CN109932695A - A kind of method and device improving object recognition speed - Google Patents
A kind of method and device improving object recognition speed Download PDFInfo
- Publication number
- CN109932695A CN109932695A CN201910151429.3A CN201910151429A CN109932695A CN 109932695 A CN109932695 A CN 109932695A CN 201910151429 A CN201910151429 A CN 201910151429A CN 109932695 A CN109932695 A CN 109932695A
- Authority
- CN
- China
- Prior art keywords
- frequency
- signal
- object recognition
- freuqncy signal
- algorithm
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 42
- 238000012545 processing Methods 0.000 claims abstract description 35
- 238000001228 spectrum Methods 0.000 claims abstract description 15
- 238000005070 sampling Methods 0.000 claims abstract description 11
- 230000035559 beat frequency Effects 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 9
- 230000010355 oscillation Effects 0.000 claims abstract description 5
- 238000001514 detection method Methods 0.000 claims description 19
- 230000009466 transformation Effects 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 14
- 230000003595 spectral effect Effects 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 4
- 230000002045 lasting effect Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 6
- 230000033001 locomotion Effects 0.000 description 5
- 230000003044 adaptive effect Effects 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000003672 processing method Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 238000006424 Flood reaction Methods 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 241001464837 Viridiplantae Species 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000002401 inhibitory effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 230000002829 reductive effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 230000026676 system process Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Landscapes
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a kind of methods for improving object recognition speed, comprising the following steps: frequency mixing stages: after receiving echo-signal, echo-signal is mixed with local oscillation signal to obtain intermediate-freuqncy signal；Sampling step: digitized processing is carried out to intermediate-freuqncy signal by sampling module；Clutter cancellation step: clutter cancellation processing is carried out to the intermediate-freuqncy signal for being digitized processing；Adding window step: windowing process is carried out to the intermediate-freuqncy signal by clutter cancellation processing；Difference frequency obtaining step: the intermediate-freuqncy signal after windowing process is handled to obtain beat frequency by Fast Fourier Transform (FFT) and frequency spectrum refinement algorithm；It calculates step: the speed and distance of corresponding object being calculated according to object calculation formula.The method of raising object recognition speed of the invention is handled echo-signal by using Fast Fourier Transform (FFT) and frequency spectrum refinement algorithm, so that signal processing computation amount, effectively reduces the calculating cost of system.
Description
Technical field
The present invention relates to a kind of automobile technical field more particularly to a kind of methods and dress for improving object recognition speed
It sets.
Background technique
Currently, with the development of science and technology with the progress in epoch, intelligent automobile have become future automobile development certainty
Trend.Meanwhile as the development of intelligent automobile technology, orthodox car technology are faced with more and more challenges, auto industry is each
The development space of a research field is also increasing.The intelligent vehicle mainly quick obtaining in starting, driving and braking process
Critical performance parameters, application of the acquisition of these key parameters dependent on highly sensitive automobile sensor.Wherein trailer-mounted radar passes
Sensor is one of the key sensor in Intelligent Vehicle System.Core force of the millimetre-wave radar as trailer-mounted radar sensor,
The always research hotspot of onboard sensor technology.
Vehicle-mounted millimeter wave radar realizes that the key of its function is the various information that will obtain from radar sensor in the short time
Inside be effectively treated, wherein the information some received include target information, some only have clutter noise and it is various interference at
Point, the interference being mingled in echo-signal and noise contribution are filtered out, the form easily analyzed is converted the signal to, is that automobile is real
Its existing active safety function is ready to be vital.It would therefore be desirable to handle signal using a variety of methods, subtract
In few reaction time, the accuracy of identification is improved, reduces false alarm, the reliability of whole system is improved, to guarantee the peace of automobile
Entirely.
For the signal processing of vehicle-mounted millimeter wave radar, traditional signal processing method is that target ginseng is obtained from time domain
Number carries out ranging to static object according to the method for measurement echo intermediate frequency average frequency；Subsequent signal processing method is from frequency
The parameter that moving target, multiple target are obtained on domain, generallys use structure as " beat-Fourier transform ", this is steady height
The optimum detection of this effective section of white noise midpoint target echo signal receives system, but there is also several significant for the system simultaneously
The shortcomings that,
Arithmetic speed once the excessive telephone system of the points of, signal processing will be greatly reduced, and will affect system work
Real-time.
Two, the precision and accuracy of target identification and measurement be not high.
