CN114924237A - Filtering method for dynamically detecting abnormal values of radar data - Google Patents
Filtering method for dynamically detecting abnormal values of radar data Download PDFInfo
- Publication number
- CN114924237A CN114924237A CN202210559010.3A CN202210559010A CN114924237A CN 114924237 A CN114924237 A CN 114924237A CN 202210559010 A CN202210559010 A CN 202210559010A CN 114924237 A CN114924237 A CN 114924237A
- Authority
- CN
- China
- Prior art keywords
- measurement
- function
- value
- data
- time
- 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
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 44
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000001914 filtration Methods 0.000 title claims abstract description 36
- 238000005259 measurement Methods 0.000 claims abstract description 77
- 238000005070 sampling Methods 0.000 claims abstract description 26
- 238000012937 correction Methods 0.000 claims abstract description 21
- 238000001514 detection method Methods 0.000 claims abstract description 11
- 239000002245 particle Substances 0.000 claims description 57
- 238000012952 Resampling Methods 0.000 claims description 7
- 238000005315 distribution function Methods 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 abstract description 5
- 238000005312 nonlinear dynamic Methods 0.000 abstract description 5
- 238000011897 real-time detection Methods 0.000 abstract description 2
- 238000004088 simulation Methods 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 230000003068 static effect Effects 0.000 description 4
- 230000001133 acceleration Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000013450 outlier detection Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/40—Means for monitoring or calibrating
- G01S7/4004—Means for monitoring or calibrating of parts of a radar system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention provides a filtering method for dynamically detecting abnormal values of radar data, which determines a system state equation and a measurement equation according to radar measurement and a target state; setting a corresponding sampling interval according to the motion condition of the target, and sampling a series of dynamic measurement data; filtering by adopting a nonlinear dynamic filtering method without assuming that noise contained in the measured data is white noise; calculating a measurement correction value by applying the state estimation value according to a measurement equation; and analyzing the residual error between the measured value of the measured data and the corrected value by adopting a box diagram method, thereby realizing the real-time detection of the abnormal value of the measured data. The method has the advantages of simple and clear principle, convenience for realization on a computer, accurate and reliable detection result, great reduction of error of further data processing, capability of dynamically detecting and correcting abnormal values of radar measurement data in real time, and great significance for improving the target tracking track precision.
Description
Technical Field
The invention relates to the technical field of radar data processing, in particular to a filtering method for radar data abnormal values.
Background
In radar systems, the target signal is typically resolved by a data logger into distance, azimuth, pitch, etc. measurements of the target, also known as the footprint. The radar data processing generally refers to processing a measured value by using a data processor, acquiring target state information (namely position, speed, acceleration and the like) to the maximum extent, and generally includes links such as navigation, track association, data filtering and the like. In actual engineering, the phenomenon that abnormal values appear in measurement data is often generated. Whether static measurement or dynamic measurement is performed, some erroneous measurement, called abnormal value or outlier, may be included in the measurement data due to the measurement device itself, data transmission or manual operation. These outliers in the measurement data are not detected and corrected or rejected, which can introduce significant errors into further data processing.
