Rotating object speed sensing method and system based on FMCW millimeter wave radar

Abstract

The present invention provides a method and system for sensing the rotational speed of rotating objects based on FMCW millimeter-wave radar, comprising: Step S1: generating and transmitting a frequency-varying chirp signal using FMCW millimeter-wave radar chirp, and collecting radar signal data for estimating the rotating object's rotational speed; Step S2: extracting features related to the rotating object's rotational speed from the received radar signal, including the initial peak index (IFP) of the autocorrelation sequence; Step S3: isolating the rotational speed-related features of the target rotating object's reflected signal by self-separating the rotating object's reflected signal; and Step S4: searching for the object's rotational speed using radar signals collected at multiple chirp-generated frequencies based on a rotating object speed search algorithm. This method, using FMCW millimeter-wave radar, enables contactless, long-distance, and accurate sensing of the rotating object's rotational speed, with measurement accuracy exceeding that of commercial laser tachometers and robustness to ambient lighting conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensing and ubiquitous computing, and in particular to a method and system for sensing the rotational speed of a rotating object based on an FMCW millimeter-wave radar. Background Art

[0002] Machines with rotor components play an important role in everyday life and various industrial fields (e.g., machining, automotive, and aviation). Accurate and safe speed measurement is crucial for monitoring the operating status of machines. Operators in machine shops need to regularly check the spindle speed to monitor its operating status, and checking the tire speed has become a standard item in vehicle inspections. In addition to industrial applications, the speed of the motors of washing machines and air conditioners is used to diagnose the cause of abnormalities. In short, speed measurement plays a key role in many industrial and everyday applications. The main reasons are:

[0003] 1) Speed is one of the important indicators for judging the health and performance of mechanical equipment. Regularly monitoring the speed can timely detect machine failures or performance degradation and achieve intelligent maintenance of the machine.

[0004] 2) The control algorithms and performance of many devices and systems are related to the speed. Accurately measuring the speed is a prerequisite for achieving high-performance control and stable operation.

[0005] 3) The safety of some systems is closely related to the rotational speed, such as flywheels, centrifuges and other high-speed rotating equipment. Real-time monitoring of the rotational speed can effectively reduce the risk of casualties.

[0006] 4) Speed measurement is widely used in various quality inspections, such as dynamic balancing of wheels on automobile production lines.

[0007] In summary, accurate and safe speed measurement technology is essential for many industrial systems and equipment. It can achieve real-time monitoring of machine performance and safety, greatly improving the reliability and stability of the system. This is also an important driving force for the development of advanced speed measurement technology.

[0008] Patent document CN114966663A (Application Number: CN202210528501.1) discloses a method and system for measuring the speed of a ship diesel engine using continuous wave radar, comprising: S101, a radar signal transceiver module transmits a continuous wave radar signal to the diesel engine to be measured; S102, the diesel engine reflects the received continuous wave radar signal back to the radar signal transmitter module; S103, processing the signal reflected back from the diesel engine to obtain frequency domain information; S104, obtaining the corresponding speed based on the frequency domain information. This patent does not use FMCW millimeter wave radar to continuously measure the rotating target object, and therefore cannot achieve high-precision, long-distance, and low-cost speed measurement.

[0009] To date, a range of sensing technologies have been developed to develop different types of rotational speed sensors (i.e., tachometers), which can be categorized as contact and non-contact. Specifically, contact tachometers are physically attached to the axis of rotation of the target object, which limits the distance between the operator and the rotating object and creates safety risks. Existing non-contact tachometers use electromagnetic or optical signals to detect rotational speed. Electromagnetic-based methods employ electrostatic or Hall-effect sensors to detect changes in the electromagnetic field caused by rotation. However, the sensing distance of these methods is typically less than 10 cm, which is very limited. Optical-based methods utilize optical sensors, such as cameras and laser receivers, for rotational speed detection. However, camera-based tachometers have strict lighting requirements. Laser tachometers will not work if the target object is highly reflective. In this case, the laser tachometer cannot distinguish between laser pulses reflected by its reflective tag and those reflected by the object.