Three, vehicle-mounted millimeter wave radar in practical applications, ever-changing due to road environment, signal often floods
Not in noise, it is difficult to the phenomenon that identifying true signal from noise, often will appear false dismissal or false-alarm in this way,
Then certain methods are needed to remove the influence of noise to improve stability of the radar system under complex road condition and accurate
Degree.
Four, when vehicle in real roads when driving, the radar for being mounted on vehicle front will identify that in detection range
All targets, the interference information including lane vehicle, guardrail, green plants etc..It is practical since the performance of radar itself limits
Target can lose in a short time, and the data for causing radar to return cannot be directly used to the screening of effective target.Then one is needed
A effective track algorithm carries out target to continue tracking.
Summary of the invention
For overcome the deficiencies in the prior art, one of the objects of the present invention is to provide a kind of raising object identification speed
The method of degree.
The second object of the present invention is to provide a kind of electronic equipment.
The third object of the present invention is to provide a kind of computer readable storage medium.
An object of the present invention adopts the following technical scheme that realization:
A method of improving object recognition speed, comprising the following steps:
Frequency mixing stages: after receiving echo-signal, echo-signal is mixed with local oscillation signal to obtain intermediate frequency letter
Number；
Sampling step: digitized processing is carried out to intermediate-freuqncy signal by sampling module；
Clutter cancellation step: clutter cancellation processing is carried out to the intermediate-freuqncy signal for being digitized processing；
Adding window step: windowing process is carried out to the intermediate-freuqncy signal by clutter cancellation processing；
Difference frequency obtaining step: the intermediate frequency after windowing process is believed by Fast Fourier Transform (FFT) and frequency spectrum refinement algorithm
It number is handled to obtain beat frequency；
It calculates step: the speed and distance of corresponding object being calculated according to object calculation formula；The object
Calculation formula are as follows:
Wherein, VfFor object speed, c is the light velocity, f0For the centre frequency of transmitted wave, f+Be positive difference frequency value, f-It is negative
Difference frequency value, T are the modulated signal period, and B is modulation bandwidth, and R is the distance to object.
Further, among the difference frequency obtaining step, the frequency spectrum refinement algorithm is CZT algorithm, and the difference frequency obtains
Step is taken to specifically include following sub-step:
Fourier transformation step: the transformation of N point quick Fourier is done to the intermediate-freuqncy signal after windowing process, and searches spectral line
Amplitude maxima point P；
Frequency calculates step: calculating the frequency values f1 and f2 of (P-1) and (P+1) two o'clock.
Difference frequency calculates step: M point CZT transform operation done in f1 to the section f2, searches spectral line amplitude maximum of points P1,
And the frequency values of the point are calculated, which is beat frequency.
Further, in Fourier transformation step: being detected by horizontal false-alarm detection module through Fast Fourier Transform (FFT)
Intermediate-freuqncy signal that treated.
Further, the horizontal false-alarm detection in the Fourier transformation step specifically includes following sub-step:
The critical element value is chosen by log-likelihood estimation function；
Judge that object is to be in strong clutter area in weak clutter area according to the critical element value, if it is in weak
Clutter area then selects SOCA-CFAR algorithm, believes if it is in strong clutter area to detect the intermediate frequency through Fast Fourier Transform (FFT)
Number, then GOCA-CFAR algorithm is selected to detect the intermediate-freuqncy signal through Fast Fourier Transform (FFT).
Further, it is calculated in step in the difference frequency, spectral line amplitude maximum of points is searched by peak detection module
P1。
Further, the windowed function used in the windowing process is Hanning window or hamming window, the clutter cancellation
Using MTI algorithm.
It further, further include tracking step after calculating step: by tracking module by the speed of obtained object
With distance and the velocity and acceleration of combination vehicle itself is to realize the lasting tracking to vehicle forward target.
Further, the tracking module uses expanded Kalman filtration algorithm.
The second object of the present invention adopts the following technical scheme that realization:
A kind of electronic equipment can be run on a memory and on a processor including memory, processor and storage
Computer program, the processor are realized described in any one of one of the object of the invention when executing the computer program
A method of improving object recognition speed.
The third object of the present invention adopts the following technical scheme that realization:
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
A kind of method of raising object recognition speed as described in one of the object of the invention any one is realized when row.
