measurement probability value set includes values ​​obtained by evaluating the probability density function 31 at the candidate phase values.

[0154] A set of data tuples is generated, wherein a data tuple in the set of data tuples associates each corresponding candidate phase value in the set of candidate phase values ​​with a candidate measurement probability value in the set of candidate probability values ​​obtained by evaluating a probability density function 31 at the corresponding candidate phase value. For example, a data tuple in the set of data tuples indicated by arrow 33 associates a candidate phase value of 20° with a candidate measurement probability value obtained by evaluating a probability density function 31 at 20°. Similarly, a data tuple in the set of data tuples indicated by arrow 34 associates a candidate phase value of 40° with a candidate measurement probability value corresponding to 40°. Another data tuple in the set of data tuples indicated by arrow 35 associates a candidate phase value of 60° with a candidate measurement probability value corresponding to 60°. Arrows 33, 34, and 35 are shown for illustration purposes only; as mentioned, the set of data tuples includes a data tuple for each candidate phase value in the set of candidate phase values.

[0155] The set of data tuples is mapped to a set of PC-SAR images. Each PC-SAR image in the set of PC-SAR images includes a grid map, and the set of data tuples generated for the radar signal is mapped to cells of the grid map of the PC-SAR image corresponding to the area in the environment from which the radar signal was received.

[0156] According to equation (2), the candidate measurement probability values ​​of the corresponding cells of the grid map are updated based on the update formula for the occupancy grid map (OGM). Since the update formula for the OGM is based on a scalar, only a scalar update step can be performed.

[0157] Therefore, not only one PC-SAR image is processed, but a set of PC-SAR images is processed. Each PC-SAR image in the set of PC-SAR images is associated with a corresponding candidate phase value in the set of candidate phase values ​​and represents a specific phase value of the resulting image (i.e., of the SAR image to be generated).

[0158] The set of data tuples is mapped to the set of PC-SAR images such that the candidate measurement probability value of each data tuple is assigned to a cell in the PC-SAR image associated with the candidate phase value of the corresponding data tuple that corresponds to the radar signal (i.e., corresponds to an area in the environment that has reflected the radar signal).

[0159] Figure 4 The mapping of a set of data tuples to a set of PC-SAR images according to an embodiment is illustrated. Figure 4 Shown by Figure 3 The arrows 33, 34 and 35 in FIG. 3 indicate how the three data tuples are mapped to the corresponding PC-SAR images.

[0160] The PC-SAR image 41 in the PC-SAR image set is associated with the candidate phase value 20°. Figure 3 The data tuple indicated by arrow 33 of , which also corresponds to the candidate phase value 20°, is mapped to the cell 42 of the PC-SAR image 41 corresponding to the radar signal. This is illustrated in the cell 42 by arrow 43 .

[0161] Likewise, PC-SAR image 44 in the PC-SAR image set is associated with candidate phase value 40° and is represented by Figure 3 The data tuple indicated by arrow 34 of , which also corresponds to the candidate phase value 40°, is mapped to a cell 45 of the PC-SAR image 44 that corresponds to the radar signal. This is illustrated in the cell 45 by arrow 46 .

[0162] In addition, PC-SAR image 47 in the PC-SAR image set is associated with the candidate phase value 60° and is represented by Figure 3 The data tuple indicated by arrow 35 of , which also corresponds to the candidate phase value 60°, is mapped to a cell 48 of the PC-SAR image 47 that corresponds to the radar signal. This is illustrated in the cell 48 by arrow 49 .

[0163] Then, for the candidate measurement probability values ​​of the corresponding data tuples in the set of data tuples, the candidate measurement probability values ​​associated with the corresponding cells 42, 45 and 48 are updated in a probability update based on the update formula according to equation (2). The probability update in the PC-SAR images in the set of PC-SAR images allows compensation for different candidate phase values, which cannot be directly considered by the update formula according to equation (2).

[0164] The first and second steps are repeated for additional radar signals received from different locations within the environment, wherein the candidate measurement probability values ​​for the same set of PC-SAR images are updated for all radar signals.

[0165] A final result is determined using a maximum search within all PC-SAR images in the PC-SAR image set, generating a probabilistic SAR image of the environment. As a final result, the probabilistic SAR image is generated using a grid map. For each cell of the grid map of the probabilistic SAR image, a maximum candidate measurement probability value is selected from cells of the grid map of the PC-SAR image set corresponding to the same area in the environment as the cell in the probabilistic SAR image, and the selected candidate measurement probability value is assigned to the cell in the probabilistic SAR image.

[0166] The first and second steps may be performed by, for example, Figure 1 The circuit system 1 and / or through Figure 2 The method 20 is executed.

