KR20200004583A - Method and apparatus for detecting object - Google Patents

Abstract

Provided are a method for detecting an object and an apparatus thereof. The method for detecting an object determines a first chirp sequence including a preset number of chirp signals in a first order to detect the object, determines a second chirp sequence including the chirp signals in a second order different from the first order, transmits a transmission signal including the first chirp sequence and the second chirp sequence, generates a mixed signal based on the transmitted signal and a received signal, and detects the object based on the mixed signal.

Description

Object detection method and apparatus {METHOD AND APPARATUS FOR DETECTING OBJECT}

The following embodiments relate to a method and apparatus for detecting an object, and more particularly, to a method and apparatus for detecting an object by removing a false object generated by noise or a surrounding radar signal.

An object detecting apparatus mounted on a vehicle detects an object around the vehicle using a radar. Automotive radars use Frequency Modulated Continuous Waveform (FMCW). The object detecting apparatus propagates a predetermined waveform around the vehicle and receives a signal reflected by the object. The object detecting apparatus generates a bit signal by down-converting a received signal and a transmitted signal, samples a predetermined sampling period, and converts the sampled signal into a frequency domain through fast fourier transformation (FFT). The bit frequency of the object is calculated using a constant false alarm rate (CFAR) detection method for the signal converted into the frequency domain. Based on the calculated bit frequency, the distance and relative velocity of the object is calculated.

One embodiment may provide an apparatus and a method for detecting an object.

One embodiment can provide an object detection apparatus and method for removing false objects caused by noise and interference signals.

According to an aspect, an object detecting method includes determining a first chirp sequence including a preset number of chirp signals in a first order, and performing the chirp signals in a second order different from the first order. Determining a second chirp sequence comprising, transmitting a transmission signal comprising the first chirp sequence and the second chirp sequence, receiving a received signal, the transmitted signal and the received signal Generating a mixing signal based on the step S, and detecting the object based on the mixed signal.

The determining of the second chirp sequence may include assigning a chirp signal that is different from a chirp signal allocated to a first time slot of the first chirp sequence to a first time slot of the second chirp sequence. .

The maximum frequencies of each of the preset number of chirp signals may be different from each other.

The predetermined number of chirp signals may appear in the form of sawtooth waves or triangle waves on the time axis and the frequency axis.

The determining of the second chirp sequence may include determining the second order so as not to correspond to the cyclic permutation of the first order.

The object detecting method may further include generating the transmission signal such that the first chirp sequence and the second chirp sequence are continuous in time.

Detecting an object based on the mixed signal comprises: extracting one or more bit frequencies for each of the chirp signals, determining a bit frequency for the object among the extracted bit frequencies, and the determined Detecting the object based on a bit frequency.

Determining a bit frequency for the object of the extracted bit frequencies, comprising determining a bit frequency for the object of the extracted bit frequencies using a constant false alarm rate (CFAR) detection method can do.

Determining a bit frequency for the object of the extracted bit frequencies, calculating at least four straight lines for the axis of distance and the axis of relative speed based on the bit frequencies for the two intervals, Calculating one or more intersections represented by the at least four straight lines, and determining a bit frequency for the object based on the one or more intersections.

Determining a bit frequency for an object based on the one or more intersections includes determining a intersection group corresponding to each of the one or more object candidates, calculating a distance between the intersections in the intersection group, and calculating And determining a bit frequency for the object based on a distance of a predetermined intersection group and a preset threshold value.

The determining of the bit frequency for the object based on the one or more intersections may include determining the number of extracted bit frequencies for the interval having the smallest interference-to-noise ratio (INR) value as the maximum object number; And determining a final object based on the maximum number of objects.

According to another aspect, an object detecting apparatus includes a memory in which a program for detecting an object is recorded, and a processor for executing the program, wherein the program includes a predetermined number of chirp signals in a first order. Determining a first chirp sequence including the first chirp sequence; determining a second chirp sequence including the chirp signals in a second order different from the first order; and determining the first chirp sequence and the second chirp sequence. Transmitting a transmission signal, including a transmission signal, receiving a reception signal, generating a mixing signal based on the transmission signal and the received signal, and generating an object based on the mixed signal. The detecting step is performed.

The object detecting apparatus may further include a radar that transmits the transmission signal by using a multiple input multiple output (MIMO) antenna and receives the reception signal.

The object detecting apparatus may be included in a vehicle, and the object detecting apparatus may be used to detect an object located around the vehicle.

According to yet another aspect, a method of calculating accuracy of object detection may include generating a bit signal of an object based on an object profile, generating an interference bit signal based on an interference profile, generating white noise, and Generating a merge signal by merging a bit signal, the interference bit signal, and the white noise, sampling the merge signal, and applying a fast fourier transform (FFT) to the sampled merged signal; CFAR to the result of the FFT (constant false alarm rate) detecting the object by applying a detection method, and calculating the accuracy of object detection based on the detected object and object profile.

