CN116577763A - Combined detection method aiming at improving action distance of two heterogeneous nodes - Google Patents
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- 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
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/52001—Auxiliary means for detecting or identifying sonar signals or the like, e.g. sonar jamming signals
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- 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
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
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Abstract
The invention provides a joint detection method aiming at improving the action distance of two heterogeneous nodes, which comprises the following steps of constructing a multi-constraint two-node joint automatic detection optimization model according to the relation among detection probability, false alarm probability, signal to noise ratio, integration time, propagation loss and action distance to the target, adopting the detection probability and the false alarm probability of sonar with stronger meshing partition performance, obtaining the detection probability and the false alarm probability of the other corresponding sonar by utilizing the relation among the two sonar detection probabilities and the false alarm probability under the joint detection rule, and screening grid points according to the relation among the detection probability, the false alarm probability, the signal to noise ratio, the integration time, the propagation loss and the action distance to the target to obtain an iteration initial value. The invention has rapid calculation and provides technical support for distributed joint detection of UUV, submerged buoy, buoy and other equipment.
Description
Technical field:
the invention belongs to the field of sonar multi-array joint detection, and particularly relates to a joint detection method aiming at improving the action distance of two heterogeneous nodes.
The background technology is as follows:
with the wide application of unmanned platforms such as UUV, buoy, submerged buoy and the like, the underwater sound detection means gradually develop to the automatic, intelligent and clustering directions. The individual nodes (matrices) are limited by the installation space, which further improves the detection capability. The multi-node (matrix) joint detection can break through the limitation of single-node (matrix) array space in theory, and becomes one of the important means for improving the underwater sound detection performance. At present, the means of multi-array combined detection are mainly divided into three types of array element level combined detection, beam level combined detection and contact level combined detection. The array element level and the wave beam level fusion have strict requirements on clocks, hydrophone types, working frequencies, array shapes and sizes of all arrays and large data interaction quantity, so that the application range of the array is limited. The contact level fusion generally fuses the results after detection of each array, and has low requirements and wide application range. However, how to use multi-array contact level fusion/joint detection to obtain performance (especially improving the action distance) superior to that of single array has been a difficulty in the research of the underwater sound field.
At present, a plurality of distributed detection fusion methods mainly aim at improving the detection probability or the reaction speed under the condition of constant false alarm, and the detection fusion methods aiming at improving the action distance are less.
The invention comprises the following steps:
the invention aims to solve the technical problem of providing a joint detection method aiming at improving the action distance of two heterogeneous nodes, which converts the two-array joint passive automatic detection problem into a multi-constraint nonlinear strong correlation optimization solving model, and obtains the detection threshold and the joint detection rule of each array by solving the optimization model so as to improve the joint detection action distance.
The technical proposal of the invention is to provide a joint detection method aiming at improving the action distance of two heterogeneous nodes, which comprises the following steps,
step 1, a multi-constraint two-node joint automatic detection optimization model is constructed according to the relation among detection probability, false alarm probability, signal to noise ratio, integration time, propagation loss and acting distance to a target, wherein two assumed sonars comprise a sonar 1 and a sonar 2, and a joint detection optimization model is respectively constructed under two different joint detection rules, wherein the two different joint detection rules are respectively at least one of the two sonars and at least two of the two sonars;
step 2, gridding the detection probability and the false alarm probability of the sonar 1, calculating the acting distance to the target according to the relation between the detection probability, the false alarm probability, the signal-to-noise ratio, the integration time, the propagation loss and the acting distance to the target, and screening the detection probability and the false alarm probability grid points by using the acting distance to form a set consisting of the detection probability and the false alarm probability pair of the sonar 1;
step 3, according to the functional relation between the detection probability of the two sonars and the false alarm probability under the two detection rules, calculating the detection probability and the false alarm probability pair corresponding to the sonar 2 and the corresponding acting distance, and screening the set according to the corresponding acting distance;
step 4, respectively based on two joint detection rules, selecting two smaller lifting distances serving as detection probability and false alarm probability pairs according to the lifting conditions of the two acting distances of the two arrays, and selecting a pair with the largest prompting distance from all the probability pairs; selecting the detection probability and false alarm probability pairs of two sonar corresponding to the maximum lifting action distance in the two joint detection rules as iteration initial values of accurate calculation;
step 5, selecting the four nearest groups according to the iteration initial value, and refining the search maximum value by using a two-dimensional dichotomy;
and 6, obtaining a detection threshold corresponding to the farthest action distance and an optimization solving model of the joint detection rule.
