A weak target energy accumulation strategy and method for multi-base heterogeneous platforms

Abstract

The present invention discloses a weak target energy accumulation strategy and method for multi-base heterogeneous platforms; first, for three types of heterogeneous platforms, namely air-based, land-based and sea-based, a long-time coherent accumulation algorithm suitable for the three types of heterogeneous receiving platforms is screened out based on the algorithm operation complexity and performance, so as to realize the target energy accumulation within the node; in addition, considering that there are obvious differences in target distance and Doppler parameters in the indirect return echoes of multiple bases, the alignment and fusion of target echo energy between multiple nodes are completed by establishing a non-coherent accumulation mathematical mapping relationship of each receiving platform, thereby achieving effective improvement of target energy and enhancing the detection capability of maneuvering weak targets; the present invention particularly addresses the signal-level accumulation problem of existing distributed heterogeneous platforms, and provides a relatively clear long-time accumulation method selection strategy and a signal accumulation method of different transform domain accumulation results; therefore, the present invention has high practical value and broad application prospects.

Description

Technical Field

[0001] The present invention relates to the field of radar detection technology, and in particular to a weak target energy accumulation strategy and method for a multi-base heterogeneous platform. Background Art

[0002] With the increasing complexity of electromagnetic space and the increasingly severe battlefield situation, multi-platform collaborative observation can help achieve a significant improvement in combat effectiveness. The "three-level" fusion method of signal level, data level, and intelligence level has the ability to systematically enhance the collaborative detection capabilities of multiple platforms. Among them, compared with the data level and intelligence level, which mine and obtain target information at the point and track levels, the signal level accumulation directly uses the original echo signal for information fusion, thereby maximizing the acquisition of target feature information. Therefore, the multi-platform joint signal-level target energy accumulation method is of great significance to improving weak target detection capabilities.

[0003] Existing distributed coherent reception methods primarily focus on the coherent synthesis of pulse signals. Multiple mobile radar units or antennas are deployed in a dispersed manner. By performing signal-level coherent synthesis of the radar echoes, they effectively form a powerful detection radar. After time delay and phase compensation, the received signals are aligned in both time and phase, allowing for coherent superposition of signals from different receiving channels. In this case, summing the signals from N receiving channels can provide an N-fold signal-to-noise ratio benefit. Currently, distributed coherent reception techniques impose stringent requirements on the spatial geometry between the target and the distributed nodes. The radial acceleration of high-speed maneuvering targets also causes Doppler nonlinear spread in the echo signal, resulting in severe defocusing of the target energy, severely limiting the performance of this method. For multi-node observation of highly maneuverable targets, the typical approach currently employed is a multi-channel target energy accumulation method based on a distributed MIMO architecture. This method utilizes full-dimensional search methods such as GRFT and RFT within multiple channels to first perform intra-channel accumulation, and then constructs inter-channel envelope and phase compensation functions to complete inter-channel accumulation.

[0004] Coherent synthesis techniques, exemplified by distributed receiver coherence, are also known as distributed aperture synthesis. However, these platforms primarily utilize multiple similar receiving platforms, and multi-node echo synthesis at the receiving end is highly susceptible to parameter estimation. For faint targets maneuvering at high speed, the received echoes are typically weak, making it difficult for distributed receiver coherence to directly estimate motion parameters. This significantly limits its use in weak target environments. Furthermore, the aforementioned MIMO-GRFT multi-channel integration technique also presents three major challenges in practical application: 1) This method still targets the same type of platform. In heterogeneous multi-receiving platform scenarios, the range migration and Doppler spread of received echoes from airborne, land-based, and sea-based nodes vary during the long-term integration process due to the varying geometric configurations of the transmitter, target, and receiving station. Using the GRFT method across all platforms would incur significant overhead, as the full-dimensional search computation significantly increases the processing load on the airborne platform. Different receiving payload platforms should select more appropriate algorithms, rather than sharing the same integration algorithm across multiple platforms. Therefore, for actual detection scenarios, it is particularly important to select a long-term accumulation algorithm that is suitable for each platform. 2) Heterogeneous multi-platforms are usually deployed in a large detection range. Even if the transmitter adopts wide beam coverage, the multi-angle span difference will still lead to unstable fluctuation characteristics of the target scattering cross-section. The echo coherence of the receiving end between multiple platforms is still difficult to meet, and the coherent accumulation results of multiple platforms have a large performance loss. Non-coherent technology can efficiently aggregate energy from heterogeneous multi-platforms, thereby greatly improving the detection capability of weak targets, but how to aggregate target energy from multiple platforms urgently needs in-depth research. 3) The fusion configuration of the results of different long-term accumulation algorithms puts forward new requirements for the processing algorithm. At present, the industry lacks corresponding methods to solve the signal-level fusion accumulation problem of accumulation results in different transform domains, parameter domains and time-frequency domains. Summary of the Invention

[0005] The present invention provides a weak target energy accumulation strategy and method for a multi-base heterogeneous platform, thereby solving the signal energy accumulation problem of maneuvering targets in the beam space dimension or signal level in the existing technology under the satellite-borne illumination source-multi-base heterogeneous platform receiving system. It realizes the rapid estimation of parameter differences between nodes through the graph node set and voting accumulation method, and then constructs a compensation function to achieve envelope alignment and accelerate non-coherent accumulation.