The generating of the bit signal of the object may include determining a first chirp sequence including a predetermined number of chirp signals in a first order, and the chirp in a second order different from the first order. Determining a second chirp sequence comprising signals, generating a transmission signal comprising the first chirp sequence and the second chirp sequence, generating a received signal based on a distance of a preset object, and Generating a mixing signal as the bit signal based on the transmission signal and the received signal.

The determining of the second chirp sequence may include assigning a chirp signal different from the chirp signal allocated to the first time slot of the first chirp sequence to the first time slot of the second chirp sequence. .

According to another aspect, an apparatus for calculating the accuracy of object detection includes a memory in which a program for calculating the accuracy of object detection is recorded, and a processor for executing the program, the program based on an object profile Generating a bit signal of the object, generating an interference bit signal based on the interference profile, generating white noise, generating a merge signal by merging the bit signal, the interference bit signal and the white noise Sampling the merge signal and applying fast fourier transformation (FFT) to the sampled merge signal; detecting an object by applying a constant false alarm rate (CFAR) detection method to the result of the FFT; and detecting To calculate the accuracy of object detection based on compiled objects and object profiles Perform the system.

An object detecting apparatus and method using a radar may be provided.

An object detecting apparatus and method for removing false objects caused by noise and interference signals may be provided.

1 illustrates an object detection method according to an example.
2 is a block diagram of an object detecting apparatus according to an exemplary embodiment.
3 is a flowchart of an object detecting method according to an exemplary embodiment.
4 illustrates a first chirp sequence and a second chirp sequence, according to an example.
5 is a flowchart of a method of detecting an object based on a mixed signal according to an example.
6 is a flowchart of a method of detecting an object based on bit frequencies according to an example.
7 shows straight lines drawn in distance-relative velocity planes based on chirp signals to solve a multi-target problem according to an example.
8 is a flowchart of a method of determining a bit frequency for an object based on a distance of an intersection group according to an example.
9 shows straight lines with different slopes drawn in the distance-relative velocity planes based on chirp signals to solve a multi-target problem according to an example.
10 illustrates a method of determining a final object based on the determined maximum number of objects according to an example.
11 illustrates a method for calculating the accuracy of object detection according to an example.
12 illustrates object detection probabilities according to object detection methods according to an example.
13 illustrates the number of detected false targets according to object detection methods according to an example.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these embodiments. Like reference numerals in the drawings denote like elements.

Various modifications may be made to the embodiments described below. The examples described below are not intended to be limited to the embodiments and should be understood to include all modifications, equivalents, and substitutes for them.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of examples. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as "comprise" or "have" are intended to indicate that there is a feature, number, step, action, component, part, or combination thereof described on the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in the commonly used dictionaries should be construed as having meanings consistent with the meanings in the context of the related art and shall not be construed in ideal or excessively formal meanings unless expressly defined in this application. Do not.

In addition, in the description with reference to the accompanying drawings, the same components regardless of reference numerals will be given the same reference numerals and duplicate description thereof will be omitted. In the following description of the embodiment, when it is determined that the detailed description of the related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

1 illustrates an object detection method according to an example.

As a method for detecting an object 120 (eg, a person, etc.) located in the vicinity of the vehicle 110, the transmission signal 112 propagates to the periphery of the vehicle 110 and is reflected by the object 120. A method for detecting the reflected signal 114 can be considered. For example, the radar of the vehicle 110 propagates the transmission signal 112 for the detection of the object 120 to the periphery of the vehicle 110 and reflects the reflected signal 114 reflected by the object 120. The distance between the vehicle 110 and the object 120 may be calculated by calculating a time of flight (ToF), and the relative speed of the object 120 may be calculated in consideration of the Doppler effect.

The signal received by the radar may further include a transmission signal 132 and noise transmitted from another vehicle 130 in addition to the reflection signal 114. The transmission signal 132 and the noise transmitted by the other vehicle 130 may cause a false target (or a false object) to be detected. Accordingly, a method of including a feature in the transmission signal 112 to be distinguished from other signals may be considered.

The radar may propagate a modulated transmission signal to the periphery of the vehicle 110 to distinguish noise such as the reflected signal 114 reflected from the object 120 and the other transmission signal 132. According to an aspect, the modulated transmission signal 132 may be a frequency modulated continuous waveform (FMCW). For example, the signal of the FMCW may be a signal whose frequency is modulated based on 79 gigahertz (GHz). The bandwidth (BW) of the signal of the FMCW may be 77 GHz to 81 GHz, but is not limited thereto. Higher resolution can be provided when millimeter waves in the 79 GHz band are used.