[0167] In some embodiments, the present technology enables robust and almost power-independent SAR processing. In some embodiments, the resulting map is no longer power-dependent, but probability-based. Therefore, strong and weak targets that cause constructive overlap of complex pointers can be represented as "1", and targets that overlap destructively can be represented as "0". This can allow environmental mapping of all targets, not just those with high backscatter cross sections (RCS).

[0168] As mentioned, in some embodiments, the present technique enables probabilistic SAR processing based on radar raw data.

[0169] Today, SAR processing is based on amplitude-based complex summation in some instances. Therefore, in some instances, the resulting representation of the environment is strongly amplitude-dependent, where weak targets are no longer discernible due to strong targets and the dynamics of the map.

[0170] In some embodiments of the present disclosure, this is no longer the case, as the present technology provides a probabilistic environment representation. Thus, in some embodiments, each cell has an occupancy probability that can be determined based on phase and amplitude. In some embodiments, this results in a map where weak and strong targets have similar occupancy probabilities, as long as the corrected phase results in a constructive superposition of all complex pointers. In some embodiments, this results in significant robustness gains due to the independence of amplitude.

[0171] In some embodiments, no additional hardware is required to implement the present technique, only adapted software is required.Thus, in some embodiments, the algorithm can be used for any SAR processing and is not limited to, for example, specific hardware, such as a specific sensor configuration.

[0172] Next, examples of applications of the present technology are discussed.

[0173] The technology according to the embodiments of the present disclosure can be applied to various products. For example, the technology according to the embodiments of the present disclosure can be implemented as a device included in a mobile body, which is any one of a car, an electric vehicle, a hybrid electric vehicle, a motorcycle, a bicycle, a personal mobility vehicle, an airplane, a drone, a ship, a robot, a construction machine, an agricultural machine (tractor), etc.

[0174] Figure 5 7000 is a block diagram illustrating an example of a schematic configuration of a vehicle control system 7000 as an example of a mobile vehicle body control system to which the technology according to an embodiment of the present disclosure can be applied. The vehicle control system 7000 includes a plurality of electronic control units connected to each other via a communication network 7010. Figure 5 In the example depicted in FIG, a vehicle control system 7000 includes a drive system control unit 7100, a body system control unit 7200, a battery control unit 7300, an external information detection unit 7400, an internal information detection unit 7500, and an integrated control unit 7600. A communication network 7010 that connects a plurality of control units to each other may be, for example, an in-vehicle communication network conforming to any standard, such as a controller area network (CAN), a local interconnect network (LIN), a local area network (LAN), FlexRay (registered trademark), and the like.

[0175] Each of the control units includes: a microcomputer that performs arithmetic processing according to various programs; a storage unit that stores programs executed by the microcomputer, parameters for various operations, etc.; and a drive circuit that drives various control target devices. Each of the control units also includes: a network interface (I / F) for communicating with other control units via the communication network 7010; and a communication I / F for communicating with devices, sensors, etc. inside and outside the vehicle through wired communication or radio communication. Figure 5 The functional configuration of the integrated control unit 7600 illustrated in FIG. 76 includes a microcomputer 7610, a general communication I / F 7620, a dedicated communication I / F 7630, a positioning unit 7640, a beacon receiving unit 7650, an in-vehicle device I / F 7660, a sound / image output unit 7670, an in-vehicle network I / F 7680, and a storage unit 7690. Other control units similarly include a microcomputer, a communication I / F, a storage unit, and the like.

[0176] The drive system control unit 7100 controls the operation of devices related to the drive system of the vehicle according to various programs. For example, the drive system control unit 7100 is used as a control device for a drive force generating device (such as an internal combustion engine, a drive motor, etc.) for generating the drive force of the vehicle, a drive force transmission mechanism for transmitting the drive force to the wheels, a steering mechanism for adjusting the steering angle of the vehicle, a braking device for generating the braking force of the vehicle, etc. The drive system control unit 7100 may have a function as a control device for an anti-lock braking system (ABS), an electronic stability control (ESC), etc.

[0177] The drive system control unit 7100 is connected to the vehicle state detection unit 7110. For example, the vehicle state detection unit 7110 includes at least one of a gyro sensor for detecting the angular velocity of the axial rotational movement of the vehicle body, an acceleration sensor for detecting the acceleration of the vehicle, and a sensor for detecting the operation amount of the accelerator pedal, the operation amount of the brake pedal, the steering angle of the steering wheel, the engine speed or the rotation speed of the wheel, etc. The drive system control unit 7100 performs arithmetic processing using the signal input from the vehicle state detection unit 7110, and controls the internal combustion engine, the drive motor, the electric power steering device, the brake device, etc.

[0178] The body system control unit 7200 controls the operation of various devices provided to the body according to various programs. For example, the body system control unit 7200 is used as a control device for a keyless entry system, a smart key system, a power window device, or various lights (such as headlights, reverse lights, brake lights, turn signal lights, fog lights, etc.). In this case, radio waves transmitted from a mobile device as a substitute for a key or signals of various switches may be input to the body system control unit 7200. The body system control unit 7200 receives these input radio waves or signals and controls the door lock device, power window device, lights, etc. of the vehicle.