Compared with prior art, the beneficial effects of the present invention are:
The method of raising object recognition speed of the invention is calculated by using Fast Fourier Transform (FFT) and frequency spectrum refinement
Method handles echo-signal, so that signal processing computation amount, effectively reduces the calculating cost of system.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the raising object recognition speed of embodiment one；
The waveform diagram of FMCW when Fig. 2 is static；
The waveform diagram of FMCW when Fig. 3 is movement；
Fig. 4 is the structural diagrams of three pulse canceller.
Specific embodiment
In the following, being described further in conjunction with attached drawing and specific embodiment to the present invention, it should be noted that not
Under the premise of conflicting, new reality can be formed between various embodiments described below or between each technical characteristic in any combination
Apply example.
Embodiment one
In the Different Modulations of vehicle-mounted millimeter wave radar, FMCW (CW with frequency modulation) radar passes through the company to transmitting
The frequency of continuous wave is modulated, to be measured according to acquired difference on the frequency, phase difference etc. target information, cost is opposite
Low, exploitation is also easier, and measurement accuracy is higher, therefore becomes the first choice of vehicle-mounted millimeter wave radar exploitation gradually,
Then this technology is in such a way that FMCW (CW with frequency modulation) is as radar modulation.
Basic functional principle under vehicle-mounted millimeter wave radar FMCW mode: general modulated signal is triangular signal, transmitting letter
Number with receive signal frequency variation as shown in Figures 2 and 3, the shape of back wave is identical.Only there is a delay in time
Δ t, the relationship of Δ t and target range R are as follows:
Wherein: Δ t: the time of transmitted wave and back wave prolongs；R: target range；C: the light velocity 3 × 108m/s。
According to triangle relation, obtain:
Wherein: Δ f: transmitting signal and the difference on the frequency for reflecting signal
T: modulated signal period
B: modulation bandwidth
Target range R are as follows:
It can be concluded that target range R is directly proportional to the IF frequency Δ f that radar front end exports.
When target and radar are not opposing stationary, that is, when having relative motion, reflect in signal comprising one by
Doppler frequency shift fd caused by the relative motion of target, when moving target is close to radar, as shown in Fig. 2:
A difference frequency can be obtained respectively in the rising edge and failing edge of triangular wave, be formulated are as follows:
f+=Δ f-fd (4)
F-=Δ f+fd (5)
Wherein Δ f is IF frequency when target is opposing stationary, and f+ represents the difference frequency of first half cycle forward direction frequency modulation, f- generation
The later half resulting difference frequency of period negative sense frequency modulation of table, fd is the Doppler frequency shift for the target for having relative motion, according to Doppler
Effect obtains:
To the value of speed vr are as follows:
Wherein: vr is the radial velocity of target and radar, f0For the centre frequency of transmitted wave, can be obtained by formula 4 and formula 5:
Wushu 8 is brought into the expression formula of available speed and distance in formula 3 and formula 7:
Speed vrSymbol and direction of relative movement have relationship, when object with respect to radar close to when vrFor positive value.Work as mesh
Mark v when opposite radar leavesrFor negative value.It can be seen that the worth object out of positive and negative difference frequency can be passed through after radar detection object
Distance and speed, so as to by these obtained information come so that vehicle carries out different work.
In order to be accurately obtained positive and negative difference frequency value and objects ahead can be carried out in normal vehicle operation with
Track establishes a complete signal processing system, and entire signal processing system is made of following components: sampling module,
Clutter cancellation module, fourier transformation module, CFAR (horizontal false-alarm detection) module, peak detection module and tracking module.
As shown in Figure 1, present embodiments providing a kind of method for improving object recognition speed, comprising the following steps:
S1: after receiving echo-signal, echo-signal is mixed with local oscillation signal to obtain intermediate-freuqncy signal；
S2: digitized processing is carried out to intermediate-freuqncy signal by sampling module；Sampling module uses A/D sampling technique, by mould
Quasi- intermediate-freuqncy signal carries out digitized processing, is convenient for subsequent carry out Fourier transformation.
S3: clutter cancellation processing is carried out to the intermediate-freuqncy signal for being digitized processing；The clutter cancellation uses MTI algorithm.
The method that clutter cancellation uses MTI (moving-target instruction), MTI is a kind of effective ways of filtering clutter, it is that multiple groups pulse is returned
The weighted sum of the same distance unit of wave, to obtain result；That is multi input and an output；It is equivalent to for inhibiting fixed mesh
The high-pass filter of mark and slow clutter.In the case where individually eliminating MTI filter, from the echo of the second transmission pulse
The echo of the first transmission pulse is subtracted, and removes fixed target and at a slow speed clutter, while retaining the information of mobile target.Fig. 4
For three pulse MTI cancellers, transmission function is H (z)=1-2z-1+z-2, can filter out phase by three pulse MTI cancellers
As clutter component.