For example, the radar may include transmit antennas for transmitting a signal, and the transmit antennas may be arranged at different direction angles, or may be arranged or implemented to adjust the direction angles. The radar may include a receiving antenna for receiving a signal, and the receiving antennas may be arranged at different directivity angles, or may be arranged or implemented to adjust the directivity angles. That is, the radar may include a multiple input multiple output (MIMO) antenna.

For example, when the object 120 is detected through the reflection signal 114, the vehicle 110 may inform the driver of the information of the object 120. As another example, the vehicle 110 may assist the driver through advanced driver assistance systems (ADAS). As another example, when the vehicle 110 is an autonomous vehicle, the vehicle 110 may set a driving route of the vehicle based on the object 120. For example, the information of the object 120 may include the position, speed, and direction of the object 120.

The object detection method will be described in detail below with reference to FIGS. 2 to 11.

2 is a block diagram of an object detecting apparatus according to an exemplary embodiment.

The object detecting apparatus 200 includes a communication unit 210, a processor 220, and a memory 230. The object detecting apparatus 200 may be included in a vehicle. For example, the object detecting apparatus 200 may be an electronic control unit (ECU) of a vehicle. As another example, the object detecting apparatus 200 may be connected to the ECU of the vehicle.

The communication unit 210 is connected to the processor 220 and the memory 230 to transmit and receive data. The communication unit 210 may be connected to another external device to transmit and receive data. The communication unit 210 may include the radar described above with reference to FIG. 1.

The communication unit 210 may be implemented as a circuit in the object detecting apparatus 200. For example, the communication unit 210 may include an internal bus and an external bus. As another example, the communication unit 210 may be an element connecting the object detection apparatus 200 and an external device. The communication unit 210 may be an interface. The communication unit 210 may receive data from an external device and transmit data to the processor 220 and the memory 230.

The processor 220 processes data received by the communication unit 210 and data stored in the memory 230. A "processor" may be a data processing device implemented in hardware having circuitry having a physical structure for performing desired operations. For example, desired operations may include code or instructions included in a program. For example, data processing devices implemented in hardware may include a microprocessor, a central processing unit, a processor core, a multi-core processor, and a multiprocessor. , An application-specific integrated circuit (ASIC), and a field programmable gate array (FPGA).

The processor 220 executes computer readable code (eg, software) stored in the memory (eg, memory 230) and instructions caused by the processor 220.

The memory 230 stores data received by the communication unit 210 and data processed by the processor 220. For example, the memory 230 may store a program. The stored program may be a set of syntaxes coded to detect an object and executable by the processor 220.

According to one aspect, the memory 230 may include one or more volatile memory, nonvolatile memory and random access memory (RAM), flash memory, hard disk drive, and optical disk drive.

The memory 230 stores a set of instructions (eg, software) for operating the object detecting apparatus 200. The instruction set for operating the object detecting apparatus 200 is executed by the processor 220.

The communication unit 210, the processor 220, and the memory 230 will be described in detail with reference to FIGS. 3 to 11 below.

3 is a flowchart of an object detecting method according to an exemplary embodiment.

Steps 310 to 370 below are performed by the object detecting apparatus 200 described above with reference to FIG. 2.

In operation 310, the object detecting apparatus 200 determines a first chirp sequence including a preset number of chirp signals in a first order. For example, the preset number may be four. The four chirp signals a ₁ , a ₂ , a ₃ , a ₄ all have the same center frequency, and the maximum frequency of each of the four chirp signals may be different from each other. For example, the first order may be an order of the first chirp signal a ₁ , the second chirp signal a ₂ , the third chirp signal a ₃ , and the fourth chirp signal a ₄ .

The chirp sequence may be divided into four time slots, and each time slot may be named first time slot, second time slot, third time slot, and fourth time slot from the front. In a first order, a first chirp signal a ₁ is allocated to a first time slot, a second chirp signal a ₂ is assigned to a _second time slot, and a third chirp signal a ₃ is assigned to a third time slot. ) May be allocated, and the fourth chirp signal a ₄ may be allocated to the fourth time slot. The first chirp sequence may be a signal in which a first chirp signal, a second chirp signal, a third chirp signal, and a fourth chirp signal are continuously connected.

For example, the chirp signal may appear in the form of sawtooth or triangle waves on the time axis and the frequency axis. An i-th chirp sequence including chirp signals in the form of a sawtooth wave or triangle wave is represented by Equation 1 below.

는 제i 처프 시퀀스이고, N은 처프 시퀀스에 포함되는 미리 설정된 처프 신호들의 개수이고,

는 제i 처프 시퀀스의 제i 순서에 의해 각 시간 슬롯에 할당된 처프 신호의 기울기이다. 예를 들어, 제1 순서에 따르면, 제1 처프 신호, 제2 처프 신호, 제3 처프 신호 및 제4 처프 신호가 순서대로

에 대응한다. T는 하나의 처프 신호의 지속 기간(time duration)이다. 아래에서 도 4를 참조하여 제1 처프 시퀀스에 대해 설명된다.In [Equation 1],

Is the i th chirp sequence, N is the number of preset chirp signals included in the chirp sequence,

Is the slope of the chirp signal assigned to each time slot by the i th order of the i th chirp sequence. For example, according to the first order, the first chirp signal, the second chirp signal, the third chirp signal, and the fourth chirp signal are in order.

