CN112632763A - Data analysis method for millimeter wave radar - Google Patents
Data analysis method for millimeter wave radar Download PDFInfo
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- CN112632763A CN112632763A CN202011494822.1A CN202011494822A CN112632763A CN 112632763 A CN112632763 A CN 112632763A CN 202011494822 A CN202011494822 A CN 202011494822A CN 112632763 A CN112632763 A CN 112632763A
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- 238000000034 method Methods 0.000 title claims abstract description 14
- 238000007405 data analysis Methods 0.000 title claims abstract description 11
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 30
- 238000005457 optimization Methods 0.000 claims abstract description 11
- 238000004088 simulation Methods 0.000 claims abstract description 11
- 238000012795 verification Methods 0.000 claims abstract description 5
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012804 iterative process Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 abstract description 4
- 230000033772 system development Effects 0.000 abstract description 2
- 230000008859 change Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 238000012544 monitoring process Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
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Abstract
The invention relates to a data analysis method for a millimeter wave radar, which comprises the following steps: collecting data information of the millimeter wave radar according to the use scene of the radar; analyzing according to the acquired data information and a specified protocol format; designing corresponding algorithm models according to different working scenes so as to input and output specified alarm messages; inputting the analyzed data, and carrying out simulation operation on a verification algorithm model to obtain an operation result; comparing the result of the simulation operation with an expected theoretical result; and performing repeated iterative optimization on the algorithm model according to the comparison result of the fifth step to obtain the optimal algorithm model. According to the method, a real road test scene is restored through simulation operation, and the algorithm model is optimized through repeated iteration to obtain the optimal algorithm model, so that the system development cycle is accelerated without multiple road tests, and meanwhile, the strategy is comprehensively verified to ensure the stability of the system.
Description
Technical Field
The invention relates to the technical field of radar electronics, in particular to a data analysis method for a millimeter wave radar.
Background
77GHz millimeter wave radar has the capability of positioning targets in all weather, long distance and high resolution, so that the radar is widely used for detecting the distance, the speed and the angular orientation of surrounding targets in the field of intelligent automobile application. Can realize various driving auxiliary functions of the automobile.
However, in actual use of the radar, the radar has multiple scenes, short function development period and high requirements on accuracy and stability, so that the development and verification workload is large.
Disclosure of Invention
In order to solve the technical problem, the invention provides a data analysis method for a millimeter wave radar. The technical problem to be solved by the invention is realized by adopting the following technical scheme:
a data analysis method for a millimeter wave radar, comprising the steps of:
the first step is as follows: collecting data information of the millimeter wave radar according to the use scene of the radar;
the second step is that: analyzing according to the acquired data information and a specified protocol format;
the third step: designing corresponding algorithm models according to different working scenes so as to input and output specified alarm messages;
the fourth step: inputting the analyzed data, and carrying out simulation operation on a verification algorithm model to obtain an operation result;
the fifth step: comparing the result of the simulation operation with an expected theoretical result;
and a sixth step: and performing repeated iterative optimization on the algorithm model according to the comparison result of the fifth step to obtain the optimal algorithm model.
The sixth iterative iteration comprises the steps of:
the first step is as follows: determining variables needing iterative optimization through an algorithm;
the second step is that: establishing an iterative relational expression to calculate the variables;
the third step: and the iterative process is controlled, so that endless repeated operation is prevented, and an optimization result cannot be obtained.
The iterative control includes two cases: one is that the iteration number is a determined value, the iteration variable can be accurately calculated, and the other is that the iteration number cannot be determined.
If the iteration number can not be determined, the control is carried out by the following steps:
the first step is as follows: designing a radar alarm condition to collect road condition data;
the second step is that: setting a theoretical expected value of radar alarm, and determining an error range of an iteration result;
the third step: iterative calculation is carried out by continuously changing the set variable value, so that the result is continuously close to the theoretical expected value, and the optimal algorithm model is achieved when the calculation result is within the error range.
The invention has the beneficial effects that: according to the method, a real drive test scene is restored through simulation operation, and the algorithm model is optimized through repeated iteration to obtain the optimal algorithm model, so that the system development cycle is accelerated without multiple tests, and meanwhile, the strategy is comprehensively verified to ensure the stability of the system.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the present invention will be described more clearly and more completely below, and it is to be understood that the described embodiments are only a part of the present invention and not all of the present invention, and based on the embodiments, other embodiments obtained by those skilled in the art without inventive exercise are within the protection scope of the present invention.
A data analysis method for a millimeter wave radar, comprising the steps of:
the first step is as follows: collecting data information of the millimeter wave radar according to the use scene of the radar; if the millimeter wave radar is applied to an automobile, the use scene of the radar comprises: guardrails, tunnels, high speed, cities, villages, mountainous areas; the acquired scene data includes: target data detected by the radar, vehicle CAN network data, scene video data and differential GPS position data.
The second step is that: analyzing according to the acquired data information and a specified protocol format;
the third step: designing corresponding algorithm models according to different working scenes so as to input and output specified alarm messages; the algorithm model is used for making corresponding strategies according to the function requirements of the function specification, and the strategies can specify alarm messages according to input and output; if the radar is applied to the automobile, the algorithm model comprises the following steps: blind area monitoring, lane change assisting, vehicle backward crossing early warning, door opening early warning, rear collision early warning and rear-end collision early warning prevention;
the fourth step: inputting the analyzed data, and carrying out simulation operation on a verification algorithm model to obtain an operation result;
the running result and the data information are displayed on a computer screen;
the fifth step: comparing the result of the simulation operation with an expected theoretical result;
and a sixth step: and performing repeated iterative optimization on the algorithm model according to the comparison result of the fifth step to obtain the optimal algorithm model.