Aiming at the problem of how to utilize two heterogeneous nodes to carry out combined passive automatic detection so as to improve the action distance, the invention analyzes the relation among single-node detection probability, false alarm probability, signal to noise ratio and integral time, provides a combined detection method aiming at improving the action distance under the conditions of given detection probability and false alarm probability, converts the two-array combined passive automatic detection problem into a multi-constraint nonlinear strong-correlation optimization solving model, and obtains detection thresholds and combined detection rules of each array by solving the optimization model so as to improve the combined detection action distance.
1. Relation among single-node detection probability, false alarm probability, signal-to-noise ratio and integration time
In sonar passive target detection, both the target radiation noise and the background noise may be approximated as zero-mean gaussian random processes. Let the variance of the target radiation noise beThe variance of background noise is->The power spectral density is +.>The target radiation noise and the background noise are independent of each other, then the optimal receiver (in discrete form) is:
the detection probability and the false alarm probability are as follows:
wherein the method comprises the steps of
η is the detection threshold.
Probability of detection P D And false alarm probability P F The functional relationship of (2) can be expressed as:
2. two-node joint detection optimization model construction
And constructing a multi-constraint optimization model taking the farthest acting distance as an objective function according to the detection probability, the false alarm probability, the signal to noise ratio, the propagation loss and the joint detection rule. The two sonars are arranged by adopting a unified coordinate system, and the working frequencies are f respectively 1 And f 2 Corresponding propagation losses are TL (f 1 ,r 1 ) And TL (f) 2 ,r 2 ) The integration time is N respectively 1 And N 2 The detection capability of sonar 1 is not weaker than that of sonar 2. The detection probability and the false alarm probability which are required to be achieved are respectively P D And P F The output signal-to-noise ratio of the sonar 1 at this time isThe acting distance to the target is R 0 Sonar 2 pair distance is R 0 The output signal-to-noise ratio of the target is +.>Let the detection probability achieved by the two sonar parts required by the joint detection be P respectively d1 And P d2 The false alarm probabilities are P respectively f1 And P f2 Requirement P d_down ＜P d1 ＜1，0＜P f1 ＜P f_up ，P d_down ＜P d2 ＜1，0＜P f2 ＜P f_up 。
The joint detection optimization model under two different joint detection rules is:
(1) at least one of the two sonar detects, i.e. decides that the target is detected:
(2) at least two sonar detection, namely, judging that the target is detected:
wherein the method comprises the steps ofIndicating f for the operating frequency 1 Propagation loss at output signal-to-noise ratio +.>Corresponding to the action distance R 0 Under the condition of (1), the output signal-to-noise ratio is SNR 1 Corresponding action distance.
The goal of the joint detection is: given the joint detection probability P D And false alarm probability P F Under the condition of (1), a single sonar detection threshold (or detection probability and false alarm probability) and a joint detection rule are determined, so that the action distance to the target is furthest. From the equation (5) and the equation (6), it can be seen that the model is a multi-constraint strong-correlation nonlinear optimization model, but the solution in the prior art is complex, and how to solve is a difficult problem.