[0006] The present invention provides a weak target energy accumulation strategy and method for a multi-base heterogeneous platform, the method comprising:

[0007] Constructing a bistatic distance history mathematical model for a heterogeneous receiving platform, and obtaining a bistatic distance history expression of the bistatic distance history based on the bistatic distance history mathematical model;

[0008] Acquire target echo signals corresponding to the plurality of heterogeneous receiving platforms, perform matched filtering on the target echo signals to obtain range-compressed echo signals, and perform the following processing on the range-compressed echo signals corresponding to each heterogeneous receiving platform:

[0009] Combining the range-compressed echo signal with the bistatic range history expression to obtain a first echo signal; wherein the heterogeneous receiving platform includes: an air-based platform, a land-based platform, and a sea-based platform;

[0010] Performing intra-node target energy accumulation calculation on the first echo signal using different algorithms for different base platforms in the heterogeneous receiving platform, to obtain a first frequency domain echo signal corresponding to each base platform and an intra-node coherent accumulation result;

[0011] A low threshold method is used to roughly estimate the coherent accumulation results within the nodes corresponding to each base platform, and a graph node set of the platform dual base is constructed based on the rough estimation results; the graph node set is screened using a voting accumulation method to obtain the maximum value of the graph node set; and the optimal estimation node corresponding to the optimal accumulation node is determined based on the maximum value;

[0012] A compensation function corresponding to each base platform in the heterogeneous receiving platform is constructed according to the optimal estimation node, and the first frequency domain echo signal corresponding to each base platform is compensated by using the compensation function. Non-coherent accumulation is performed on each base platform to obtain a non-coherent accumulation result, thereby completing the signal-level weak target energy accumulation of multiple heterogeneous platforms.

[0013] In a possible implementation, the dual-base distance history mathematical model is expressed as:

[0014] R(t m )=‖R TP (t m )‖+‖R RP (t m )‖-‖R TR (t m )||;

[0015] Among them, R TP (t m ) represents the distance history between the irradiation source satellite and the target; R TR (t m ) represents the distance history between the illumination source satellite and the multi-base heterogeneous receiving platform; R RP (t m ) represents the distance history between the multi-base heterogeneous receiving platform and the target; ||·|| represents the Euclidean norm of the vector.

[0016] In a possible implementation, acquiring target echo signals corresponding to the plurality of heterogeneous receiving platforms, and performing matched filtering on the target echo signals to obtain range-compressed echo signals includes:

[0017] Determine the transmission signal emitted by the irradiation source satellite to the ground;

[0018] Obtaining target echo signals corresponding to the plurality of heterogeneous receiving platforms according to a bistatic distance relationship;

[0019] Cross-correlation processing is performed on the target echo signal and the direct wave to obtain range-compressed echo signals corresponding to the plurality of heterogeneous receiving platforms.

[0020] In a possible implementation, the range-compressed echo signal is expressed as:

[0021]

[0022] Among them, A i represents the signal amplitude after pulse compression at the i-th receiving platform; B represents the signal bandwidth; R TP (t m ) represents the distance history between the irradiation source satellite and the target; R RP,i (t m ) represents the distance history between the multi-base heterogeneous receiving platform and the target; R TR,i (t m ) represents the distance history between the illumination source satellite and the multi-base heterogeneous receiving platform.

[0023] In a possible implementation, the first echo signal is expressed as:

[0024]

[0025] Among them, A i represents the signal amplitude after pulse compression at the i-th receiving platform; B represents the signal bandwidth; t n Indicates fast time; t m Indicates slow time; R 0,i represents the equivalent dual-base initial distance between the i-th receiving platform and the target; v i represents the equivalent radial velocity between the i-th receiving platform and the target; a i represents the equivalent radial acceleration between the i-th receiving platform and the target; c represents the speed of light; λ represents the carrier wavelength.

[0026] In a possible implementation, performing intra-node target energy accumulation calculation on the first echo signal using different algorithms for different base platforms in the heterogeneous receiving platform includes:

[0027] The air-based platform adopts TRT-based algorithm to achieve target energy accumulation within the node;

[0028] The land-based platform adopts GRFT-based algorithm to achieve target energy accumulation within the node;

[0029] The sea-based platform adopts a KT-based algorithm to achieve target energy accumulation within the node.

[0030] In one possible implementation, constructing a compensation function corresponding to each base platform in the heterogeneous receiving platform according to the optimal estimation node, and using the compensation function to compensate the first frequency domain echo signal corresponding to each base platform includes:

[0031] Determining optimal estimation parameters of each base platform in the heterogeneous receiving platform according to the optimal estimation node;

[0032] determining a parameter compensation function corresponding to each base platform according to the optimal estimated parameters, and compensating the first frequency domain echo signal corresponding to each base platform using the parameter compensation function corresponding to each base platform to obtain a first compensated signal corresponding to each base platform;

[0033] The reference node of each base platform is determined, and a compensation function is constructed based on the parameter differences corresponding to the remaining base platforms according to the reference node and the optimal estimation node.

[0034] In a possible implementation, performing non-coherent accumulation on each base platform to obtain a non-coherent accumulation result and completing signal-level weak target energy accumulation on multiple base heterogeneous platforms includes:

[0035] constructing a compensation function according to the parameter difference to correct the first frequency domain echo signal corresponding to each base platform to obtain a correction signal corresponding to each base platform;

[0036] By utilizing the correction signals corresponding to the base platforms, non-coherent accumulation is performed on each base platform to obtain non-coherent accumulation results, thereby completing the signal-level weak target energy accumulation of multiple base heterogeneous platforms.

[0037] In a possible implementation, the optimal estimation node is represented as:

[0038]

[0039] Among them, Y m,n,q represents the voting result; m represents the number of nodes in the first set of the graph node set; n represents the number of nodes in the second set of the graph node set; q represents the number of nodes in the third set of the graph node set; argmax{·} represents the maximum value function.

[0040] In a possible implementation, the non-coherent accumulation result is expressed as:

[0041]

[0042] Among them, f r represents the distance frequency domain variable; t m represents the slow time variable; Indicates the signal after compensation by the airborne platform; represents the distance difference compensation function of the land-based platform; represents the signal after compensation of the land-based platform; Indicates the signal after compensation of the sea-based platform; represents the distance difference compensation function of the sea-based platform; t n represents the fast time variable; f m represents the pulse-dimensional frequency domain variable.

