Distance and distance rate estimation for step frequency waveforms with constant pulse repetition period

Abstract

The invention relates to distance and distance rate estimation for step frequency waveforms with constant pulse repetition periods. A system and method are provided and include a radar system that transmits a radar signal within a frame having a radar chirp. The radar chirps have a stepped frequency waveform such that for subsequent chirps, the initial and end frequencies are changed with a constant pulse repetition period between the chirps. The radar system performs distance FFT processing and Doppler FFT processing on a received value corresponding to a reflected signal from an object. The radar system estimates a distance and distance change rate information about the object based on the distance FFT value and the Doppler FFT value using a signal model, and determines information about the object based on the estimated distance and distance change rate information. The signal model includes (1) fast time dimension terms corresponding to samples within the radar chirps and (2) slow time dimension terms corresponding to individual radar chirps within the frame, the slow time dimension terms including second order terms.

Description

Technical Field

[0001] The present disclosure relates to radar systems and methods, and more particularly, to radar systems and methods for determining range and range rate estimates for a stepped frequency waveform having a constant pulse repetition period. Background Art

[0002] This section provides background information related to the present disclosure which is not necessarily prior art.

[0003] Automotive radar sensors are used in vehicle sensing systems to determine information about objects in the vehicle environment, such as the location, size, orientation, velocity, and acceleration of objects in the vehicle environment. The sensed information can be used, for example, by other vehicle systems, such as autonomous driving systems and / or advanced driver assistance systems (ADAS), to control steering, braking, throttle, and / or other vehicle systems.

[0004] The radar system may transmit multiple radar chirps within a particular transmit frame and then receive signals corresponding to the transmit chirps within the transmit frame reflected from objects within the vehicle environment. The frequency of the radar signal may vary during each individual chirp. For example, in some radar systems, the transmit signal may start with an initial frequency and then increase or decrease to an end frequency over the time period of the chirp. In some conventional radar systems, the initial and end frequencies of each chirp may remain the same for each chirp during the transmit frame. However, other radar systems may utilize a stepped frequency waveform so that the initial and end frequencies of the transmit signal change chirp by chirp. For example, the radar system may utilize a stepped frequency waveform so that the initial and end frequencies of each subsequent chirp are lower than the initial and end frequencies of the previous chirp. Alternatively, the radar system may utilize a stepped frequency waveform so that the initial and end frequencies of each subsequent chirp are higher than the initial and end frequencies of the previous chirp.

[0005] Additionally, in some radar systems, the time period between individual chirps, called the pulse repetition period (PRP), can be kept constant for all chirps within a transmit frame. Alternatively, other radar systems can be configured to vary the PRP between chirps over a transmit frame, such that the time between chirps varies over the course of a transmit frame.

[0006] Radar systems can use signal models to process received radar signals reflected from objects and determine the object's range and range rate information. However, in radar systems that utilize a constant PRP, using a signal model developed for a stepped frequency waveform with a variable PRP introduces errors. Summary of the invention

[0007] This section provides a general summary of the disclosure, and is not a comprehensive disclosure of its full scope or all of its features.

[0008] According to the present disclosure, a radar system includes a processor and a memory, wherein the processor and the memory are configured to: transmit a radar signal within at least one frame having a plurality of radar chirps, wherein the plurality of radar chirps have a stepped frequency waveform, so that for each subsequent chirp within the plurality of radar chirps, an initial transmission frequency and an end transmission frequency are changed, and the plurality of radar chirps have a constant pulse repetition period between chirps. The processor and the memory are also configured to: receive and sample a radar signal reflected from at least one object to generate a reception value from the received and sampled radar signal. The processor and the memory are also configured to: perform a range fast Fourier transform (FFT) process and a Doppler FFT process on the reception value to generate a range FFT value and a Doppler FFT value. The processor and the memory are also configured to: estimate the distance and the distance change rate information about the at least one object based on the range FFT value and the Doppler FFT value using a signal model. The processor and memory are further configured to determine information about the object based on the estimated distance and distance rate information, the information about the object including at least one of a position, size, orientation, velocity, and acceleration of the at least one object. The signal model includes: (1) at least one fast time dimension term corresponding to a single received sample within each of the plurality of radar chirps, and (2) at least one slow time dimension term corresponding to a single radar chirp within the plurality of radar chirps; and the at least one slow time dimension term includes: at least one second order term corresponding to a single radar chirp within the plurality of radar chirps.

[0009] In other features, the at least one slow-time dimension term is quadratic.

[0010] In other features, the at least one slow-time dimension term comprises an index for an individual radar chirp within the plurality of radar chirps.

[0011] In other features, the at least one fast time dimension term includes at least one second order term corresponding to an individual received sample within each of the plurality of radar chirps.

[0012] In other features, the at least one fast-time dimension term is quadratic.

