Active and passive collaborative sensing fusion method for dual-function radar communication system

Abstract

Disclosed in the present invention is an active and passive collaborative sensing fusion method for a dual-function radar communication system. The method comprises: 1, embedding a dedicated sensing symbol in a signal sent by a base station; 2, using an optimization problem to solve power distribution; 3, a receiving access point receiving a passive sensing signal, and using a whitening filter to process direct path interference, such that the direct path interference becomes white noise; 4, the receiving access point using a generalized likelihood ratio detector to determine whether there is a target, and then the receiving access point and the base station transmitting a binary decision result and a target detection probability to a fusion center; and 5, the fusion center performing vote aggregation on received information, and determining whether there is a target. The present invention can improve the accuracy and efficiency of target detection, target positioning and environment information acquisition and reconstruction, and may significantly improve the performance of a wireless communication sensing network.

Description

An active-passive collaborative perception fusion method for radar-communication dual-function systems Technical Field

[0001] The present invention belongs to the field of radar power distribution, and in particular relates to an active-passive collaborative perception fusion method for a radar communication dual-function system. Background Art

[0002] Communication systems are evolving from 5G to 6G, posing unique challenges for networks that achieve global coverage, green intelligence, sensory connectivity, and inter-sensory fusion. To fully realize the potential of 6G, it is necessary to acquire environmental perception information, enable seamless information exchange and sharing, and intelligently control information processing. Therefore, researchers have identified dual-function radar communication (DFRC) as a key technology for 6G, integrating communication systems and radar sensors, driving research on DFRC systems. However, DFRC research also presents challenges. First, sensing uncertainty caused by the fading and interference characteristics of wireless communication systems can imperfect data received by a single radar. Second, DFRC research has primarily focused on waveform design and signal processing, with limited consideration of multi-stationary sensing capabilities, resulting in a waste of sensing resources.

[0003] Summary of the Invention

[0004] Purpose of the invention: The purpose of the present invention is to provide an active-passive collaborative perception fusion method for a radar communication dual-function system, integrating active and passive perception in the DFRC system to remedy the waste of sensing resources.

[0005] Technical solution: The present invention provides an active-passive collaborative perception fusion method for a dual-function radar communication system. The dual-function radar communication system includes a dual-function radar communication base station BS equipped with a fusion center FC and receiving access points RAPs. The dual-function radar communication base station BS performs active perception while communicating with users. The receiving access points RAPs perform passive perception and are subject to direct path interference from the dual-function radar communication base station BS's transmitted signal. The receiving access points RAPs are connected to the dual-function radar communication base station BS via a backhaul link, but the link capacity is limited and does not support the transmission of large amounts of data. The method includes the following steps:

[0006] Step 1: embed dedicated sensing symbols into the signal sent by the dual-function radar communication base station BS and precode it through a zero-forcing radar beamformer;

[0007] Step 2: The optimization problem is modeled as maximizing the average target detection probability while ensuring that the total transmission power does not exceed the budget of the dual-function radar communication base station BS and that the user's signal-to-interference-and-noise ratio (SINR) meets the minimum requirements. This problem is solved using a power allocation algorithm based on the ergodic algorithm.

[0008] Step 3: The receiving access point (RAPs) receives the passive sensing signal transmitted from the dual-function radar communication base station (BS) to the target and then to the receiving access point (RAPs), and uses a whitening filter to process the direct path interference in the passive sensing signal to convert it into white noise.

[0009] Step 4: The receiving access point RAPs uses the generalized likelihood ratio test (GLRT) detector to determine whether the target exists. The receiving access point RAPs and the dual-function radar communication base station BS transmit the judgment result and target detection probability to the fusion center FC.

[0010] Step 5: The fusion center FC aggregates the received information by voting to determine whether the target exists.

[0011] Furthermore, step 1 is specifically as follows: the dedicated sensing symbol is s0, and the zero-forcing radar beamformer is used for precoding, and the precoding vector is

[0012] Where H=[h1,h2,...,h K ] H is the communication channel matrix, a(θ) is the transmit steering vector, θ is the azimuth angle of the target relative to the BS, and the operator |||| represents the 2-norm operation.

[0013] Furthermore, the optimization problem described in step 2 is as follows:

[0014] in, is the target detection probability of BS and RAPs, R is the number of RAPs, is the noncentral chi-squared cumulative distribution function with 2 degrees of freedom, ρ r is the non-centrality parameter of BS and RAPs, which can be given by the generalized likelihood ratio test detector used, ξ r is the threshold of BS and RAPs detector, is the SINR of the i-th user, is the normalized communication transmission precoding vector, λ is the regularization parameter, p=[p0,p1,...,p K ] is the power distribution of sensing symbols and communication symbols, is the variance of additive white Gaussian noise AWGN, K is the number of users, Γ is the required SINR threshold, P T is the power budget of the BS.