[0179] The battery control unit 7300 controls the secondary battery 7310 as a power source for driving the motor according to various programs. For example, information about the battery temperature, the battery output voltage, the remaining power in the battery, etc. is supplied to the battery control unit 7300 from the battery device including the secondary battery 7310. The battery control unit 7300 performs arithmetic processing using these signals, and performs control for adjusting the temperature of the secondary battery 7310 or controlling a cooling device provided to the battery device, etc.

[0180] The vehicle exterior information detection unit 7400 detects information about the exterior of the vehicle including the vehicle control system 7000. For example, the vehicle exterior information detection unit 7400 is connected to at least one of the imaging unit 7410 and the vehicle exterior information detection unit 7420. The imaging unit 7410 includes at least one of a time-of-flight (ToF) camera, a stereo camera, a monocular camera, an infrared camera, and other cameras. The vehicle exterior information detection unit 7420 includes, for example, an environmental sensor for detecting current atmospheric conditions or weather conditions and at least one of a peripheral information detection sensor for detecting another vehicle, an obstacle, a pedestrian, etc. on the periphery of the vehicle including the vehicle control system 7000.

[0181] For example, the environmental sensor may be at least one of a raindrop sensor for detecting rainfall, a fog sensor for detecting fog, a sunshine sensor for detecting the degree of sunshine, and a snow sensor for detecting snowfall. The peripheral information detection sensor may be at least one of an ultrasonic sensor, a radar device, and a LIDAR device (a light detection and ranging device or a laser imaging detection and ranging device). Each of the imaging unit 7410 and the vehicle exterior information detection unit 7420 may be provided as an independent sensor or device, or may be provided as a device in which a plurality of sensors or devices are integrated.

[0182] Figure 6 An example of the installation position of the imaging section 7410 and the vehicle exterior information detection section 7420 is depicted. The imaging sections 7910, 7912, 7914, 7916, and 7918 are provided, for example, at least one of the positions on the front nose, side mirrors, rear bumper, and rear door of the vehicle 7900 and the upper portion of the windshield inside the vehicle. The imaging section 7910 provided to the front nose inside the vehicle and the imaging section 7918 provided to the upper portion of the windshield mainly acquire an image of the front of the vehicle 7900. The imaging sections 7912 and 7914 provided to the side mirrors mainly acquire an image of the side of the vehicle 7900. The imaging section 7916 provided to the rear bumper or rear door mainly acquires an image of the rear of the vehicle 7900. The imaging section 7918 provided to the upper portion of the windshield inside the vehicle is mainly used to detect a front vehicle, a pedestrian, an obstacle, a signal, a traffic sign, a lane, etc.

[0183] By the way, Figure 6An example of the shooting range of the corresponding imaging units 7910, 7912, 7914, and 7916 is depicted. Imaging range a represents the imaging range of the imaging unit 7910 provided to the front nose. Imaging ranges b and c respectively represent the imaging ranges of the imaging units 7912 and 7914 provided to the side mirrors. Imaging range d represents the imaging range of the imaging unit 7916 provided to the rear bumper or rear door. For example, by superimposing the image data imaged by the imaging units 7910, 7912, 7914, and 7916, a bird's-eye view image of the vehicle 7900 viewed from above can be acquired.

[0184] The vehicle exterior information detection units 7920, 7922, 7924, 7926, 7928, and 7930 provided to the front, rear, sides, and corners of the vehicle 7900 and the upper portion of the windshield inside the vehicle may be, for example, ultrasonic sensors or radar devices. The vehicle exterior information detection units 7920, 7926, and 7930 provided to the front nose of the vehicle 7900, the rear bumper, the rear door of the vehicle 7900, and the upper portion of the windshield inside the vehicle may be, for example, LIDAR devices. These vehicle exterior information detection units 7920 to 7930 are mainly used to detect vehicles ahead, pedestrians, obstacles, and the like.

[0185] Return to Figure 5 , will continue to describe. The vehicle exterior information detection unit 7400 enables the imaging unit 7410 to image the image outside the vehicle, and receives the image data of the imaging. In addition, the vehicle exterior information detection unit 7400 receives detection information from the vehicle exterior information detection unit 7420 connected to the vehicle exterior information detection unit 7400. In the case where the vehicle exterior information detection unit 7420 is an ultrasonic sensor, a radar device, or a LIDAR device, the vehicle exterior information detection unit 7400 sends ultrasonic waves, electromagnetic waves, etc., and receives information of the received reflected waves. Based on the received information, the vehicle exterior information detection unit 7400 can perform processing for detecting objects (such as people, vehicles, obstacles, signs, characters on the road surface, etc.) or processing for detecting the distance to the object. The vehicle exterior information detection unit 7400 can perform environmental recognition processing for identifying rainfall, fog, road conditions, etc. based on the received information. The vehicle exterior information detection unit 7400 can calculate the distance to the object outside the vehicle based on the received information.