[0013] In other features, the at least one fast time dimension term comprises an index for an individual received sample within each of the plurality of radar chirps.

[0014] In other features, the signal model includes at least one coupling term, the at least one coupling term including at least one fast time dimension term corresponding to individual received samples within each of the plurality of radar chirps and at least one slow time dimension term corresponding to an individual radar chirp within the plurality of radar chirps.

[0015] In other features, the radar system is located in a vehicle and information about the object is transmitted to a vehicle system that controls at least one of a steering system, a braking system, and a throttle system of the vehicle based on the information about the object.

[0016] In other features, the vehicle system includes at least one of an autonomous driving system and a driver assistance system.

[0017] According to the present disclosure, a method is provided, comprising the following steps: using a radar system having a processor and a memory to transmit a radar signal within at least one frame having a plurality of radar chirps, the plurality of radar chirps having a stepped frequency waveform, so that for each subsequent chirp within the plurality of radar chirps, an initial transmission frequency and an end transmission frequency are changed, and the plurality of radar chirps have a constant pulse repetition period between chirps. The method also includes: using the radar system to receive and sample a radar signal reflected from at least one object to generate a reception value from the received and sampled radar signal. The method also includes: using the radar system to perform a range fast Fourier transform (FFT) process and a Doppler FFT process on the reception value to generate a range FFT value and a Doppler FFT value. The method also includes: using the radar system to estimate the distance and the distance change rate information about the at least one object based on the range FFT value and the Doppler FFT value using a signal model. The method further includes determining, using the radar system, information about the object based on the estimated range and range rate information, the information about the object including at least one of a position, size, orientation, velocity, and acceleration of the at least one object. The signal model includes: (1) at least one fast time dimension term corresponding to a single received sample within each of the plurality of radar chirps, and (2) at least one slow time dimension term corresponding to a single radar chirp within the plurality of radar chirps. The at least one slow time dimension term includes: at least one second order term corresponding to a single radar chirp within the plurality of radar chirps.

[0018] In other features, the at least one slow-time dimension term is quadratic.

[0019] In other features, the at least one slow-time dimension term comprises an index for an individual radar chirp within the plurality of radar chirps.

[0020] In other features, the at least one fast time dimension term includes at least one second order term corresponding to an individual received sample within each of the plurality of radar chirps.

[0021] In other features, the at least one fast-time dimension term is quadratic.

[0022] In other features, the at least one fast time dimension term comprises an index for an individual received sample within each of the plurality of radar chirps.

[0023] In other features, the signal model includes at least one coupling term, the at least one coupling term including at least one fast time dimension term corresponding to individual received samples within each of the plurality of radar chirps and at least one slow time dimension term corresponding to an individual radar chirp within the plurality of radar chirps.

[0024] In other features, the radar system is located in a vehicle and information about the object is transmitted to a vehicle system that controls at least one of a steering system, a braking system, and a throttle system of the vehicle based on the information about the object.

[0025] In other features, the vehicle system includes at least one of an autonomous driving system and a driver assistance system.

[0026] Further areas of applicability will become apparent from the description provided herein.The description and specific examples in this summary are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are for illustrative purposes only and may not depict all possible implementations, and are not intended to limit the scope of the present disclosure.

[0028] Figure 1 is a block diagram of a vehicle having a radar system according to the present disclosure.

[0029] Figure 2 is another block diagram of a vehicle having a radar system according to the present disclosure.

[0030] Figure 3 is a diagram illustrating a transmitted radar signal chirp within a frame having a stepped frequency waveform and a constant pulse repetition period in accordance with the present disclosure.

[0031] Figure 4 The values ​​of the time vector of the radar chirp within a frame according to the present disclosure are illustrated.

[0032] Figure 5 The values ​​of the time vector of a radar chirp frame according to the present disclosure are illustrated.

[0033] Figure 6 The mathematical formula for the signal model according to the present disclosure is illustrated.

[0034] Figure 7 Additional equations for a signal model according to the present disclosure are illustrated.

[0035] Figure 8 and Fig. 9 The range and range rate errors obtained when using an existing signal model developed for a stepped frequency waveform (SFW) with a varying PRP in a radar system utilizing a SFW with a constant PRP are illustrated.

[0036] Fig.10 and Fig.11 The range and range rate errors obtained when using a signal model according to the present disclosure are illustrated.

[0037] Corresponding reference numerals indicate corresponding parts throughout the several views of the drawings. DETAILED DESCRIPTION

[0038] Example embodiments will now be described more fully with reference to the accompanying drawings.