[0010] In summary, existing non-contact speed sensing technology has the following major disadvantages:

[0011] 1) Electromagnetic methods have a very short measurement distance, usually less than 10 cm;

[0012] 2) Camera-based methods are easily affected by ambient lighting conditions and have low measurement accuracy;

[0013] 3) Laser sensing systems cannot work under highly reflective surfaces;

[0014] Therefore, an effective non-contact speed sensing technology is urgently needed to solve the above problems and achieve high-precision, long-distance and low-cost speed measurement. Summary of the Invention

[0015] In view of the defects in the prior art, the purpose of the present invention is to provide a method and system for sensing the rotation speed of a rotating object based on FMCW millimeter wave radar.

[0016] The method for sensing the rotation speed of a rotating object based on an FMCW millimeter-wave radar provided by the present invention includes:

[0017] Step S1: Generate a frequency-modulated chirp signal using FMCW millimeter-wave radar chirp and transmit it to collect radar signal data for estimating the rotational speed of a rotating object;

[0018] Step S2: extracting features related to the rotation speed of the rotating object from the radar received signal, including the initial peak index IFP of the autocorrelation sequence;

[0019] Step S3: separating the rotation speed-related features of the reflected signal of the target rotating object by self-separating the reflected signal of the rotating object;

[0020] Step S4: Based on a rotating object rotation speed search algorithm, the rotation speed of the object is searched using radar signals collected at multiple chirp generation frequencies.

[0021] Preferably, the step S1 includes:

[0022] Step S1.1: Set the chirp signal generation frequency set Where T i is the time interval between two consecutive chirp signal frames corresponding to the i-th chirp signal generation frequency, where H is the size of the chirp signal frequency generation set set in advance;

[0023] Step S1.2: at each chirp generation frequency, N frames of chirp signals are transmitted, and each frame of transmitted chirp signal and its reflected signal are mixed by a mixer to generate a corresponding frame of intermediate frequency signal.

[0024] Preferably, the step S2 includes:

[0025] Step S2.1: Establish a radar signal transmission model for measuring the rotational speed of a rotating object using an FMCW millimeter-wave radar signal. The k-th chirp signal emitted by the radar is expressed as follows:

[0026] S Tx (t)=exp(j(2πf c t+πLt 2 ))

[0027] Among them, f c is the starting frequency of the chirp signal; L is the slope of the chirp signal; j is the imaginary unit; t is the time;

[0028] After being reflected by the effective reflecting surface ERS of the rotating object, the received signal of the radar is expressed as:

[0029]

[0030] Where R(t) is the distance from ERS to radar; α(t) is the path transmission loss; c is the speed of light;

[0031] The intermediate frequency signal is obtained by mixing the transmitted signal and the received signal through a mixer. The expression is:

[0032]

[0033] The radar outputs the sampling sequence s[n] of the intermediate frequency signal s(t). After obtaining the intermediate frequency signal sample s[n] of the kth chirp signal, it is subjected to the distance Fourier transform. The component of the transformed spectrum corresponding to the distance from the ERS to the radar is F k , as an ERS element, its expression is:

[0034] Fk =α((k-1)T I )exp(j4πf c R((k-1)T I ) / c

[0035] Where T I is the time interval between two consecutive frames of radar signals;

[0036] F k is the sampling signal of the continuous signal F(t), and its expression is:

[0037] F(t)=α(t)exp(j4πf c R(t) / c)

[0038] Since the periods of α(t) and R(t) are both T, the period of F(t) is also T, which is the rotation period of the object;

[0039] After obtaining N chirp signal ERS element sequences {F k} k∈[N] Then, find the autocorrelation sequence X of the sequence, where X is a sequence of length N-1, and its p-th element X[p] satisfies:

[0040]

[0041] where F′[n] is given by {F k} k∈[N] The value of the nth point in the sequence, For {F k} k∈[N] The conjugate of the value of the (n+p)th point in the constructed sequence;

[0042] The first maximum peak of X appears at position p * ,satisfy:

[0043] p * T I =mT

[0044] Where m is the rotation period coefficient;

[0045] Step S2.2: decomposing the original chirp signal into a plurality of sub-chirp signals of equal length, and decomposing the original intermediate frequency signal into a plurality of corresponding sub-intermediate frequency signals;

[0046] Step S2.3: Perform Fourier transform on all sub-IF signals to obtain the distance Fourier sequence of each sub-IF signal. After bitwise summing of all distance Fourier sequences, the sum of the ERS elements is the group sum value GSV. The position of the first peak of the autocorrelation sequence obtained according to the GSV sequence is the target extracted IFP value that is robust to approximately centrally rotated objects.

