CN107102308B - Distributed radar heterogeneous signal level fusion target detection method - Google Patents
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Abstract
The invention belongs to the technical field of radar target detection, and discloses a distributed radar heterogeneous signal level fusion target detection method, which comprises the following steps: setting a distributed radar system comprising N local radar stations and a fusion detection center; constructing local detection statistics of each local radar station; setting false alarm probability p for distributed radar systemsf(ii) a Each local radar station calculates the signal-to-noise ratio weighting coefficient of the local radar station according to the corresponding local detection statistic and the false alarm probability of the distributed radar system; setting a fusion judgment threshold, and carrying out signal-to-noise ratio weighting on local detection statistics of N local radar stations by a fusion detection center to obtain fused detection statistics; and if the fused detection statistic is larger than the fusion judgment threshold, determining that the target is detected and the detection performance of the target can be achieved.
Description
Technical Field
The invention belongs to the technical field of radar target detection, and particularly relates to a distributed radar heterogeneous signal level fusion target detection method.
Background
Distributed radar is a hot topic in the field of radar and is a key problem of a target detection data fusion algorithm.
The traditional distributed radar target detection fusion algorithm has better detection performance under the condition that each local radar station has the same parameter and statistic structure, but when the parameters of the local radar stations are different, namely the signal-to-noise ratio loss of each channel (each local radar station is also called as one channel) is different, the detection performance of the traditional detection algorithm is deteriorated, the deterioration degree is increased along with the enlargement of the difference, and actually, each local radar station is not necessarily the same type radar and the construction modes of the parameters and the statistic are not necessarily the same, so that the single-station detection performance of each local radar station is inconsistent, and when the actual difference is larger, the traditional distributed radar target detection algorithm cannot meet the task of efficiently detecting targets.
Disclosure of Invention
In view of the above drawbacks of the prior art, an object of the present invention is to provide a method for detecting a heterogeneous signal level fusion target of a distributed radar, which can improve the problem of detection performance degradation caused by differences in parameters of different local radar stations and large differences in distribution of structured detection statistics.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme.
A distributed radar heterogeneous signal level fusion target detection method comprises the following steps:
step 1, setting a distributed radar system comprising N local radar stations and a fusion detection center; constructing local detection statistics of each local radar station meeting the constant false alarm property (the constant false alarm property means that the false alarm probability is only related to a decision threshold and does not change along with the fluctuation of background noise power);
step 2, setting the total false alarm probability p of the distributed radar systemfThe false alarm probability of each local radar station evaluation detection performance curve is equal to the total false alarm probability of the distributed radar system;
step 3, each local radar station detects according to the corresponding localFalse alarm probability p of statistic and local radar station evaluation detection performance curvefCalculating the signal-to-noise ratio weighting coefficient of the local radar station;
step 4, setting a fusion judgment threshold, and weighting the signal-to-noise ratio of the local detection statistics of the N local radar stations by the fusion detection center according to the local detection statistics of each local radar station and the signal-to-noise ratio weighting coefficient of the local radar station to obtain fused detection statistics;
and if the fused detection statistic is larger than the fusion judgment threshold, determining that the target is detected.
The technical scheme of the invention has the characteristics and further improvements that:
(1) in step 1, there are many methods for constructing the local detection statistics satisfying the constant false alarm property for each local radar station, for example, the following local detection statistics obtained by this construction method satisfy the constant false alarm property:
local detection statistic q for nth local radar stationnExpressed as:
wherein D isnA sampling value of a sampling unit of the nth local radar station is represented, N being 1niDenotes the sampled value of the ith reference cell of the nth local radar station, and i is 1n，lnIs the total number of reference units of the nth local radar station.