Therefore, the invention adopts the detection probability and the false alarm probability of the sonar with stronger meshing partition performance, obtains the detection probability and the false alarm probability of the other sonar corresponding to the relation between the two sonar detection probabilities and the false alarm probability under the joint detection rule, screens the grid points according to the relation between the detection probability, the false alarm probability, the signal-to-noise ratio, the integration time, the propagation loss and the target acting distance to obtain an iteration initial value, selects the point of the iteration initial value accessory to carry out dichotomy iteration to obtain the optimal solution, finally obtains the detection threshold and the joint detection rule corresponding to the furthest acting distance, and provides technical support for the distributed joint detection of UUV, the submerged buoy, the buoy and other equipment.
Compared with the prior art, the invention has the following advantages:
aiming at the problem of how to utilize a plurality of asynchronous matrixes to combine passive automatic detection to improve the passive automatic detection performance, the method can construct a multi-constraint two-node combined automatic detection optimization model according to the relation of detection probability, false alarm probability, signal to noise ratio, integration time, propagation loss and acting distance to a target, adopts the detection probability and the false alarm probability of sonar with stronger meshing division performance, obtains the detection probability and the false alarm probability of the other corresponding sonar by utilizing the relation of the two sonar detection probabilities and the false alarm probability under the combined detection rule, screens grid points according to the relation of the detection probability, the false alarm probability, the signal to noise ratio, the integration time, the propagation loss and the acting distance to the target to obtain an iteration initial value, selects the point of an iteration initial value accessory to perform binary iteration to obtain an optimal solution, finally obtains the detection threshold and the combined detection rule which correspond to the furthest acting distance, is calculated quickly, and provides technical support for distributed combined detection of UUV, latent marks, buoys and other equipment.
Description of the drawings:
FIG. 1 is a graph showing the signal-to-noise ratio required for each array in the case where at least one of the two arrays detects, i.e., determines, the presence of a target;
FIG. 2 is a graph showing the signal-to-noise ratio required for each array under the condition that at least two arrays detect, i.e., determine, the presence of a target;
FIG. 3 is a schematic diagram showing the relationship between the improvement of the detection performance and the difference of the detection capability of two arrays;
FIG. 4 is a diagram showing the relationship between the improvement of the detection performance and the detection probability of two arrays.
The specific embodiment is as follows:
the invention is further described in terms of specific embodiments in conjunction with the following drawings:
a joint detection method aiming at improving the action distance of two heterogeneous nodes comprises the following steps,
step 1, a multi-constraint two-node joint automatic detection optimization model is constructed according to the relation among detection probability, false alarm probability, signal to noise ratio, integration time, propagation loss and acting distance to a target, wherein two assumed sonars comprise a sonar 1 and a sonar 2, and a joint detection optimization model is respectively constructed under two different joint detection rules, wherein the two different joint detection rules are respectively at least one of the two sonars and at least two of the two sonars; that is, a multi-constraint two-node joint automatic detection optimization model constructed according to the relation of detection probability, false alarm probability, signal to noise ratio, integration time, propagation loss and acting distance to a target is shown in a formula (5) and a formula (6), and the model converts the joint automatic detection problem aiming at improving the acting distance under the condition of the given detection probability and the false alarm probability into a detection threshold (or detection probability and false alarm probability) and an optimization solving model of a joint detection rule under the multi-constraint condition;
step 2, gridding the detection probability and the false alarm probability of the sonar 1, calculating the acting distance to the target according to the relation between the detection probability, the false alarm probability, the signal-to-noise ratio, the integration time, the propagation loss and the acting distance to the target, and screening the detection probability and the false alarm probability grid points by using the acting distance to form a set consisting of the detection probability and the false alarm probability pair of the sonar 1;
step 3, according to the functional relation between the detection probability of the two sonars and the false alarm probability under the two detection rules, calculating the detection probability and the false alarm probability pair corresponding to the sonar 2 and the corresponding acting distance, and screening the set according to the corresponding acting distance;
step 4, respectively based on two joint detection rules, selecting two smaller lifting distances serving as detection probability and false alarm probability pairs according to the lifting conditions of the two acting distances of the two arrays, and selecting a pair with the largest prompting distance from all the probability pairs; selecting the detection probability and false alarm probability pairs of two sonar corresponding to the maximum lifting action distance in the two joint detection rules as iteration initial values of accurate calculation;
step 5, selecting the four nearest groups according to the iteration initial value, and refining the search maximum value by using a two-dimensional dichotomy;
and 6, obtaining a detection threshold corresponding to the farthest action distance and an optimization solving model of the joint detection rule.