[0015] Furthermore, in step 2, the power allocation algorithm based on the traversal algorithm specifically includes:

[0016] Step 2.1: When the power p0 allocated to the sensing symbol gradually increases and meets the BS power budget, the joint detection probability Gradually increase, initialize: p′0=P T ,Δp,p sum =p′0, where p′0 is the initialization parameter, Δp is the step size, and p sum For the conditions of judgment;

[0017] Step 2.2, set p0 = p′0 - Δp;

[0018] Step 2.3: Use the CVX toolkit to solve the convex optimization problem min||p||1s.t.γ k ≥Γ, where ||||1 is a norm;

[0019] Step 2.4, set p sum =||p||1'p′0=p0;

[0020] Step 2.5, when p sum ≥P T , go to step 2.2, otherwise output p.

[0021] Furthermore, step 3 is specifically as follows:

[0022] When there is a target, the passive sensing signal received by the rth RAP is:

[0023] Among them, n′ r [l] represents the AWGN matrix, α r is the combined perceptual channel gain, is the steering vector of the receiving antenna, is the azimuth of the target relative to the rth RAP, X=WS is the DFRC signal matrix, S=[s0,s1,...,s K ],s i ,i=1,2,...,K is the communication symbol of the i-th user, G r In the absence of a target, the targetless channel between the BS and the r-th RAP is represented by the signal passing through the matched filter and vectorized:

[0024] in W=WW H ,vec() represents the vectorized operator, ε r is zero-mean, complex Gaussian distributed, and has a block covariance matrix of:

[0025] in, The whitening filter is obtained as Depend on The Cherosky decomposition of is obtained.

[0026] Furthermore, step 4 is specifically as follows:

[0027] The binary hypothesis of the GLRT detector after the whitening filter is:

[0028] in The corresponding GLRT detector is then given by:

[0029] and is the joint probability density function of the received signal, and the test statistic obtained after calculation is:

[0030] in, Then a binary decision is made according to (8).

[0031] Furthermore, step 5 is specifically as follows:

[0032] FC makes the following binary assumptions

[0033] Among them D r are the binary reasoning results of BS and the i-th RAP, respectively, D r =0 means no target, D r =1 indicates the existence of a target, n represents the voting threshold, and n is given by the following formula,

[0034] in,

[0035] P FAr is the false alarm probability of BS and RAPs.

[0036] Beneficial effects: Compared with the existing technology, the present invention has the following significant advantages: Compared with the existing technology, the present invention adopts the above technical solution to integrate active and passive perception in the DFRC system, improve the accuracy and efficiency of target detection, and its perception performance is significantly higher than that of the DFRC system that only uses passive perception; and through the binary judgment result voting aggregation scheme, it solves the huge overhead problem caused by directly sending sensor signals to the fusion center, and has the potential to significantly improve the performance of the wireless communication perception network. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] FIG1 is a diagram of a DFRC system model of the present invention.

[0038] FIG2 is a schematic flow chart of the method of the present invention.

[0039] FIG3 is a diagram showing the relationship between the average detection probability and the number of RAPs provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0041] The technical problem to be solved by the embodiments of the present invention is to provide an active-passive collaborative perception fusion method for a radar communication dual-function system, which integrates active and passive perception in a DFRC system to remedy the waste of sensing resources.

[0042] As shown in Figure 1, the multi-input multi-output dual-function radar communication base station (MIMO DFRC BS) described in this example is equipped with a fusion center (FC) that performs active sensing and communicates with users; receiving access points (RAPs) perform passive sensing; RAPs and the BS are connected via a backhaul link, but the link capacity is limited and does not support the transmission of large amounts of data.

[0043] As shown in FIG2 , an embodiment of the present invention provides a flow chart of an active-passive collaborative perception fusion method for a radar communication dual-function system. The method includes the following steps:

[0044] Step 1: Dedicated sensing symbols are embedded in the signal sent by the BS and precoded using a zero-forcing radar beamformer.

[0045] Step 2: Model the optimization problem as maximizing the average target detection probability while ensuring that the total transmission power does not exceed the BS budget and the user's signal-to-interference-and-noise ratio (SINR) meets the minimum requirements. This problem is solved using a power allocation algorithm based on the ergodic algorithm.

[0046] Step 3: RAPs receives the passive sensing signal and processes the direct path interference (DPI) using a whitening filter to convert it into white noise;

[0047] Step 4: RAPs use the generalized likelihood ratio test (GLRT) detector to determine whether the target exists. Then RAPs and BS transmit the binary decision results and the target detection probability to FC;

[0048] Step 5: FC aggregates the received information through voting to determine whether the target exists.