Corresponds to. T is the time duration of one chirp signal. The first chirp sequence is described below with reference to FIG. 4.

In operation 320, the object detecting apparatus 200 determines a second chirp sequence including a preset number of chirp signals in a second order. The second order is different from the first order.

A chirp signal different from the chirp signal allocated to the first time slot of the first chirp sequence according to an aspect may be allocated to the first time slot of the second chirp sequence. When the first chirp signal is assigned to the first time slot of the first chirp sequence, the second chirp signal, the third chirp signal, and the fourth chirp signal, not the first chirp signal, are assigned to the first time slot of the second chirp sequence. Can be assigned.

According to another aspect, the second order may be determined not to correspond to the cyclic permutation of the first order. When the first order is the order of the first chirp signal a ₁ , the second chirp signal a ₂ , the third chirp signal a ₃ and the fourth chirp signal a ₄ , for example, The cyclic permutation of the order may be an order of the second chirp signal a ₂ , the third chirp signal a ₃ , the fourth chirp signal a ₄ , and the first chirp signal a ₁ .

For example, the determined second order may be an order of the second chirp signal a ₂ , the fourth chirp signal a ₄ , the first chirp signal a ₁ , and the third chirp signal a ₃ . A second chirp sequence is described below with reference to FIG. 4.

In operation 330, the object detecting apparatus 200 generates a transmission signal such that the first chirp sequence and the second chirp sequence are continuous in time. The transmission signal may further include a third chirp sequence that is temporally coupled with the second chirp sequence. The third chirp sequence includes a preset number of chirp signals in a third order. The third order is different from the second order.

In operation 340, the object detecting apparatus 200 transmits (or propagates) a transmission signal to the periphery of the object detecting apparatus 200. For example, the object detecting apparatus 200 may transmit a transmission signal using a radar.

In operation 350, the object detecting apparatus 200 receives a received signal. The received signal may include reflected signals reflected by the object, noise and transmitted signals transmitted by other radars. When the object is detected using the received signal as it is, a false object (or a false target) may be detected in addition to the real object. The following steps 360 and 370 are performed to detect and remove false objects.

In operation 360, the object detecting apparatus 200 generates a mixing signal based on the transmission signal and the reception signal. The mixed signal may be a signal obtained by low pass filtering or down-converting a product of a transmission signal and a reception signal. When the received signal is expressed by Equation 2 below, the mixed signal may be expressed by Equation 3 below.

는 수신 신호이고,

는 반사 신호이고,

는 간섭에 의한 신호이고,

는 잡음 신호이다.

및

는 감쇠 계수(attenuation coefficient)들이고,

는 시간 지연(time delay)이고,

은 간섭 신호들의 개수이고,

는 기울기 순서 인덱스(slope sequence index)이고,

는 타겟 오브젝트들의 개수이고,

는 간섭들의 개수이다.In Equation 2,

Is the received signal,

Is the reflected signal,

Is a signal caused by interference,

Is a noise signal.

And

Are attenuation coefficients,

Is the time delay,

Is the number of interfering signals,

Is a slope sequence index,

Is the number of target objects,

Is the number of interferences.

는 혼합 신호이고,

는 비트 신호(beat signal)이고,

는 간섭 비트 신호(interference beat signal)이고,

는 잡음 신호이다.In Equation 3,

Is a mixed signal,

Is a beat signal,

Is an interference beat signal,

Is a noise signal.

According to an aspect, the object detecting apparatus 200 may remove a false object by using a constant false alarm rate (CFAR) detection method, extract one or more bit frequencies, and detect the object by using the extracted bit frequencies. have. A method of detecting an object by removing a false object using CFAR is described in detail with reference to FIGS. 5 to 10 below.

4 illustrates a first chirp sequence and a second chirp sequence, according to an example.

The first chirp sequence 410 may include a first chirp signal a ₁ , a second chirp signal a ₂ , a third chirp signal a ₃ , and a fourth chirp signal a ₄ arranged in a first order. Include. A first chirp signal a ₁ is assigned to the first time slot 411, a second chirp signal a ₂ is assigned to the second time slot 412, and a third time slot 413 is assigned to the third time slot 413. A chirp signal a ₃ may be allocated and a fourth chirp signal a ₄ may be allocated to the fourth time slot 414.