At present, an abnormal value in static measurement data is easy to judge, and a mature theory exists. For a dynamic measurement data outlier detection method, a linear kalman filtering method is currently studied more frequently. Linear kalman filtering is limited to linear systems and relies on the white noise assumption. In the paper 'application of an outlier elimination method in Radar Cross Section (RCS) measurement data processing', aiming at a large amount of original data obtained by RCS measurement of a target, a Grabas inspection method and a Kalman filtering method are respectively used for analyzing and eliminating outliers of static and dynamic test data; xinxin et al applied the adjacent mean filtering and sliding window type detection method to study the problem of abnormal parameters of radar transmitting power, angle and distance in the study of abnormal value detection and correction method.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a filtering method for dynamically detecting abnormal values of radar data. The invention aims to provide a filtering method for dynamically detecting radar data abnormal values, aiming at the problems that the prior art mainly detects the abnormal values of static measurement data, lacks the detection of the abnormal values of dynamic data and excessively depends on a white noise hypothesis. The technical scheme is as follows: determining a system state equation and a measurement equation according to radar measurement and a target state; setting a corresponding sampling interval according to the motion condition of the target, and sampling a series of dynamic measurement data; filtering by adopting a nonlinear dynamic filtering method, namely a Cost Reference Particle Filtering (CRPF) method, which does not need to assume that noise contained in the measured data is white noise; calculating a measurement correction value by applying the state estimation value according to a measurement equation; and analyzing the residual error between the measured value of the measured data and the corrected value by adopting a box diagram method, thereby realizing the real-time detection of the abnormal value of the measured data.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
the method comprises the following steps: according to radar measurement and a target state, establishing a system equation:
the state equation is: s k ＝g(s k-1 ,u k-1 )+v k-1 ；
The measurement equation is: m is a unit of k ＝h(s k ,u k )+w k ；
Where k is the sampling time, s k Is a state variable at time k, m k For the measured variable at time k, g (-) and h (-) are the state transfer function and the measurement function, u (-) respectively k Is an input quantity at time k, v k-1 State noise at time k-1, w k Measuring noise at the moment k, wherein the state and the measurement noise are not related to each other;
step two:setting a corresponding sampling interval delta T according to a sampling theorem, and sampling the dynamic measurement data set { m } according to a sampling time k which is 1,2,3 k } k＝1...T Wherein T is the current sampling moment;
step three: filtering by adopting a Cost Reference Particle Filtering (CRPF) method, and constructing a cost function and a risk function; a set of random state particles of a known a posteriori Probability Distribution Function (PDF)
Can be used, k is more than or equal to 1,
is a state particle, i is a particle index, N is the total number of particles,
is a posterior probability distribution function; cost function
Is a measure of the "particle quality" of all current and past particles, and the cost function at time k for the ith particle is defined as
In which λ represents the forgetting factor, 0<λ<1, q is greater than or equal to 1, symbol
A norm representing the vector v; risk function
Is to increment a cost function
Is defined as
Step four: based on the state estimate
And equation of measurement
The measurement data is corrected in such a way that,
a measurement correction value representing time k; detecting an abnormal value of the measurement data in real time by the measurement correction value;
step five: if the measured data m k If the detected value is a normal value, returning to the real-time state estimation value
Correction value
If m is k If abnormal value is detected, m is measured k Compensating, and performing data correction and state estimation by using the compensated observation value;
the abnormal value compensation calculation formula of the observed data is as follows
m′ k ＝m k -r k
Wherein r is k For the measurement residual error of the observation data at the moment k, the increment of the cost function in the CRPF filtering process is updated as follows:
the risk function is updated as:
updating the probability mass function by the updated risk function:
renormalization of probability mass function
And for joint cost particle sets
Repeating the steps 3.2.2-3.2.5 to obtain an accurate target state estimation value
Based on the state estimate
Using the equation of measurement
Obtaining accurate correction values of measured data
The CRPF filtering comprises the following specific steps:
3.1 first initialize the filter from the initial distribution function p 0 (s 0 ) The middle sampling obtains N initial particles, and the ith particle state is expressed as
The initial value of the cost function is
Obtaining an initial joint cost particle set
Setting an initial variance of
3.2 with time k 1.., T, obtain the real-time state estimate through CRPF filtering, the concrete steps are as follows:
3.2.1 for each particle i ═ 1., N, according to the equation of state s k ＝g(s k-1 ,u k-1 )+v k-1 And the measurement equation m k ＝h(s k ,u k )+w k Generating a set of joint cost particles
Calculating a risk function from the distance of the particle from the measurement:
calculating a probability mass function from the risk function:
in the formula delta>0，β>0 and delta is 0.1 for ensuring that the denominator is not zero and then normalizing the probability mass function
And for joint cost particle sets
Resampling of (d);
3.2.2 probability mass function according to Risk function
Systematic resampling is carried out on the particles to obtain a new combined cost particle set
3.2.3 particles i pass from time k-1 to time k, the passing process particles obey a Gaussian distribution of adaptively adjusted variances:
wherein
I is an identity matrix with s in the same dimension;
3.2.4 after resampling, calculate the cost function of particle i at time k
Calculating probability quality function according to cost function
And normalizing
3.2.5 according to probability mass function
Estimating a target state at time k
Updating a cost function
Updating a set of particles
The specific steps of detecting abnormal values of the measurement data in real time by measuring the correction values include:
4.1 calculating the measurement data m at time k k And a correction value
The residual error of (a):
wherein
N m Representing the total number of measurements, using residual errors
Detecting abnormal values as a result;
4.2 analyzing the measurement residual r by using a boxplot method k Thereby detecting m k Whether it is an outlier.