S4: windowing process is carried out to the intermediate-freuqncy signal by clutter cancellation processing；The adding window used in the windowing process
Function is Hanning window or hamming window；Echo-signal is subjected to adding window before progress Fourier transformation, wherein window function can select
Hanning window or hamming window are selected, different window functions can choose according to different situations to be handled, let out with reducing frequency spectrum
Dew and truncated error, the detection performance of Lai Tigao target.
S5: the intermediate-freuqncy signal after windowing process is handled by Fast Fourier Transform (FFT) and frequency spectrum refinement algorithm
To obtain beat frequency；In step s 5, the frequency spectrum refinement algorithm is CZT algorithm, and the step S5 specifically includes following son
Step:
S51: the transformation of N point quick Fourier is done to the intermediate-freuqncy signal after windowing process, fourier transformation module uses FFT
(Fast Fourier Transform (FFT)) transformation, FFT is the fast algorithm of discrete Fourier transform, it be according to the surprise of discrete fourier transform,
The characteristics such as even, empty, real, improve acquisition to the algorithm of Discrete Fourier Transform.Calculation amount can be greatly simplified, is passed through
Signal after clutter cancellation is carried out the transformation of time domain to frequency domain by FFT transform.Warp is detected by horizontal false-alarm detection module
Fast Fourier Transform (FFT) treated intermediate-freuqncy signal.Horizontal false-alarm detection in the step S51 specifically includes following sub-step:
The critical element value is chosen by log-likelihood estimation function；
Judge that object is to be in strong clutter area in weak clutter area according to the critical element value, if it is in weak
Clutter area then selects SOCA-CFAR algorithm, believes if it is in strong clutter area to detect the intermediate frequency through Fast Fourier Transform (FFT)
Number, then GOCA-CFAR algorithm is selected to detect the intermediate-freuqncy signal through Fast Fourier Transform (FFT).CFAR (horizontal false-alarm detection) module
Using improved adaptive CFAR algorithm, the estimation of statistical property is carried out to cell data, to determine the opposite of target and clutter
Relationship, to carry out the different Processing Algorithm of adaptive selection, which is established at one including unit to be checked, by N
The window of a reference unit composition, and window crosses over two different distributions clutter areas.Clutter edge is assumed to occur in sample M and M+1
Between, it enablesIt represents and the sample 1 for forming the first clutter area is averaged to MEstimated value,It is to be formed
Mean value of the M+1 sample in the second clutter area to n-th sample.Algorithm is calculated since M=1 It is only single
The sample average of member 1, while calculating the sample average of N-1 unitWhen taking M=2 ..., when N-1, above-mentioned mistake is repeated
Journey.Therefore, it for N-1 possible the critical elements, calculates separately to obtain the equal value sequence of a pair of sampleWith
Next most probable the critical element Mt value is chosen, the maximal possibility estimation of the critical point is exactly so that below
The maximized M value of log-likelihood estimation function:
Once find Mt value, just simultaneously establish unit to be checked be located at weak clutter region or strong clutter region, when to
It is just to select SOCA-CFAR (cell-average selects small) algorithm that inspection unit, which is located at weak clutter region, when unit to be checked is positioned at strong miscellaneous
GOCA-CFAR (cell-average choosing is big) algorithm is just selected in wave region.
S52: the frequency values f1 and f2 of (P-1) and (P+1) two o'clock are calculated；
S53: M point CZT transform operation is done in f1 to the section f2, spectral line amplitude is searched by peak detection module most
Big value point P1, and the frequency values of the point are calculated, which is beat frequency.By to the echo-signal for transforming to frequency domain
Spectral peak scan for, by the work of above-mentioned several modules, clutter is basically eliminated, target information be retained, then find
Frequency corresponding to peak value maximum point, the as frequency of the difference frequency up and down of target needed for us.
