Patent ID: 12210114

DETAILED DESCRIPTION

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

As used in this specification and claims, the terms “for example,” “for instance,” and “such as,” and the verbs “comprising,” “having,” “including,” and their other verb forms, when used in conjunction with a listing of one or more components or other items, are each to be construed as open ended, meaning that that the listing is not to be considered as excluding other, additional components or items. The term “based on” means at least partially based on. Further, it is to be understood that the phraseology and terminology employed herein are for the purpose of the description and should not be regarded as limiting. Any heading utilized within this description is for convenience only and has no legal or limiting effect.

FIG.1illustrates an FMCW automotive radar system101configured to track an object103using FMCW, in accordance with an example embodiment of the present disclosure. The FMCW automotive radar system (also referred to as “radar system”)101comprises a transceiver101aconfigured to transmit105FMCW in a radio frequency (RF) band over a sequence of pulse repetition intervals (PRI) to detect the object103, where the object103may be located at a distance d from the radar system101. The transmitted FMCW105is reflected by the object103, where the reflected FMCW107is received by the transceiver101a. The reflected FMCW107is analyzed by the radar system101to track the object103in the scene.

In some embodiments, the radar system101may comprise a transmitter to transmit105the FMCW and a receiver to receive the reflected107FMCW. The radar system101is configured to sample radar measurements of the scene in a time-frequency domain and collect the sample radar measurements of the scene within an intermediate frequency (IF) bandwidth. The radar system101quantizes a frequency dimension of the time-frequency domain into multiple frequency bins forming the frequency bandwidth. Similarly, the radar system101quantizes a time dimension of the time-frequency domain into multiple time instances forming a time interval corresponding to the PRI.

Based on the quantized time and frequency domain, the radar system101counts the number of amplitude peaks of the radar measurements for each frequency bin at different instances of time, where each amplitude peak of the number of amplitude peaks corresponds to maximum energy within each frequency bin at a specific instance of time. The radar system101is further configured to identify a number of frequency bins with their count of the number of peaks above a pre-determined threshold and further, configured to determine at least the distance, a velocity, or a combination thereof for the object103based on frequency analysis of the radar measurements within the identified frequency bins while ignoring radar measurements at most of the other frequency bins within the frequency bandwidth.

The radar system101may be able to perform the same measurements for multiple objects in the scene by using a different FMCW for each of the multiple objects, and performing corresponding detection described above for each object.

FIG.2illustrates a block diagram200of the radar system101for tracking the object103, in accordance with an example embodiment. The radar system101includes a processor201configured to execute stored instructions. The radar system101further comprises a memory203that stores instructions that are executable by the processor201. The processor201can be a single core processor, a multi-core processor, a computing cluster, or any number of other configurations. The memory203can include random access memory (RAM), read only memory (ROM), flash memory, or any other suitable memory systems. The processor201is connected through a bus205to one or more input and output devices. Further, the radar system101includes an FMCW generator207, where the FMCW generator207is configured to generate FMCW over the sequence of PRI. The radar system101further comprises a transmitter209configured to transmit105the FMCW over the sequence of PRIs.

The radar system101comprises a receiver211configured to receive incoming signals within an RF bandwidth. The incoming signals include a delayed FMCW reflected107from the object103, and one or multiple interference signals. The incoming signals are provided to a mixer213, where the mixer213is configured to mix the incoming signals with the copy of the FMCW to shift the incoming signals into the IF bandwidth, such that a sequence of peak amplitudes at different time instances of a target beat signal formed by mixing the copy of the FMCW with a coherent incoming signal forms a ridge falling within a frequency bin, while a sequence of peak amplitudes at different time instances of an interference beat signal formed by mixing the copy of the FMCW with an incoherent incoming signal crosses multiple frequency bins at an angle, such that an output of the mixer213includes a combination of the target beat signal and one or multiple interference beat signals. The mixer213may comprise a local oscillator using which the mixer213converts the RF band FMCW signals into IF band. The combination of the target beat signal and one or multiple interference beat signals are required to be sampled in fast time to obtain the fTF representation of a scene comprising the object103.

To that end, the processor201is configured to use an analog-to-digital converter (ADC)215. The ADC215is configured to sample the output of the mixer in fast time to produce the radar measurements providing the fTF representation of the scene. Based on the fTF representation obtained from the ADC215, the processor201is further configured to generate a high-resolution fTF representation of the scene by using a frequency reassignment-based Fourier synchrosqueezing transform (FSST). The high-resolution fTF representation of the scene comprises target ridges corresponding to the object103and interference ridges corresponding to the one or multiple interference signals. The interference ridges are stronger than the target ridges.