(2) The step 3 specifically comprises the following substeps:
(3a) evaluating the false alarm probability p of the detection performance curve at the local radar stationfUnder the condition, the nth local radar station detects statistic q according to the corresponding localnPerforming single-station detection to obtain a detection performance curve corresponding to the local radar station; the abscissa of the detection performance curve is a signal-to-noise ratio, and the ordinate is a detection probability value when a target exists;
(3b) setting a preset probability value p for the presence of a targetdN th local radar stationAccording to the preset probability value p of the target existencedDetermining a preset probability value p of the existence of the target on a detection performance curve of the local radar stationdCorresponding signal-to-noise ratio SNRn；
(3c) N is set to be 1, so as to obtain signal-to-noise ratios corresponding to the N local radar stations at the preset probability value of the existence of the target;
(3d) the signal-to-noise ratio weighting coefficient of the nth local radar station
Wherein the SNRminAnd the minimum value of the signal-to-noise ratios of the N local radar stations at the preset probability value of the existence of the target is represented.
(3) In the sub-step (3d),
another way to express the snr weighting factor w (n) of the nth local radar station is:
wherein,
(4) the step 4 specifically comprises the following steps:
(4a) setting a fusion decision threshold η;
(4b) the fusion detection center performs signal-to-noise ratio weighting on the local detection statistics of N local radar stations according to the local detection statistics of each local radar station and the signal-to-noise ratio weighting coefficient of the local radar station to obtain fused detection statistics t:
(4c) and if the fused detection statistic t is smaller than the fusion judgment threshold η, the target is judged to be not detected.
By the method, the detection performance deterioration of the distributed radar heterogeneous signal level fusion can be reduced to a certain extent.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a distributed radar heterogeneous signal level fusion target detection method according to an embodiment of the present invention;
FIG. 2 is a diagram of parameter l in a simulation experiment according to an embodiment of the present invention1＝8,l2＝12,l3＝16,l4The detection performance curves of the 4 local radar stations are shown as 20 hours;
FIG. 3 is a diagram illustrating a parameter l in a simulation experiment according to an embodiment of the present invention1＝8,l2＝16,l3＝24,l4The detection performance curves of 4 local radar stations are shown as 32 hours;
fig. 4 is a schematic diagram illustrating comparison of detection performance curves without signal-to-noise ratio weighting and after signal-to-noise ratio weighting in a simulation test according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a distributed radar heterogeneous signal level fusion target detection method, which comprises the following steps of:
step 1, setting a distributed radar system comprising N local radar stations and a fusion detection center; and constructing local detection statistics for each local radar station that satisfy the constant false alarm property.
In step 1, the local detection statistic of each local radar station is constructed, and the following construction method is selected but not unique, and the constructed local detection statistic can be used as the local detection statistic in the embodiment of the invention as long as the constructed local detection statistic satisfies the constant false alarm property.
Local detection statistic q for nth local radar stationnExpressed as:
wherein D isnA sampling value of a sampling unit of the nth local radar station is represented, N being 1niDenotes the sampled value of the ith reference cell of the nth local radar station, and i is 1n，lnIs the total number of reference units of the nth local radar station.
It should be noted that each local radar station has one sampling unit, the sampling unit includes a target signal and noise, and each local radar station has a plurality of reference units, and each reference unit includes only noise.
In particular, let t benIs the observed value received by the nth local radar station, and the local detection statistic q of the nth local radar stationnIs the observed value tnMonotonic transformation function of qnHas constant false alarm property (the false alarm probability is only related to the decision threshold and does not change with the fluctuation of background noise power), qnThe larger the amplitude, the greater the likelihood that the target is present.
For example, when N local radar stations are in an independent white gaussian noise background with the same distribution zero mean value and a target echo signal is a Swerling i model, a sampling unit D is sampled after passing through a square rate detectornProbability density function of
And a reference cell xniProbability density function of
Obeying an exponential distribution, as specified by the following formula:
λnrepresenting the signal-to-noise ratio (in units of 1) of the nth local radar station and mu representing the background noise power level, the local detection statistic q of the nth local radar station can be derivednCumulative distribution function Fqn(x) The following were used:
step 2, setting the false alarm probability p of the distributed radar systemfWhen the signal-to-noise ratio weighting coefficient of the local radar station is obtained, the false alarm probability of each local radar station is equal to the false alarm probability of the distributed radar system and is also pf。
In particular, the false alarm probability p of the distributed radar systemf＝10-4。
Step 3, each local radar station detects statistics and false alarm probability p of local radar station according to its corresponding localfAnd calculating the signal-to-noise ratio weighting coefficient of the local radar station.