[0049] In the embodiment, the dedicated sensing symbol in step 1 is s0, and a zero-forcing radar beamformer is used for precoding, and the precoding vector is

[0050] Where H=[h1,h2,...,h K ] His the communication channel matrix, a(θ) is the transmit steering vector, θ is the azimuth angle of the target relative to the BS, and the operator |||| represents the 2-norm operation.

[0051] In an embodiment, the optimization problem in step 2 is as follows:

[0052] in, is the target detection probability of BS and RAPs, R is the number of RAPs, is the noncentral chi-squared cumulative distribution function with 2 degrees of freedom, ρ r is the non-centrality parameter of BS and RAPs, which can be given by the generalized likelihood ratio test detector used, ξ r is the threshold of BS and RAPs detector, is the SINR of the i-th user, is the normalized communication transmission precoding vector, λ is the regularization parameter, p=[p0,p1,...,p K ] is the power distribution of sensing symbols and communication symbols, is the variance of additive white Gaussian noise (AWGN), K is the number of users, Γ is the required SINR threshold, P T is the power budget of the BS.

[0053] In an embodiment, when p0 gradually increases and satisfies the BS power budget, So the power allocation algorithm based on the traversal algorithm in step 2 is as follows.

[0054] (1) Initialization: p′0=P T ,Δp,p sum =p′0;

[0055] (2) Set p0 = p′0 - Δp;

[0056] (3) Using the CVX toolkit to solve the convex optimization problem min||p||1s.t.γ k ≥Γ;

[0057] (4) Set p sum =||p||1, p′0=p0;

[0058] (5) When p sum ≥P T , go to (2), otherwise output p;

[0059] In the embodiment, in step 3, when there is a target, the passive sensing signal received by the rth RAP is

[0060] Among them, n′r [l] represents the AWGN matrix, α r is the combined perceptual channel gain, is the steering vector of the receiving antenna, is the azimuth of the target relative to the rth RAP, X=WS is the DFRC signal matrix, S=[s0,s1,...,s K ],s i ,i=1,2,...,K is the communication symbol of the i-th user, G r Indicates the target-free channel between the BS and the rth RAP in the absence of a target. The signal is vectorized by the matched filter to obtain

[0061] in W=WW H ,vec() represents the vectorized operator, ε r is a zero-mean, complex Gaussian distribution with a block covariance matrix of

[0062] in, So the whitening filter is Depend on The Cherosky decomposition of is obtained.

[0063] In an embodiment, the binary hypothesis of the GLRT detector after the whitening filter is

[0064] in The corresponding GLRT detector is then given by

[0065] and is the joint probability density function of the received signal. After calculation, the test statistic is

[0066] in, Then a binary decision is made according to (8).

[0067] In the embodiment, in step 5, FC makes the following binary assumptions:

[0068] Among them D r are the binary reasoning results of BS and the i-th RAP, respectively, D r =0 means no target, D r =1 indicates the existence of a target, and n represents the voting threshold. n can be given by the following formula,

[0069] in,

[0070] P FAr is the false alarm probability of BS and RAPs.

[0071] In this example, we prepared 10,000 samples for simulation. The DFRC system parameter configuration is as follows:

[0072] This example is a special case of the embodiment of the present invention and can be extended to other similar situations.

[0073] Figure 3 shows the relationship between the average detection probability and the number of RAPs. We observed the change in average detection probability when the variance of the combined perceptual channel gain was -37 / -36 / -35dB (-37dB, -36dB, and -35dB in the example). When the variance of the combined perceptual channel gain was greater than or equal to -36dB, the average detection probability increased with the increase in RAP. However, the growth rate when the variance of the combined perceptual channel gain was -35dB was much greater than when the variance was -36dB. Furthermore, we found that when the variance of the combined perceptual channel gain was -37dB, the average detection probability barely increased with the increase in RAP, always approaching 0. This is because the binary inference results of the FC are obtained through voting aggregation in a limited backhaul capacity scenario, which focuses on the quality of the perceptual signal of each single RAP. Therefore, as the variance of the combined perceptual channel gain increases, the detection probability of a single RAP increases. Furthermore, as the number of RAPs increases, the probability of misjudgment after voting aggregation further decreases.