Some embodiments are based on the realization that the high-resolution fTF representation enables higher degree of separation between the target ridges and the inference ridges. To detect the target ridges, the radar system101comprises a ridge detector217. The ridge detector217is configured to sequentially detect the strongest signal ridge in the fTF representation. The radar system101stores a count of the number of peaks corresponding to detect ridge signals in each pulse of multiple pulses of the FMCW within each PRI.

Additionally, the radar system101may include an output interface219. In some embodiments, the radar system101is further configured to submit, via the output interface219, a sequence of control forces to a controller221, where the control forces are generated based on the detection of the object103. The controller221may be configured to control a load such as a self-driving vehicle installed with the radar system101, based on the sequence of control forces.

Mathematical Implementation:

Target and Interference Signal Model

FIG.3Aillustrates time-frequency domain FMCW radar interferences over multiple pulses in RF band300a, in accordance with an example embodiment. For ease of describing only two FMCW pulses are shown inFIG.3A.FIG.3Aillustrates two pulses corresponding to a reference signal301and corresponding two pulses of backscattered target signal303. The reference signal301corresponds to transmitted FMCW105to detect the object103, and the backscattered target signal303corresponds to the reflected FMCW107. As illustrated inFIG.3A, the FMCW is affected by two interfering pulses: interference 1305and interference 2307.

Assuming the FMCW-based automotive radar101, sub-indexed by u, transmits a sequence of chirp pulses (i.e., the reference signal301) with a carrier frequency modulated at the RF bands and the PRI {tilde over (T)}u:

xuR⁢F(T)=auR⁢F⁢ej⁢2⁢π⁡(f0⁢t+0.5hu⁢t2),∀∈[0,Tu](1)
where auRFis the RF amplitude, f0is the central frequency, huis the chirp rate, Tuis the chirp sweep duration, and {tilde over (T)}u-Tuis the inter-pulse idle duration. Without loss of generality, all automotive radars operate at the same central frequency f0, e.g., f0=77 GHZ, but with different chirp sweep duration Tu, PRI Tu, and chirp rate hu.

In the following mathematical implementation, the FMCW-based automotive radar101is a victim radar, where the victim radar is referred to as the radar0, i.e., u=0. For the l-th chirp pulse, the dechirped signal of the victim radar is a multi-component signal:

xl(T)=xlo(T)+xli(T)=∑k=1Ko⁢xl,ko(t)+∑k=1Ko⁢xl,ui(t)(2)

where xlo(t) consists of Kotarget components, and xli(t) is the sum of Uliinterference components. In some embodiments, the number of interferences Ulimay vary over the pulse.

An expression for the target component xl,ko(t), can be derived by multiplying the attenuated and delayed copy (i.e., the backscattered target signal303) with the transmitted105FMCW (i.e., the reference signal301) in equation (1) at a local oscillator

xl,ko(t)=ako⁢ej⁢2⁢π⁢ϕl,ko(t),∀∈[0,T0](3)
where akois the IF amplitude of target k, and the phase term is given as:

ϕl,ko=(fr,ko+fD,ko)⁢t+fD,ko(l-1)⁢To(4)
with fr,koand fD,kodenoting the beat frequency and, respectively, the Doppler frequency of the k-th target.

On the other hand, the interference components (305and307) may or may not be dechirped into the IF band of the victim radar, depending on FMCW configurations between the victim radar {f0, h0, T0, {tilde over (T)}0} and the u-th interfering radar {f0, hu, Tu, {tilde over (T)}u} as well as their relative time offset at l-th chirp cycle τu,l.

FIG.3Billustrates time-frequency domain FMCW radar interferences over multiple pulses in IF band300b, in accordance with an example embodiment. The IF band300bcomprises a first deciphered interference (also referred to as “deciphered interference 1”)309corresponding to interference component305and a second deciphered interference (also referred to as “deciphered interference 2”)311corresponding to interference component307along with the deciphered target signal313. Assuming that the u-th interference at the l-th pulse turns out to be a chirp signal

xl,ui(T)=aui⁢ej⁢2⁢π⁢ϕl,ui(t)⁢∀t∈Tl,ui(5)
where auiis the IF amplitude of interference u, the phase term is given as:

ϕl,ui=0.5⁢(hu⁢τu,l-fD,ui)⁢t+ϕl,u,0i(6)
with fD,uidenoting the Doppler frequency of interference u and ϕl,u,oidenoting initial phase difference between interferer radar and victim radar at the l-th pulse, and Tl,uidenotes the contaminated fast-time interval of the l-th pulse due to the u-th interference

Tl,ui={t|(hu-hu)⁢t-(hu⁢τu,l-fD,ui)|≤fL}(7)
which is determined from the fact that the interference signal xl,ui(t) must lie in the IF band of the victim radar with IF bandwidth fL.
II. Interference Mitigation Method

It is an objective of some embodiments to reconstruct xlo(t) from xl(t). This is achieved by separating the targets from the interferences in the fTF domain via high-resolution time-frequency tools, identifying the target ridge by utilizing the consistent patterns of the target and varying patterns of the interferences, and directly reconstructing target signals via the fTFMR.

High-Resolution Fast-Time-Frequency (fTF) Representation

For the l-th pulse, the fast-time samples of the received signal xl(t) can be transformed to the fTF representation Xl(t,ω) via standard time-frequency analysis such as the STFT. The radar system101is configured to use the FSST that highly focuses on the target pattern and allows fTFMR, and directly reconstruct the fast-time target signals from the focused target portions of the fTF representation with limited inclusion of interference and noise.

The FSST can be considered as a frequency-domain reassignment of the STFT. Given the STFT of xl(t):

Vxlg(t,ω)=∫Rxl(τ)⁢g*(τ-t)⁢e-i⁢ω⁢τ⁢d⁢τ,(8)
where g(t) is the time-domain window function, (.)* denotes the complex conjugate, and w is the frequency variable, its centroid (local energy) of the spectrogram, i.e.,

❘"\[LeftBracketingBar]"Vxlg(t,ω)❘"\[RightBracketingBar]"2
is computed as:

ω^xl(t,ω)=ω-𝒥⁢{Vxlg′(t,ω)Vxlg(t,ω)},(9)
where

Vxlg′(t,ω)
denotes the STFT with the window function given by the derivative of g(t), andtakes the imaginary part of the input. The FSST reassigns the STFT from the point of computation to its centroid along the frequency (ω) domain over each fast-time instant t:

Xl(t,ω)=∫ℝVx10(t,v)⁢ei⁢ω⁢t⁢δ⁡(ω-ω^x⁢l(t,v))⁢dv2⁢π⁢g*(0),(10)
where δ(.) denotes the Dirac function. The FSST-based fTFR is highly focused on sinusoid-like target signals
B. Robust Ridge Detection of Underwhelmed Targets

Some embodiments are based on the realization that reconstructing the target signal

xlo(t)=al,ko⁢ej⁢2⁢πϕl,ko(t)
requires separating it from interferences and other target signals. This is done by detecting its ridge

Ωl,ko(t)
in the fTF domain, i.e., the estimation of its instantaneous frequency dϕi,ko(t)/dt.

FIG.4Aillustrates a ridge detection in an fTF representation400aof a pulse comprising only target signal, in accordance with an example embodiment. The frequency dimension of the time-frequency domain in the fTF representation400ais quantized into multiple frequency bins (for example, from −15 MHz-15 MHz, where each frequency bin is of size 5 MHz) forming the frequency bandwidth, and a time dimension of the time-frequency domain in the fTF representation400ais quantized into multiple time instances forming a time interval corresponding to the PRI (for example, from 0 μs to 30 μs, where each time interval or time bin is of size 5 μs).

Further, as illustrated inFIG.4A, the target is a single-tone signal401(as shown in equation (4)), the target ridge

Ωl,ko(t)
403is a constant function of t and is a straight line in the fTF representation400a. The target ridge

Ωl,ko(t)
403stays the same over the multiple pulses within a coherent processing interval (CPI). The radar system101is configured to count the number of amplitude peaks of the radar measurements in the fTF representation400afor each frequency bin at different instances of time.

In some embodiments, the radar system101is configured to plot a histogram for the number of amplitude peaks in each frequency bin.FIG.4Billustrates a histogram400bfor the number of amplitude peaks405in frequency bins of the fTF representation400a, in accordance with an example embodiment.FIG.4Bis described below in conjunction withFIG.4A. The histogram400bcomprises frequency in the unit of MHz on the X-axis and the number of ridges detected and time bins on each frequency bin on the Y-axis. As the target ridge

Ωl,ko(t)
stays the same over the multiple pulses within the CPI as illustrated inFIG.4A, the number of amplitude peaks (or ridges)405are accumulated at the base band frequency i.e., 5 MHz of the target signal.