[0039] The present disclosure relates to a radar system that transmits a radar signal by changing the frequency of a radar chirp within a transmission frame according to a stepped frequency waveform, so that the initial frequency and the end frequency of each subsequent chirp increase or decrease from the previous chirp. In addition, the radar system of the present disclosure can use a constant pulse repetition period (PRP) between each chirp within each transmission frame to transmit the radar chirp. The radar system of the present disclosure uses a signal model to perform a range fast Fourier transform (FFT) and a Doppler FFT on a received signal reflected from an object, as discussed in further detail below. In particular, the radar system according to the present disclosure utilizes a signal model to process the received signal in a fast time dimension corresponding to the time period of a single chirp and a slow time dimension corresponding to the time period of a single transmission frame. As discussed in further detail below, the signal model for processing the received signal according to the present disclosure includes: high-order terms in both the fast time dimension and the slow time dimension, such as second-order terms, which advantageously reduce the range rate error compared to the previous model used by the radar system with a constant PRP.

[0040] Reference Figure 1 and Figure 2 , shows a radar system 100 of a vehicle 102 according to the present disclosure. The radar system 100 transmits a signal 104 that is reflected from an object 106 in the environment of the vehicle 102. Although for illustration purposes only, Figure 11 , a single object 106 is shown, but in reality the object 106 may include multiple objects. The radar system 100 receives a signal 108 reflected from the object 106. Based on the characteristics of the received signal 108, the radar system 100 determines information about the object, such as the range and the rate of change of the range, which is then used to determine the position, size, orientation, velocity, and acceleration of the object 106.

[0041] The radar system 100 includes a processor 200 and a memory 202, the memory 202 storing code executed by the processor 200 to perform the functions required to send and receive signals 104, 108 and determine information about the object 106 based on the characteristics of the received signal 108. Although the exemplary implementation illustrates a single processor 200, multiple processors and / or modules working together may alternatively be used. The radar system 100 includes a transmitter / receiver 204 that sends and receives signals 104, 108. The transmitter / receiver 204 includes one or more transmit (Tx) antennas and one or more receive (Rx) antennas for sending and receiving signals 104, 108. For example, the processor 200 executes the transmit / receive code 206 stored in the memory 202 to send and receive signals 104, 108. The memory 202 also stores information for a signal model 208. The memory 202 also stores code 210 for performing a range FFT on the received radar signal 108 using the signal model 208 and code 212 for performing a Doppler FFT. As discussed in further detail below, the processor 200 determines the distance and the rate of change of the distance information about the object 106 based on performing the range FFT and the Doppler FFT on the received radar signal 108, respectively. For example, the processor 200 performs the range FFT using the range FFT code 210 to generate a range index, and performs the Doppler FFT using the Doppler FFT code 212 to generate a Doppler index. The processor 200 then processes the range and the Doppler index using the signal model 208 to determine the distance and the rate of change of the distance, respectively. In addition, the memory 202 also stores code 214 for performing object detection by the processor 200 to determine information about the object 106 based on the determined distance and the rate of change of the distance information. The processor 200 may then transmit information about the object 106, such as the position, size, direction, velocity, and acceleration of the object 106 determined based on the distance and distance rate information, to other vehicle systems 220, such as an autonomous driving system and / or an advanced driver assistance system (ADAS), etc. The other vehicle systems 220 may then utilize and process the information about the object 106 to appropriately control the steering, braking, throttle, and / or other vehicle operations of the vehicle 102 based on the information about the object 106.

[0042] Radar system 100 also includes an analog-to-digital converter 205 for sampling received radar signal 108 using a specified sampling rate and converting analog information about the received radar signal (e.g., received frequency) into a digital format. Analog-to-digital converter 205 is also referred to as ADC 205. ADC 205 may be implemented using a single processor, multiple processors, and / or modules configured to perform analog-to-digital radar signal processing according to the present disclosure.

[0043] Processor 200 may control transmitter / receiver 204 to transmit radar signal 104 using a stepped frequency waveform. Figure 3 , illustrates a diagram 300 showing the number of transmitted radar signal chirps 302. Figure 3 In the example of FIG. 3 , a single frame 304 having a 14 ms frame length is shown for illustration purposes, wherein time in milliseconds (ms) is shown along the horizontal axis and frequency in GHz is shown along the vertical axis. Figure 3 A single frame 304 is shown in FIG, but the radar system 100 is configured to send chirps 302 and process the received signals over multiple subsequent frames. Each chirp 302 is sent at an initial frequency, and then, during the course of the chirp 302, that frequency is modulated to a different ending frequency. Figure 3 In the example of FIG. 3 , a step-down waveform is shown in which the initial frequency and the end frequency of each chirp 302 are higher than the initial frequency and the end frequency of the next subsequent chirp 302. For example, the initial end and the end frequency of the chirp 302-1 are higher than the initial end and the end frequency of the next subsequent chirp 302-2. Figure 3 In the example of the step-down waveform shown in FIG. 1 , the initial end and end frequencies of the chirp 302 decrease within the period of the frame. Figure 3 A step-down waveform is illustrated, but a step-up waveform may also be used, wherein the initial end and end frequencies of each chirp 302 are lower than the next subsequent chirp 302 , such that the initial end and end frequencies of the chirp 302 increase within the period of the frame 304 .