The step 3 specifically comprises the following substeps:
(3a) false alarm probability p at the local radar stationfUnder the condition, the nth local radar station detects statistic q according to the corresponding localnPerforming single-station detection (namely the radar target detection system only has one radar station) to obtain a detection performance curve corresponding to the local radar station; the abscissa of the detection performance curve is the signal-to-noise ratio, and the ordinate is the detection probability that the single-station radar target detection system can detect the target when the target existsA value;
specifically, N local radar stations use their own detection statistics qnThe single station detection is carried out, and the judgment method comprises the following steps:
ηnwhen the single station detection is carried out for the nth local radar station, the false alarm probability is pfTemporal detection threshold (i.e. q in the case of no target in a local radar station)nHas a value of greater than ηnHas a probability of pf) Then, the probability of false alarm is plotted as pfDetecting performance curves of N single-station radar target detection systems;
(3b) setting a preset probability value p for the presence of a targetdThe nth local radar station presets a probability value p according to the existence of the targetdDetermining a preset probability value p of the existence of the target on a detection performance curve of the local radar stationdCorresponding signal-to-noise ratio SNRn；
In particular, a preset probability value p of the existence of the target is setd＝50％；
(3c) N is set to be 1, so as to obtain signal-to-noise ratios corresponding to the N local radar stations at the preset probability value of the existence of the target;
(3d) the signal-to-noise ratio weighting coefficient of the nth local radar station
Wherein the SNRminAnd the minimum value of the signal-to-noise ratios of the N local radar stations at the preset probability value of the existence of the target is represented.
In particular, SNRn-SNRminRepresenting the signal-to-noise ratio loss of the nth local radar station.
In the sub-step (3d),
another way to express the snr weighting factor w (n) of the nth local radar station is:
wherein,
step 4, setting a fusion judgment threshold, and weighting the signal-to-noise ratio of the local detection statistics of the N local radar stations by the fusion detection center according to the local detection statistics of each local radar station and the signal-to-noise ratio weighting coefficient of the local radar station to obtain fused detection statistics;
and if the fused detection statistic is larger than the fusion judgment threshold, determining that the target is detected.
The step 4 specifically comprises the following steps:
(4a) setting a fusion decision threshold η;
specifically, η is the false alarm probability p at a given distributed radar systemfFusion decision threshold found under the condition (i.e. the probability p that the fused value is greater than η under the condition of no target is pf)；
(4b) The fusion detection center performs signal-to-noise ratio weighting on the local detection statistics of N local radar stations according to the local detection statistics of each local radar station and the signal-to-noise ratio weighting coefficient of the local radar station to obtain fused detection statistics t:
(4c) and if the fused detection statistic t is smaller than the fusion judgment threshold η, the target is judged to be not detected.
Simulation experiment:
using MATLAB software, draw 4 local radar stations with a false alarm probability of 10-4According to the technical scheme of the invention, the signal-to-noise ratio weighting coefficient corresponding to each local radar station is calculated, and the detection performance curve which is not weighted and weighted is drawn by using a Monte Carlo method.
As can be seen from fig. 2 and 3, for single-station detection, the detection performance of the single-station detection becomes better with the increase of the reference units, so that at the same detection probability, the signal-to-noise ratio based on single-station detection under different reference units is different, and the single-station detection with less reference units and more reference units has a relative signal-to-noise ratio loss.