[0043] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0044] The present invention first establishes a mathematical model of bistatic range history in a three-dimensional geometric configuration with multiple receivers and a single transmitter. This model constructs a universal received echo signal expression for heterogeneous multi-receiving platforms. In the first processing step, the present invention integrates intra-node signals. For airborne nodes, a TRT-based method is used to accumulate target energy within the node. For land-based platforms, a GRFT-based method is used to transform the target state into the parameter domain while simultaneously accumulating target energy. For sea-based platforms, a robust KT-based method is used to achieve energy focusing on weak targets, balancing complexity and performance. In the second processing step, the present invention integrates inter-node signals. By setting a low threshold, the accumulated results of each platform are roughly extracted to construct a target graph node set. Furthermore, based on the concept of voting accumulation, target state information is transferred and accumulated between graph nodes. After obtaining preprocessing results, peak relationships are used to eliminate remaining false targets and retain the optimal graph nodes. The parameter estimation results are then used to construct a compensation function to compensate for envelope differences between each node relative to a reference node. Finally, a non-coherent accumulation operation is used to achieve weak target energy accumulation between the three heterogeneous nodes: airborne, land-based, and sea-based. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a weak target energy accumulation strategy and method steps for a multi-base heterogeneous platform provided by an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of the spatial geometric configuration of a multi-base heterogeneous receiving platform provided in an embodiment of the present invention;

[0047] Figure 3A schematic diagram of graph node link transmission provided by an embodiment of the present invention;

[0048] Figure 4a A diagram showing the result of receiving an echo range compression signal from an airborne platform according to an embodiment of the present invention;

[0049] Figure 4b This is a graph showing the result of receiving echo range compression signals from a land-based platform according to an embodiment of the present invention;

[0050] Figure 4c A diagram showing the result of receiving an echo range compression signal from a sea-based platform according to an embodiment of the present invention;

[0051] Figure 5a The accumulated results after parameter compensation of the airborne receiving platform provided in the embodiment of the present invention;

[0052] Figure 5b The accumulated results after parameter compensation of the land-based receiving platform provided in the embodiment of the present invention;

[0053] Figure 5c The accumulated results after parameter compensation of the sea-based receiving platform provided in the embodiment of the present invention;

[0054] Figure 5d This is the signal-level non-coherent accumulation result of the multi-base heterogeneous platform provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0056] The present invention provides a weak target energy accumulation strategy and method for a multi-base heterogeneous platform, such as Figure 1 As shown, the method includes the following steps S101 to S105.

[0057] S101, constructing a bistatic distance history mathematical model for a heterogeneous receiving platform, and obtaining a bistatic distance history expression of the bistatic distance history based on the bistatic distance history mathematical model;

[0058] Specifically, in step S101, the dual-base distance history mathematical model is expressed as:

[0059] R(t m )=||R TP (t m )||+||R RP (t m )||-||RTR (t m )||;

[0060] Among them, R TP (t m ) represents the distance history between the irradiation source satellite and the target; R TR (t m ) represents the distance history between the illumination source satellite and the multi-base heterogeneous receiving platform; R RP (t m ) represents the distance history between the multi-base heterogeneous receiving platform and the target; ||·|| represents the Euclidean norm of the vector.

[0061] For example, this invention primarily addresses the detection of maneuvering weak aerial targets in a distributed radar system using a single-transmit-multiple-receiver (STM) configuration, with a satellite illumination source as the external radiation source and heterogeneous receiving platforms deployed in air, land, and sea as receiving nodes. First, assuming the illumination source system transmits a linear frequency modulated (LFM) signal, and using three heterogeneous multi-base platforms to receive the reflected signals from the maneuvering target, mathematical models for speed and range measurement in a STM configuration with a common geometric configuration are derived, and a corresponding mathematical model for the received signal is established.

[0062] Specifically, if Figure 2 Figure 1 shows the spatial geometry of the multi-base heterogeneous receiving platform. The parameters that need to be initialized mainly include the spatial position, velocity, and acceleration parameters of the multi-base heterogeneous receiving platform; the spatial position, velocity, and acceleration parameters of the satellite illumination source; and the spatial position, velocity, and acceleration parameters of the maneuvering target.

[0063] According to the above parameters, the distance history between the illumination source satellite and the target, the distance history between the multi-base heterogeneous receiving platform and the target, and the distance history between the illumination source satellite and the multi-base heterogeneous receiving platform can be expressed as:

[0064]

[0065] Among them, R TP0 represents the initial distance vector between the source satellite and the target, v T represents the spatial velocity vector of the irradiation source satellite, a T R represents the spatial acceleration vector of the irradiation source satellite; RP0,i represents the initial distance vector between the multi-base heterogeneous receiving platform and the target, v R,i represents the spatial velocity vector of the i-th multi-base heterogeneous receiving platform, a R,i Represents the spatial acceleration vector of the i-th multi-base heterogeneous receiving platform. Convert the above distance history vector into a scalar and calculate it at t m =0, and the second-order approximate Taylor expansion is obtained:

[0066]

[0067] According to the spatial geometric configuration relationship, the dual-base distance history of the target can be expressed as:

[0068] R(t m )=||R TP (t m )||+||R RP (t m )||-||R TR (t m )|| (7)

[0069] Where ||·|| represents the Euclidean norm of the vector. Substituting the above distance history scalar, the target's dual-base distance history scalar is further expressed as:

[0070]

[0071] Simplifying the above formula, we can get:

[0072]

[0073] Therefore, the bistatic distance history between the multi-base heterogeneous receiving platform and the target can be simplified as:

[0074]

[0075] Among them, R i represents the bi-base distance history between the i-th receiving platform and the target, R 0,i represents the equivalent dual-base initial distance between the i-th receiving platform and the target, v i represents the equivalent radial velocity between the i-th receiving platform and the target, a i represents the equivalent radial acceleration between the i-th receiving platform and the target.

[0076] S102: Acquire target echo signals corresponding to multiple heterogeneous receiving platforms, perform matched filtering on the target echo signals to obtain range-compressed echo signals, and perform the following processing on the range-compressed echo signals corresponding to each of the heterogeneous receiving platforms:

[0077] The first echo signal is obtained by combining the range-compressed echo signal and the bistatic range history expression; wherein the heterogeneous receiving platform includes: an air-based platform, a land-based platform, and a sea-based platform;

[0078] Specifically, in step S102, target echo signals corresponding to multiple heterogeneous receiving platforms are acquired, and matched filtering is performed on the target echo signals to obtain range-compressed echo signals, which includes the following steps S1021 to S1023.