Specifically, in the step 1,
the two sonars adopt a unified coordinate system, and the working frequencies are f respectively 1 And f 2 Corresponding propagation losses are TL (f 1 ,r 1 ) And TL (f) 2 ,r 2 ) The integration time is N respectively 1 And N 2 The detection capability of the sonar 1 is not weaker than that of the sonar 2; the detection probability and the false alarm probability which are required to be achieved are respectively P D And P F The output signal-to-noise ratio of the sonar 1 at this time isThe acting distance to the target is R 0 Sonar 2 pair distance is R 0 The output signal-to-noise ratio of the target is +.>Let the detection probability achieved by the two sonar parts required by the joint detection be P respectively d1 And P d2 The false alarm probabilities are P respectively f1 And P f2 Requirement P d_down ＜P d1 ＜1，0＜P f1 ＜P f_up ，P d_down ＜P d2 ＜1，0＜P f2 ＜P f_up ；
The joint detection optimization model is represented by formula (5) and formula (6) under two different joint detection rules, wherein at least one of the two sonar is detected, namely, the detection of the target is judged:
at least two sonar detection, namely, judging that the target is detected:
wherein the method comprises the steps ofIndicating f for the operating frequency 1 Propagation loss of (1) inOutput signal to noise ratio +.>Corresponding to the action distance R 0 Under the condition of (1), the output signal-to-noise ratio is SNR 1 Corresponding action distance.
Step 2 is specifically operated as follows, the number N of integral points of the two sonar is input 1 And N 2 Detecting center frequency f 1 And f 2 Corresponding propagation loss curve TL (f 1 R) and TL (f 2 R), inputting the expected detection probability P D And false alarm probability P F The sonar 1 achieves the detection probability P D And false alarm probability P F Output signal to noise ratio at timeAnd a distance of action R on the target 0 The method comprises the steps of carrying out a first treatment on the surface of the 2 pairs of input sonar with distance R 0 Output signal-to-noise ratio of the target->Input single-array false alarm probability upper limit P f_up Lower limit of detection probability P d_down False alarm probability scanning step length delta P f And a detection probability scan step Δp d The method comprises the steps of carrying out a first treatment on the surface of the Constructing a set consisting of pairs of detection probability and false alarm probability>
Calculating the corresponding signal to noise ratio->
According to the operating frequency f of sonar 1 1 Signal to noise ratioCorresponding working distance R 0 Propagation loss curve TL (f 1 R), calculating the signal-to-noise ratio +.>Corresponding distance of action-> Screening the set according to the action distance to obtain a new set A 1 ：
The specific operation of the step 3 is as follows,respectively calculating the corresponding +.>Andrespectively get the collection->And->
Calculating the signal to noise ratio corresponding to the two detection strategiesAnd->
According to the operating frequency f of the matrix 2 2 Signal to noise ratio SNR 2 Corresponding working distance R 2 Propagation loss curve TL (f 2 R), calculate signal to noise ratioAnd->Corresponding distance of action->And->Screening the collection to obtain a new collection A 3 And A 4 ：
The specific operation of step 4 is as follows,
construction set A 5 ：
Selection ofMake->
Construction set A 6 ：
Selection ofMake->
If it isThen
c＝0，/>
Otherwisec＝1，/>
The specific operation of step 5 is as follows,
taking P f01 ,P d01 Adjacent point P f01_r ,P f01_l ,P d01_r ,P l01_r Forming four groups of combinations, calculating the detection probability, the false alarm probability and the delta R of the corresponding matrix 2, taking one group corresponding to the maximum delta R, and combining (P) f01 ,P d01 ,P f02 ,P d02 ) By means of binary value iterative calculation, deltaR if DeltaR > DeltaR max DeltaR is then max =Δr, corresponding probability pair substitution (P f01 ,P d01 ,P f02 ,P d02 ) If the number of iterations is greater than M or |DeltaR max If ΔR| < ε, then stop iterating, output (P f01 ,P d01 ,P f02 ,P d02 )。