The specific steps of the step 4.2 for detecting the abnormal value are as follows:
4.2.1 will measure the residual error r 1 ,r 2 ,...,r k ]Sorting from small to large to find out the first quartile F L And a third quartile F U ；
4.2.2 calculating the measurement residual four-bit distance d F ＝F U -F L And an abnormal value cutoff point F U +ηd F And F L -ηd F Wherein the value of eta is determined according to the actual condition;
4.2.3 detection of outliers: if r is k ≥F U +ηd F Or r k ≤F L -ηd F Then measure the data m k Detecting as an abnormal value; otherwise, measuring data m k Detected as normal.
The invention has the advantages of simple and clear principle and convenient realization on a computer. The method is very suitable for dynamically detecting the abnormal value of the radar data, so that the detection result is accurate and reliable. The error of further data processing is greatly reduced. The invention has the beneficial effects that: the abnormal value of the radar measurement data can be dynamically detected and corrected in real time, and the method has important significance for improving the target tracking track precision.
Drawings
Fig. 1 is a flowchart of a filtering method for dynamically detecting outliers of radar data according to the present invention.
FIG. 2 is a schematic diagram of detection of an azimuth anomaly value in an embodiment of a pure azimuth-seeking missile guidance system.
FIG. 3 is a schematic diagram of detection of an abnormal pitch angle value in an embodiment of a pure azimuth-seeking missile guidance system.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a schematic diagram of the general structure of the filtering method for dynamically detecting radar data outliers of the present invention. The invention discloses a filtering method for dynamically detecting abnormal values of radar data, which specifically comprises the following steps:
the method comprises the following steps: according to radar measurement and a target state, establishing a system equation:
equation of state s k ＝g(s k-1 ,u k-1 )+v k-1
Measurement equation m k ＝h(s k ,u k )+w k )
Wherein s is k Is a state variable, m k For measuring variables, k is the sampling time, g (-) and h (-) are the state transfer function and the measurement function, respectively, u k As an input quantity, v k-1 And w k Respectively, state and measurement noise.
Step two: setting a sampling interval delta T, and sampling a series of dynamic measurement data { m } according to sampling time k ═ 1,2,3 k } k＝1...T And T is the current sampling moment.
Step three: filtering by adopting a CRPF nonlinear dynamic filtering method, which comprises the following specific steps:
3.1: initializing the CRPF filter from the initial distribution function p 0 (s 0 ) The middle sampling obtains N initial particles, and the ith particle state is expressed as
Setting the initial value of the cost function as
Obtaining an initial set of particles
Setting an initial variance of
3.2: iteratively updating k to 1.. times, T according to time, and obtaining a real-time state estimation value through nonlinear dynamic filtering, wherein the method specifically comprises the following steps of:
3.2.1: for each particle i 1, N, according to the equation of state s k ＝g(s k-1 ,u k-1 )+v k-1 And the measurement equation m k ＝h(s k ,u k )+w k Generating a set of joint cost particles
Calculating a risk function based on the distance of the particle from the measurement:
and its probability mass function
And normalizing
3.2.2: probability mass function from risk function
Systematic resampling is carried out on the particles to obtain a new combined cost particle set
3.2.3: the particles i are transferred from the moment k-1 to the moment k, and the Gaussian distribution of the particles subjected to the self-adaptive variance adjustment in the transfer process is subjected to the Gaussian distribution
Wherein
I is an identity matrix with dimensions s.