The second chirp sequence 420 receives the second chirp signal a ₂ , the fourth chirp signal a ₄ , the first chirp signal a ₁ , and the third chirp signal a ₃ arranged in a _{second order} . Include. A second chirp signal a ₂ is assigned to the first time slot 421, a fourth chirp signal a ₄ is assigned to the second time slot 422, and a first chirp is assigned to the third time slot 423. The chirp signal a ₁ may be allocated and a third chirp signal a ₃ may be allocated to the fourth time slot 424.

The transmission signal may be generated such that the first chirp sequence 410 and the second chirp sequence 420 are continuous.

5 is a flowchart of a method of detecting an object based on a mixed signal according to an example.

Step 370 described above with reference to FIG. 3 includes steps 510 and 520 below.

In operation 510, the object detecting apparatus 200 extracts bit frequencies for each of the chirp signals in order to obtain a bit frequency for the object that is not a false object. For example, the object detecting apparatus 200 may extract bit frequencies for each of the chirp signals by sampling the mixed signal and applying Fast Fourier Transformation (FFT) to the sampled signal.

In operation 520, the object detecting apparatus 200 determines a bit frequency of the object among the extracted bit frequencies. The bit frequency not determined as the bit frequency for the object may be the bit frequency for the false object. For example, the object detecting apparatus 200 may determine the bit frequency of the object among the extracted bit frequencies using the CFAR detection method.

In operation 530, the object detecting apparatus 200 detects an object based on the determined bit frequency. For example, if an object candidate is detected as a real object, the distance and relative velocity of the real object may be determined together.

6 is a flowchart of a method of detecting an object based on bit frequencies according to an example.

Steps 520 described above with reference to FIG. 5 include steps 610 through 630 below.

In operation 610, the object detecting apparatus 200 calculates at least four straight lines about the axis of distance and the axis of relative speed based on the bit frequencies for the two sections. The R-v plane is formed by the axis of distance and the axis of relative velocity, and straight lines can appear on the R-v plane.

 The mixed signal may include a plurality of sections, and each section may correspond to one chirp signal. For example, if one chirp sequence includes two chirp signals, the mixed signal includes two intervals. One section may include an up-ramp section and a down-ramp section.

는 k 번째의 처프 신호에 대해 추출된 비트 주파수들의 개수이고, k는 1 내지 N 사이이고, N은 하나의 처프 시퀀스에 포함되는 처프 신호들의 개수이다.

가 큰 두 개의 구간들을 선택하는 이유는 오브젝트의 검출 확률을 높이기 위함이다.The object detecting apparatus 200 selects two sections from among the plurality of sections. In the two sections, sections having a large number of extracted bit frequencies for each chirp signal among a plurality of sections may be selected.

Is the number of bit frequencies extracted for the k-th chirp signal, k is between 1 and N, and N is the number of chirp signals included in one chirp sequence.

The reason for selecting two intervals with is to increase the detection probability of the object.

는 i 번째 비트 주파수이고,

는 k 번째 처프 신호의 대역폭(bandwidth)이다.For example, when k is represented by k ₁ and k ₂ , straight lines in the Rv plane are represented by Equation 4 below.

Is the i-th bit frequency,

Is the bandwidth of the k th chirp signal.

In operation 620, the object detecting apparatus 200 calculates one or more intersections represented by at least four straight lines. The distance (R) and relative velocity (v) profiles of the object candidates are represented by Equation 5 below.

In operation 630, the object detecting apparatus 200 determines a bit frequency for the object based on the intersections. In the absence of noise and interference signals, intersections caused by different straight lines can occur at the same location. For example, the first intersection point generated by the first straight line and the second straight line, the second intersection point generated by the second straight line and the third straight line, and the third intersection point generated by the first straight line and the third straight line are Can occur at the same location. The slopes of the first straight line, the second straight line, and the third straight line are all different. The bit frequency for the corresponding intersection may be determined as the bit frequency for the object.

7 shows straight lines drawn in distance-relative velocity planes based on chirp signals to solve a multi-target problem according to an example.

는 업-비트 주파수이고,

는 다운-비트 주파수이다. B₁은 첫 번째 처프 신호의 대역폭이고, B₂는 두 번째 처프 신호의 대역폭이다.In order to solve the multi-target (or object) problem, the existing method uses the CFAR detection method to obtain up- and down-bit frequencies. Based on the obtained up-bit frequency and down-bit frequency, straight lines such as Equation 6 below can be expressed in the Rv plane. In Equation 6,

Is the up-bit frequency,

Is the down-bit frequency. B ₁ is the bandwidth of the first chirp signal and B ₂ is the bandwidth of the second chirp signal.

For example, if the straight lines 711, 712, 721, 722 of FIG. 7 are assumed, four intersections 731, 732, 733, 734 are defined by the straight lines 711, 712, 721, 722. Occurs. In practice, even if the real objects correspond to the intersections 731 and 734, and the false object corresponds to the intersections 732 and 733, the false object is the intersections 732 and 733 by using the equation (6). ) Is detected as a real object.