[0079] S1021, determining the transmission signal emitted by the illumination source satellite to the ground;

[0080] S1022, obtaining target echo signals corresponding to multiple heterogeneous receiving platforms based on a bistatic distance relationship;

[0081] S1023 , cross-correlation processing is performed on the target echo signal and the direct wave to obtain range-compressed echo signals corresponding to multiple heterogeneous receiving platforms.

[0082] For example, assuming that the illumination source satellite transmits a linear frequency modulation signal to the ground, the transmitted signal can be expressed as:

[0083]

[0084] Where rect(·) represents the rectangular window function, T p Indicates pulse width, μ=B / T P represents the modulation frequency, B represents the signal bandwidth, f c represents the carrier frequency, and t represents the full-dimensional time.

[0085] Ignoring noise and clutter, the two-dimensional echo signal emitted by the source satellite and received by the i-th heterogeneous platform can be expressed as:

[0086]

[0087] in, represents the complex amplitude of the echo signal of the i-th heterogeneous receiving platform, β i represents the target echo signal level value of the i-th receiving platform, φ i ~N(μ,σ 2 ) represents the phase change of the received signal due to different viewing angles. Performing down-conversion operation on equation (12) yields:

[0088]

[0089] At the same time, the direct wave signal corresponding to the i-th receiving platform can be expressed in the two-dimensional time domain as:

[0090]

[0091] Next, the target signal is range-compressed by performing cross-correlation processing on the target echo and the direct wave at the i-th receiving platform. The echo signal after pulse compression (range-compressed echo signal) at the i-th receiving platform can be expressed as:

[0092]

[0093] Among them, A irepresents the signal amplitude after pulse compression at the i-th receiving platform; B represents the signal bandwidth; R TP (t m ) represents the distance history between the irradiation source satellite and the target; R RP,i (t m ) represents the distance history between the multi-base heterogeneous receiving platform and the target; R TR,i (t m ) represents the distance history between the illumination source satellite and the multi-base heterogeneous receiving platform.

[0094] Substituting the simplified distance history expression, the above formula can be simplified to the first echo signal:

[0095]

[0096] Among them, A i represents the signal amplitude after pulse compression at the i-th receiving platform; B represents the signal bandwidth; t n Indicates fast time; t m Indicates slow time; R 0,i represents the equivalent dual-base initial distance between the i-th receiving platform and the target; v i represents the equivalent radial velocity between the i-th receiving platform and the target; a i represents the equivalent radial acceleration between the i-th receiving platform and the target; c represents the speed of light; λ represents the carrier wavelength.

[0097] S103, performing intra-node target energy accumulation calculation on the first echo signal using different algorithms for different base platforms in the heterogeneous receiving platform, to obtain a first frequency domain echo signal corresponding to each base platform and an intra-node coherent accumulation result;

[0098] Specifically, in step S103, different algorithms are used for different base platforms in the heterogeneous receiving platform to perform accumulation calculation of target energy within the node on the first echo signal, including:

[0099] The air-based platform uses a TRT-based algorithm to achieve target energy accumulation within the node;

[0100] The land-based platform uses the GRFT-based algorithm to achieve target energy accumulation within the node;

[0101] The sea-based platform adopts the KT-based algorithm to achieve target energy accumulation within the node.

[0102] Here, due to the limited payload space and load-bearing capacity of airborne platforms, signal processing capabilities are limited. Therefore, airborne platforms are well-suited for long-term accumulation algorithms with lower computational complexity. The TRT-based algorithm, by flipping the range frequency domain along the slow time dimension and then conjugating and multiplying it with the original data, can quickly eliminate odd-order terms in high-order maneuvering parameters, significantly reducing range migration and Doppler spread, and achieving high computational efficiency. This algorithm is suitable for payload-constrained platforms such as airborne platforms.

[0103] Land-based platforms utilize computers equipped with large-scale, powerful processing units to enhance system computational power, reducing the need for algorithm complexity and efficiency. The GRFT-based algorithm has proven to offer excellent coherent accumulation performance. Its multidimensional search operations can be broken down into multiple parallel processing units for simultaneous execution, making it well-suited for execution on ground-based platforms equipped with large-scale parallel computing processors.

[0104] Sea-based platforms are often deployed on buoys or ships exposed to rough seas, placing high demands on algorithm robustness and seeking a balance between computational complexity and accumulation performance. In this context, the KT-based algorithm uses a first-order KT transform to eliminate the first-order range migration caused by unambiguous velocity. It then constructs a two-dimensional compensation function of the fuzzy factor and acceleration to perform matched filtering on the frequency-domain echo signal after the KT. This effectively addresses the accumulation of weak targets on sea-based platforms.

[0105] For example, the airborne platform uses a TRT-based algorithm to perform range migration and Doppler spread correction to achieve coherent accumulation of target energy within the node.

[0106] First, the received echo signal of the airborne node is converted to the range frequency domain, and the first echo range frequency domain signal is expressed as:

[0107]

[0108] Among them, σ air represents the target scattering coefficient of the airborne node, f r Represents the frequency variable in the distance frequency domain, R air represents the equivalent dual-base initial distance between the airborne receiving platform and the target, v air The equivalent radial velocity between the airborne receiving platform and the target, α air It represents the equivalent radial acceleration between the airborne receiving platform and the target.

[0109] Usually, the spaceborne illumination source continuously transmits signals to the ground at a low pulse repetition frequency. Therefore, the target equivalent true radial velocity can be expressed as:

[0110] v air =v b,air +Mv,air V u (18)

[0111] Among them, v b,air represents the unambiguous velocity corresponding to the airborne node target echo, M v,air represents the velocity ambiguity number corresponding to the airborne node target echo, V u Indicates the blind speed corresponding to the air-based node.