3.2.4: after resampling, calculating a particle cost function at the k moment
And its probability mass function
And normalizing the probability mass function
3.2.5: computing a real-time state estimate from a probability mass function of a cost function
The cost function is then updated
Updating a joint cost particle set
Step four: according to the equation of measurement
Calculating a correction value of the measurement data
The method for detecting the abnormal value of the measurement data in real time comprises the following specific steps:
4.1: calculating measurement data m at k sampling moments k And a correction value
Residual error of (2)
4.2: analysis of residual r using boxplot method k Thereby detecting the measurement data m k Whether the abnormal value is detected or not, the specific steps comprise:
4.2.1: will measure the residual error r 1 ,r 2 ,...,r k ]Sorting from small to large to find out the first quartile F L And a third quartile F U 。
4.2.2: calculating the four-bit distance d of the measurement residual F ＝F U -F L And an outlier cutoff point F U +ηd F And F L -ηd F And the value of eta is determined by actual conditions.
4.2.3: an abnormal value is detected. If r is k ≥F U +ηd F Or r k ≤F L -ηd F Then measure the data m k Detecting as an abnormal value; otherwise measuring data m k Detected as normal.
Step five: if the measured data m k If the detected value is a normal value, returning to the real-time state estimation value
Correction value
If m is k If the detected value is abnormal, the value is m k And compensating, and performing data correction and state estimation by using the compensated observation value.
In the embodiment of the pure azimuth-seeking missile guidance system for simulation test, a system equation is established according to radar angle measurement and the position and the motion state of a target, the state equation is a linear equation, and the state variable is the relative position r of a missile and the target x (k)，r y (k)，r z (k) Relative velocity v x (k)，v y (k)，v z (k) And relative acceleration speed a x (k)，a y (k)，a z (k) (ii) a The observation equation is a nonlinear dynamic model, and the observation variables are an azimuth viewing distance theta and an elevation viewing distance phi. And setting the sampling period delta t to be 0.05s, and setting the simulation time length t of the guidance process to be 3.7 s. Randomly introducing 9 abnormal values, and applying the methodThe results of simulation tests are shown in fig. 2 and fig. 3. Further, 50 simulation tests were performed, and the statistical result was that the number of abnormal values accumulated in correct detection was 450 in the 50 simulation tests, and the number of abnormal values accumulated in erroneous detection was only 14.
Claims (4)
1. A filtering method for dynamically detecting radar data outliers, comprising the steps of:
the method comprises the following steps: according to radar measurement and a target state, establishing a system equation:
the state equation is: s k ＝g(s k-1 ,u k-1 )+v k-1 ；
The measurement equation is: m is k ＝h(s k ,u k )+w k ；
Where k is the sampling time, s k Is a state variable at time k, m k For measuring variables at time k, g (-) and h (-) are the state transfer function and the measurement function, respectively, u k Is an input quantity at time k, v k-1 State noise at time k-1, w k Measuring noise at the moment k, wherein the state and the measurement noise are not related to each other;
step two: setting a corresponding sampling interval delta T according to a sampling theorem, and sampling a dynamic measurement data set { m } according to a sampling time k as 1,2,3 k } k＝1...T Wherein T is the current sampling moment;
step three: filtering by adopting a cost reference particle filtering method, and constructing a cost function and a risk function; a set of randomly-stateful particles of known posterior probability distribution function
Can be used, k is more than or equal to 1,
is a state particle, i is a particle index, N is the total number of particles,
is a posterior probability distributionA function; cost function
Is a measure of the "particle quality" of all current and past particles, and the cost function at time k for the ith particle is defined as
In which λ represents a forgetting factor, 0<λ<1, q is greater than or equal to 1, symbol
A norm representing the vector v; risk function
Is to increment a cost function
Is defined as
Step four: based on the state estimate
And equation of measurement
The measurement data is corrected in such a way that,