[0047] Preferably, step S3 includes:

[0048] Step S3.1: For k frames of continuously acquired intermediate frequency signals, after chirp decomposition, calculate the autocorrelation sequence of each distance unit sequence, where the sequence length is k;

[0049] Step S3.2: For each obtained set of autocorrelation sequences, find its peak value. The autocorrelation sequence with the highest peak value is the autocorrelation sequence corresponding to the reflected signal of the rotating object, and its IFP value is the IFP value of the target extraction.

[0050] Preferably, the step S4 includes:

[0051] Step S4.1: The radar-based chirp generation frequency is And the IFP set {p i} i∈[H] , calculate the minimum search range D = min i∈[H] p i ;

[0052] Step S4.2: For any integer m∈[D], first assume that the rotation period of the object is Calculate the jth rotation speed of each chirp generation frequency under this rotation period The speed residual under Where round(·) represents the integer function; then the total speed residual under this rotation period is calculated

[0053] Step S4.3: Take the rotation period with the smallest rotation speed residual as the true rotation period of the object, and its reciprocal is the rotation speed of the object.

[0054] The rotating object speed sensing system based on FMCW millimeter wave radar provided by the present invention includes:

[0055] Module M1: Generates and transmits a frequency-modulated chirp signal using FMCW millimeter-wave radar chirp, collecting radar signal data used to estimate the rotational speed of rotating objects.

[0056] Module M2: extracts features related to the rotational speed of the rotating object from the radar received signal, including the initial peak index (IFP) of the autocorrelation sequence;

[0057] Module M3: Separates the rotation speed-related features of the target rotating object's reflected signal by self-separating the reflected signal of the rotating object;

[0058] Module M4: Based on the rotating object rotation speed search algorithm, the radar signals collected at multiple chirp generation frequencies are used to search for the rotation speed of the object.

[0059] Preferably, the module M1 includes:

[0060] Module M1.1: Setting the chirp signal generation frequency set Where T i is the time interval between two consecutive chirp signal frames corresponding to the i-th chirp signal generation frequency, where H is the size of the chirp signal frequency generation set set in advance;

[0061] Module M1.2: At each chirp generation frequency, it transmits N frames of chirp signals. Each frame of transmitted chirp signal and its reflected signal are mixed by a mixer to generate a corresponding frame of intermediate frequency signal.

[0062] Preferably, the module M2 includes:

[0063] Module M2.1: Establish a radar signal transmission model for measuring the rotational speed of a rotating object using an FMCW millimeter-wave radar signal. The expression for the kth chirp signal emitted by the radar is as follows:

[0064] S Tx (t)=exp(j(2πf c t+πLt 2 ))

[0065] Among them, f c is the starting frequency of the chirp signal; L is the slope of the chirp signal; j is the imaginary unit; t is the time;

[0066] After being reflected by the effective reflecting surface ERS of the rotating object, the received signal of the radar is expressed as:

[0067]

[0068] Where R(t) is the distance from ERS to radar; α(t) is the path transmission loss; c is the speed of light;

[0069] The intermediate frequency signal is obtained by mixing the transmitted signal and the received signal through a mixer. The expression is:

[0070]

[0071] The radar outputs the sampling sequence s[n] of the intermediate frequency signal s(t). After obtaining the intermediate frequency signal sample s[n] of the kth chirp signal, it is subjected to the distance Fourier transform. The component of the transformed spectrum corresponding to the distance from the ERS to the radar is F k , as an ERS element, its expression is:

[0072] F k =α((k-1)T I )exp(j4πfc R((k-1)T I ) / c

[0073] Where T I is the time interval between two consecutive frames of radar signals;

[0074] F k is the sampling signal of the continuous signal F(t), and its expression is:

[0075] F(t)=α(t)exp(j4πf c R(t) / c)

[0076] Since the periods of α(t) and R(t) are both T, the period of F(t) is also T, which is the rotation period of the object;

[0077] After obtaining N chirp signal ERS element sequences {F k} k∈[N] Then, find the autocorrelation sequence X of the sequence, where X is a sequence of length N-1, and its p-th element X[p] satisfies:

[0078]

[0079] Among them F ′ [n] is due to {F k} k∈[N] The value of the nth point in the sequence, For {F k} k∈[ N ] The conjugate of the value of the (n+p)th point in the constructed sequence;

[0080] The first maximum peak of X appears at position p * ,satisfy:

[0081] p * T I =mT

[0082] Where m is the rotation period coefficient;

[0083] Module M2.2: decomposes the original chirp signal into multiple sub-chirp signals of equal length, and decomposes the original intermediate frequency signal into multiple corresponding sub-intermediate frequency signals;

[0084] Module M2.3: Perform Fourier transform on all sub-IF signals to obtain the distance Fourier sequence of each sub-IF signal. After bitwise summing of all distance Fourier sequences, the sum of the ERS elements is the group sum value GSV. The position of the first peak of the autocorrelation sequence obtained according to the GSV sequence is the target extracted IFP value that is robust to approximately centrally rotated objects.