[0044] Additionally, radar system 100 utilizes a time delay between individual chirps 302, referred to as a pulse repetition period (PRP), between the end of one chirp 302 and the beginning of the next chirp 302. For example, the time period between the end of transmission of chirp 302-1 and the beginning of transmission of chirp 302-2 is referred to as the PRP. Figure 3 In the example of , a constant PRP is used so that the time delay between individual chirps 302 is the same for all chirps 302 over a frame.

[0045] In addition, the leverage factor (LF) refers to the ratio of (i) the frequency change of all chirps 302 over a frame 304 to (ii) the frequency change for one chirp 302. For example, the frequency change of all chirps 302 corresponds to the difference between the initial frequency of the first chirp 302-1 in the frame 304 and the initial frequency of the last chirp in the frame 304. The frequency change of one chirp 302 corresponds to the difference between the initial frequency of the chirp 302 and the ending frequency of the same chirp 302. In general, although each chirp 302 within a frame 304 starts and ends with different initial and ending frequencies, the amount of frequency change of each chirp 302 is the same for all chirps 302 within a frame 304. The ratio of these two numbers, i.e., the frequency change of all chirps 302 over a frame 304 divided by the frequency change of one chirp, is referred to as the leverage factor (LF). In existing systems using conventional signal models, the resulting errors in the calculated range and range rate increase as the LF of the frame increases.

[0046] In this disclosure, the following abbreviations are used:

[0047] “c” refers to the speed of light;

[0048] “S” refers to the chirp slope, which is labeled such that an increasing chirp corresponding to an increase in the transmitted signal frequency over the chirp period is positive, and a decreasing chirp corresponding to a decrease in the transmitted signal frequency over the chirp period is negative;

[0049] “Δf” refers to the chirp-by-chirp frequency shift, which is signed such that rising frames corresponding to initial end and ending frequencies increasing chirp-by-chirp are positive, and falling frames corresponding to initial end and ending frequencies decreasing chirp-by-chirp are negative;

[0050] “f 0 ” refers to the starting frequency of ADC 205 on all frequency samples of the received frame;

[0051] “f s ” refers to the sampling frequency or sampling rate of ADC 205;

[0052] “PRP” refers to the pulse repetition period or time delay between adjacent chirps within a frame;

[0053] “M” refers to the number of samples within a particular chirp;

[0054] “K” refers to the number of chirps within a particular frame;

[0055] “m” refers to a specific sample within a specific chirp;

[0056] “k” refers to a specific chirp within a specific frame;

[0057] “M RFFT” refers to the number of range FFT bins of the processed received signal used in performing the range FFT process;

[0058] “K DFFT ” refers to the number of Doppler FFT bins of the processed received signal used in performing Doppler FFT processing;

[0059] "p" and "q" refer to the index of a particular detection in the 2D FFT plot;

[0060] “P * ” refers to the distance score interval estimate;

[0061] "q * ” refers to the Doppler fractional interval estimate;

[0062] “R” refers to distance; and

[0063] "v" refers to velocity.

[0064] Reference Figure 4 and Figure 5 , illustrates the time vector t for the chirp within a frame chirp 400 and a time vector t for the entire frame having m×k elements frame 500. Figure 4 and Figure 5 As shown, time vectors 400, 500 are two-dimensional arrays that are further processed for range FFT and Doppler FFT based on signal models described in further detail below. As described above, M refers to the number of samples per chirp and K refers to the number of chirps per frame. Each time stamp in the time vector 400 for a chirp references the start of each chirp. Each time stamp in the time vector 500 for an entire frame references the start of the frame.

[0065] The signal model 208 used by the system and method of the present disclosure is described below with reference to the following formula. Specifically, the instantaneous frequency f of the transmitted signal is calculated based on the following formulas 1 and 2: tx :

[0066] Formula 1: f tx =f start +Slope·t cnirp

[0067] Formula 2: f start (k) = f 0 +k·Δf

[0068] Among them, f 0 is the starting frequency of the entire frame, and Δf is the frequency step between chirps.

[0069] The instantaneous phase Φ of the transmitted signal is calculated based on the following equation 3:tx :

[0070] Formula 3:

[0071] This corresponds to integrating the frequency over time.

[0072] The instantaneous phase Φ of the received signal is calculated based on the following equations 4 and 5: rx :

[0073] Formula 4:

[0074] Formula 5:

[0075] Here, τ is the added time delay.