[0186] In addition, based on the received image data, the vehicle exterior information detection unit 7400 can perform image recognition processing for identifying a person, a vehicle, an obstacle, a sign, characters on the road surface, etc., or a process for detecting the distance of an object. The vehicle exterior information detection unit 7400 can subject the received image data to processing such as distortion correction, alignment, etc., and combine image data imaged by a plurality of different imaging units 7410 to generate a bird's-eye view image or a panoramic image. The vehicle exterior information detection unit 7400 can perform viewpoint conversion processing using image data imaged by the imaging unit 7410 including different imaging parts.

[0187] The in-vehicle information detection unit 7500 detects information about the interior of the vehicle. For example, the in-vehicle information detection unit 7500 is connected to a driver state detection unit 7510 that detects the state of the driver. The driver state detection unit 7510 may include a camera that images the driver, a biosensor that detects the driver's biometric information, a microphone that collects sounds inside the vehicle, and the like. The biosensor is, for example, arranged in a seat surface, a steering wheel, and the like, and detects biometric information of a passenger sitting on the seat or a driver holding the steering wheel. Based on the detection information input from the driver state detection unit 7510, the in-vehicle information detection unit 7500 can calculate the driver's fatigue level or the driver's concentration level, or can determine whether the driver is taking a nap. The in-vehicle information detection unit 7500 may subject the audio signal obtained by collecting the sound to processing such as noise elimination processing.

[0188] The integrated control unit 7600 controls the general operation within the vehicle control system 7000 according to various programs. The integrated control unit 7600 is connected to the input unit 7800. The input unit 7800 is implemented by a device capable of inputting an operation by a passenger (such as, for example, a touch panel, a button, a microphone, a switch, a lever, etc.). The integrated control unit 7600 may be supplied with data acquired by voice recognition of a voice input via a microphone. The input unit 7800 may be, for example, a remote control device using infrared or other radio waves, or an external connection device supporting the operation of the vehicle control system 7000, such as a mobile phone, a personal digital assistant (PDA), etc. The input unit 7800 may be, for example, a camera. In this case, the passenger may input information by gestures. Alternatively, data acquired by detecting the movement of a wearable device worn by the passenger may be input. In addition, the input unit 7800 may include, for example, an input control circuit, etc., which generates an input signal based on the information input by the passenger, etc. using the above-mentioned input unit 7800, and outputs the generated input signal to the integrated control unit 7600. The passenger or the like inputs various data or gives instructions for processing operations to the vehicle control system 7000 through the operation input unit 7800 .

[0189] The storage unit 7690 may include a read-only memory (ROM) storing various programs executed by the microcomputer and a random access memory (RAM) storing various parameters, operation results, sensor values, etc. In addition, the storage unit 7690 may be implemented by a magnetic storage device (such as a hard disk drive (HDD)), a semiconductor storage device, an optical storage device, a magneto-optical storage device, etc.

[0190] The general communication I / F 7620 is a widely used communication I / F that mediates communication with various devices present in the external environment 7750. The general communication I / F 7620 can implement a cellular communication protocol (such as Global System for Mobile Communications (GSM (registered trademark)), Worldwide Interoperability for Microwave Access (WiMAX (registered trademark)), Long Term Evolution (LTE (registered trademark)), Advanced LTE (LTE-A)), etc.) or another wireless communication protocol (such as wireless LAN (also known as Wireless Fidelity (Wi-Fi (registered trademark))), Bluetooth (registered trademark), etc.). The general communication I / F 7620 can be connected to a device (e.g., an application server or a control server) present on an external network (e.g., the Internet, a cloud network, or a company-specific network) via a base station or an access point, for example. In addition, the general communication I / F 7620 can be connected to a terminal (the terminal is, for example, a terminal of a driver, a pedestrian, or a store, or a machine type communication (MTC) terminal) present near the vehicle using, for example, a peer-to-peer (P2P) technology.

[0191] The dedicated communication I / F 7630 is a communication I / F that supports a communication protocol developed for use in a vehicle. The dedicated communication I / F 7630 may implement a standard protocol such as, for example, Wireless Access in Vehicular Environments (WAVE) (which is a combination of Institute of Electrical and Electronics Engineers (IEEE) 802.11p as a lower layer and IEEE 1609 as a higher layer), Dedicated Short Range Communication (DSRC), or a cellular communication protocol. The dedicated communication I / F 7630 generally performs V2X communication as a concept including one or more of communication between vehicles and vehicles (vehicle to vehicle), communication between roads and vehicles (vehicle to infrastructure), communication between vehicles and homes (vehicle to home), and communication between pedestrians and vehicles (vehicle to pedestrian).