[0085] Preferably, the module M3 includes:

[0086] Module M3.1: For k frames of continuously acquired intermediate frequency signals, after chirp decomposition, find the autocorrelation sequence of each range unit sequence, where the sequence length is k;

[0087] Module M3.2: For each set of autocorrelation sequences obtained, find its peak value. The autocorrelation sequence with the highest peak value is the autocorrelation sequence corresponding to the reflected signal of the rotating object, and its IFP value is the IFP value of the target extraction.

[0088] Preferably, the module M4 includes:

[0089] Module M4.1: Radar-based chirp generation frequency is And the IFP set {p i} i∈[ H ] , calculate the minimum search range D = min i∈[ H ] p i ;

[0090] Module M4.2: For any integer m∈[D], first assume that the rotation period of the object is Calculate the jth rotation speed of each chirp generation frequency under this rotation period The speed residual under Where round(·) represents the integer function; then the total speed residual under this rotation period is calculated

[0091] Module M4.3: Take the rotation period with the smallest speed residual as the object's true rotation period, and its reciprocal is the object's rotation speed.

[0092] Compared with the prior art, the present invention has the following beneficial effects:

[0093] (1) Compared with the traditional method for measuring the rotational speed of rotating objects, the present invention is more robust to the light reflectivity of the object being measured and the ambient light, and has higher measurement accuracy and a longer measurement distance;

[0094] (2) The present invention utilizes FMCW millimeter-wave radar to continuously measure a rotating target object and accurately estimates the target's rotation speed by analyzing the radar's continuous measurement results. This can achieve high-precision, long-distance, and low-cost rotation speed measurement, overcome the main limitations of the existing technology, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0096] Figure 1 is an example diagram of the system flow in an embodiment of the present invention;

[0097] Figure 2 2 is a diagram of a rotating object speed sensing system based on FMCW millimeter wave radar in an embodiment of the present invention;

[0098] Figure 3a is the autocorrelation sequence of the ERS element obtained experimentally, Figure 3b This is the GSV autocorrelation sequence diagram obtained from the experiment. DETAILED DESCRIPTION

[0099] The present invention will be described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the scope of the present invention. These all fall within the scope of protection of the present invention.

[0100] Example 1:

[0101] The embodiment of the present invention provides a method for sensing the rotation speed of a rotating object based on FMCW millimeter wave radar. Figure 1 and Figure 2 As shown, the method specifically includes the following contents:

[0102] Step S1: Data collection: a method for transmitting a chirp signal with a frequency change generated by FMCW millimeter-wave radar chirp is proposed to collect radar signal data that can be used to estimate the rotation speed of a rotating object;

[0103] Step S2: Propose a method for extracting features related to the rotation speed of the rotating object from the radar received signal, and extract the features related to the rotation speed of the rotating object, namely, the initial peak index IFP of the autocorrelation sequence;

[0104] Step S3: Propose a self-separation method for the reflected signal of the rotating object to separate the rotation speed related features of the reflected signal of the target rotating object;

[0105] Step S4: A rotating object rotation speed search algorithm is proposed to accurately search the rotation speed of the object using radar signals collected at multiple chirp generation frequencies.

[0106] The step S1 comprises:

[0107] Step S1.1: Set the chirp signal generation frequency set Where T iis the time interval between two consecutive frames of chirp signals corresponding to the chirp signal generation frequency in the i-th frame.

[0108] Step S1.2: at each chirp generation frequency, N frames of chirp signals are transmitted, and each frame of transmitted chirp signal and its reflected signal are mixed by a mixer to generate a corresponding frame of intermediate frequency signal.