FIG.5Aillustrates a ridge detection in an fTF representation500aof a pulse comprising one target signal and two interference signals, in accordance with an example embodiment. Maximum energy501is detected as per the plot representation500a. The time-frequency dimension of the fTF representation500ais quantized like that of the fTF representation400a. As illustrated inFIG.5A, a target ridge503stays the same over multiple pulses within the CPI while the FMCW interference ridges (corresponding to the maximum energy detection501) are likely to vary over multiple pulses due to the non-coherence between the interfering and victim radars.

FIG.5Billustrates a histogram500bfor the number of amplitude peaks in frequency bins of the fTF representation500a, in accordance with an example embodiment.FIG.5Bis described below in conjunction withFIG.5A. As the target ridge stays the same over the multiple pulses within the CPI while the FMCW interference ridges vary over multiple pulses, the number of amplitude peaks (or ridges)505that gets accumulated at the beat frequency, i.e., 5 MHz, of the target signal is still significantly larger than the number of amplitude ridges at any other frequency.

In some embodiments, to detect the target ridges from the fTF representation (for example,500a) a multi-pulse ridge detection method is used.FIG.6illustrates a flow diagram600of multi-pulse ridge detection method, in accordance with an example embodiment. At step601, a maximum energy ridge detection is performed on an input FFST Xl(t,ω) to detect target ridge

Ωl,kd(t).
To that end, the multi-pulse ridge detection is performed using a maximum-energy ridge detector that uses a penalized forward-backward greedy algorithm to sequentially detect the strongest signal ridge. The detected target ridges may be a mixture of target and interferences.

At step603, it is validated whether the detected ridges correspond to the target ridges from other pulses in the same CPI if necessary. The detected ridges are validated by checking if:

∑n=1N1⁢(Ωl,kd(n⁢Δ⁢T)=mode({Ωl,kd(n⁢Δ⁢T),n=1,…,N}))>NTH,(11)
where N is the number of time bins in the fTF domain, ΔT is the time resolution of fTF such that NΔT=T0, mode (.) means the value that appears the most often in a set of values, NTHis a threshold.

If the target ridge, at step605, is reassigned to be a constant function over the fast time with the beat frequency equal to the most frequent bin of the detected ridge:

Ωl,ko(t)=mode({Ωl,kd(n⁢Δ⁢T),n=1,…,N}).(12)

Alternatively, if the target ridge is not detected, at step607, the history of the most frequent ridge over multiple pulses in the same CPI are used and assign the most frequent ridge in the pulse l,

Ωl,ko(t)=mode({Ωl′,ko(t),l′=[1,…,L]⁢\⁢{l}}),(13)
where L is the number of pulses in a CPI. For instance, as illustrated inFIG.5A, the detected ridge

Ωl,kd(t)
fails to pass the validation of equation (11) and the target ridge is re-assigned by looking into the target ridges from other pulses.
C. Fast-Time-Frequency Mode Retrieval of Targets

Finally, to directly reconstruct the target signal xlo(t) from its fTF representation Xl(t,ω) and the detected target ridges

Ωl,ko(t),
k=1, 2, . . . , Ko. In some embodiments, to avoid in interference component on the target ridge

Ωl,ko(t),
the tTFMR uses the interference-free portion along the detected ridges:

X^l,k(t,ω)={Xl(t,ω),t∈𝒯^l,ko⁢(ω),❘"\[LeftBracketingBar]"ω-Ωl,ko⁢(t)❘"\[RightBracketingBar]"<ϵ,0,otherwise,
where

𝒯^l,ko(ω)
is the estimated interference-free portion of the target ridge k. Then, the fTFMR directly integrates {circumflex over (X)}l,k(t,ω) over a small frequency interval around

Ωl,ko(t):

x^l,ko(t)=1g⁡(0)⁢∫❘"\[LeftBracketingBar]"ω-Ωl,ko(t)❘"\[RightBracketingBar]"<∈X^l,k(t,ω)⁢d⁢ω,(14)
where {circumflex over (x)}l,ko(t) is the reconstructed fast-time signal of target k.