[0192] For example, the positioning unit 7640 performs positioning by receiving a global navigation satellite system (GNSS) signal (e.g., a GPS signal from a global positioning system (GPS) satellite) from a GNSS satellite, and generates position information including the latitude, longitude, and altitude of the vehicle. Incidentally, the positioning unit 7640 may identify the current position by exchanging signals with a wireless access point, or may acquire position information from a terminal (such as a mobile phone, a personal handyphone system (PHS), or a smart phone with a positioning function).

[0193] For example, the beacon receiving section 7650 receives radio waves or electromagnetic waves transmitted from a radio station installed on a road or the like, and thereby acquires information on the current position, congestion, closed roads, necessary time, etc. Incidentally, the function of the beacon receiving section 7650 may be included in the above-mentioned dedicated communication I / F 7630.

[0194] The in-vehicle device I / F 7660 is a communication interface that mediates the connection between the microcomputer 7610 and various in-vehicle devices 7760 present in the vehicle. The in-vehicle device I / F 7660 can establish a wireless connection using a wireless communication protocol such as wireless LAN, Bluetooth (registered trademark), near field communication (NFC), or wireless universal serial bus (WUSB). In addition, the in-vehicle device I / F 7660 can establish a wired connection through a universal serial bus (USB), a high-definition multimedia interface (HDMI (registered trademark)), a mobile high-definition link (MHL), etc. via a connection terminal not depicted in the figure (and a cable when necessary). The in-vehicle device 7760 may, for example, include at least one of a mobile device and a wearable device owned by a passenger and an information device carried into or attached to the vehicle. The in-vehicle device 7760 may also include a navigation device that searches for a path to an arbitrary destination. The in-vehicle device I / F 7660 exchanges control signals or data signals with these in-vehicle devices 7760.

[0195] The in-vehicle network I / F 7680 is an interface that mediates communication between the microcomputer 7610 and the communication network 7010. The in-vehicle network I / F 7680 transmits and receives signals and the like according to a predetermined protocol supported by the communication network 7010.

[0196] The microcomputer 7610 of the integrated control unit 7600 controls the vehicle control system 7000 according to various programs based on information acquired via at least one of the general communication I / F 7620, the dedicated communication I / F 7630, the positioning unit 7640, the beacon receiving unit 7650, the in-vehicle device I / F 7660, and the vehicle network I / F 7680. For example, the microcomputer 7610 may calculate a control target value of a driving force generation device, a steering mechanism, or a braking device based on the acquired information about the inside and outside of the vehicle, and output a control command to the drive system control unit 7100. For example, the microcomputer 7610 may perform cooperative control aimed at realizing functions of an advanced driver assistance system (ADAS), including collision avoidance or shock absorption for a vehicle, following driving based on a following distance, vehicle speed keeping driving, vehicle collision warning, vehicle lane departure warning, and the like. In addition, microcomputer 7610 can perform cooperative control intended for autonomous driving by controlling a drive force generating device, a steering mechanism, a braking device, etc. based on information acquired about the vehicle's surroundings, which enables the vehicle to drive autonomously without relying on the driver's operation, etc.

[0197] The microcomputer 7610 may generate three-dimensional distance information between the vehicle and an object (such as a surrounding structure, a person, etc.) based on information acquired via at least one of the general communication I / F 7620, the dedicated communication I / F 7630, the positioning unit 7640, the beacon receiving unit 7650, the in-vehicle device I / F 7660, and the vehicle network I / F 7680, and generate local map information including information about the surroundings of the current position of the vehicle. In addition, the microcomputer 7610 may predict danger (such as a vehicle collision, a pedestrian approaching, etc.), entry into a closed road, etc. based on the acquired information, and generate a warning signal. For example, the warning signal may be a signal for generating a warning sound or lighting a warning light.

[0198] The sound / image output unit 7670 transmits an output signal of at least one of sound and image to an output device capable of visually or auditorily notifying the vehicle occupants or the outside of the vehicle of information. Figure 5In the example of , the audio speaker 7710, the display unit 7720 and the dashboard 7730 are illustrated as output devices. For example, the display unit 7720 may include at least one of an onboard display and a head-up display. The display unit 7720 may have an augmented reality (AR) display function. The output device may be a device other than these devices, and may be another device (such as headphones), a wearable device (such as a glasses-type display worn by a passenger, etc.), a projector, a lamp, etc. In the case where the output device is a display device, the display device visually displays the results obtained by various processes performed by the microcomputer 7610 or information received from another control unit in various forms (such as text, images, tables, charts, etc.). In addition, in the case where the output device is an audio output device, the audio output device converts an audio signal consisting of reproduced audio data or sound data, etc. into an analog signal, and outputs the analog signal auditorily.