[0076] The intermediate frequency signal IF is calculated based on the following formula 6:

[0077] Formula 6:

[0078] The terms of Equation 3 and Equation 5 can then be used for substitution to simplify Equation 4 to the following Equations 7 to 9:

[0079] Formula 7:

[0080] Formula 8:

[0081] Formula 9:

[0082]

[0083] However, the following higher-order terms from Equation 9 have negligible effects and can be omitted:

[0084]

[0085] Omitting those higher-order terms and using substitutions based on Equation 3 yields Equation 10:

[0086]

[0087] like Figure 6 and Figure 7 As shown, further derivation, substitution and approximation result in a signal model term including both R and v.

[0088] Table 1 provides a number of terms used in the signal model, along with the term type and term description for each term.

[0089] Table 1: Signal model terms

[0090]

[0091] The present disclosure utilizes a signal model that includes high-order terms in both the fast time dimension and the slow time dimension. As described above, the fast time dimension corresponds to the time period of a single chirp, while the slow time dimension corresponds to the time period of a single transmission frame. In Table 1 above, the items including "m" corresponding to a specific sample within a chirp are referred to as fast time dimension items, while the items including "k" corresponding to a specific chirp within a frame are referred to as slow time dimension items. The items including both "m" and "k" are referred to as coupled items because they include items from both the fast time dimension and the slow time dimension. The "m" fast time dimension item, for example, may correspond to an index indicating a specific sample within a specific radar chirp. The "k" slow time dimension item may correspond to an index indicating a specific chirp within a frame.

[0092] In addition, the signal model of the present disclosure includes high-order terms in the slow time dimension, as shown in Table 1 above, and herein:

[0093] Slow time dimension high-order terms:

[0094] This slow time dimension term is a "high order" term because the "k" value corresponding to a particular chirp within a frame is a second order, squared term. In other words, the slow time dimension high order term includes a quadratic k term. This is distinguishable from existing signal models that do not include high order terms in the slow time dimension.

[0095] In addition, the signal model of the present disclosure also includes high-order terms in the fast time dimension, as shown in Table 1 above, and herein:

[0096] Fast time dimension high-order terms:

[0097] This fast time dimension term is a "high order" term because the "m" value corresponding to a particular sample within the chirp is a second order, squared term. In other words, the fast time dimension high order term includes a quadratic m term.

[0098] In this way, when interpreting the target position in the two-dimensional FFT map as distance and distance change rate information, the signal model according to the present disclosure takes into account the above-mentioned quadratic term.

[0099] The complex intermediate frequency (IF) formula is shown in Equation 11:

[0100]

[0101] In addition, the equations of the two-dimensional Doppler FFT are shown as the following equations 12 to 14:

[0102] Formula 12:

[0103] Formula 13:

[0104]

[0105] Formula 14:

[0106] As shown above, Equation 13 includes high-order terms from both the slow and fast time dimensions, expressed as:

[0107] Slow time dimension high-order terms:

[0108] Fast time dimension high-order terms:

[0109] When performing the range Doppler FFT according to the present disclosure, the processor 200 searches for a peak in the two-dimensional FFT map through the above equations 12 to 14, and determines the position of the peak in the two-dimensional FFT map and the interval index (p, q) and (p * ,q * ). Specifically, the processor 200 sets the following equation 15 for the fast time dimension to be equal to 0:

[0110] Formula 15:

[0111]

[0112] This can be seen in the phase complex vector e -j2πΦ(m,k) The slowest case of rotation in the fast time dimension is considered. This yields the following equation 16:

[0113] Formula 16:

[0114]

[0115] According to experience, p * It can be set equal to the average of p(m, k) according to the following formula 17:

[0116] Formula 17:

[0117]

[0118] The processor 200 then sets the following equation 18 for the slow time dimension equal to 0:

[0119] Formula 18:

[0120]

[0121] This is the phase complex vector e -j2πΦ(m,k) In the case of the slowest rotation in the slow time dimension, the following equation 19 is obtained:

[0122] Formula 19:

[0123]

[0124] According to experience, q * It can be set equal to the average of q(m, k) according to the following equation 20:

[0125]

[0126] Processor 200 then determines F using the following equations 21 and 22 of the signal model: bFast and F bSlow :

[0127] Formula 21:

[0128]

[0129] Formula 22:

[0130]

[0131] in,

[0132] in, as well as

[0133] in,

[0134] According to the present disclosure, in the above equation 22, the high-order terms (in as well as The contribution of (shown at) is included in the model and is not ignored. Based on equations 21 and 22, and when p and q are known, the processor 200 can then use these two linear equations to solve for the two unknowns R and speed v.