[0109] The step S2 comprises:

[0110] Step S2.1: Establish a radar signal transmission model for measuring the rotational speed of a rotating object using an FMCW millimeter-wave radar signal. Specifically, the kth chirp signal emitted by the radar is expressed as follows:

[0111] S Tx (t)=exp(j(2πf c t+πLt 2 ))

[0112] where f c is the starting frequency of the chirp signal, and L is the slope of the chirp signal. After being reflected by the effective reflecting surface (ERS) of the rotating object, the received signal of the radar is expressed as:

[0113]

[0114] Where R(t) is the distance from the ERS to the radar, and α(t) is the path transmission loss. The intermediate frequency signal is obtained by mixing the transmitted signal and the received signal through a mixer:

[0115]

[0116] The radar outputs the sampling sequence s[n] of the intermediate frequency signal s(t).

[0117] After obtaining the intermediate frequency signal sample s[n] of the kth chirp signal, perform the distance Fourier transform on it. The component of the transformed spectrum corresponding to the distance from the ERS to the radar is F k , called ERS element, its expression is:

[0118] F k =α((k-1)T I )exp(j4πf c R((k-1)T I ) / c

[0119] In particular, F k is the sampling signal of the continuous signal F(t), and its expression is:

[0120] F(t)=α(t)exp(j4πf cR(t) / c)

[0121] Since the periods of α(t) and R(t) are both T, the period of F(t) is also T, which is the rotation period of the object.

[0122] After obtaining N chirp signal ERS element sequences {F k} k∈[N] Then, find the autocorrelation sequence X of the sequence, where X is a sequence of length N-1, and its p-th element X[p] satisfies:

[0123]

[0124] According to the properties of the autocorrelation sequence, the first maximum peak of X appears at position p * ,satisfy:

[0125] p * T I =mT

[0126] Where m is called the rotation period coefficient, which is a positive integer of unknown value, and is called the index of the first peak (IFP).

[0127] Measuring the rotational speed of rotating objects requires extracting signal features that disambiguate nearly centrosymmetric objects. Specifically, if the autocorrelation sequence of the ERS element of a nearly centrosymmetric object is calculated, as shown in Figure 3, in addition to the peaks corresponding to periodic rotation, there are also many ambiguous peaks due to the high similarity between the centrosymmetric parts of the object. If these ambiguous peaks are mistakenly interpreted as peaks corresponding to periodic rotation, the obtained IFP value will be incorrect, resulting in an error in the calculated object's rotation period. To address this issue, a feature extraction algorithm with disambiguation is proposed. This algorithm consists of two parts: chirp decomposition and feature extraction.

[0128] Step S2.2: In the chirp decomposition part, we decompose the original chirp signal into multiple sub-chirp signals of equal length, and decompose the original intermediate frequency signal into corresponding multiple sub-intermediate frequency signals using the same decomposition method.

[0129] Step S2.3: In the feature extraction section, we first perform a Fourier transform on all sub-IF signals to obtain the bitwise summation of the distance Fourier sequence of each sub-IF signal. The sum of the ERS elements is called the group sum value (GSV). We note that the difference between the GSV values of different centrosymmetric parts of a nearly centrosymmetric object is greater than the ERS elements after the Fourier transform of the original IF signal, because the difference in GSV is the cumulative value of the difference in ERS elements. Therefore, if the autocorrelation sequence of the GSV sequence is calculated, the ambiguous peaks will be greatly reduced. The position of the first peak of the autocorrelation sequence obtained from the GSV sequence is the IFP value that we aim to extract that is robust to nearly centrally rotated objects.

[0130] The step S3 comprises:

[0131] Although the IFP value of the GSV sequence's autocorrelation sequence is a rotational feature that can disambiguate, obtaining it requires first determining the ERS element. This requires either knowing the distance between the rotating target and the radar or the frequency of the intermediate frequency signal corresponding to the target's reflected signal. To address this issue, we made the key observation that only the autocorrelation sequence corresponding to the signal reflected from the rotating object has a distinct peak. This information allows us to determine whether the signal corresponds to a rotating object.

[0132] Step S3.1: For k frames of intermediate frequency signals obtained continuously, after chirp decomposition, calculate the autocorrelation sequence of each distance unit sequence (length is k).

[0133] Step S3.2: For each obtained set of autocorrelation sequences, calculate its peak value. The autocorrelation sequence with the highest peak value is the autocorrelation sequence corresponding to the reflected signal of the rotating object, and its IFP value is the IFP value we want to extract.