To reduce this error, additional straight lines different in slope from the straight lines 711, 712, 721, 722 can be drawn on the R-v plane. In order to draw additional straight lines with different slopes on the R-v plane, chirp signals having different slopes may be included in the transmission signal. Since the additional straight line is different in slope from the straight lines 711, 712, 721, 722, it passes through the intersection corresponding to the real object and does not pass through the intersection corresponding to the false object. An intersection where no additional straight line passes may be determined to correspond to the false object.

8 is a flowchart of a method of determining a bit frequency for an object based on a distance of an intersection group according to an example.

Theoretically, the intersections caused by the straight lines on the R-v plane determined based on the received signal (i.e. the reflected signal) without the interference signal and the noise signal are accumulated for the same object. However, since the received signal generally includes an interference signal and a noise signal, errors due to the interference signal and the noise signal may be considered.

Step 630 described above with reference to FIG. 6 includes steps 810 through 830 below.

In operation 810, the object detecting apparatus 200 determines an intersection group corresponding to each of the object candidates. For example, intersections of similar locations can be clustered into intersection groups for one object candidate. Theoretically, the intersections should be accumulated in one position for one object, but considering the interference signal and the noise signal, the intersections of unequal but similar positions may be grouped into intersection groups.

In operation 820, the object detecting apparatus 200 calculates a distance between intersections in the intersection group. The object detecting apparatus 200 may calculate an average of distances.

In operation 830, the object detecting apparatus 200 determines a bit frequency for the object based on the calculated distance and threshold value of the intersection group.

For example, when the calculated distance (or average of distances) of the intersection group is greater than the threshold value, the intersection group may be determined as the intersection group for the false object. The intersections for the false objects can be excluded in the object detection process.

If the distance of the computed intersection group is within the threshold, the intersections in that intersection group can be determined as the intersections for the real object, and the bit frequency for the object is determined based on the intersections.

According to an embodiment that differs from the steps 810-830, the intersections occurring on the R-v plane may be considered as a group. The intersections may be object candidates, and distances to other object candidates may be calculated for each object candidate. The sum of the distances or the average of the distances can be calculated based on the calculated distances. If the sum of the distances or the average of the distances is larger than the threshold value, the corresponding object candidate (or intersection point) is determined as a false object and may be excluded in the object detection process.

9 shows straight lines with different slopes drawn in the distance-relative velocity planes based on chirp signals to solve a multi-target problem according to an example.

When the chirp signals having different slopes are used, additional straight lines 911 and 912 may be calculated in addition to the straight lines 711, 712, 721 and 722 illustrated in FIG. 7. If the objects corresponding to the intersection 731 and the intersection 734 are genuine, the additional straight lines 911, 912 pass through the intersection 731 and the intersection 734. And, if the intersections 732 and the objects corresponding to the intersection 732 are false, the additional straight lines 911, 912 do not pass through the intersection 732 and the intersection 732.

In order to take into account the effects of the interference signal and the noise signal, the intersections 732 and 922 may be formed as one intersection group, but if the intersections 732 and 922 correspond to a false object, the intersections 732 , 922 will exceed the threshold.

Using additional straight lines 911, 912, false objects corresponding to intersections 732, 733 that could not be detected in the embodiment for FIG. 7 may be determined.

10 illustrates a method of determining a final object based on the determined maximum number of objects according to an example.

Steps 630 described above with reference to FIGS. 6 and 8 may further include steps 1010 and 1020 below. After step 830 is performed, step 1010 may be performed.

를 진짜 오브젝트들의 총 개수(

)로 설정한다.

는 진짜 오브젝트들의 최대 개수일 수 있다. INR은 아래의 [수학식 7]로 정의된다. P _inrerference 는 간섭의 신호 파워이고, P _AWGN 는 AWGN(additive white gaussian noise)의 신호 파워이다.In operation 1010, the object detecting apparatus 200 may determine a region having a smallest interference-to-noise ratio (INR) among intervals corresponding to a plurality of chirp signals in one chirp signal sequence.

Is the total number of real objects (

Set to).

May be the maximum number of real objects. INR is defined by Equation 7 below. P _inrerference is the signal power of interference, and P _AWGN is the signal power of additive white gaussian noise (AWGN).

에 기초하여 최종 오브젝트를 결정한다. 예를 들어, 오브젝트 후보들의 개수가 최대 오브젝트 개수

를 초과하는 경우, 각각의 오브젝트 후보에 대해 계산된 거리들의 합 또는 거리 평균이 작은 순서대로, 오브젝트 후보들 중 최대 오브젝트 개수

를 선택한다.In operation 1020, the object detecting apparatus 200 may determine the maximum number of objects.

Determine the final object based on For example, the number of object candidates is the maximum number of objects

If exceeds, the maximum number of objects among the object candidates, in order of the sum of the distances calculated for each object candidate or the average of the distances being small

Select.