In embodiment, in order to improve the accuracy and calculating speed of signal processing system, while Fourier transformation
Using the technology of frequency spectrum refinement, frequency spectrum refinement uses CZT algorithm, operates together with FFT, in order to improve arithmetic speed, first to difference
Frequency signal does the FFT operation of N point, finds out the maximum peak point P of frequency spectrum, then calculates (P-1) and (P+1) two dot frequency, then right
The frequency spectrum of this point-to-point transmission does the CZT transform operation zoom FFT of M point, and the spectral line peak point obtained after frequency spectrum local refinement is corresponding
Frequency values be beat frequency.The key of FFT/CZT unified algorithm is that the selection of spectral range, selection could properly be protected
Demonstrate,prove the correctness of Frequency Estimation.FFT/CZT unified algorithm embodies a kind of small echo processing thought of partial enlargement, can improve
Speed, and precision can be obtained.
For N*M point sequence, FFT operation, operand are as follows: (N*M) lg2 (N*M) are directly used.According to FFT/CZT
Unified algorithm needs first to carry out to carry out after N point FFT operation the CZT transformation calculations of M point again to reach identical resolution ratio,
Total operand are as follows: Nlg2N+2 (N+M)+(N+M) lg2 (N+M).Identical resolution ratio, using the operation of FFT/CZT unified algorithm
Amount far smaller than directly calculates the operand of FFT, so not will increase operation again while improving precision using unified algorithm
Time.
S6: the speed and distance of corresponding object are calculated according to object calculation formula；The object calculates public
Formula are as follows:
Wherein, Vf is object speed, and c is the light velocity, and f0 is the centre frequency of transmitted wave, and f+ is positive difference frequency value, and f- is negative
Difference frequency value, T are the modulated signal period, and B is modulation bandwidth, and R is the distance to object.
S7: by tracking module by the speed of obtained object and distance and in conjunction with the velocity and acceleration of vehicle itself
To realize the lasting tracking to vehicle forward target.The tracking module uses expanded Kalman filtration algorithm.
The method that tracking module uses Extended Kalman filter is exactly the Kalman filtering calculation suitable for nonlinear system
Method.The system equation of Linear system model is replaced with nonlinear system model equation in expanded Kalman filtration algorithm.It is logical
It crosses introducing Jacobian matrix to linearize nonlinear system, is then carrying out relevant treatment using Kalman filtering.
Firstly, passing through speed and distance equivalent, the introducing one in conjunction with the Velocity-acceleration of this vehicle oneself of obtained object
The system of a discrete control process.Wherein system model can choose constant speed, often acceleration or current statistical model, the system
Useable linear stochastic differential equation describes, and introduces system state equation:
X (k)=AX (k-1)+BU (k)+W (k)
Along with the measurement equation of system: Z (k)=HX (k)+V (k)
Kalman filter equation is as follows:
Present condition prediction result: X (k | k-1)=AX (k-1 | k-1)+B U (k) ... ... (11)
The corresponding covariance of present condition prediction: P (k | k-1)=AP (k-1 | k-1) A '+Q ... ... (12)
Present condition optimal result: X (k | k)=X (k | k-1)+Kg (k) (Z (k)-H X (k | k-1)) ... ... (13)
Gain: Kg (k)=P (k | k-1) H '/(H P (k | k-1) H '+R) ... ... (14)
The optimal corresponding covariance P of present condition (k | k)=(I-Kg (k) H) P (k | k-1) ... ... (15)
By being continuously updated above-mentioned formula, so that it may carry out continuing tracking to vehicle front object.
After the processing of above-mentioned signal processing module, obtained data can be connected by CAN communication with Vehicular system,
After vehicle receives relevant information, the processing such as braking or alarm will do it, so that AEB may be implemented is (active for vehicle
Emergency braking system), ACC (adaptive cruise), the functions such as BSD (blind area monitoring).
Signal processing system process are as follows: after radar receives echo-signal, be mixed to obtain intermediate frequency letter with local oscillation signal
Number, intermediate-freuqncy signal, which is sent in clutter cancellation after sampling module samples, carries out clutter cancellation, the signal after clutter cancellation is complete
Frequency domain is transformed from the time domain to after fourier transformation module, transforms to the signal of frequency domain to further filter out clutter noise
Deng interference, need to carry out CFAR (horizontal false-alarm detection) processing, the interference such as clutter noise quilt substantially after the processing of above-mentioned module
It filters out, leaves behind target information, then can be obtained by required beat frequency by peak detection again, convolution 9, then
The speed and distance of the object detected can be drawn, the speed of object by obtaining and distance in conjunction with this vehicle from
Oneself Velocity-acceleration, so that it may objects in front be carried out by tracking module to continue tracking.