[0112] Perform the time domain conjugate flip self-multiplication operation on the first echo range frequency domain signal to obtain the first echo range compensation signal:

[0113]

[0114] Further using the traditional KT wedge transformation method to eliminate the range migration phenomenon caused by radial velocity, we can obtain:

[0115]

[0116] The compensation function for the residual distance movement is constructed as follows:

[0117]

[0118] Further use of compensation function and s TRT-KT (f r ,t a ) to eliminate the fuzzy factor effect, and perform IFFT operation along the range frequency domain to obtain the first echo time-frequency domain signal:

[0119]

[0120] From formula (22), it can be seen that the time-frequency domain signal envelope of the first echo has been concentrated in the same distance unit. At this time, the peak extraction technology can be used to estimate the unambiguous speed of the target, and the actual speed of the airborne node target echo can be estimated according to the searched speed ambiguity factor as follows:

[0121]

[0122] The compensation function is constructed using the velocity estimation result to eliminate the velocity term of the original range frequency domain echo, and the second echo range frequency domain echo can be obtained:

[0123]

[0124] Furthermore, the range-frequency domain flip multiplication operation is performed on the second echo range-frequency domain echo to obtain:

[0125]

[0126] Perform IFFT transformation on the above equation to the fast time domain:

[0127]

[0128] The acceleration compensation function is constructed as follows:

[0129]

[0130] By using this compensation function to eliminate the acceleration ambiguity phase term, we can obtain:

[0131]

[0132] Performing NUFFT operation on the above signal along the slow time dimension yields:

[0133]

[0134] Using the acceleration fuzzy factor and peak estimation results, the acceleration is estimated as follows:

[0135]

[0136] Using the above velocity and acceleration estimates to compensate for the first echo of the range frequency, we can obtain:

[0137]

[0138] Transforming the above signal into the distance time domain, we can get:

[0139]

[0140] Perform FFT along the slow time dimension and use peak detection techniques to estimate the equivalent initial distance At this time, for the airborne receiving node, the target's equivalent dual-base initial distance, equivalent initial velocity and equivalent acceleration can be effectively estimated.

[0141] The land-based platform uses the GRFT-based algorithm to perform a three-dimensional joint search of distance, velocity and acceleration, and gives the accumulated results.

[0142] Get the received echo after distance compression from the land-based platform:

[0143]

[0144] Among them, A land Indicates the amplitude of the echo signal received by the land-based node, R land represents the equivalent dual-base initial distance between the land-based receiving platform and the target, v land is the equivalent radial velocity between the land-based receiving platform and the target, α land It represents the equivalent radial acceleration between the land-based receiving platform and the target.

[0145] The GRFT-based algorithm essentially extracts and accumulates echo signal energy along the trajectory found by a multi-dimensional parameter search. Each search value combination of distance, velocity, and acceleration corresponds to a target trajectory to be extracted, and coherent superposition is achieved through phase compensation. If and only if the search value matches the true value, the target echo signal energy is fully extracted and coherently superimposed, resulting in a maximum value. Using this concept, executing the GRFT-based algorithm on the received echo after range compression from a land-based platform yields the following first echo signal in the parameter domain:

[0146]

[0147] Where R' represents the equivalent bistatic initial distance search value, v' represents the equivalent initial velocity search value, and α' represents the equivalent acceleration search value. After processing by this method, the target energy is coherently accumulated in the parameter domain, and the signal form is transformed from the time-frequency domain to the parameter domain.

[0148] The sea-based platform adopts the KT-based algorithm to accumulate target energy within the node.

[0149] The range frequency third echo signal corresponding to the received echo after the sea-based platform range compression is expressed as:

[0150]

[0151] Among them, σ sea represents the target scattering coefficient of the sea-based node, f r Represents the frequency variable in the distance frequency domain, R sea represents the equivalent dual-base initial distance between the sea-based receiving platform and the target, v sea represents the equivalent radial velocity between the sea-based receiving platform and the target, α sea It represents the equivalent radial acceleration between the airborne receiving platform and the target.

[0152] Performing the traditional KT transform on the signal, the corrected echo signal is expressed as:

[0153]

[0154] The matched filter function is constructed as follows:

[0155]

[0156] This function is used to compensate for the velocity ambiguity factor and acceleration. When the search value matches the true value, we can get:

[0157]

[0158] Transforming the result into the range-time domain, we can obtain:

[0159]

[0160] Performing FFT along slow time yields:

[0161]

[0162] Therefore, when traversing the parameter search space of velocity ambiguity factor and acceleration, the coherent accumulation energy of the target echo signal reaches a peak when the search value matches the true value.

[0163] At this point, the peak extraction technology can be used to estimate the unambiguous speed of the target, and the actual speed of the sea-based node target echo can be estimated according to the searched speed ambiguity factor as follows:

[0164]

[0165] S104, make a rough estimate of the intra-node coherent accumulation results corresponding to each base platform, and construct a graph node set of the platform dual base based on the rough estimation results; and use the voting accumulation method to screen the graph node set to obtain the maximum value of the graph node set; determine the optimal estimation node corresponding to the optimal accumulation node based on the maximum value; wherein, a low threshold method is used to make a rough estimate of the intra-node coherent accumulation results corresponding to each base platform.

[0166] Specifically, when performing coarse threshold detection, the amplitude mean Z is calculated using the time-frequency domain or transform domain accumulation results, and the product of Z and the threshold factor K0 is used as a fixed threshold to screen local maximum points.

[0167]

[0168] All the above local maximum points are used to construct the graph node set Θ of the corresponding receiving platform.

[0169] Furthermore, a coarse threshold detection is performed on the node coherent accumulation results of the airborne receiving platform, and the graph node set is constructed in combination with the parameter estimation results of velocity and acceleration. Used to characterize the rough estimation results accumulated over a long period of time by airborne platforms.

[0170] Furthermore, a coarse threshold detection is performed on the node coherence accumulation results of the land-based receiving platform, and the graph node set is used Characterize the rough estimation results of long-term accumulation of land-based platforms.

[0171] Furthermore, the coarse threshold detection is performed on the node coherent accumulation results of the sea-based receiving platform, and the parameter estimation results of the velocity and acceleration are combined with the graph node set Rough estimates used to characterize long-term accumulation on land-based platforms.

[0172] like Figure 3 The figure shows a schematic diagram of the node link transmission. Now define Ω∈{Θ α vΘ β ∪Θ χ} is the union of the coarse threshold detection estimation results of the three heterogeneous platform nodes, where Θ α ,Θ β ,Θ χ As a subset, it contains several mutually communicating nodes used to represent rough estimation results. There is a unique effective communication link between the subsets. There is at least one direct or indirect information path between any two nodes in the subset. The information of two adjacent subsets is interoperable, but the data link transmission between subsets must pass through the subset level.

