6284results about "Wave based measurement systems" patented technology

The invention belongs to the technical field of electromagnetic confrontation and intelligent decision making, and particularly relates to an electromagnetic situation intelligent confrontation decision making method and system based on deep learning. Threat level evaluation and resource adaptation degree analysis are carried out based on a multi-head self-attention mechanism, and a dynamic electromagnetic confrontation strategy knowledge graph is generated; a game confrontation engine driven by deep reinforcement learning is used for deducing an enemy countering path and a situation evolution result under different interference strategies of our party, interference parameters are optimized through a multi-agent collaborative learning model, weight parameters of a strategy network are updated in real time through online confrontation element learning, and historical confrontation efficiency data and current task constraints are combined to obtain a confrontation result. And dynamically generating a target interference decision sequence and an electromagnetic confrontation efficiency prediction report, and synchronously updating the electromagnetic confrontation strategy library. Therefore, the problems of serious disjunction of deduction results, low real-time antagonism and the like in the prior art are solved.

There is disclosed a method of operating a radio node in a wireless communication network, the radio node being adapted for wireless communication, and being adapted for sensing and / or for radar operation. The method includes transmitting sensing signalling of a second type based on transmission and / or reception of sensing signalling of a first type. The disclosure also pertains to related devices and methods.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention relates to the technical field of semiconductor luminescence, and discloses a manufacturing method of a lens module, a mold, the lens module and a sensor device. The manufacturing method comprises the following steps: S1, arranging at least two cavity structures with upper and lower openings in the bearing part; the upper mold and the lower mold are each internally provided with a mold cavity with an opening in one side. And mold cavities of the upper mold and the lower mold extend into the cavity structure from the upper opening end and the lower opening end of the bearing part correspondingly to be aligned and closed. And a gap is reserved between the upper mold and the lower mold in the mold closing process. And S2, a mold cavity for mold closing is filled with the light-transmitting glue, and the redundant light-transmitting glue flows out through gaps in the peripheries of the upper mold and the lower mold and adheres to the inner wall of the bearing part. And S3, after curing, the upper mold and the lower mold are moved away, and a first lens and a second lens are formed in the two cavity structures respectively. The manufacturing method is simple and high in efficiency, and the manufactured sensor device is simple and stable in structure, good in sealing performance, high in precision and easy to optically design.

The invention belongs to the technical field of ocean surface flow velocity remote sensing measurement, and particularly relates to a Luoga No.2 SAR (Synthetic Aperture Radar) flow velocity correction method based on Bragg orbit velocity, which comprises the following steps of: verifying the physical correctness of an orbit velocity mechanism relative to a phase velocity mechanism; under a DopRIM framework, a physically consistent sea wave error correction model is established; calibrating parameters of the sea wave error correction model; applying the calibrated sea wave error correction model to along-orbit interference SAR observation data of the Luoma No. 2 satellite, and calculating the radial surface flow velocity and sea wave error of each resolution unit; comparing an output result with a matched reference truth value to evaluate the effectiveness of the sea wave error correction model; comparing the sea wave error correction model with a KaDOP model, and respectively counting and analyzing simulated sea wave errors under different methods; the sea wave error simulation result is visually displayed, and the improvement effect is quantitatively evaluated through in-situ observation data and a KaDOP simulation result.

The invention provides a method and a device for predicting influence of sea wave fluctuation on signal intensity based on ship data. The method provided by the invention comprises the following steps: acquiring multi-attitude angle data of a ship based on a nine-axis inertial measurement unit; the multi-attitude angle data comprises a rolling angle, a pitch angle and a yawing angle; establishing a Cartesian coordinate system taking the gravity center of the ship as an original point; based on the multi-attitude angle data, calculating spatial transformation of the antenna position in the Cartesian coordinate system through an automatic spatial transformation algorithm to obtain the transformed antenna position; determining the direction of the transformed antenna through vector operation by combining the initial orientation of the antenna and the position of the transformed antenna; and determining a corresponding angle through an angle resolving algorithm based on the initial antenna direction and the transformed antenna direction, matching a corresponding antenna gain in an antenna gain database based on the angle, and determining a signal strength change prediction value based on the antenna gain.