Of the invention has the advantages of following several respects:
1. FFT-CZT algorithm is used, so that signal processing computation amount, effectively reduces being calculated as system
This.
2. using adaptive CFAR algorithm, it can adapt to various complex environments, so that entire signal processing system accuracy
Height, false dismissal and false-alarm probability when substantially improving target detection.
3. using extended BHF approach algorithm, so that whole system tracking target is accurate, stability is high.
4. whole system calculating speed is fast, consumption resource is few, target recognition and tracking accuracy is high.
Embodiment two
Embodiment two discloses a kind of electronic equipment, which includes processor, memory and program, wherein
One or more can be used in processor and memory, and program is stored in memory, and is configured to be held by processor
Row when processor executes the program, realizes the method for improving object recognition speed of embodiment one.The electronic equipment can be with
It is a series of electronic equipment of mobile phone, computer, tablet computer etc..
Embodiment three
Embodiment three discloses a kind of computer readable storage medium, and the storage medium is for storing program, and the journey
When sequence is executed by processor, the method for improving object recognition speed of embodiment one is realized.
Certainly, a kind of storage medium comprising computer executable instructions, computer provided by the embodiment of the present invention
The method operation that executable instruction is not limited to the described above, can also be performed in method provided by any embodiment of the invention
Relevant operation.
By the description above with respect to embodiment, it is apparent to those skilled in the art that, the present invention
It can be realized by software and required common hardware, naturally it is also possible to which by hardware realization, but in many cases, the former is more
Good embodiment.Based on this understanding, technical solution of the present invention substantially in other words contributes to the prior art
Part can be embodied in the form of software products, which can store in computer-readable storage medium
Floppy disk, read-only memory (Read-Only Memory, ROM), random access memory (Random in matter, such as computer
Access Memory, RAM), flash memory (FLASH), hard disk or CD etc., including some instructions use so that an electronic equipment
(can be personal computer, server or the network equipment etc.) executes method described in each embodiment of the present invention.
It is worth noting that, in the above-mentioned embodiment based on content update notice device, included each unit and mould
Block is only divided according to the functional logic, but is not limited to the above division, and is as long as corresponding functions can be realized
It can；In addition, the specific name of each functional unit is also only for convenience of distinguishing each other, the protection being not intended to restrict the invention
Range.
The above embodiment is only the preferred embodiment of the present invention, and the scope of protection of the present invention is not limited thereto,
The variation and replacement for any unsubstantiality that those skilled in the art is done on the basis of the present invention belong to institute of the present invention
Claimed range.
Claims (10)
1. a kind of method for improving object recognition speed, which comprises the following steps:
Frequency mixing stages: after receiving echo-signal, echo-signal is mixed with local oscillation signal to obtain intermediate-freuqncy signal；
Sampling step: digitized processing is carried out to intermediate-freuqncy signal by sampling module；
Clutter cancellation step: clutter cancellation processing is carried out to the intermediate-freuqncy signal for being digitized processing；
Adding window step: windowing process is carried out to the intermediate-freuqncy signal by clutter cancellation processing；
Difference frequency obtaining step: the intermediate-freuqncy signal after windowing process is carried out by Fast Fourier Transform (FFT) and frequency spectrum refinement algorithm
Processing is to obtain beat frequency；
It calculates step: the speed and distance of corresponding object being calculated according to object calculation formula；The object calculates
Formula are as follows:
Wherein, VfFor object speed, c is the light velocity, f0For the centre frequency of transmitted wave, f+Be positive difference frequency value, f-Be negative difference frequency value,
T is the modulated signal period, and B is modulation bandwidth, and R is the distance to object.
2. a kind of method for improving object recognition speed as described in claim 1, which is characterized in that obtained in the difference frequency
Among step, the frequency spectrum refinement algorithm is CZT algorithm, and the difference frequency obtaining step specifically includes following sub-step:
Fourier transformation step: the transformation of N point quick Fourier is done to the intermediate-freuqncy signal after windowing process, and searches spectral line amplitude most
Big value point P；
Frequency calculates step: calculating the frequency values f1 and f2 of (P-1) and (P+1) two o'clock；
Difference frequency calculates step: doing M point CZT transform operation in f1 to the section f2, searches spectral line amplitude maximum of points P1, and calculate
The frequency values of the point, the frequency values are beat frequency.