[0199] By the way, in Figure 5 In the example depicted in , at least two control units connected to each other via the communication network 7010 can be integrated into one control unit. Alternatively, each individual control unit may include multiple control units. In addition, the vehicle control system 7000 may include another control unit not depicted in the figure. In addition, part or all of the functions performed by one of the control units described above can be assigned to another control unit. That is, a predetermined arithmetic processing can be performed by any one of the control units, as long as information is sent and received via the communication network 7010. Similarly, a sensor or device connected to one of the control units can be connected to another control unit, and multiple control units can send and receive detection information to each other via the communication network 7010.

[0200] By the way, for reference Figure 5 The computer program of the functions of the information processing device 100 according to the present embodiment described can be implemented in one of the control units, etc. In addition, a computer-readable recording medium storing such a computer program can also be provided. For example, the recording medium is a magnetic disk, an optical disk, a magneto-optical disk, a flash memory, etc. In addition, the above-mentioned computer program can be distributed, for example, via a network without using a recording medium.

[0201] It should be appreciated that the embodiments describe methods with an exemplary ordering of method steps. However, the specific ordering of method steps is given for illustrative purposes only and should not be interpreted as binding. For example, Figure 2 Other changes in the order of the method steps may be obvious to those skilled in the art.

[0202] Note that the division of circuit system 1 into units 2 to 11 and the division of unit 7 into units 12 and 13 is made for illustrative purposes only, and the present disclosure is not limited to any particular division of functions in particular units. Figure 5 The division of the control unit or circuit system 7600 into units 7610 to 7690 is for illustrative purposes only, and the present disclosure is not limited to any particular division of functions in a particular unit. For example, at least part of the circuit system 1 or 7600 can be implemented by a corresponding programmed processor, a field programmable gate array (FPGA), a dedicated circuit, etc.

[0203] If not stated otherwise, all units and entities described in this specification and claimed in the appended claims may be implemented as integrated circuit logic (e.g. on a chip), and if not stated otherwise, the functions provided by such units and entities may be implemented by software.

[0204] To the extent that the above disclosed embodiments are implemented at least in part using software controlled data processing apparatus, it will be appreciated that providing such software controlled computer programs and transmission, storage or other media providing such computer programs are contemplated as aspects of the present disclosure.

[0205] Note that the present technology can also be configured as described below.

[0206] (1) A circuit system for generating a synthetic aperture radar image, wherein the circuit system is configured to:

[0207] acquiring radar measurement data indicating received power and received phase of the radar signal based on the radar signal detected by the radar sensor;

[0208] Mapping the received power of radar signals to measurement probabilities;

[0209] generating a set of data tuples by associating each candidate phase value in the set of candidate phase values ​​with a candidate measurement probability value in the set of candidate measurement probability values, the candidate measurement probability values ​​in the set of candidate measurement probability values ​​corresponding to a distribution of measurement probabilities according to phase uncertainty of a received phase;

[0210] Mapping the set of data tuples to a set of phase candidate PC-SAR images; and

[0211] The target phase value is determined based on the PC-SAR image set.

[0212] (2) The circuit system of (1), wherein the mapping of the received power comprises:

[0213] determining a signal-to-noise ratio of the radar signal based on the received power; and

[0214] The measurement probability is determined based on the signal-to-noise ratio of the radar signal.

[0215] (3) A circuit system according to (1) or (2), wherein the distribution of candidate measurement probability values ​​for the set of data tuples corresponds to a probability distribution, wherein at least one of a mean and a mode of the probability distribution corresponds to a received phase of the radar signal and has a probability value corresponding to a received power of the radar signal.

[0216] (4) A circuit system according to any one of (1) to (3), wherein determining the target phase value includes selecting a candidate phase value of a PC-SAR image associated with a highest candidate measurement probability value mapped to the PC-SAR image set as the target phase value.

[0217] (5) The circuit system according to any one of (1) to (4), further configured as follows:

[0218] acquiring, based on at least one further radar signal detected by the radar sensor, further radar measurement data indicative of a received power and a received phase of the at least one further radar signal;

[0219] performing mapping of the received power, generating of the set of data tuples and mapping of the set of data tuples respectively for the at least one further radar signal;

[0220] updating, for the at least one additional radar signal, a candidate measurement probability value associated with the corresponding PC-SAR image based on the set of data tuples generated for the at least one additional radar signal; and

[0221] A target phase value is determined based on the updated candidate measurement probability values.

[0222] (6) The circuit system of (5), wherein the updating is based on a product of a first factor and a second factor, wherein the first factor is based on a candidate measurement probability value associated with the corresponding PC-SAR image and the second factor is based on a corresponding candidate measurement probability value of the at least one additional radar signal.

[0223] (7) The circuit system of (5) or (6), wherein the radar signal and the at least one additional radar signal are detected by the radar sensor from different locations.