[0135] Thus, according to the present disclosure, the distance and distance change rate information of the object is determined based on the above-mentioned signal model, and the distance R and speed v of the object are accurately estimated. In addition, the present disclosure provides a signal model that includes high-order terms in the slow time dimension as well as the fast time dimension, and more accurately determines the estimation of the distance and the distance change rate based on the signal received from the radar system 100 using a stepped frequency waveform with a constant pulse repetition period (PRP) compared to the previous signal model.

[0136] Thus, as described above, when interpreting the target position of an object in a two-dimensional FFT plot as a distance and a rate of change of distance, the signal model according to the present disclosure takes into account quadratic terms in both the slow time dimension and the fast time dimension. Taking these terms into account provides improved accuracy and significantly reduces the resulting error for the estimated distance and rate of change of distance. For example, Figure 8Included are graphs illustrating simulated range and range rate errors when a signal model developed for a stepped frequency waveform (SFW) with a varying PRP is used in a radar system utilizing a SFW with a constant PRP. In other words, Figure 8 The errors illustrated in are obtained in the case of a radar system using SFW with a constant PRP by using a signal model that does not include or ignore the second-order or quadratic terms in the slow time dimension. Fig. 9 As shown, when the SFW leverage factor (LF) increases, the range rate error suffers greater degradation.

[0137] On the other hand, when interpreting the target position in the two-dimensional FFT plot as range and range rate information, the signal model according to the present disclosure takes into account the second-order or quadratic terms in the slow time dimension as well as the fast time dimension. In addition, when used with a radar system utilizing SFW and constant PRP, the signal model according to the present disclosure provides improved accuracy and reduced error rate compared to existing models. Fig.10 For example, a graph illustrating simulated range and range rate errors when using a signal model according to the present disclosure in the case of a radar system utilizing SFW and constant PRP, including utilizing second order or quadratic terms in both the slow and fast time dimensions. The accuracy is improved and the resulting errors are reduced compared to existing systems. In addition, Fig.11 The use of a signal model according to the present disclosure in the case of a radar system with increased LF and utilizing SFW and constant PRP is illustrated. Furthermore, the accuracy is improved and the resulting error is reduced compared to existing systems.

[0138] The foregoing description of the embodiments has been provided for the purpose of illustration and description, and is not intended to be exhaustive or limit the present disclosure. The individual elements or features of a particular embodiment are generally not limited to the particular embodiment, but where applicable, they are interchangeable and can be used in another embodiment, even if not explicitly shown or described. Various embodiments may also be changed in many ways. Such changes should not be considered as departing from the present disclosure, and all such modifications are intended to be included within the scope of the present disclosure. Although each embodiment is described as having certain features above, any one or more of those features described with respect to any embodiment of the present disclosure may be implemented in the features of any other embodiment and / or combined with the features of any other embodiment, even if the combination is not explicitly described. In other words, the embodiments described are not mutually exclusive, and the replacement of one or more embodiments with each other is still within the scope of the present disclosure.

[0139] Exemplary embodiments are provided so that the present disclosure will be thorough and will fully convey its scope to those skilled in the art. Specific details are set forth, including examples of specific components, devices, and methods, to provide a thorough understanding of the embodiments of the present disclosure. It will be apparent to those skilled in the art that specific details need not be employed, and exemplary embodiments may be embodied in many different forms and should not be construed as limiting the scope of the present disclosure. In some example embodiments, well-known processes, well-known device structures, and well-known techniques are not described in detail.

[0140] In the written description and claims, one or more steps within a method may be performed in a different order (or simultaneously) without changing the principles of the present disclosure. Similarly, one or more instructions stored in a non-transitory computer-readable medium may be executed in a different order (or simultaneously) without changing the principles of the present disclosure. Unless otherwise indicated, numbering or other labeling of instructions or method steps is for ease of reference and does not indicate a fixed order.

[0141] Spatial and functional relationships between elements (e.g., between modules) are described using various terms, including "connection," "engagement," "interface," and "coupling." Unless explicitly described as "direct," when describing a relationship between a first element and a second element in the above disclosure, the relationship includes a direct relationship in which no other intermediate elements exist between the first element and the second element, and an indirect relationship in which one or more intermediate elements exist between the first element and the second element (in space or function).

[0142] The phrase "at least one of A, B, and C" should be interpreted to mean a logic (AOR BOR C) using a non-exclusive logical OR, and should not be interpreted to mean "at least one of A, at least one of B, and at least one of C". The term "set" does not necessarily exclude the empty set. The term "non-empty set" may be used to indicate the exclusion of the empty set. The term "subset" does not necessarily require a proper subset. In other words, a first subset of a first set may be contiguous with (equal to) the first set.

[0143] In the drawings, the direction of the arrow indicated by the arrow generally indicates the flow of information (e.g., data or instructions) of interest to the illustration. For example, when element A and element B exchange various information, but the information sent from element A to element B is relevant to the illustration, the arrow may point from element A to element B. This unidirectional arrow does not mean that no other information is sent from unit B to unit A. In addition, for information sent from unit A to unit B, unit B may send a request for the information to unit A or receive an acknowledgement of the information.