3. a kind of method for improving object recognition speed as claimed in claim 2, which is characterized in that walked in Fourier transformation
In rapid: detected by horizontal false-alarm detection module through Fast Fourier Transform (FFT) treated intermediate-freuqncy signal.
4. a kind of method for improving object recognition speed as claimed in claim 3, which is characterized in that the Fourier transformation
Horizontal false-alarm detection in step specifically includes following sub-step:
The critical element value is chosen by log-likelihood estimation function；
Judge that object is to be in strong clutter area in weak clutter area according to the critical element value, if it is in weak clutter
Area then selects SOCA-CFAR algorithm, detects the intermediate-freuqncy signal through Fast Fourier Transform (FFT) if it is strong clutter area is in, then
GOCA-CFAR algorithm is selected to detect the intermediate-freuqncy signal through Fast Fourier Transform (FFT).
5. a kind of method of raising object recognition speed as described in any one of claim 1-4, which is characterized in that
The difference frequency calculates in step, and spectral line amplitude maximum of points P1 is searched by peak detection module.
6. a kind of method for improving object recognition speed as claimed in claim 5, which is characterized in that in the windowing process
For the windowed function used for Hanning window or hamming window, the clutter cancellation uses MTI algorithm.
7. a kind of method for improving object recognition speed as claimed in claim 5, which is characterized in that after calculating step
It further include tracking step: by tracking module by the speed of obtained object and distance and in conjunction with the speed and acceleration of vehicle itself
Degree is to realize the lasting tracking to vehicle forward target.
8. a kind of method for improving object recognition speed as claimed in claim 7, which is characterized in that in the tracking module
Track algorithm use expanded Kalman filtration algorithm.
9. a kind of electronic equipment including memory, processor and stores the meter that can be run on a memory and on a processor
Calculation machine program, which is characterized in that the processor realizes any one of claim 1-8 institute when executing the computer program
A kind of method for the raising object recognition speed stated.
10. a kind of computer readable storage medium, is stored thereon with computer program, it is characterised in that: the computer program
A kind of method of raising object recognition speed as described in claim 1-8 any one is realized when being executed by processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910151429.3A CN109932695A (en) | 2019-02-28 | 2019-02-28 | A kind of method and device improving object recognition speed |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910151429.3A CN109932695A (en) | 2019-02-28 | 2019-02-28 | A kind of method and device improving object recognition speed |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109932695A true CN109932695A (en) | 2019-06-25 |
Family
ID=66986063
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910151429.3A Pending CN109932695A (en) | 2019-02-28 | 2019-02-28 | A kind of method and device improving object recognition speed |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109932695A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110501702A (en) * | 2019-09-09 | 2019-11-26 | 厦门精益远达智能科技有限公司 | Real-time flight height measurement method, device, equipment and the storage medium of unmanned plane |
| CN111750972A (en) * | 2020-05-26 | 2020-10-09 | 北京理工大学 | Target vibration measurement method suitable for dual-frequency continuous laser radar |
| CN111964773A (en) * | 2020-07-22 | 2020-11-20 | 清华大学 | Vibration signal extraction method and device based on multiple overlapped linear frequency modulation sets |
| CN112540630A (en) * | 2020-06-16 | 2021-03-23 | 深圳优地科技有限公司 | Method and device for processing motor speed signal, robot and storage medium |
| CN112617773A (en) * | 2019-09-24 | 2021-04-09 | 新加坡国立大学 | Signal processing method and signal processing device for health monitoring |
| CN112748429A (en) * | 2020-12-28 | 2021-05-04 | 中国人民解放军空军工程大学 | Rapid noise cancellation filtering method |
| CN114355328A (en) * | 2021-12-29 | 2022-04-15 | 加特兰微电子科技(上海)有限公司 | Radar signal processing method, radio signal processing method and application device |
-
2019
- 2019-02-28 CN CN201910151429.3A patent/CN109932695A/en active Pending
Non-Patent Citations (4)
| Title |
|---|
| HAROLD M.FINN等: ""A CFAR Design for a Window Spanning Two Clutter Fields"", 《IEEE TRANSACTION ON AEROSPACE AND ELECTRONIC SYSTEMS》 * |