[0134] The step S4 comprises:

[0135] Step S4.1: Calculate the minimum search range D=min i∈[ H ] p i ;

[0136] Step S4.2: For any integer m∈[D], perform the following operation:

[0137] Step S4.2.1: First assume that the rotation period of the object is

[0138] Step S4.2.2: Calculate the frequency of each chirp generated under this rotation period. The speed residual u j : r j′ =round(r j ),u j =|r j -r j '|;

[0139] Step S4.2.3: Calculate the total rotational speed residual for this rotation period

[0140] Step S4.3: Take the rotation period with the smallest rotation speed residual as the true rotation period of the object, and its reciprocal is the rotation speed of the object.

[0141] Example 2:

[0142] The present invention also provides a rotating object speed sensing system based on FMCW millimeter wave radar. The rotating object speed sensing system based on FMCW millimeter wave radar can be implemented by executing the process steps of the rotating object speed sensing method based on FMCW millimeter wave radar. That is, those skilled in the art can understand the rotating object speed sensing method based on FMCW millimeter wave radar as the operating mode of the rotating object speed sensing system based on FMCW millimeter wave radar.

[0143] Module M1: Generates a frequency-modulated chirp signal using FMCW millimeter-wave radar chirp to collect radar signal data that can be used to estimate the rotational speed of a rotating object.

[0144] Module M2: Feature IFP related to the rotational speed of rotating objects;

[0145] Module M3: Rotating object reflection signal self-separation method, which separates the rotation speed related features of the reflection signal of the target rotating object;

[0146] Module M4: Accurately search for the rotation speed of an object using radar signals collected at multiple chirp generation frequencies.

[0147] Preferably, the step M1 includes:

[0148] Module M1.1: Setting the chirp signal generation frequency set Where T i is the time interval between two consecutive frames of chirp signals corresponding to the chirp signal generation frequency in the i-th frame.

[0149] Module M1.2: At each chirp generation frequency, it transmits N frames of chirp signals. Each frame of transmitted chirp signal and its reflected signal are mixed by a mixer to generate a corresponding frame of intermediate frequency signal.

[0150] Preferably, the step M2 includes:

[0151] Module M2.1: Decompose the original chirp signal into multiple sub-chirp signals of equal length, and decompose the original intermediate frequency signal into multiple corresponding sub-intermediate frequency signals using the same decomposition method.

[0152] Module M2.3: First, all sub-IF signals are Fourier transformed to obtain the distance Fourier sequence of each sub-IF signal and sum them bitwise. The sum of the ERS elements is called the group sum value (GSV). We note that the difference between the GSV values of different centrosymmetric parts of a nearly centrosymmetric object is greater than the ERS elements after the Fourier transform of the original IF signal, because the difference in GSV is the cumulative value of the difference in ERS elements. Therefore, if the autocorrelation sequence of the GSV sequence is calculated, the ambiguous peaks will be significantly reduced. The position of the first peak in the autocorrelation sequence obtained from the GSV sequence is the IFP value that we aim to extract that is robust to nearly central rotation of the object.

[0153] The step M3 includes:

[0154] Module M3.1: For the intermediate frequency signal obtained continuously for k frames, after chirp decomposition, calculate the autocorrelation sequence of each distance unit sequence (length is k).

[0155] Module M3.2: For each obtained autocorrelation sequence, find its peak value. The autocorrelation sequence with the highest peak value is the autocorrelation sequence corresponding to the reflected signal of the rotating object, and its IFP value is the IFP value we want to extract.

[0156] The step M4 includes:

[0157] Assume that the radar chirp generation frequency in step S1 is At each chirp generation frequency, the obtained IFP set is {p i} i∈[H] , the module takes these two sets as input and follows the steps below:

[0158] Module M4.1: Calculate the minimum search range D = min i∈[H] p i ;

[0159] Module M4.2: For any integer m∈[D], perform the following operation:

[0160] Module M4.2.1: First, assume that the rotation period of the object is

[0161] Module M4.2.2: Calculate the frequency of each chirp generated under this rotation period The speed residual u j : rj ′=round(r j ),u j =|r j -r j '|;

[0162] Module M4.2.3: Calculate the total speed residual for the rotation period

[0163] Module M4.3: Take the rotation period with the smallest speed residual as the object's true rotation period, and its reciprocal is the object's rotation speed.