[0173] To address this phenomenon, the maximum screening problem is transformed into a voting accumulation problem between nodes. First, a voting matrix Y is established for all possible accumulation values. m,n,q , its size is designed to be M×N×Q, where M is the graph node set Θ α The number of internal nodes, N represents the graph node set Θ β The number of internal nodes, Q represents the graph node set Θ χ The number of internal nodes. Then all voting results of the voting matrix are expressed as:

[0174] Y m,n,q =Θ α (A air ) m +Θ β (A ground ) n +Θ χ (A sea ) q (43)

[0175] After discrete voting for all possible accumulated values. Since the voting value is an important parameter reflecting the true distance value of the target, the larger the voting value of the accumulated unit, the greater the probability that it corresponds to the true distance value of the target. Conversely, the smaller the accumulated value, the smaller the probability that it corresponds to the target distance value. Therefore, the position estimate corresponding to the optimal accumulated node in the graph node set is expressed as:

[0176]

[0177] Among them, Y m,n,q represents the voting result; m represents the number of nodes in the first set of the graph node set; n represents the number of nodes in the second set of the graph node set; q represents the number of nodes in the third set of the graph node set; argmax{·} represents the maximum value function; It represents the optimal estimated node after voting accumulation of the three subsets.

[0178] S105: Construct a compensation function corresponding to each base platform in the heterogeneous receiving platform based on the optimal estimation node, and use the compensation function to compensate the first frequency domain echo signal corresponding to each base platform. Perform non-coherent accumulation on each base platform to obtain a non-coherent accumulation result, completing the signal-level weak target energy accumulation of multiple heterogeneous platforms.

[0179] Specifically, in step S105, a compensation function corresponding to each base platform in the heterogeneous receiving platform is constructed according to the optimal estimation node, and the first frequency domain echo signal corresponding to each base platform is compensated using the compensation function, including:

[0180] (1) Determine the optimal estimation parameters of each base platform in the heterogeneous receiving platform based on the optimal estimation node;

[0181] (2) determining a parameter compensation function corresponding to each base platform according to the optimal estimated parameters, and compensating the first frequency domain echo signal corresponding to each base platform using the parameter compensation function corresponding to each base platform to obtain a first compensated signal corresponding to each base platform;

[0182] (3) Determine the reference node of each base platform, and construct the compensation function based on the parameter differences corresponding to the remaining base platforms based on the reference node and the optimal estimation node.

[0183] For example, the optimal estimation parameters corresponding to each node are obtained using the optimal estimation node. Three parameter compensation functions are constructed as follows:

[0184]

[0185] use Eliminating the speed and acceleration differences within the node, the signals after parameter compensation for each heterogeneous platform are:

[0186]

[0187] It can be seen from the above formula that the Doppler effect of each node has been eliminated, but there are differences in envelope positions, and non-coherent accumulation between nodes needs to be completed.

[0188] For the optimal estimation node, the reference node is first selected, and then the parameter difference compensation function is constructed. The original echoes of the air-based, land-based, and sea-based signals are corrected in the time-frequency domain respectively. The corrected signals of the three nodes are non-coherently accumulated, and finally the maximum value after non-coherent accumulation is obtained.

[0189] In this embodiment, an airborne node is selected as a directional reference node for signal-level energy enhancement, and the following distance correction function is constructed:

[0190]

[0191] The distance difference between the land-based node and the air-based node is expressed as The distance difference between the sea-based node and the air-based node is expressed as

[0192] Specifically, in step S105, non-coherent accumulation is performed on each base platform to obtain a non-coherent accumulation result, completing the signal-level weak target energy accumulation of the multi-base heterogeneous platform, including:

[0193] (1) constructing a compensation function according to the parameter difference to correct the first frequency domain echo signal corresponding to each base platform to obtain the correction signal corresponding to each base platform;

[0194] (2) Using the correction signals corresponding to each base platform, non-coherent accumulation is performed on each base platform to obtain non-coherent accumulation results, thus completing the signal-level weak target energy accumulation of multiple base heterogeneous platforms.

[0195] Here, the non-coherent accumulation result is expressed as:

[0196]

[0197] Among them, f r represents the distance frequency domain variable; t m represents the slow time variable; Indicates the signal after compensation by the airborne platform; represents the distance difference compensation function of the land-based platform; represents the signal after compensation of the land-based platform; Indicates the signal after compensation of the sea-based platform; represents the distance difference compensation function of the sea-based platform; t n represents the fast time variable; f m represents the pulse-dimensional frequency domain variable.

[0198] For example, after the distance alignment processing of the proposed method, the three-node echoes realize signal-level non-coherent accumulation in the time-frequency domain, and the signal accumulation energy is the sum of the peak values ​​of multiple stations, which effectively improves the target detection capability.

[0199] The present invention is mainly verified by simulation experiments, and all steps and conclusions are verified to be correct on Matlab 2022b. The beneficial results of the present invention are further described in detail below in conjunction with the simulation configuration parameters shown in Tables 1 and 2.

[0200] (1) Simulation experimental conditions

[0201] The radar system simulation parameters for this experiment are shown in Table 1. It should be noted that, for the sake of convenience, receiving station 1 refers to the receiving node of the air-based platform, receiving station 2 refers to the receiving node of the land-based platform, and receiving station 3 refers to the receiving node of the sea-based platform.

[0202] Table 1 Radar system simulation parameters

[0203] System parameters Numerical System parameters Numerical carrier frequency 1.2GHz Receiving station 1 received echo signal-to-noise ratio -13dB bandwidth 2MHz Receiving station 2 received echo signal-to-noise ratio -20dB Pulse repetition frequency 1000 Receiving station 3 received echo signal-to-noise ratio -15dB Pulse Width 50us Sampling rate 4MHz Number of pulse accumulation 1000

[0204] Table 2 gives the initial position, velocity, and acceleration configuration information of the irradiation source satellite, three heterogeneous receiving nodes, and the moving target in the 3D rectangular coordinate system in this simulation experiment.