The invention discloses a full waveform decomposition method based on a multi-branch convolutional neural network, which belongs to the technical field of airborne depth sounding laser radars, is used for airborne depth sounding laser radar echo signal decomposition, and comprises the following steps: obtaining and preprocessing an original echo sequence, constructing a multi-branch one-dimensional convolutional neural network model, and performing neural network training; and based on the trained multi-branch one-dimensional convolutional neural network, outputting a three-channel probability heat map, performing multi-peak sub-pixel decoding on the three-channel probability heat map, and outputting a peak accurate position, a peak accurate position normalized value and a peak confidence after de-weighting. According to the method, the multi-branch one-dimensional convolutional neural network model is constructed and multi-peak sub-pixel decoding is carried out, so that stable decomposition of full-waveform echoes and high-precision positioning of multi-echo peak values are realized under the conditions of multi-peak superposition, high noise and weak signals, and false detection and false peaks caused by noise are remarkably reduced.

The invention belongs to the technical field of underwater acoustic engineering and digital signal processing, and particularly relates to a fractional delay FIR (Finite Impulse Response) phase calibration method and system of a multichannel sonar system. Comprising the steps of signal acquisition and reference channel selection; high-precision relative delay estimation; calculating a cross-correlation function of other channel signals and the reference channel signal, performing coarse estimation by searching a peak point of the amplitude of the cross-correlation function, and performing fine estimation on the peak position by using a parabola interpolation method to obtain the delay time of each channel relative to the reference channel; designing an FIR fractional delay filter; digital filtering and phase calibration; verifying a calibration effect; and calculating the phase of each channel signal at the target frequency point after calibration, and counting the standard deviation of each channel signal so as to evaluate the improvement degree of phase consistency. The method aims at overcoming the defect that traditional integral multiple sampling delay correction precision is insufficient, high-precision phase compensation of a sub-sampling point level is achieved, and therefore the performance of algorithms such as beam forming of a multi-channel sonar system is remarkably improved.

The invention relates to a sonar fault intelligent diagnosis method and system based on a knowledge graph and a large language model. The sonar fault intelligent diagnosis method comprises the following steps: receiving an initial fault description text input by a user through a natural language interaction interface; carrying out integrity analysis on the initial fault description text, and judging whether the initial fault description text contains all preset key information dimensions or not; if the judgment result of the integrity analysis is no, starting an interactive guide mechanism until a complete fault description text is obtained; based on the complete fault description text, performing reasoning in a pre-trained large language model configured with a sonar fault knowledge graph; and based on the conclusion of the joint reasoning, generating and outputting a structured fault diagnosis report. According to the invention, by introducing a fault description integrity evaluation and interaction guide mechanism, the problem that the system diagnosis accuracy is reduced due to fuzzy or incomplete fault description input by a user in the prior art is solved, and the accuracy and stability of overall diagnosis are improved.

The present disclosure relates to the technical field of electromagnetic wave sensors, in particular to a signal transmitting, calibration, compensation and transceiving links, an integrated circuit, an electromagnetic wave sensor and a device, wherein the transmitting link includes an analog signal source and a digital phase shifter, the analog signal source may be configured to provide an initial analog signal, and the digital phase shifter may be configured to provide a phase shift signal in a digital domain and to perform phase shifting on the initial analog signal based on the phase shift signal, so as to perform a preset phase shifting operation on the initial analog signal, thereby effectively improving the phase modulation resolution and phase modulation accuracy.