11 illustrates a method for calculating the accuracy of object detection according to an example.

The method for calculating the accuracy of object detection may be performed through the following steps 1110 to 1180. The object detecting apparatus 200 may perform steps 1110 to 1180. The object detecting apparatus 200 may be referred to as an accuracy calculating device for detecting an object.

In operation 1110, the apparatus for calculating accuracy of object detection generates an object profile. The object profile may include the range and relative velocity of the object. The object profile may be for a plurality of objects. For example, the range of the object may be set to 0 to 150m, the relative speed may be set to -100m / s to 50m / s.

In operation 1120, the apparatus for calculating accuracy of object detection generates a bit signal of an object. For example, the bit signal of the object may be generated based on the transmission signal and the object profile.

According to an aspect, an apparatus for calculating accuracy of object detection may determine a first chirp sequence including a preset number of chirp signals in a first order, and output the chirp signals in a second order different from the first order. Determine a second chirp sequence to include, generate a transmission signal comprising the first chirp sequence and the second chirp sequence, generate a received signal based on a distance of a preset object, and based on the transmitted signal and the received signal Generate a mixed signal as a bit signal.

In step 1130, the accuracy calculation device of object detection generates an interference profile. For example, the interference profile may be generated based on the signal transmitted by another object detection apparatus.

In step 1140, the accuracy calculation device of object detection generates an interference bit signal based on the interference profile.

In step 1150, the accuracy calculation device of object detection generates white noise.

An apparatus for calculating the accuracy of object detection merges the bit signal, the interference bit signal and the white noise of the object. The merged signal corresponds to the bit signal generated by down-converting the received signal.

In operation 1160, the apparatus for calculating accuracy of object detection samples the merge signal and applies Fast Fourier Transformation (FFT) to the sampled signal.

In operation 1170, the apparatus for calculating accuracy of object detection applies the CFAR detection method to the result of the FFT. For example, the CFAR detection method applied may be a hybrid method of order statistics (OS) -CFAR and cell-averaging (CA) -CFAR.

In operation 1180, the accuracy calculation device of the object detection detects the object. Accuracy calculation device of the object detection The device can calculate the accuracy of the object detection by comparing the object profile and the detected object.

The simulation parameters used in steps 1110 to 1180 are as shown in Table 1 below.

One chirp sequence used for the detection simulation includes four chirp signals with different slopes. Each chirp signal lasts for 0.5 ms and the four chirp signals are ± 1200 / msec, ± 900 / msec, ± 600 / msec, and ± 300 / msec. As the pattern of the chirp sequences, the following [Table 2] was used.

A comparison of the results of the detection simulation for the proposed object detection method and the results of the simulation for the existing object detection method is shown with reference to FIGS. 12 and 13. The detection simulation for the proposed object detection method and the simulation for the existing object detection method were performed 10,000 times, respectively, and the average of the performed results is shown in FIGS. 12 and 13.

12 illustrates object detection probabilities according to object detection methods according to an example.

The detection probability is defined by Equation 8 below.

The matching target (matching object) means detecting the range and speed of the actual target (object) within a range of threshold values.

In the range of -20dB to 0dB, the proposed object detection method is better than the conventional object detection method.

13 illustrates the number of detected false targets according to object detection methods according to an example.

The number of false targets detected in FIG. 13 is the number of targets (objects) that do not actually exist or the targets detected to exist.

It can be seen that the proposed object detection method outperforms the conventional object detection method in the range of -30dB to -10dB. Even in the range of -10 dB to 0 dB, the proposed object detection method shows better performance than the existing object detection method.

The method according to the embodiment may be embodied in the form of program instructions that can be executed by various computer means and recorded in a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the media may be those specially designed and constructed for the purposes of the embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the embodiments have been described with reference to the accompanying drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques may be performed in a different order than the described method, and / or components of the described systems, structures, devices, circuits, etc. may be combined or combined in a different form than the described method, or other components. Or even if replaced or substituted by equivalents, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims are within the scope of the following claims.

200: object detection device
210: communication unit
220: processor
230: memory

Claims (20)