[0164] Those skilled in the art will appreciate that, in addition to implementing the system, device, and various modules provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, and the like by logically programming the method steps. Therefore, the system, device, and various modules provided by the present invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; the modules for implementing various functions can also be considered both software programs for implementing the method and structures within the hardware component.

[0165] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. A method for sensing the rotation speed of a rotating object based on FMCW millimeter wave radar, characterized in that: include: Step S1: Generate a frequency-modulated chirp signal using FMCW millimeter-wave radar chirp and transmit it to collect radar signal data for estimating the rotational speed of a rotating object; Step S2: extracting features related to the rotation speed of the rotating object from the radar received signal, including the initial peak index IFP of the autocorrelation sequence; Step S3: separating the rotation speed-related features of the reflected signal of the target rotating object by self-separating the reflected signal of the rotating object; Step S4: Based on a rotating object rotation speed search algorithm, the rotation speed of the object is searched using radar signals collected at multiple chirp generation frequencies.

2. The method for sensing the rotation speed of a rotating object based on FMCW millimeter wave radar according to claim 1, characterized in that: The step S1 comprises: Step S1.1: Set the chirp signal generation frequency set Where T i is the time interval between two consecutive chirp signal frames corresponding to the i-th chirp signal generation frequency, where H is the size of the chirp signal frequency generation set set in advance; Step S1.2: at each chirp generation frequency, N frames of chirp signals are transmitted, and each frame of transmitted chirp signal and its reflected signal are mixed by a mixer to generate a corresponding frame of intermediate frequency signal.

3. The method for sensing the rotation speed of a rotating object based on FMCW millimeter wave radar according to claim 1, characterized in that: The step S2 comprises: Step S2.1: Establish a radar signal transmission model for measuring the rotational speed of a rotating object using an FMCW millimeter-wave radar signal. The k-th chirp signal emitted by the radar is expressed as follows: S Tx (t)=exp(j(2πf c t+πLt 2 )) Among them, f c is the starting frequency of the chirp signal; L is the slope of the chirp signal; j is the imaginary unit; t is the time; After being reflected by the effective reflecting surface ERS of the rotating object, the received signal of the radar is expressed as: Where R(t) is the distance from ERS to radar; α(t) is the path transmission loss; c is the speed of light; The intermediate frequency signal is obtained by mixing the transmitted signal and the received signal through a mixer. The expression is: The radar outputs the sampling sequence s[n] of the intermediate frequency signal s(t). After obtaining the intermediate frequency signal sample s[n] of the kth chirp signal, it is subjected to the distance Fourier transform. The component of the transformed spectrum corresponding to the distance from the ERS to the radar is F k , as an ERS element, its expression is: F k =α((k-1)T I )exp(j4πf c R((k-1)T I ) / c Where T I is the time interval between two consecutive frames of radar signals; F k is the sampling signal of the continuous signal F(t), and its expression is: F(t)=α(t)exp(j4πf c R(t) / c) Since the periods of α(t) and R(t) are both T, the period of F(t) is also T, which is the rotation period of the object; After obtaining N chirp signal ERS element sequences {F k } k∈[N] Then, find the autocorrelation sequence X of the sequence, where X is a sequence of length N-1, and its p-th element X[p] satisfies: Among them F ′ [n] is due to {F k } k∈[N] The value of the nth point in the sequence, For {F k } k∈[N] The conjugate of the value of the (n+p)th point in the constructed sequence; The first maximum peak of X appears at position p * ,satisfy: p * T I =mT Where m is the rotation period coefficient; Step S2.2: decomposing the original chirp signal into a plurality of sub-chirp signals of equal length, and decomposing the original intermediate frequency signal into a plurality of corresponding sub-intermediate frequency signals; Step S2.3: Perform Fourier transform on all sub-IF signals to obtain the distance Fourier sequence of each sub-IF signal. After bitwise summing of all distance Fourier sequences, the sum of the ERS elements is the group sum value GSV. The position of the first peak of the autocorrelation sequence obtained according to the GSV sequence is the target extracted IFP value that is robust to approximately centrally rotated objects.

4. The method for sensing the rotation speed of a rotating object based on FMCW millimeter wave radar according to claim 1, characterized in that: The step S3 comprises: Step S3.1: For k frames of continuously acquired intermediate frequency signals, after chirp decomposition, calculate the autocorrelation sequence of each distance unit sequence, where the sequence length is k; Step S3.2: For each obtained set of autocorrelation sequences, find its peak value. The autocorrelation sequence with the highest peak value is the autocorrelation sequence corresponding to the reflected signal of the rotating object, and its IFP value is the IFP value of the target extraction.