| 史林 等, 西安电子科技大学出版社 * |
| 樊小倩: ""自适应恒虚警算法研究"", 《中国优秀硕博士学位论文全文数据库(硕士) 信息科技辑》 * |
| 钟鹏: ""FMCW雷达近程测距系统的设计与研究"", 《中国优秀博硕士学位论文全文数据库(硕士) 信息科技辑》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110501702A (en) * | 2019-09-09 | 2019-11-26 | 厦门精益远达智能科技有限公司 | Real-time flight height measurement method, device, equipment and the storage medium of unmanned plane |
| CN112617773A (en) * | 2019-09-24 | 2021-04-09 | 新加坡国立大学 | Signal processing method and signal processing device for health monitoring |
| CN111750972A (en) * | 2020-05-26 | 2020-10-09 | 北京理工大学 | Target vibration measurement method suitable for dual-frequency continuous laser radar |
| CN112540630A (en) * | 2020-06-16 | 2021-03-23 | 深圳优地科技有限公司 | Method and device for processing motor speed signal, robot and storage medium |
| CN111964773A (en) * | 2020-07-22 | 2020-11-20 | 清华大学 | Vibration signal extraction method and device based on multiple overlapped linear frequency modulation sets |
| CN112748429A (en) * | 2020-12-28 | 2021-05-04 | 中国人民解放军空军工程大学 | Rapid noise cancellation filtering method |
| CN112748429B (en) * | 2020-12-28 | 2023-09-08 | 中国人民解放军空军工程大学 | Fast noise cancellation filtering method |
| CN114355328A (en) * | 2021-12-29 | 2022-04-15 | 加特兰微电子科技(上海)有限公司 | Radar signal processing method, radio signal processing method and application device |
| WO2022233346A3 (en) * | 2021-12-29 | 2022-12-29 | 加特兰微电子科技(上海)有限公司 | Radar signal processing method, radio signal processing method and application apparatuses |
| CN114355328B (en) * | 2021-12-29 | 2024-04-09 | 加特兰微电子科技(上海)有限公司 | Radar signal processing method, radio signal processing method and application device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109932695A (en) | A kind of method and device improving object recognition speed | |
| CN110531336A (en) | A kind of object detection recognition methods and system | |
| WO2021134449A1 (en) | Method, apparatus, computer device, and storage medium for detection by a frequency-modulated continuous-wave (fmcw) array radar of weak signals of multiple moving targets under strong clutter, | |
| US10627481B2 (en) | Multi-resolution doppler processing | |
| CN107076836A (en) | For method, driver assistance system and the motor vehicles to the object classification in motor vehicles peripheral region | |
| CN110058239A (en) | A kind of vehicle-mounted millimeter wave radar installations and object detection method | |
| Zhang et al. | Support vector machines for classification of automotive radar interference | |
| CN113009441B (en) | Method and device for identifying multipath target of radar moving reflecting surface | |
| CN112731307B (en) | RATM-CFAR detector based on distance-angle joint estimation and detection method | |
| CN108693523A (en) | The method and system to be tested the speed based on sawtooth wave linear FM radar multi-Goal Measure | |
| CN110907929A (en) | Vehicle-mounted radar target detection method and device based on double-threshold detection | |
| CN111929685A (en) | Radar detection method and device based on linear frequency modulation continuous wave, radar device and motor vehicle | |
| CN111044987B (en) | Method, system and medium for resolving ambiguity of environmental target speed based on automobile radar | |
| CN113311398A (en) | Tracking method for high maneuvering dim small target with strong clutter complex background | |
| CN116224274A (en) | Low-speed small target detection method, device and equipment based on dynamic threshold factor | |
| Holder et al. | Modeling and simulation of radar sensor artifacts for virtual testing of autonomous driving | |
| CN113325408A (en) | Noise estimation method, device and related equipment | |
| CN111175715B (en) | Auxiliary driving system and method capable of inhibiting radar close-range harmonic waves | |
| CN111175714A (en) | Driving assistance method capable of suppressing short-range harmonic waves of radar and storage medium | |
| US11892557B2 (en) | Radar device | |
| CN110907930B (en) | Vehicle-mounted radar target detection and estimation method and device based on angle estimation | |
| CN105242272B (en) | Vehicle-mounted millimeter wave Anticollision Radar CFAR detection method based on auto-regressive time series model | |
| CN116047442A (en) | Method and device for detecting target angle and electronic equipment | |
| US20240280692A1 (en) | Fine-near-range estimation method for automotive radar applications | |
| CN111175717A (en) | Auxiliary driving method capable of restraining short-distance harmonic waves of radar and scene application |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190625 |
|
| RJ01 | Rejection of invention patent application after publication |