The sixth iterative iteration comprises the steps of:
the first step is as follows: determining variables needing iterative optimization through an algorithm;
the second step is that: establishing an iterative relational expression to calculate the variables;
the third step: and the iterative process is controlled, so that endless repeated operation is prevented, and an optimization result cannot be obtained.
The iterative control includes two cases: one is that the iteration number is a determined value, the iteration variable can be accurately calculated, and the other is that the iteration number cannot be determined.
If the iteration number can not be determined, the control is carried out by the following steps:
the first step is as follows: designing a radar alarm condition to collect road condition data;
the second step is that: setting a theoretical expected value of radar alarm, and determining an error range of an iteration result;
the third step: iterative calculation is carried out by continuously changing the set variable value, so that the result is continuously close to the theoretical expected value, and the optimal algorithm model is achieved when the calculation result is within the error range.
If the automobile lane change auxiliary algorithm is to be verified, firstly inputting data information of a radar, then verifying the automobile lane change auxiliary algorithm through simulation operation, then determining a set automobile iteration relation according to collision time and the speed of an incoming automobile, simultaneously determining a collision time variable t, judging whether the variable t meets alarm time, if so, finishing optimization, and if not, modifying the alarm strategy again.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A data analysis method for a millimeter wave radar is characterized in that: the method comprises the following steps:
the first step is as follows: collecting data information of the millimeter wave radar according to the use scene of the radar;
the second step is that: analyzing according to the acquired data information and a specified protocol format;
the third step: designing corresponding algorithm models according to different working scenes so as to input and output specified alarm messages;
the fourth step: inputting the analyzed data, and carrying out simulation operation on a verification algorithm model to obtain an operation result;
the fifth step: comparing the result of the simulation operation with an expected theoretical result;
and a sixth step: and performing repeated iterative optimization on the algorithm model according to the comparison result of the fifth step to obtain the optimal algorithm model.
2. The data analysis method for a millimeter wave radar according to claim 1, characterized in that: the sixth iterative iteration comprises the steps of:
the first step is as follows: determining variables needing iterative optimization through an algorithm;
the second step is that: establishing an iterative relational expression to calculate the variables;
the third step: and the iterative process is controlled, so that endless repeated operation is prevented, and an optimization result cannot be obtained.
3. The data analysis method for a millimeter wave radar according to claim 2, characterized in that: the iterative control includes two cases: one is that the iteration number is a determined value, the iteration variable can be accurately calculated, and the other is that the iteration number cannot be determined.
4. The data analysis method for a millimeter wave radar according to claim 3, characterized in that: if the iteration number can not be determined, the control is carried out by the following steps:
the first step is as follows: designing a radar alarm condition to collect road condition data;
the second step is that: setting a theoretical expected value of radar alarm, and determining an error range of an iteration result;
the third step: iterative calculation is carried out by continuously changing the set variable value, so that the result is continuously close to the theoretical expected value, and the optimal algorithm model is achieved when the calculation result is within the error range.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202011494822.1A CN112632763A (en) | 2020-12-17 | 2020-12-17 | Data analysis method for millimeter wave radar |
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| CN202011494822.1A CN112632763A (en) | 2020-12-17 | 2020-12-17 | Data analysis method for millimeter wave radar |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108693508A (en) * | 2018-03-26 | 2018-10-23 | 天津大学 | Cognition radar waveform optimization method based on particle cluster algorithm |
| CN110456344A (en) * | 2019-08-13 | 2019-11-15 | 成都电科慧安科技有限公司 | To the estimation method of wall parameter in through-wall radar imaging |
| CN111458629A (en) * | 2019-01-21 | 2020-07-28 | 平高集团有限公司 | Inversion method and device for mechanical fault of high-voltage switch |
| CN111650598A (en) * | 2019-02-19 | 2020-09-11 | 北京京东尚科信息技术有限公司 | External parameter calibration method and device for vehicle-mounted laser scanning system |
-
2020
- 2020-12-17 CN CN202011494822.1A patent/CN112632763A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108693508A (en) * | 2018-03-26 | 2018-10-23 | 天津大学 | Cognition radar waveform optimization method based on particle cluster algorithm |
| CN111458629A (en) * | 2019-01-21 | 2020-07-28 | 平高集团有限公司 | Inversion method and device for mechanical fault of high-voltage switch |
| CN111650598A (en) * | 2019-02-19 | 2020-09-11 | 北京京东尚科信息技术有限公司 | External parameter calibration method and device for vehicle-mounted laser scanning system |
| CN110456344A (en) * | 2019-08-13 | 2019-11-15 | 成都电科慧安科技有限公司 | To the estimation method of wall parameter in through-wall radar imaging |
Non-Patent Citations (1)
| Title |
|---|
| 裴晓飞 等: ""汽车主动避撞***的安全距离模型和目标检测算法"", 《汽车安全与节能学报》, vol. 3, no. 1, 31 January 2012 (2012-01-31), pages 26 - 33 * |
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