The invention discloses a transmitting power determination method and device, electronic equipment, a medium and a product, and relates to the technical field of radars. The transmitting power determination method comprises the following steps: acquiring historical time sequence characteristic information and environmental point cloud data acquired by a radar sensor, and determining target point cloud data and traffic scene perception characteristics according to the environmental point cloud data; determining a motion mode of a traffic participant in the environmental point cloud data according to the traffic scene perception feature and the historical time sequence feature information; determining participant categories of traffic participants in the target point cloud data based on a preset target classification model and the motion mode, and constructing a radar scene model according to the traffic scene perception features, the historical time sequence feature information and the participant categories; and obtaining target scene data of the radar scene model, and determining the target transmitting power of the radar sensor through the preset power determination model and the target scene data, thereby realizing adaptive determination of the target transmitting power according to the traffic scene perception characteristics and historical time sequence characteristic information.

An optical detector system providing electrical output signals. The system includes an apertured surface and a transparent window, with a metasurface on the window's opposite side. The metasurface imparts spatially varying phase delay and / or amplitude or polarization modulation to deflect ILR based on their angle of incidence, producing exiting light rays. These rays are detected by a two-dimensional tetralateral position-sensing detector (TLD) with a single resistive photo-absorption layer and four electrodes. The TLD generates X-Y position signals proportional to the azimuth and elevation of the incoming ILR. A computer may be included to calculate these angular values from the position signals.

A method includes detecting interaction by a user with an entry detector; collecting credentials from the user; and, if the credentials indicate that the user is authorized to enter, accessing a material database associating each of a set of materials with one or more corresponding resonance frequencies; and for each material of the set of materials: transmitting, into an environment associated with the user, an RF signal at a first resonance frequency for a material of the set of materials; receiving a response signal from the environment; analyzing the response signal from the environment for resonance characteristics that indicate a presence of the material; and in response to the presence of the material being indicated: determining whether an action is required for the material; and if the action is required for the material, actuating an access apparatus to perform the action.

The invention belongs to the technical field of electrical equipment detection, and discloses a defect image enhancement method fusing reasoning and generation, which integrates visible light, infrared and laser radar data through a multi-modal feature fusion network, breaks through the limitation that a contrast file CN114281093A only depends on a visible light image, and improves the detection accuracy. The dynamic attention mechanism can flexibly deploy visual, spatial and semantic feature weights according to defect types, key features can still be captured in complex environments such as strong light and shielding, and meanwhile, the spatial form of the defects is analyzed by means of three-dimensional point cloud; by means of the design, missing detection caused by insufficient characteristics of tiny parts such as hardware fittings and pins is effectively avoided. Aiming at the problem of distortion of a sample generated by a traditional data enhancement method in a comparison file, the sample quality is guaranteed through double mechanisms of reasoning constraint and physical verification, defect features output by a reasoning model directly constrain feature distribution of the generated sample, and meanwhile, a material mechanics rule is introduced to verify the physical rationality of the generated sample.

The invention discloses a radar communication radiation source identification method and system based on multi-modal feature alignment, and belongs to the technical field of electronic reconnaissance and signal processing. The method comprises the following steps: preprocessing a received radar signal to generate a standardized time-frequency graph; extracting a signal feature vector through a specially designed convolutional neural network encoder; extracting a text feature vector by using a Transform encoder; through joint optimization of cosine similarity loss and physical parameter constraint loss, alignment of signal-text features in a unified vector space is realized; and finally, zero sample identification of unknown radar signals is realized through vector similarity calculation. According to the method, the problems of low recognition rate and insufficient cross-modal information fusion in a low signal-to-noise ratio environment of a traditional method are effectively solved, and the recognition precision and the system robustness are remarkably improved.