a measurement correction value representing time k; detecting abnormal values of the measurement data in real time through the measurement correction values;
step five: if the measured data m k If the detected value is a normal value, returning to the real-time state estimation value
Correction value
If m is k If the detected value is abnormal, the value is m k Compensating, and performing data correction and state estimation by using the compensated observation value;
the abnormal value compensation calculation formula of the observed data is as follows
m′ k ＝m k -r k
Wherein r is k For the measurement residual error of the observation data at the moment k, the increment of the cost function in the CRPF filtering process is updated as follows:
the risk function is updated as:
updating the probability mass function by the updated risk function:
renormalization of probability mass function
And for combining sets of cost particles
Repeating the steps 3.2.2-3.2.5 to obtain an accurate target state estimation value
Based on the state estimate
Using the equation of measurement
Obtaining accurate correction value of measured data
2. The filtering method for dynamically detecting outliers of radar data as set forth in claim 1, wherein: the CRPF filtering comprises the following specific steps:
3.1 first initialize the filter from the initial distribution function p 0 (s 0 ) The middle sampling obtains N initial particles, and the ith particle state is expressed as
The initial value of the cost function is
Obtaining an initial joint cost particle set
Setting an initial variance of
3.2 obtaining real-time state estimation values by CRPF filtering with time k ═ 1., T, specifically including the steps of:
3.2.1 for each particle i ═ 1., N, according to the equation of state s k ＝g(s k-1 ,u k-1 )+v k-1 And the measurement equation m k ＝h(s k ,u k )+w k Generating a set of joint cost particles
Calculating a risk function from the distance of the particle from the measurement:
calculating a probability mass function from the risk function:
in the formula of>0，β>0 and delta is 0.1 to ensure that the denominator is not zero, and then normalizing the probability mass function
And for combining sets of cost particles
Resampling of (1);
3.2.2 probability quality function according to Risk function
The particles are systematically resampled to obtain a new combined cost particle set
3.2.3 particles i pass from time k-1 to time k, the passing process particles obey a Gaussian distribution of adaptively adjusted variances:
wherein
I is a unit of same dimension as sA matrix;
3.2.4 after resampling, calculate the cost function of particle i at time k
Calculating probability quality function according to cost function
And normalizing
3.2.5 according to a probability mass function
Estimating a target state at time k
Updating a cost function
Updating a set of particles
3. The filtering method for dynamically detecting outliers of radar data of claim 1, further comprising:
the specific steps of detecting abnormal values of the measurement data in real time by measuring the correction values include:
4.1 calculating the measurement data m at time k k And a correction value
The residual error of (a):
wherein
N m Representing the total number of measurements, using the residual r k ＝[r 1,k ,r 2,k ,...,r Nm,k ] T Detecting abnormal values as a result;
4.2 analysis of the measurement residual r by boxplot method k Thereby detecting m k Whether it is an outlier.
4. The filtering method for dynamically detecting outliers of radar data as set forth in claim 1, wherein: the specific steps of the step 4.2 for detecting the abnormal value are as follows:
4.2.1 will measure the residual error r 1 ,r 2 ,...,r k ]Sorting from small to large to find out the first quartile F L And a third quartile F U ；
4.2.2 calculating the measurement residual four-bit distance d F ＝F U -F L And an abnormal value cutoff point F U +ηd F And F L -ηd F Wherein the value of eta is determined according to the actual condition;
4.2.3 detection of outliers: if r is k ≥F U +ηd F Or r k ≤F L -ηd F Then measuring data m k Detecting as an abnormal value; otherwise measuring data m k Detected as a normal value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210559010.3A CN114924237A (en) | 2022-05-21 | 2022-05-21 | Filtering method for dynamically detecting abnormal values of radar data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210559010.3A CN114924237A (en) | 2022-05-21 | 2022-05-21 | Filtering method for dynamically detecting abnormal values of radar data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114924237A true CN114924237A (en) | 2022-08-19 |