[0224] (8) The circuit system of any one of (1) to (7), wherein the radar measurement data further indicates a range of the radar signal;

[0225] The mapping of the data tuple set includes mapping the data tuple set to a portion of the corresponding PC-SAR image corresponding to the radar signal.

[0226] (9) The circuit system of (8), wherein the PC-SAR images in the set of PC-SAR images include a grid map, and the portions of the PC-SAR images correspond to cells of the grid map.

[0227] (10) The circuit system according to (8) to (9), further configured as follows:

[0228] correcting a received phase of the radar signal based on the range of the radar signal; and

[0229] The generation and mapping of the set of data tuples is performed based on the corrected phase.

[0230] (11) A method for generating a synthetic aperture radar image, the method comprising:

[0231] acquiring radar measurement data indicating received power and received phase of the radar signal based on the radar signal detected by the radar sensor;

[0232] Mapping the received power of radar signals to measurement probabilities;

[0233] generating a set of data tuples by associating each candidate phase value in the set of candidate phase values ​​with a candidate measurement probability value in the set of candidate measurement probability values, the candidate measurement probability values ​​in the set of candidate measurement probability values ​​corresponding to a distribution of measurement probabilities according to phase uncertainty of a received phase;

[0234] Mapping the set of data tuples to a set of phase candidate PC-SAR images; and

[0235] The target phase value is determined based on the PC-SAR image set.

[0236] (12) The method according to (11), wherein the mapping of the received power comprises:

[0237] determining a signal-to-noise ratio of the radar signal based on the received power; and

[0238] The measurement probability is determined based on the signal-to-noise ratio of the radar signal.

[0239] (13) A method according to (11) or (12), wherein the distribution of candidate measurement probability values ​​for the set of data tuples corresponds to a probability distribution, wherein at least one of a mean and a mode of the probability distribution corresponds to a received phase of the radar signal and has a probability value corresponding to a received power of the radar signal.

[0240] (14) A method according to any one of (11) to (13), wherein determining the target phase value includes selecting a candidate phase value of a PC-SAR image associated with a highest candidate measurement probability value mapped to the PC-SAR image set as the target phase value.

[0241] (15) The method according to any one of (11) to (14), further comprising:

[0242] acquiring, based on at least one further radar signal detected by the radar sensor, further radar measurement data indicative of a received power and a received phase of the at least one further radar signal;

[0243] performing mapping of the received power, generating of the set of data tuples and mapping of the set of data tuples respectively for the at least one further radar signal;

[0244] updating, for the at least one additional radar signal, a candidate measurement probability value associated with the corresponding PC-SAR image based on the set of data tuples generated for the at least one additional radar signal; and

[0245] A target phase value is determined based on the updated candidate measurement probability values.

[0246] (16) A method according to (15), wherein the updating is based on a product of a first factor and a second factor, wherein the first factor is based on a candidate measurement probability value associated with the corresponding PC-SAR image and the second factor is based on a corresponding candidate measurement probability value of the at least one additional radar signal.

[0247] (17) The method of (15) or (16), wherein the radar signal and the at least one additional radar signal are detected by the radar sensor from different locations.

[0248] (18) The method according to any one of (11) to (17), wherein the radar measurement data further indicates a range of the radar signal;

[0249] The mapping of the data tuple set includes mapping the data tuple set to a portion of the corresponding PC-SAR image corresponding to the radar signal.

[0250] (19) The method according to (18), wherein the PC-SAR images in the PC-SAR image set include a grid map, and the portions of the PC-SAR images correspond to cells of the grid map.

[0251] (20) The method according to (18) or (19), further comprising:

[0252] correcting a received phase of the radar signal based on the range of the radar signal; and

[0253] The generation and mapping of the set of data tuples is performed based on the corrected phase.

[0254] (21) A computer program comprising a program code which, when executed on a computer, causes the computer to execute the method according to any one of (11) to (20).

[0255] (22) A non-transitory computer-readable recording medium storing a computer program product, which, when executed by a processor, causes execution of the method according to any one of (11) to (20).

Claims

1. A circuit system for generating a synthetic aperture radar image, wherein: The circuit system is configured to: acquiring radar measurement data indicating received power and received phase of the radar signal based on the radar signal detected by the radar sensor; Mapping the received power of the radar signal to a measurement probability; generating a set of data tuples by associating each candidate phase value in a set of candidate phase values ​​with a candidate measurement probability value in a set of candidate measurement probability values, the candidate measurement probability values ​​in the set of candidate measurement probability values ​​corresponding to a distribution of the measurement probabilities according to phase uncertainty of the received phase; Mapping the data tuple set to a phase candidate synthetic aperture radar PC-SAR image set; as well as A target phase value is determined based on the PC-SAR image set.

2. The circuit system according to claim 1, wherein: The mapping of the received power includes: determining a signal-to-noise ratio of the radar signal based on the received power; and The measurement probability is determined based on a signal-to-noise ratio of the radar signal.