[0205] Table 2 Motion parameters

[0206] Configuration parameters Numerical Satellite position of irradiation source [-100e3, -100e3, 3.6e7]m Satellite speed of irradiation source [10, 10, 10]m / s Receiving station 1 starting position [60e3,100e3,22e3]m Receiving station 1 speed [200, 300, -50] m / s Receiving station 1 acceleration <![CDATA[[20,15,30]m / s 2 ]]> Receiving station 2 starting position [10e3,10e3,500]m Receiving station 2 speed [[0,0,0]m / s Receiving station 2 acceleration <![CDATA[[0,0,0]m / s 2 ]]> Receiving station 3 starting position [80e3,4e3,0]m Receiving station 3 speed [4, 5, 0] m / s Receiving station 3 acceleration <![CDATA[[0,0,1]m / s 2 ]]> Target initial position 150e3,150e3,10e3]m Target speed [-800, -600, -500]m / s Target acceleration <![CDATA[[-20,-20,0]m / s 2 ]]>

[0207] (2) Simulation experiment content and result analysis

[0208] In an embodiment of the present invention, a multi-base heterogeneous platform is constructed under a one-transmit-multiple-receive system to receive echo data based on a signal model. In the first step, the long-term accumulation algorithm described in the present invention is used to select a strategy distribution to perform intra-node coherent accumulation on the three heterogeneous platforms. A compensation function is constructed based on the first-level accumulation results, and then a second-level non-coherent accumulation is completed between nodes, ultimately achieving weak target energy enhancement and detection.

[0209] Figure 4a A graph showing the result of the echo range compression signal received by the airborne platform; Figure 4b A graph showing the result of echo range compression signal received by the land-based platform; Figure 4c The figure shows the result of receiving echo range compression signal from the sea-based platform. The horizontal axis represents the number of pulses and the vertical axis represents the distance unit. Figure 4a 、 Figure 4b and Figure 4c It can be seen that for high-speed maneuvering weak targets, due to the large differences in the viewing angles of the three heterogeneous platforms and the fact that the air-based platform itself has certain maneuvering characteristics, the projection in the radial direction shows a large equivalent velocity and acceleration. Therefore, the target trajectory spans 70 range units in the air-based receiving echo, and there is a relatively obvious range bending phenomenon. The echoes of land-based and sea-based receiving platforms are mainly based on first-order range migration or range movement. In addition, according to the geometric configuration, the land-based node layout usually has a long horizontal straight-line distance from the target. Figure 4a , Figure 4b , Figure 4cIt can also be found that the target starts at a dual-base distance of 116 km in the air-based reception echo, starts at a dual-base distance of 190 km in the land-based reception echo, and starts at a dual-base distance of 153 km in the sea-based reception echo. The equivalent initial distance of the dual-base distance history of the land-based node is farther than the other two initial distances, which is consistent with the geometric structure analysis.

[0210] Figure 5a Given the accumulated results after compensation of the airborne receiving platform parameters, Figure 5b Given the accumulated results after compensation of the airborne receiving platform parameters, Figure 5c The accumulated results after the compensation of the sea-based receiving platform parameters are given. In the embodiment of the present invention, the air-based platform receiving node, namely receiving station 1, is selected as the reference node, and the target parameter positions of other nodes will move closer to the reference node. Figure 5d It can be seen that the non-coherent accumulation results are specifically manifested in that the time-frequency domain distance and Doppler envelope position are aligned with the reference node, the peak level is significantly improved, and the noise around the peak is more stable than that of the reference node, achieving the enhancement of weak echo signals and can be used for effective detection of weak targets.

[0211] Furthermore, the present invention provides a comparison of the computational complexity of the disclosed method and the algorithm proposed by MIMO-GRFT. Assume that the number of receiving platforms is set to 3, the number of acceleration searches, the number of speed searches, the number of distance searches, the number of distance units, and the number of pulse accumulations are N respectively. a 、N v 、N r , N and M.

[0212] The computational complexity of the nodes in the airborne receiving platform in the embodiment of the present invention is expressed as follows:

[0213] O[(K1+1)NMlog2N+(K1+K2+7)NM+4NMlog2M+(K1+K2)Mlog2M];

[0214] The computational complexity of the algorithm used for the land-based receiving platform is:

[0215] O(N v N a NM);

[0216] The computational complexity of the long-term coherent integration algorithm within the sea-based receiving platform node is:

[0217] O[(K1+4)NM+(N a +K1+4)NMlog2M];

[0218] The freight complexity of non-coherent accumulation between heterogeneous nodes is expressed as O(4NM), so the total computational complexity is:

[0219]

[0220] During the accumulation process of the MIMO-GRFT algorithm, the target trajectory within the node needs to be searched in three dimensions: distance, speed, and acceleration. The computational complexity is: O(3N v N a NM); the computational complexity accumulated between nodes is simplified as: O(4NM)O(4NM), so the total computational complexity of this method can be expressed as: O(3N v N a NM+4NM).

[0221] Table 3 compares the time complexity of the examples under simulation conditions, using 64GB of RAM and MATLAB version 2022b. The disclosed method runs in 217.21 seconds, while the MIMO-GRFT method runs in 349.18 seconds. Therefore, the proposed algorithm significantly reduces time overhead while achieving the target signal-level capability accumulation for a multi-base heterogeneous platform.

[0222] Table 3 Algorithm running time comparison

[0223] Comparison Method Runtime Proposed algorithm 217.21s MIMO-GRFT 349.18s

[0224] The above simulation experiments also verify the effectiveness and reliability of the method proposed in this invention.

[0225] The various embodiments in this specification are described in a progressive manner. References to the same or similar parts between the various embodiments are sufficient. Each embodiment focuses on the differences from other embodiments. All or part of the present invention can be used in a variety of general or specialized computer system environments or configurations. For example, personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above systems or devices.

[0226] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the present invention.