Then, take P f01 ,P d01 Adjacent point P f01_r ,P f01_l ,P d01_r ,P l01_r Forming four groups of combinations, calculating the detection probability, the false alarm probability and the delta R of the corresponding matrix 2, taking one group corresponding to the maximum delta R, and combining (P) f01 ,P d01 ,P f02 ,P d02 ) By means of binary value iterative calculation, deltaR if DeltaR > DeltaR max DeltaR is then max =Δr, corresponding probability pair substitution (P f01 ,P d01 ,P f02 ,P d02 ) If the number of iterations is greater than M or |DeltaR max If ΔR| < ε, then stop iterating, output (P f01 ,P d01 ,P f02 ,P d02 )。
According to (P f01 ,P d01 ,P f02 ,P d02 ) Calculating a detection threshold eta 1 ，η 2 ：
Finally, an optimal detection method is obtained: calculating likelihood ratio of two arrays:when c=0, at least one of the two arrays is larger than the corresponding threshold, judging that the object exists, otherwise, judging that the object does not exist; when c=1, both arraysIf the target is larger than the corresponding threshold, judging that the target exists, otherwise, judging that the target does not exist.
On the basis, the given detection probability is set to be 0.9, and the false alarm probability is set to be 10 -3 The number of integration points is 512 points, and the output signal to noise ratio required by the node is-5.272 dB. Considering the actual scene, the single-array detection probability is set to be larger than 0.3, the false alarm probability is set to be smaller than 0.3, and the combination detection performance is analyzed through a simulation test. Setting the background noise of the two receiving arrays and the received target signal as 0 mean Gaussian white noise, and setting the variance as 1, wherein the detection frequencies of the two arrays are the same (namely, the detection threshold is reduced by the same decibel value, and the corresponding acting distance is increased by the same distance). Matrix 2 has a 2dB difference in detection capability compared to matrix 1. At this time, under two detection rules, input signal noise required by the base arrays 1 and 2 corresponding to different detection probabilities and false alarm probabilities is as shown in fig. 1 and 2. The results of the analysis are shown in Table 1,
TABLE 1 analysis of two arrays of joint detection effect
Selecting 'two arrays at least two arrays found to judge that a target exists' as a joint detection rule by taking the maximum performance improvement as a principle, wherein the detection probability of the array 1 is 0.9213, and the false alarm probability is 0.0033; the detection probability of the matrix 2 is 0.9769, and the false alarm probability is 0.3049. At this time, the joint detection probability is 0.9, the false alarm probability is 0.001, and the joint detection performance is 0.2206dB compared with the single-array lifting.
Furthermore, in order to analyze the influence of the single-array detection performance difference on the performance improvement of the combined detection, the detection capability difference of the two receiving arrays is set to be 0 to 4dB, and the performance improvement of the combined detection under different capability differences compared with the optimal single-array detection is analyzed. The experimental conditions were the same as (1). As can be seen from fig. 3, the closer the two arrays are, the better the joint detection performance improvement effect is; the larger the difference of the detection performance of the two arrays is, the worse the improvement effect of the joint detection performance is. When the detection performance of the two arrays is the same, the combined detection is improved by about 0.87dB compared with the optimal single array, and when the detection performance of the two arrays is different by more than 3dB, the combined detection has no increase compared with the optimal basic array in action distance.