As can be seen from FIG. 4, the detection performance of the algorithm after signal-to-noise ratio weighting is better than that of the algorithm without weighting, and the parameter l is1＝8,l2＝12,l3＝16,l4At 20, when the preset probability value of the presence of the target is 50%, the signal-to-noise ratio is weighted in w1 with a gain of about 0.3dB relative to the unweighted time, and the weighted in w2 with a gain of about 0.25 dB. At the parameter l1＝8,l2＝16,l3＝24,l4At 32, when the preset probability value of the presence of the target is 50%, the signal-to-noise ratio is weighted by w1 with a gain of about 0.45dB relative to the unweighted time, and the weighted by w2 with a gain of about 0.4 dB. It can be known from the above that the gain effect is better when the parameter difference is larger, and w1 in the signal-to-noise ratio weighting proposed by the algorithm is better than w 2.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (3)
1. A distributed radar heterogeneous signal level fusion target detection method is characterized by comprising the following steps:
step 1, setting a distributed radar system comprising N local radar stations and a fusion detection center; constructing local detection statistics of each local radar station; the local detection statistic has a constant false alarm characteristic;
in step 1, constructing local detection statistics of each local radar station, specifically:
local detection statistic q for nth local radar stationnExpressed as:
wherein D isnA sampling value of a sampling unit of the nth local radar station is represented, N being 1n,iDenotes the sampled value of the ith reference cell of the nth local radar station, and i is 1n，lnThe total number of reference units of the nth local radar station;
step 2, setting the total false alarm probability p of the distributed radar systemfThe false alarm probability of each local radar station is equal to the total false alarm probability of the distributed radar system;
step 3, each local radar station detects statistics and false alarm probability p of local radar station according to its corresponding localfCalculating the signal-to-noise ratio weighting coefficient of the local radar station;
the step 3 specifically comprises the following substeps:
(3a) evaluating the false alarm probability p of the detection performance curve at the local radar stationfUnder the condition, the nth local radar station detects statistic q according to the corresponding localnPerforming single-station detection to obtain a detection performance curve corresponding to the local radar station; the abscissa of the detection performance curve is a signal-to-noise ratio, and the ordinate is a detection probability value when a target exists;
(3b) setting a preset probability value p for the presence of a targetdThe nth local radar station presets a probability value p according to the existence of the targetdDetermining a preset probability value p of the existence of the target on a detection performance curve of the local radar stationdCorresponding signal-to-noise ratio SNRn；
(3c) N is set to be 1, so as to obtain signal-to-noise ratios corresponding to the N local radar stations at the preset probability value of the existence of the target;
(3d) signal-to-noise ratio weighting coefficient of nth local radar station
Wherein the SNRminRepresenting the minimum value of signal-to-noise ratios corresponding to the preset probability values of the N local radar stations at the target existence;
step 4, setting a fusion judgment threshold, and weighting the signal-to-noise ratio of the local detection statistics of the N local radar stations by the fusion detection center according to the local detection statistics of each local radar station and the signal-to-noise ratio weighting coefficient of the local radar station to obtain fused detection statistics;
and if the fused detection statistic is larger than the fusion judgment threshold, determining that the target is detected.
2. The distributed radar heterogeneous signal level fusion target detection method according to claim 1, wherein in the sub-step (3d),
another way to express the snr weighting factor w (n) of the nth local radar station is:
wherein,
3. the method for detecting the distributed radar heterogeneous signal level fusion target according to claim 1, wherein the step 4 specifically comprises:
(4a) setting a fusion decision threshold η;
(4b) the fusion detection center detects the statistic according to the local detection of each local radar station and the local detection statisticAnd the signal-to-noise ratio weighting coefficient of the local radar stations weights the signal-to-noise ratio of the local detection statistics of the N local radar stations to obtain the fused detection statistics t:
(4c) and if the fused detection statistic t is smaller than the fusion judgment threshold η, the target is judged to be not detected.
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| CN109521412B (en) * | 2018-12-26 | 2021-08-20 | 西安电子科技大学 | Radar networking airspace target detection method based on local statistic fusion |
| CN110488276B (en) * | 2019-06-10 | 2021-06-22 | 西安电子科技大学 | Heterogeneous radar network optimal resource on-demand distribution method oriented to multi-target tracking task |
| CN111751811B (en) * | 2020-05-20 | 2023-07-25 | 西安电子科技大学 | Distance expansion target detection method under multi-base station radar configuration |
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