The invention relates to the technical field of information, and discloses a radar dynamic anti-interference method and system based on interference source positioning. The method comprises the following steps: acquiring current signal data and a historical signal sequence, and determining an initial deviation value; extracting a historical deviation sequence according to the initial deviation value to obtain a deviation change trend vector; calculating a trend slope and a fluctuation amplitude, and determining an adjustment trigger signal; extracting feature interaction influence according to the adjustment trigger signal, and determining a weight coefficient update increment; iterative optimization is carried out in combination with the convergence rate parameter, and an optimized convergence rate value is obtained; adjusting deviation compensation model parameters according to the convergence speed value, and outputting a deviation compensation result when the compensation residual error is lower than a preset threshold value; and carrying out positioning calculation according to a deviation compensation result, and determining a corrected positioning coordinate. The model is dynamically optimized through technologies such as a sliding window and gradient descent, the problem of positioning deviation caused by complex environment signal interference is solved, and the positioning precision and the response speed are improved.

The invention discloses a multi-scale sea surface electromagnetic scattering calculation method and device based on marine environment elements and a storage medium, and belongs to the technical field of marine detection and physical remote sensing. The method comprises the following steps: calculating a tension wave sea spectrum according to an Elfouhail sea spectrum in combination with a Longuet-Higgins directional spectrum; a JONSWAP sea spectrum is combined with a Longuet-Higgins directional spectrum to calculate a gravity wave sea spectrum; superposing the tension wave on the gravity wave according to the slope by using a linear superposition method, and calculating a self-adaptive cut-off wave number; the contribution of small-scale waves to scattering is calculated by adopting an integral equation method, mirror scattering of large-scale waves is counted by adopting a Kirchhoff approximation method, and statistics and fusion are carried out on each scale wave according to the self-adaptive cut-off wave number, so that an electromagnetic scattering value is obtained. According to the method, a sea surface geometric calculation method based on real marine environment element driving is adopted, and accurate calculation is performed aiming at sea surface scattering characteristics under different sea conditions in combination with an integral equation method-Kirchhoff approximation electromagnetic scattering calculation model of an adaptive cut-off wave number, so that the calculation precision of scattering intensity is improved.

The invention discloses an airborne laser radar sounding data processing method and system, and relates to the technical field of marine surveying and mapping and earth observation, and the method comprises the following steps: collecting original full-waveform data of an airborne laser radar sounding system, the original full-waveform data comprising infrared laser channel data and blue-green laser multichannel data; joint denoising processing is carried out on the original full-waveform data to obtain denoised waveform data, and the joint denoising processing comprises background noise modeling and removing based on machine learning and random noise filtering based on frequency domain low-pass filtering; a collaborative joint denoising strategy is formed by fusing background noise modeling based on machine learning and frequency domain low-pass filtering based on signal-to-noise ratio optimization. According to the method, complex background noise can be accurately estimated and removed, random noise can be adaptively filtered out, weak underwater echo signals are reserved to the maximum extent, and the problem of water depth information loss caused by excessive smoothness is effectively avoided.

The invention belongs to the technical field of underwater small target object analysis and recognition methods, and particularly relates to a sound signal feature optimization method for underwater small target object analysis and recognition. Comprising the following steps: 1, acquiring and processing original data; 2, eliminating interference line spectrums, and intercepting and splicing by adopting a time window to obtain a time recombination sound signal; using range transformation to optimize signal amplitude characteristics; processing the sound signal by using an FIR digital high-pass filter; obtaining an optimized signal by using a homomorphic filter; 3, extracting structural features, energy features, frequency features and sound intensity features of the optimized digital signals; step 4, sound signal feature fusion: carrying out underwater target sound signal feature fusion processing based on a canonical correlation analysis method; the invention provides a method for extracting effective sound signal data of an underwater target under the background of optimizing a complex marine environment, enhancing signal effectiveness during long-distance detection, and constructing effective sound signal features capable of being used for classification and recognition of the underwater target at the same time.