Claims

1. A weak target energy accumulation strategy and method for a multi-base heterogeneous platform, characterized in that: include: Constructing a bistatic distance history mathematical model for a heterogeneous receiving platform, and obtaining a bistatic distance history expression of the bistatic distance history based on the bistatic distance history mathematical model; Acquire target echo signals corresponding to the plurality of heterogeneous receiving platforms, perform matched filtering on the target echo signals to obtain range-compressed echo signals; and perform the following processing on the range-compressed echo signals corresponding to each heterogeneous receiving platform: Combining the range-compressed echo signal with the bistatic range history expression to obtain a first echo signal; wherein the heterogeneous receiving platform includes: an air-based platform, a land-based platform, and a sea-based platform; Performing intra-node target energy accumulation calculation on the first echo signal using different algorithms for different base platforms in the heterogeneous receiving platform, to obtain a first frequency domain echo signal corresponding to each base platform and an intra-node coherent accumulation result; A low threshold method is used to roughly estimate the coherent accumulation results within the nodes corresponding to each base platform, and a graph node set of the platform dual base is constructed based on the rough estimation results; the graph node set is screened using a voting accumulation method to obtain the maximum value of the graph node set; and the optimal estimation node corresponding to the optimal accumulation node is determined based on the maximum value; A compensation function corresponding to each base platform in the heterogeneous receiving platform is constructed according to the optimal estimation node, and the first frequency domain echo signal corresponding to each base platform is compensated by using the compensation function. Non-coherent accumulation is performed on each base platform according to the compensated signal to obtain a non-coherent accumulation result, thereby completing the signal-level weak target energy accumulation of multiple heterogeneous platforms.

2. The weak target energy accumulation strategy and method for a multi-base heterogeneous platform according to claim 1 is characterized in that: The dual-base distance history mathematical model is expressed as: ； in, Represents the distance history between the irradiation source satellite and the target; Represents the distance history between the illumination source satellite and the multi-base heterogeneous receiving platform; Represents the distance history between multi-base heterogeneous receiving platforms and the target; Represents the Euclidean norm of a vector.

3. The weak target energy accumulation strategy and method for a multi-base heterogeneous platform according to claim 1, characterized in that: The acquiring target echo signals corresponding to the plurality of heterogeneous receiving platforms and performing matched filtering on the target echo signals to obtain range-compressed echo signals includes: Determine the transmission signal emitted by the irradiation source satellite to the ground; acquiring target echo signals corresponding to the plurality of heterogeneous receiving platforms according to a bistatic distance relationship; Cross-correlation processing is performed on the target echo signal and the direct wave to obtain range-compressed echo signals corresponding to the plurality of heterogeneous receiving platforms.

4. The weak target energy accumulation strategy and method for a multi-base heterogeneous platform according to claim 1, characterized in that: The echo signal of the range compression is expressed as: ； in, Indicates the The signal amplitude after pulse compression on each receiving platform; Indicates the signal bandwidth; Represents the distance history between the irradiation source satellite and the target; Represents the distance history between multi-base heterogeneous receiving platforms and the target; Represents the distance history between the illumination source satellite and the multi-base heterogeneous receiving platform; Indicates the carrier frequency.

5. The weak target energy accumulation strategy and method for a multi-base heterogeneous platform according to claim 1, characterized in that: The first echo signal is expressed as: ； in, Indicates the The signal amplitude after pulse compression on each receiving platform; Indicates the signal bandwidth; Indicates fast time; Indicates slow time; Indicates the The equivalent dual-base initial distance between the receiving platform and the target; Indicates the The equivalent radial velocity between the receiving platform and the target; Indicates the The equivalent radial acceleration between the receiving platform and the target; represents the speed of light; Indicates the carrier wavelength.

6. The weak target energy accumulation strategy and method for a multi-base heterogeneous platform according to claim 1, characterized in that: The performing intra-node target energy accumulation calculation on the first echo signal using different algorithms for different base platforms in the heterogeneous receiving platform includes: The air-based platform adopts TRT-based algorithm to achieve target energy accumulation within the node; The land-based platform adopts GRFT-based algorithm to achieve target energy accumulation within the node; The sea-based platform adopts a KT-based algorithm to achieve target energy accumulation within the node.

7. The weak target energy accumulation strategy and method for a multi-base heterogeneous platform according to claim 1, characterized in that: The constructing, according to the optimal estimation node, a compensation function corresponding to each base platform in the heterogeneous receiving platform, and using the compensation function to compensate for the first frequency domain echo signal corresponding to each base platform includes: Determining optimal estimation parameters of each base platform in the heterogeneous receiving platform according to the optimal estimation node; determining a parameter compensation function corresponding to each base platform according to the optimal estimated parameters, and compensating the first frequency domain echo signal corresponding to each base platform using the parameter compensation function corresponding to each base platform to obtain a first compensated signal corresponding to each base platform; The reference node of each base platform is determined, and a compensation function is constructed based on the parameter differences corresponding to the remaining base platforms according to the reference node and the optimal estimation node.

8. The weak target energy accumulation strategy and method for a multi-base heterogeneous platform according to claim 7, characterized in that: The non-coherent accumulation is performed on each base platform to obtain a non-coherent accumulation result, and the signal-level weak target energy accumulation of the multi-base heterogeneous platform is completed, including: constructing a compensation function according to the parameter difference to correct the first frequency domain echo signal corresponding to each base platform to obtain a correction signal corresponding to each base platform; By utilizing the correction signals corresponding to the base platforms, non-coherent accumulation is performed on each base platform to obtain non-coherent accumulation results, thereby completing the signal-level weak target energy accumulation of multiple base heterogeneous platforms.

9. The weak target energy accumulation strategy and method for a multi-base heterogeneous platform according to claim 1, characterized in that: The optimal estimation node is expressed as: ； in, Indicates voting results; Indicates the number of nodes in the first set of graph nodes; Indicates the number of nodes in the second set in the graph node set; Indicates the number of nodes in the third set in the graph node set; Represents the maximum function.

10. The weak target energy accumulation strategy and method for a multi-base heterogeneous platform according to claim 1, characterized in that: The non-coherent accumulation result is expressed as: ； in, represents the distance frequency domain variable; represents the slow time variable; Indicates the signal after compensation by the airborne platform; represents the distance difference compensation function of the land-based platform; represents the signal after compensation of the land-based platform; Indicates the signal after compensation of the sea-based platform; represents the distance difference compensation function of the sea-based platform; represents fast time variable; represents the pulse-dimensional frequency domain variable.

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