[0074] The above is a schematic description of the present invention and its embodiments, which is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs a structure and embodiment similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. An active and passive collaborative perception fusion method for a radar communication dual-function system, characterized in that: The radar communication dual-function system includes a dual-function radar communication base station BS equipped with a fusion center FC and a receiving access point RAPs. The dual-function radar communication base station BS communicates with a user while performing active sensing. The receiving access point RAPs performs passive sensing and is interfered by a direct path of a signal transmitted by the dual-function radar communication base station BS. The receiving access point RAPs is connected to the dual-function radar communication base station BS through a backhaul link, and includes the following steps: Step 1: embed a dedicated sensing symbol in a signal sent by a dual-function radar communication base station BS and perform precoding through a zero-forcing radar beamformer; Step 2: Model the optimization problem as maximizing the average target detection probability while ensuring that the total transmission power does not exceed the BS budget of the dual-function radar communication base station and the user's signal-to-interference-noise ratio (SINR) meets the minimum requirements, and use a power allocation algorithm based on the traversal algorithm to solve it; Step 3, the receiving access point RAPs receives the passive sensing signal from the dual-function radar communication base station BS to the target and then to the receiving access point RAPs, and uses a whitening filter to process the direct path interference in the passive sensing signal to convert it into white noise; Step 4: The receiving access point RAPs uses the generalized likelihood ratio test GLRT detector to determine whether the target exists, and the receiving access point RAPs and the dual-function radar communication base station BS transmit the judgment result and the target detection probability to the fusion center FC; Step 5: The fusion center FC aggregates the received information by voting to determine whether the target exists.

2. The active-passive collaborative perception fusion method for a radar communication dual-function system according to claim 1 is characterized in that: Step 1 is as follows: the dedicated sensing symbol is s0, and the zero-forcing radar beamformer is used for precoding, and the precoding vector is Where H = [h1,h2,...,h K ] H is the communication channel matrix, a(θ) is the transmit steering vector, θ is the azimuth of the target relative to the BS, and the operator |||| represents the 2-norm operation.

3. The active-passive collaborative perception fusion method for a radar communication dual-function system according to claim 1 is characterized in that: The optimization problem described in step 2 is as follows, in, is the joint detection probability, is the target detection probability of BS and RAPs, R is the number of RAPs, is the noncentral chi-squared cumulative distribution function with 2 degrees of freedom, ρ r is the non-centrality parameter of BS and RAPs, which can be given by the generalized likelihood ratio test detector used, ξ r is the threshold of BS and RAPs detector, is the SINR of the ith user, is the normalized communication transmission precoding vector, λ is the regularization parameter, p=[p0,p1,...,p K ] is the power allocation of sensing symbols and communication symbols, is the variance of additive white Gaussian noise AWGN, K is the number of users, Γ is the required SINR threshold, P T is the power budget of the BS.

4. The active-passive collaborative perception fusion method for a radar communication dual-function system according to claim 1 is characterized in that: In step 2, the power allocation algorithm based on the ergodic algorithm specifically includes: Step 2.1: When the power p0 allocated to the sensing symbol gradually increases and meets the BS power budget, the joint detection probability Gradually increase, initialize: p′0=P T ,Δp,p sum =p′0, where p′0 is the initialization parameter, Δp is the step size, and p sum For the conditions of judgment; Step 2.2, set p0 = p′0 - Δp; Step 2.3: Use the CVX toolkit to solve the convex optimization problem min||p||1s.t.γ k ≥Γ, where ||||1 is a norm; Step 2.4, set p sum =||p||1, p′0=p0; Step 2.5: When p sum ≥P T , go to step 2.2, otherwise output p.

5. The active-passive collaborative perception fusion method for a radar communication dual-function system according to claim 1 is characterized in that: Step 3 is as follows: When there is a target, the passive sensing signal received by the rth RAP is: Among them, n′ r [l] represents the AWGN matrix, α r is the combined perceptual channel gain, is the steering vector of the receiving antenna, is the azimuth of the target relative to the rth RAP, X=WS is the DFRC signal matrix, S=[s0,s1,...,s K ],s i ,i=1,2,...,K is the communication symbol of the i-th user, G r In the absence of a target, the targetless channel between the BS and the rth RAP is represented. The signal is passed through a matched filter and vectorized to obtain: in W=WW H ,vec() represents the vectorization operator, ε r is a zero-mean, complex Gaussian distribution with a block covariance matrix of: in, The whitening filter is obtained as Depend on The Cherosky decomposition of is obtained.

6. The active-passive collaborative perception fusion method for a radar communication dual-function system according to claim 1 is characterized in that: Step 4 is as follows: The binary hypothesis of the GLRT detector after using the whitening filter is: in The corresponding GLRT detector is then given by: and is the joint probability density function of the received signal, and the test statistic obtained after calculation is: in, Then a binary decision is made according to (8).

7. The active-passive collaborative perception fusion method for a radar communication dual-function system according to claim 1 is characterized in that: Step 5 is as follows: FC makes the following binary assumptions Where D r are the binary reasoning results of BS and the i-th RAP, respectively, and D r =0 means no target, D r =1 indicates the existence of a target, n indicates the voting threshold, and n is given by the following formula: in, is the false alarm probability of BS and RAPs.

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