Under the condition that the output signal-to-noise ratio of the basic array is-7 dB, the relation between the two-array joint detection performance improvement and the detection probability can be seen from fig. 4, the higher the detection probability is, the larger the two-array joint detection performance improvement is, the maximum can be 1.4dB, the gain is smaller than the gain (5 lg2=1.5 dB) caused by incoherent fusion, mainly the incoherent fusion requires that the detected target directions, the processing frequency and the like of the arrays completely coincide, and the joint detection has no requirement, so that the performance loss is caused.
The foregoing is illustrative of the preferred embodiments of the present invention, and is not to be construed as limiting the claims. All equivalent flow changes made by the specification of the invention are included in the protection scope of the invention.
Claims (7)
1. A joint detection method aiming at improving the action distance of two heterogeneous nodes is characterized in that: the method comprises the steps of,
step 1, a multi-constraint two-node joint automatic detection optimization model is constructed according to the relation among detection probability, false alarm probability, signal to noise ratio, integration time, propagation loss and acting distance to a target, wherein two assumed sonars comprise a sonar 1 and a sonar 2, and a joint detection optimization model is respectively constructed under two different joint detection rules, wherein the two different joint detection rules are respectively at least one of the two sonars and at least two of the two sonars;
step 2, gridding the detection probability and the false alarm probability of the sonar 1, calculating the acting distance to the target according to the relation between the detection probability, the false alarm probability, the signal-to-noise ratio, the integration time, the propagation loss and the acting distance to the target, and screening the detection probability and the false alarm probability grid points by using the acting distance to form a set consisting of the detection probability and the false alarm probability pair of the sonar 1;
step 3, according to the functional relation between the detection probability of the two sonars and the false alarm probability under the two detection rules, calculating the detection probability and the false alarm probability pair corresponding to the sonar 2 and the corresponding acting distance, and screening the set according to the corresponding acting distance;
step 4, respectively based on two joint detection rules, selecting two smaller lifting distances serving as detection probability and false alarm probability pairs according to the lifting conditions of the two acting distances of the two arrays, and selecting a pair with the largest prompting distance from all the probability pairs; selecting the detection probability and false alarm probability pairs of two sonar corresponding to the maximum lifting action distance in the two joint detection rules as iteration initial values of accurate calculation;
step 5, selecting the four nearest groups according to the iteration initial value, and refining the search maximum value by using a two-dimensional dichotomy;
and 6, obtaining a detection threshold corresponding to the farthest action distance and an optimization solving model of the joint detection rule.
2. The joint detection method for improving the working distance of two heterogeneous nodes according to claim 1, wherein the joint detection method is characterized by comprising the following steps: in step 1, two sonars adopt a unified coordinate system, and the working frequencies are f respectively 1 And f 2 Corresponding propagation losses are TL (f 1 ,r 1 ) And TL (f) 2 ,r 2 ) The integration time is N respectively 1 And N 2 The detection capability of the sonar 1 is not weaker than that of the sonar 2; the detection probability and the false alarm probability which are required to be achieved are respectively P D And P F At this time, the output signal-to-noise ratio of the sonar 1 is SNR 1 0 The acting distance to the target is R 0 Sonar 2 pair distance is R 0 The output signal-to-noise ratio of the target isLet the detection probability achieved by the two sonar parts required by the joint detection be P respectively d1 And P d2 The false alarm probabilities are P respectively f1 And P f2 Requirement P d_down ＜P d1 ＜1，0＜P f1 ＜P f_up ，P d_down ＜P d2 ＜1，0＜P f2 ＜P f_up ；
The joint detection optimization model is represented by formula (5) and formula (6) under two different joint detection rules, wherein at least one of the two sonar is detected, namely, the detection of the target is judged:
at least two sonar detection, namely, judging that the target is detected:
wherein the method comprises the steps ofIndicating f for the operating frequency 1 Propagation loss at output signal-to-noise ratio +.>Corresponding to the action distance R 0 Under the condition of (1), the output signal-to-noise ratio is SNR 1 Corresponding action distance.