[0144] In this application, including the following definitions, the term "module" or the term "controller" may be replaced with the term "circuit". The term "module" may refer to processor hardware (shared, dedicated, or group) that executes code and memory hardware (shared, dedicated, or group) that stores code executed by the processor hardware, which is part of the processor hardware or includes the processor hardware.

[0145] The module may include one or more interface circuits. In some examples, the interface circuit may implement a wired or wireless interface connected to a local area network (LAN) or a wireless personal area network (WPAN). Examples of LANs are Institute of Electrical and Electronics Engineers (IEEE) standard 802.11-2016 (also referred to as WIFI wireless networking standards) and IEEE standard 802.3-2015 (also referred to as Ethernet wired networking standards). Examples of WPANs are Bluetooth wireless networking standards from Bluetooth Special Interest Group (SIG) (including core specification versions 3.0, 4.0, 4.1, 4.2, 5.0, and 5.1 from Bluetooth SIG).

[0146] The module can communicate with other modules using interface circuits. Although the module can be described in the present disclosure as being in logical communication directly with other modules, in various implementations, the module can actually communicate via a communication system. The communication system includes physical and / or virtual networking devices, such as hubs, switches, routers, and gateways. In some implementations, the communication system is connected to or passes through a wide area network (WAN) such as the Internet. For example, the communication system can include multiple LANs connected to each other through the Internet or point-to-point leased lines using technologies including multi-protocol label switching (MPLS) and virtual private networks (VPNs).

[0147] In various implementations, the functionality of a module may be distributed among multiple modules connected via a communication system. For example, multiple modules may implement the same functionality distributed by a load balancing system. In another example, the functionality of a module may be split between a server (also referred to as a remote or cloud) module and a client (or user) module. For example, a client module may include a native or web application that executes on a client device and communicates with a server module over a network.

[0148] As described above, the term code may include software, firmware, and / or microcode, and may refer to programs, routines, functions, classes, data structures, and / or objects. Shared processor hardware includes a single microprocessor that executes some or all of the code from multiple modules. Group processor hardware includes a microprocessor that, in combination with an additional microprocessor, executes some or all of the code from one or more modules. References to multiple microprocessors include multiple microprocessors on discrete dies, multiple microprocessors on a single die, multiple cores of a single microprocessor, multiple threads of a single microprocessor, or a combination of the above.

[0149] Shared memory hardware includes a single memory device that stores some or all code from multiple modules. Group memory hardware includes a memory device that combines with other memory devices to store some or all code from one or more modules.

[0150] The term memory hardware is a subset of the term computer-readable medium. The term computer-readable medium as used herein does not include transient electrical or electromagnetic signals propagating through a medium (e.g., on a carrier wave); the term computer-readable medium is therefore considered to be tangible and non-transitory. Non-limiting examples of non-transitory computer-readable media are non-volatile memory devices (e.g., flash memory devices, erasable programmable read-only memory devices, or mask read-only memory devices), volatile memory devices (e.g., static random access memory devices or dynamic random access memory devices), magnetic storage media (e.g., analog or digital magnetic tape or hard drive), and optical storage media.

[0151] The apparatus and methods described in this application may be implemented in part or in whole by a special-purpose computer created by configuring a general-purpose computer to perform one or more specific functions contained in a computer program. Such apparatus and methods may be described as computerized apparatus and computerized methods. The above-described functional blocks and flow chart elements serve as software specifications, which may be translated into a computer program by the routine work of a skilled technician or programmer.

[0152] The computer program includes processor executable instructions stored on at least one non-transitory computer readable medium. The computer program may also include or rely on stored data. The computer program may include a basic input / output system (BIOS) that interacts with the hardware of the special-purpose computer, device drivers that interact with specific devices of the special-purpose computer, one or more operating systems, user applications, background services, background applications, etc.

[0153] The computer program may include: (i) descriptive text to be parsed, such as HTML (Hypertext Markup Language), XML (Extensible Markup Language) or JSON (Javascript Object Notation), (ii) assembly code, (iii) object code generated by a compiler from source code, (iv) source code for execution by an interpreter, (v) source code for compilation and execution by a just-in-time compiler, etc. By way of example only, the source code may be compiled using languages ​​including C, C++, C#, ObjectiveC, Swift, Haskell, Go, SQL, R, Lisp, Fortran, Perl, Pascal, Curl, OCaml, HTML5 (Hypertext Markup Language Fifth Revision), Ada, ASP (Active Server Pages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk, Erlang, Ruby, Visual Lua, MATLAB, SIMULINK, and