Determining a first chirp sequence comprising a preset number of chirp signals in a first order;
Determining a second chirp sequence comprising the chirp signals in a second order different from the first order;
Transmitting a transmission signal comprising the first chirp sequence and the second chirp sequence;
Receiving a received signal;
Generating a mixing signal based on the transmission signal and the received signal; And
Detecting an object based on the mixed signal
Including,
Object detection method.
The method of claim 1,
Determining the second chirp sequence,
Allocating a chirp signal that is different from a chirp signal assigned to a first time slot of the first chirp sequence to a first time slot of the second chirp sequence
Including,
Object detection method.
The method of claim 1,
The maximum frequencies of each of the preset number of chirp signals are different from each other,
Object detection method.
The method of claim 1,
The predetermined number of chirp signals are represented in the form of sawtooth or triangle waves on the time axis and the frequency axis,
Object detection method.
The method of claim 1,
Determining the second chirp sequence,
Determining the second order so as not to correspond to the cyclic permutation of the first order
Including,
Object detection method.
The method of claim 1,
Generating the transmission signal such that the first chirp sequence and the second chirp sequence are contiguous in time
Further comprising,
Object detection method.
The method of claim 1,
Detecting an object based on the mixed signal,
Extracting one or more bit frequencies for each of the chirp signals;
Determining a bit frequency for the object among the extracted bit frequencies; And
Detecting the object based on the determined bit frequency
Including,
Object detection method.
The method of claim 7, wherein
Determining a bit frequency for the object of the extracted bit frequencies,
Determining a bit frequency of the object among the extracted bit frequencies using a constant false alarm rate (CFAR) detection method
Including,
Object detection method.
The method of claim 7, wherein
Determining a bit frequency for the object of the extracted bit frequencies,
Calculating at least four straight lines for the axis of distance and the axis of relative speed based on the bit frequencies for the two intervals;
Calculating one or more intersections represented by the at least four straight lines; And
Determining a bit frequency for an object based on the one or more intersections
Including,
Object detection method.
The method of claim 9,
Determining a bit frequency for an object based on the one or more intersections,
Determining an intersection group corresponding to each of the one or more object candidates;
Calculating a distance between intersections in the intersection group; And
Determining a bit frequency for the object based on the calculated distance of intersection groups and a preset threshold value
Including,
Object detection method.
The method of claim 10,
Determining a bit frequency for an object based on the one or more intersections,
Determining the number of bit frequencies extracted for the interval having the smallest interference-to-noise ratio (INR) value as the maximum number of objects; And
Determining a final object based on the maximum number of objects
Further comprising,
Object detection method.
A computer-readable recording medium containing a program for performing the method of any one of claims 1 to 11.
In the apparatus for detecting an object,
A memory in which a program for detecting an object is recorded; And
A processor that executes the program
Including,
The program,
Determining a first chirp sequence comprising a preset number of chirp signals in a first order;
Determining a second chirp sequence comprising the chirp signals in a second order different from the first order;
Transmitting a transmission signal comprising the first chirp sequence and the second chirp sequence;
Receiving a received signal;
Generating a mixing signal based on the transmission signal and the received signal; And
Detecting an object based on the mixed signal
To do,
Object detection device.
The method of claim 13,
The object detecting device,
Radar for transmitting the transmission signal by using a multiple input multiple output (MIMO) antenna and receiving the received signal
Further comprising,
Object detection device.
The method of claim 13,
The object detecting device is included in a vehicle,
The object detecting device is used to detect an object located around the vehicle,
Object detection device.
In the method of calculating the accuracy of object detection,
Generating a bit signal of the object based on the object profile;
Generating an interference bit signal based on the interference profile;
Generating white noise;
Generating a merge signal by merging the bit signal, the interference bit signal, and the white noise;
Sampling the merge signal and applying fast fourier transform (FFT) to the sampled merge signal;
Detecting an object by applying a constant false alarm rate (CFAR) detection method to the result of the FFT; And
Calculating the accuracy of object detection based on the detected object and object profile
Containing
How to calculate the accuracy of object detection.
The method of claim 16,
Generating the bit signal of the object,
Determining a first chirp sequence comprising a preset number of chirp signals in a first order;
Determining a second chirp sequence comprising the chirp signals in a second order different from the first order;
Generating a transmission signal comprising the first chirp sequence and the second chirp sequence;
Generating a received signal based on a distance of a preset object; And
Generating a mixing signal as the bit signal based on the transmission signal and the received signal
Including,
How to calculate the accuracy of object detection.
The method of claim 17,
Determining the second chirp sequence,
Allocating a chirp signal that is different from a chirp signal assigned to a first time slot of the first chirp sequence to a first time slot of the second chirp sequence
Including,
How to calculate the accuracy of object detection.
The method of claim 17,
The maximum frequencies of each of the preset number of chirp signals are different from each other,
How to calculate the accuracy of object detection.
An apparatus for calculating the accuracy of object detection,
A memory in which a program for calculating the accuracy of object detection is recorded; And
A processor that executes the program
Including,
The program,
Generating a bit signal of the object based on the object profile;
Generating an interference bit signal based on the interference profile;
Generating white noise;
Generating a merge signal by merging the bit signal, the interference bit signal, and the white noise;
Sampling the merge signal and applying fast fourier transform (FFT) to the sampled merge signal;
Detecting an object by applying a constant false alarm rate (CFAR) detection method to the result of the FFT; And
Calculating the accuracy of object detection based on the detected object and object profile
To do.
Accuracy calculation device of object detection.

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