The invention discloses a dynamic reconfigurable towed linear array sonar system and an optimization method. The system comprises a water surface mother ship, a towed cable towed by the mother ship, and a towed platform connected to the tail end of the towed cable. A sonar array storage bin and a control system are arranged in the dragging platform; a cable track is fixedly arranged on the outer surface of the dragging cable; the device further comprises a plurality of sonar sliding blocks, and the sonar sliding blocks are meshed with the cable track through sliding grooves in the sonar sliding blocks so that the sonar sliding blocks can slide along the cable track. A sonar unit is installed on the sonar sliding block. A front driving mechanism and a rear driving mechanism are arranged at the head end and the tail end of the sonar sliding block correspondingly and used for driving the sonar sliding block to ascend or descend along the cable track. According to the invention, the attitude of the towing cable is changed by actively controlling the relative spatial position of the towing platform and the mother ship, and the sonar array attached to the cable is remodeled accordingly. Finally, by monitoring acoustic performance feedback, a'perception-decision-control-optimization 'closed-loop system is formed.

The invention discloses an airborne radar system and method oriented to a signal transmitting device, and belongs to the technical field of airborne radar signal processing. Received radar task instructions are analyzed, target characteristic parameters, environment constraint conditions and performance indexes are extracted, and mapping relations between task requirements and transmitting waveforms, power and time sequence parameters are established; based on the mapping relation, the transmitting device resources are modeled into a power, frequency spectrum and time sequence three-dimensional state space, and the resource margin and the conflict risk are monitored and predicted in real time; matching a current task situation with a historical strategy library in combination with a resource state, and selecting a historical strategy with the highest similarity as a basic launching scheme; optimizing a resource allocation demand of the basic scheme according to a time sequence and a priority sequence, and introducing a resource buffer mechanism and dynamic priority adjustment to solve a multi-task resource conflict; based on real-time environment data, a basic scheme is decomposed into independently adjustable strategy units, and after optimization and recombination, a transmitting strategy adaptive to a specific scene is generated.

The invention provides a through-the-wall radar human body behavior recognition method and recognition system based on spatial-temporal characteristics, and the method comprises the steps: obtaining and processing through-the-wall radar human body behavior echo sampling signals, and obtaining a corresponding time-Doppler spectrogram as a sample set; and training a constructed TWRMama network by using the sample set, in which the network adopts a CA-Mama block as a core feature extraction module, can efficiently model and input a time sequence dynamic feature and a spatial dependency relationship of a time Doppler spectrogram, and effectively enhance the feature expression ability in a shielding scene, thereby realizing accurate recognition of human behaviors of the through-the-wall radar. Besides, information interaction between patches is introduced into a patch embedding module of the TWRMama network, so that the expression capability of the network can be further improved, and the calculation complexity of the network is effectively reduced.

The invention discloses a semi-supervised LPI radar signal modulation identification system and method based on entropy perception pseudo labels, and relates to the technical field of radar signal processing and mode identification. The system comprises a preprocessing module, a multi-scale reconstruction enhancer, a classification backbone network and a semi-supervised training module. The multi-scale reconstruction intensifier is used for reconstructing dual-channel separation through high-frequency detail enhancement and a low-frequency structure and enhancing discriminative characteristics in a noise environment; the classification backbone network introduces an adaptive contraction unit to realize channel-level noise suppression; and the semi-supervised training module dynamically evaluates the uncertainty of the unlabeled samples by adopting an entropy sensing mechanism, and generates weighted pseudo labels to carry out consistency regularization training. The method realizes signal modulation identification based on the system. According to the method, the problem of feature shielding under the condition of low signal-to-noise ratio is solved, the dependence of the model on labeled data is reduced through a reliable pseudo label generation mechanism, and stable and efficient modulation identification can still be realized in a severe channel environment with scarce labeled data.

The invention provides a high-precision angle estimation method for a distributed millimeter wave radar network, a medium and equipment, and belongs to the technical field of radar signal processing. According to the method, a large-aperture MIMO radar system is constructed based on a plurality of small millimeter wave radars, and an initial rough angle value of a target is obtained by using two-dimensional fast Fourier transform; accurately calculating the walking position of the distance spectrum peak value of each virtual antenna according to the distance resolution of the radar and the rough angle value, extracting all distance spectrum peak value data according to the walking position, and generating a one-dimensional angle vector; performing angle dimension fast Fourier transform on the one-dimensional angle vector to generate an angle spectrum; calculating a target angle estimation value according to the angle spectrum peak value position; the above process is iteratively circulated until the error between two adjacent angle estimation values is smaller than a preset threshold value. According to the method, different types of distributed millimeter wave radar network architectures can be flexibly adapted, the precision problem caused by target distance walking is effectively eliminated, and the angle estimation precision is greatly improved.