5. A rotating object speed sensing system based on FMCW millimeter wave radar, characterized in that: include: Module M1: Generates and transmits a frequency-modulated chirp signal using FMCW millimeter-wave radar chirp, collecting radar signal data used to estimate the rotational speed of rotating objects. Module M2: extracts features related to the rotational speed of the rotating object from the radar received signal, including the initial peak index (IFP) of the autocorrelation sequence; Module M3: Separates the rotation speed-related features of the target rotating object's reflected signal by self-separating the reflected signal of the rotating object; Module M4: Based on the rotating object rotation speed search algorithm, the radar signals collected at multiple chirp generation frequencies are used to search for the rotation speed of the object.

6. The rotating object speed sensing system based on FMCW millimeter wave radar according to claim 5, characterized in that: The module M1 includes: Module M1.1: Setting the chirp signal generation frequency set Where T i is the time interval between two consecutive chirp signal frames corresponding to the i-th chirp signal generation frequency, where H is the size of the chirp signal frequency generation set set in advance; Module M1.2: At each chirp generation frequency, it transmits N frames of chirp signals. Each frame of transmitted chirp signal and its reflected signal are mixed by a mixer to generate a corresponding frame of intermediate frequency signal.

7. The rotating object speed sensing system based on FMCW millimeter wave radar according to claim 5, characterized in that: The module M2 includes: Module M2.1: Establish a radar signal transmission model for measuring the rotational speed of a rotating object using an FMCW millimeter-wave radar signal. The expression for the kth chirp signal emitted by the radar is as follows: S Tx (t)=exp(j(2πf c t+πLt 2 )) Among them, f c is the starting frequency of the chirp signal; L is the slope of the chirp signal; j is the imaginary unit; t is the time; After being reflected by the effective reflecting surface ERS of the rotating object, the received signal of the radar is expressed as: Where R(t) is the distance from ERS to radar; α(t) is the path transmission loss; c is the speed of light; The intermediate frequency signal is obtained by mixing the transmitted signal and the received signal through a mixer. The expression is: The radar outputs the sampling sequence s[n] of the intermediate frequency signal s(t). After obtaining the intermediate frequency signal sample s[n] of the kth chirp signal, it is subjected to the distance Fourier transform. The component of the transformed spectrum corresponding to the distance from the ERS to the radar is F k , as an ERS element, its expression is: F k =α((k-1)T I )exp(j4πf c R((k-1)T I ) / c Where T I is the time interval between two consecutive frames of radar signals; F k is the sampling signal of the continuous signal F(t), and its expression is: F(t)=α(t)exp(j4πf c R(t) / c) Since the periods of α(t) and R(t) are both T, the period of F(t) is also T, which is the rotation period of the object; After obtaining N chirp signal ERS element sequences {F k } k∈[N] Then, find the autocorrelation sequence X of the sequence, where X is a sequence of length N-1, and its p-th element X[p] satisfies: Among them F ′ [n] is due to {F k } k∈[N] The value of the nth point in the sequence, For {F k } k∈[N] The conjugate of the value of the (n+p)th point in the constructed sequence; The first maximum peak of X appears at position p * ,satisfy: p * T I =mT Where m is the rotation period coefficient; Module M2.2: decomposes the original chirp signal into multiple sub-chirp signals of equal length, and decomposes the original intermediate frequency signal into multiple corresponding sub-intermediate frequency signals; Module M2.3: Perform Fourier transform on all sub-IF signals to obtain the distance Fourier sequence of each sub-IF signal. After bitwise summing of all distance Fourier sequences, the sum of the ERS elements is the group sum value GSV. The position of the first peak of the autocorrelation sequence obtained according to the GSV sequence is the target extracted IFP value that is robust to approximately centrally rotated objects.

8. The rotating object speed sensing system based on FMCW millimeter wave radar according to claim 5, characterized in that: The module M3 includes: Module M3.1: For k frames of continuously acquired intermediate frequency signals, after chirp decomposition, find the autocorrelation sequence of each range unit sequence, where the sequence length is k; Module M3.2: For each set of autocorrelation sequences obtained, find its peak value. The autocorrelation sequence with the highest peak value is the autocorrelation sequence corresponding to the reflected signal of the rotating object, and its IFP value is the IFP value of the target extraction.

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