3. The joint detection method for improving the working distance of two heterogeneous nodes according to claim 1, wherein the joint detection method is characterized by comprising the following steps: step 2 is specifically operated as follows, the number N of integral points of the two sonar is input 1 And N 2 Detecting center frequency f 1 And f 2 Corresponding propagation loss curve TL (f 1 R) and TL (f 2 R), inputting the expected detection probability P D And false alarm probability P F The sonar 1 achieves the detection probability P D And false alarm probability P F Output signal to noise ratio at timeAnd a distance of action R on the target 0 The method comprises the steps of carrying out a first treatment on the surface of the 2 pairs of input sonar with distance R 0 Output signal-to-noise ratio of the target->Input deviceSingle-array false alarm probability upper limit P f_up Lower limit of detection probability P d_down False alarm probability scanning step length delta P f And a detection probability scan step Δp d The method comprises the steps of carrying out a first treatment on the surface of the Constructing a set consisting of pairs of detection probability and false alarm probability>
Calculating the corresponding signal to noise ratio->
According to the operating frequency f of sonar 1 1 Signal to noise ratioCorresponding working distance R 0 Propagation loss curve TL (f 1 R), calculating the signal-to-noise ratio +.>Corresponding distance of action->Screening the set according to the action distance to obtain a new set A 1 ：
4. The joint detection method for improving the working distance of two heterogeneous nodes according to claim 1, wherein the joint detection method is characterized by comprising the following steps: the specific operation of the step 3 is as follows,respectively calculating the corresponding +.>And->Respectively get the collection->And->
Calculating the signal to noise ratio corresponding to the two detection strategiesAnd->
According to the operating frequency f of the matrix 2 2 Signal to noise ratio SNR 2 Corresponding working distance R 2 Propagation loss curve TL (f 2 R), calculate signal to noise ratioAnd->Corresponding distance of action->And->Screening the collection to obtain a new collection A 3 And A 4 ：
5. The joint detection method for improving the working distance of two heterogeneous nodes according to claim 1, wherein the joint detection method is characterized by comprising the following steps: the specific operation of step 4 is as follows,
construction set A 5 ：
Selection ofMake->
Construction set A 6 ：
Selection ofMake->
If it isThen
c＝0，/>
Otherwisec＝1，/>
6. The joint detection method for improving the working distance of two heterogeneous nodes according to claim 1, wherein the joint detection method is characterized by comprising the following steps: the specific operation of step 5 is as follows,
taking P f01 ,P d01 Adjacent point P f01_r ,P f01_l ,P d01_r ,P l01_r Forming four groups of combinations, calculating the detection probability, the false alarm probability and the delta R of the corresponding matrix 2, taking one group corresponding to the maximum delta R, and combining (P) f01 ,P d01 ,P f02 ,P d02 ) By means of binary value iterative calculation, deltaR if DeltaR > DeltaR max DeltaR is then max =Δr, corresponding probability pair substitution (P f01 ,P d01 ,P f02 ,P d02 ) If the number of iterations is greater than M or |DeltaR max If ΔR| < ε, then stop iterating, output (P f01 ,P d01 ,P f02 ,P d02 )。
7. The joint detection method for improving the working distance of two heterogeneous nodes according to claim 1, wherein the joint detection method is characterized by comprising the following steps: the specific operation of step 6 is as follows,
according to (P f01 ,P d01 ,P f02 ,P d02 ) Calculating a detection threshold eta 1 ，η 2 ：
Obtaining an optimal detection method: calculating likelihood ratio of two arrays:when c=0, at least one of the two arrays is larger than the corresponding threshold, judging that the object exists, otherwise, judging that the object does not exist; when c=1, the two arrays are larger than the corresponding threshold, the object is judged to exist, otherwise, the object is judged to exist. />
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