To obtain

𝒯^l,ko(ω),
the interfered portion on

𝒳l(ω)=Δ{❘"\[LeftBracketingBar]"Xl(n⁢Δ⁢T,ω)❘"\[RightBracketingBar]"2,n=1,2,…,N}
for each frequency bin satisfying

{ω:❘"\[LeftBracketingBar]"ω-Ωl,ko(t)❘"\[RightBracketingBar]"<ϵ}
using the median absolute deviation (MAD) detector. The MAD detector estimates the interference portion

o∈𝒪l,k
if

Xl(o⁢Δ⁢T,ω)❘"\[RightBracketingBar]"2-median(𝒳l(ω))❘"\[RightBracketingBar]">γMAD⁢mediani=1,2,…,N(Xl(i⁢Δ⁢T,ω)❘"\[RightBracketingBar]"2-median(𝒳l(ω))❘"\[RightBracketingBar]"),

Then, the interference-free portion is estimated as

𝒯^l,kMAD(ω)=⋃o∈{1,2,…,N}⁢\⁢𝒪l,k[(o-1)⁢Δ⁢T,o⁢Δ⁢T].(15)

In some embodiments, to retain the target portion as much as possible, each element in

{❘"\[LeftBracketingBar]"Xl(n⁢Δ⁢T,ω)❘"\[RightBracketingBar]"2,n=1,…,N}
with the power profile of target k, i.e.,

𝒫l,k={❘"\[LeftBracketingBar]"Xl′(n⁢Δ⁢T,ω)❘"\[RightBracketingBar]"2,n=1,…,N,l′=1,…,l-1},
and determine the set of time bins corresponding to target k

𝒬l,k={q:❘"\[LeftBracketingBar]"Xl(q⁢Δ⁢T,ω)❘"\[RightBracketingBar]"2<γHis⁢median(𝒫l,k)},(16)
where

γHis
is the target detection threshold or a pre-determined threshold in the power profile. Then, the interference-free portion of the target k is estimated as

𝒯^l,kHis(ω)=⋃q∈Ql,k[(q-1)⁢Δ⁢T,q⁢Δ⁢T].(17)

On combining equation (15) and (17), and obtain the estimated interference-free portion for target k as:

𝒯^l,ko(ω)=𝒯^l,kMAD(ω)⋃𝒯^l,kHis(ω).(18)
where

𝒯^l,ko(ω)
as the true interference-free time region of the target k on frequency.

FIG.7illustrates steps of a method700for tracking an object, in accordance with an example embodiment. The method700is executed by the radar system101. At step701, FMCW in RF band is transmitted over a sequence of pulse repetition intervals (PRI) to detect the object103, where the object103may be located at a distance d from the radar system101. The transmitted FMCW105is reflected by the object103, where the reflected FMCW107is received by the radar system101and is analyzed by the radar system101to track the object103in the scene.

At step703, radar measurements of the scene sampled in a time-frequency domain within the IF bandwidth to which reflection of the transmitted FMCW is shifted by mixing with a copy of the FMCW may be collected. A frequency dimension of the time-frequency domain is quantized into multiple frequency bins forming the frequency bandwidth. Similarly, a time dimension of the time-frequency domain is quantized into multiple time instances forming a time interval corresponding to the PRI.

At step705, the number of amplitude peaks of the radar measurements for each frequency bin at different instances of time may be counted. Each peak of the number of amplitude peaks corresponds to maximum energy within each frequency bin at a specific instance of time. At step707, the number of frequency bins with their number of peaks above the predetermined threshold is identified.

At step707, at least a distance to the object103, is determined, based on frequency analysis of the radar measurements within the identified frequency bin while ignoring radar measurements at most of the other frequency bins within the frequency bandwidth.

Embodiments

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

Specific details are given in the above description to provide a thorough understanding of the embodiments. However, understood by one of ordinary skill in the art can be that the embodiments may be practiced without these specific details. For example, systems, processes, and other elements in the subject matter disclosed may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known processes, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. Further, like reference numbers and designations in the various drawings indicate like elements.

Also, individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process may be terminated when its operations are completed but may have additional steps not discussed or included in a figure. Furthermore, not all operations in any particularly described process may occur in all embodiments. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, the function's termination can correspond to a return of the function to the calling function or the main function.

Furthermore, embodiments of the subject matter disclosed may be implemented, at least in part, either manually or automatically. Manual or automatic implementations may be executed, or at least assisted, through the use of machines, hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine readable medium. A processor(s) may perform the necessary tasks.

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

Embodiments of the present disclosure may be embodied as a method, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts concurrently, even though shown as sequential acts in illustrative embodiments. Although the present disclosure has been described with reference to certain preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the present disclosure. Therefore, it is the aspect of the append claims to cover all such variations and modifications as come within the true spirit and scope of the present disclosure.