3. The circuit system according to claim 1, wherein: The distribution of candidate measurement probability values ​​of the data tuple set corresponds to a probability distribution, wherein at least one of a mean and a mode of the probability distribution corresponds to a received phase of the radar signal and has a probability value corresponding to a received power of the radar signal.

4. The circuit system according to claim 1, wherein: The determination of the target phase value includes selecting a candidate phase value of a PC-SAR image associated with a highest candidate measurement probability value mapped to the PC-SAR image set as the target phase value.

5. The circuit system according to claim 1, further configured to: acquiring, based on at least one further radar signal detected by the radar sensor, further radar measurement data indicative of a received power and a received phase of the at least one further radar signal; performing the mapping of the received power, the generating of the set of data tuples and the mapping of the set of data tuples respectively for the at least one further radar signal; updating the candidate measurement probability values ​​associated with the corresponding PC-SAR image for the at least one further radar signal based on the set of data tuples generated for the at least one further radar signal; and The target phase value is determined based on the updated candidate measurement probability value.

6. The circuit system according to claim 5, wherein: The updating is based on a product of a first factor and a second factor, wherein the first factor is based on the candidate measurement probability values ​​associated with the corresponding PC-SAR image and the second factor is based on a corresponding candidate measurement probability value of the at least one additional radar signal.

7. The circuit system according to claim 5, wherein: The radar signal and the at least one further radar signal are detected by the radar sensor from different locations.

8. The circuit system according to claim 1, wherein: The radar measurement data also indicates a range of the radar signal; The mapping of the data tuple set includes mapping the data tuple set to a portion of a corresponding PC-SAR image corresponding to the radar signal.

9. The circuit system according to claim 8, wherein: The PC-SAR images in the set of PC-SAR images include a grid map, and the portions of the PC-SAR images correspond to cells of the grid map.

10. The circuit system according to claim 8, further configured to: correcting a received phase of the radar signal based on a range of the radar signal; and The generating and mapping of the set of data tuples is performed based on the corrected phase.

11. A method for generating a synthetic aperture radar image, the method comprising: acquiring radar measurement data indicating received power and received phase of the radar signal based on the radar signal detected by the radar sensor; Mapping the received power of the radar signal to a measurement probability; generating a set of data tuples by associating each candidate phase value in a set of candidate phase values ​​with a candidate measurement probability value in a set of candidate measurement probability values, the candidate measurement probability values ​​in the set of candidate measurement probability values ​​corresponding to a distribution of the measurement probabilities according to phase uncertainty of the received phase; Mapping the data tuple set to a phase candidate synthetic aperture radar PC-SAR image set; as well as A target phase value is determined based on the PC-SAR image set.

12. The method according to claim 11, wherein: The mapping of the received power includes: determining a signal-to-noise ratio of the radar signal based on the received power; and The measurement probability is determined based on a signal-to-noise ratio of the radar signal.

13. The method according to claim 11, wherein: The distribution of candidate measurement probability values ​​of the data tuple set corresponds to a probability distribution, wherein at least one of a mean and a mode of the probability distribution corresponds to a received phase of the radar signal and has a probability value corresponding to a received power of the radar signal.

14. The method according to claim 11, wherein: The determination of the target phase value includes selecting a candidate phase value of a PC-SAR image associated with a highest candidate measurement probability value mapped to the PC-SAR image set as the target phase value.

15. The method according to claim 11, further comprising: acquiring, based on at least one further radar signal detected by the radar sensor, further radar measurement data indicative of a received power and a received phase of the at least one further radar signal; performing the mapping of the received power, the generating of the set of data tuples and the mapping of the set of data tuples respectively for the at least one further radar signal; updating, for the at least one further radar signal, the candidate measurement probability values ​​associated with the corresponding PC-SAR image based on the set of data tuples generated for the at least one further radar signal; as well as The target phase value is determined based on the updated candidate measurement probability value.

16. The method according to claim 15, wherein: The updating is based on a product of a first factor and a second factor, wherein the first factor is based on the candidate measurement probability values ​​associated with the corresponding PC-SAR image and the second factor is based on a corresponding candidate measurement probability value of the at least one additional radar signal.

17. The method according to claim 15, wherein: The radar signal and the at least one further radar signal are detected by the radar sensor from different locations.

18. The method according to claim 11, wherein: The radar measurement data also indicates a range of the radar signal; The mapping of the data tuple set includes mapping the data tuple set to a portion of a corresponding PC-SAR image corresponding to the radar signal.

19. The method according to claim 18, wherein: The PC-SAR images in the set of PC-SAR images include a grid map, and the portions of the PC-SAR images correspond to cells of the grid map.

20. The method of claim 18, further comprising: correcting a received phase of the radar signal based on a range of the radar signal; as well as The generating and mapping of the set of data tuples is performed based on the corrected phase.

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