Claims

1. A radar system, comprising a processor and a memory, wherein the processor and the memory are configured to: transmitting a radar signal within at least one frame having a plurality of radar chirps, the plurality of radar chirps having a stepped frequency waveform such that an initial transmit frequency and an ending transmit frequency are changed for each subsequent chirp within the plurality of radar chirps, and the plurality of radar chirps have a constant pulse repetition period between chirps; receiving and sampling a radar signal reflected from at least one object to generate a reception value from the received and sampled radar signal; Performing range fast Fourier transform (FFT) processing and Doppler FFT processing on the received value to generate a range FFT value and a Doppler FFT value; estimating range and range rate information about the at least one object based on the range FFT value and the Doppler FFT value using a signal model; determining information about the object based on the estimated distance and distance rate information, the information about the object comprising at least one of a position, a size, an orientation, a velocity, and an acceleration of the at least one object; in: The signal model includes: (1) at least one fast time dimension term corresponding to an individual received sample within each radar chirp of the plurality of radar chirps, and (2) at least one slow time dimension term corresponding to an individual radar chirp within the plurality of radar chirps; and The at least one slow time dimension term includes at least one second order term corresponding to an individual radar chirp within the plurality of radar chirps.

2. The radar system according to claim 1, wherein: The at least one slow-time dimension term is quadratic.

3. The radar system according to claim 1, wherein: The at least one slow time dimension term comprises an index for an individual radar chirp within the plurality of radar chirps.

4. The radar system according to claim 1, wherein: The at least one fast time dimension term includes at least one second order term corresponding to an individual received sample within each of the plurality of radar chirps.

5. The radar system according to claim 4, wherein: The at least one fast-time dimension term is quadratic.

6. The radar system according to claim 1, wherein: The at least one fast time dimension term comprises an index for an individual received sample within each of the plurality of radar chirps.

7. The radar system according to claim 1, wherein: The signal model includes at least one coupling term including at least one fast time dimension term corresponding to individual received samples within each of the plurality of radar chirps and at least one slow time dimension term corresponding to an individual radar chirp within the plurality of radar chirps.

8. The radar system according to claim 1, wherein: The radar system is located in a vehicle, and information about the object is transmitted to a vehicle system, which controls at least one of a steering system, a braking system, and a throttle system of the vehicle based on the information about the object.

9. The radar system according to claim 8, wherein: The vehicle system includes at least one of an autonomous driving system and a driver assistance system.

10. A method comprising the following steps: transmitting, with a radar system having a processor and a memory, a radar signal within at least one frame having a plurality of radar chirps, the plurality of radar chirps having a stepped frequency waveform such that an initial transmit frequency and an ending transmit frequency are changed for each subsequent chirp within the plurality of radar chirps, and the plurality of radar chirps have a constant pulse repetition period between chirps; receiving and sampling, with the radar system, a radar signal reflected from at least one object to generate a reception value from the received and sampled radar signal; Using the radar system to perform range fast Fourier transform (FFT) processing and Doppler FFT processing on the received value to generate a range FFT value and a Doppler FFT value; estimating, with the radar system, range and range rate information about the at least one object based on the range FFT value and the Doppler FFT value using a signal model; determining, using the radar system, information about the object based on the estimated range and range rate information, the information about the object comprising at least one of a position, a size, an orientation, a velocity, and an acceleration of the at least one object; in: The signal model includes: (1) at least one fast time dimension term corresponding to an individual received sample within each radar chirp of the plurality of radar chirps, and (2) at least one slow time dimension term corresponding to an individual radar chirp within the plurality of radar chirps; and The at least one slow time dimension term includes at least one second order term corresponding to an individual radar chirp within the plurality of radar chirps.

11. The method according to claim 10, wherein: The at least one slow-time dimension term is quadratic.

12. The method according to claim 10, wherein: The at least one slow time dimension term comprises an index for an individual radar chirp within the plurality of radar chirps.

13. The method according to claim 10, wherein: The at least one fast time dimension term includes at least one second order term corresponding to an individual received sample within each of the plurality of radar chirps.

14. The method according to claim 13, wherein: The at least one fast-time dimension term is quadratic.

15. The method according to claim 10, wherein: The at least one fast time dimension term comprises an index for an individual received sample within each of the plurality of radar chirps.

16. The method according to claim 10, wherein: The signal model includes at least one coupling term including at least one fast time dimension term corresponding to individual received samples within each of the plurality of radar chirps and at least one slow time dimension term corresponding to an individual radar chirp within the plurality of radar chirps.

17. The method according to claim 10, wherein: The radar system is located in a vehicle, and information about the object is transmitted to a vehicle system, which controls at least one of a steering system, a braking system, and a throttle system of the vehicle based on the information about the object.

18. The method according to claim 17, wherein: The vehicle system includes at least one of an autonomous driving system and a driver assistance system.

Images