Family
ID=82810642
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210559010.3A Pending CN114924237A (en) | 2022-05-21 | 2022-05-21 | Filtering method for dynamically detecting abnormal values of radar data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114924237A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104977577A (en) * | 2015-07-07 | 2015-10-14 | 西安电子科技大学 | Forward-backward cost-reference particle filtering-based instantaneous frequency curve estimation method |
| WO2018119912A1 (en) * | 2016-12-29 | 2018-07-05 | 深圳大学 | Target tracking method and device based on parallel fuzzy gaussian and particle filter |
| CN108802692A (en) * | 2018-05-25 | 2018-11-13 | 哈尔滨工程大学 | A kind of method for tracking target based on maximum cross-correlation entropy volume particle filter |
| CN110865343A (en) * | 2019-11-13 | 2020-03-06 | 中国人民解放军海军航空大学 | LMB-based particle filter tracking-before-detection method and system |
-
2022
- 2022-05-21 CN CN202210559010.3A patent/CN114924237A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104977577A (en) * | 2015-07-07 | 2015-10-14 | 西安电子科技大学 | Forward-backward cost-reference particle filtering-based instantaneous frequency curve estimation method |
| WO2018119912A1 (en) * | 2016-12-29 | 2018-07-05 | 深圳大学 | Target tracking method and device based on parallel fuzzy gaussian and particle filter |
| CN108802692A (en) * | 2018-05-25 | 2018-11-13 | 哈尔滨工程大学 | A kind of method for tracking target based on maximum cross-correlation entropy volume particle filter |
| CN110865343A (en) * | 2019-11-13 | 2020-03-06 | 中国人民解放军海军航空大学 | LMB-based particle filter tracking-before-detection method and system |
Non-Patent Citations (1)
| Title |
|---|
| 夏玉洁;张兴敢;高健;: "雷达多目标交叉轨迹跟踪算法", 南京大学学报(自然科学), no. 04, 30 July 2017 (2017-07-30) * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110514391B (en) | Five-hole pressure probe measurement three-dimensional flow field uncertainty evaluation method | |
| CN110514390B (en) | Three-hole pressure probe measurement two-dimensional flow field uncertainty evaluation method | |
| CN110081923B (en) | Fault detection method and device for automatic acquisition system of field baseline environmental parameters | |
| CN109813269B (en) | On-line calibration data sequence matching method for structure monitoring sensor | |
| CN111680870A (en) | Comprehensive evaluation method for target motion trajectory quality | |
| CN112100574A (en) | Resampling-based AAKR model uncertainty calculation method and system | |
| Rao et al. | Robust Data Assimilation Using L_1 and Huber Norms | |
| CN110738275A (en) | UT-PHD-based multi-sensor sequential fusion tracking method | |
| CN116659803A (en) | Method for acquiring aerodynamic load of continuous wind tunnel based on balance zero point on-line monitoring | |
| Rach et al. | Simulation of large deflections of a flexible cantilever beam fabricated from functionally graded materials by the Adomian decomposition method | |
| CN114897675A (en) | Exponential windowing method for relevance weighting in digital image correlation | |
| CN112255612B (en) | Radar track random jitter evaluation method | |
| Wang | Analog instrument pointer monitoring and parameter estimation via line scan vision | |
| CN114239796A (en) | Power system state estimation method based on extended Kalman filtering | |
| Dichev et al. | Method of Analysis and correction of the Error from Nonlinearity of the Measurement Instruments. | |
| CN114924237A (en) | Filtering method for dynamically detecting abnormal values of radar data | |
| CN116502119A (en) | Sensor fault detection method and device of structural health monitoring system | |
| CN114266776B (en) | Digital image correlation method applying composite crack displacement field function | |
| CN114519262B (en) | Air target threat prediction method based on improved GM (1, 1) model | |
| CN112747773B (en) | Method for improving precision of gyroscope based on Allan variance and random polynomial | |
| CN108375337B (en) | Robot and method and device for measuring relative pose of process equipment of robot | |
| CN117330604B (en) | Automatic temperature compensation method, device, computer equipment and storage medium | |
| CN113236506B (en) | Industrial time delay system fault detection method based on filtering | |
| CN117131469B (en) | Error checking method of intelligent differential pressure transmitter | |
| CN111860633B (en) | Processing method of waveform sensing data |
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 |