The invention relates to the technical field of radar signal processing, and discloses a complex ground reflection environment-oriented dual-path target detection method, which comprises the following steps of: performing quantile truncation and discretization processing on original range profile data, mapping a processed signal to a D-channel discrete feature space and generating a D-dimensional discrete feature matrix; the D-dimensional discrete feature matrix is input into a dual-channel parallel processing architecture, a CFAR adaptive detection module is adopted in a first path to suppress strong clutters to generate a first response matrix, and a false attention mechanism is adopted in a second path to enhance weak targets to generate a second response matrix; performing spatial alignment and weighting processing on the first response matrix and the second response matrix, and performing cross-domain confidence fusion to output a comprehensive response matrix; and carrying out fixed threshold binarization processing on the comprehensive response matrix based on a global threshold to obtain a target detection result. The method has both local gain compensation and global adaptive suppression, and can realize more robust detection of a weak target under a complex background.

The utility model provides a multichannel staggered sampling SPAD module which is arranged in a laser radar and serves as a receiving module of a machine radar, an MCU is arranged in a laser tool, the MCU is provided with an MCU interface, and the multichannel staggered sampling SPAD module comprises an SPAD unit, a TDC circuit array, a signal processing circuit and a coding circuit; wherein the work cycles of the TDC circuits in the TDC circuit array are sequentially arranged in a staggered manner, so that time of the SPAD units is sampled in a staggered manner, a delay chain is formed, the TDC circuit array can output a second value once every a fixed time length, and the fixed time length is smaller than the work cycle of one TDC circuit. After the technical scheme is adopted, the upper limit of the signal transmission rate can be greatly improved, so that the sampling efficiency is improved, and stronger scanning performance is realized.

The invention relates to the technical field of radar testing, in particular to a radar testing system and method based on multi-mode dynamic reconstruction. The system comprises a multi-mode radio frequency link dynamic reconstruction module, a baseband signal processing module, a space positioning module, an intelligent diagnosis and visualization module and a closed-loop crosslinking control module. According to the invention, integration of three working modes of target simulation, power measurement and T / R assembly test is realized through multi-mode radio frequency link dynamic reconstruction, automatic identification and diagnosis of T / R assembly faults are realized through the intelligent diagnosis and visualization module, and the test precision is ensured through high-precision space positioning. Seamless switching among the modes is realized through closed-loop cross-linking control, and the efficiency and precision of radar testing are remarkably improved.

The invention provides a motion compensation method and device for passive sonar detection. The compensation method comprises the following steps: acquiring underwater acoustic signals acquired by a passive sonar in real time within a preset period; performing segmentation processing and Fourier transform on the underwater acoustic signal to obtain an underwater acoustic frequency domain signal segment; constructing a parameterized search matrix of each water audio frequency domain signal segment, and determining a motion compensation parameter of each water audio frequency domain signal segment through similar energy spectrum search; based on the motion compensation parameter, performing phase compensation on the water audio frequency domain signal segment to obtain a compensation frequency domain signal segment; and performing inverse discrete Fourier transform on the compensation frequency domain signal segment to obtain a compensation time domain signal segment so as to determine a long-time coherent integration result corresponding to the underwater acoustic signal. Through the method, the gain of passive sonar detection processing is improved under the condition of low signal-to-noise ratio, and the accuracy and stability of passive sonar detection are improved.

The invention discloses a cognitive interference decision-making method based on deep reinforcement learning and an application system of the cognitive interference decision-making method, which are suitable for an interference unmanned aerial vehicle to complete an intelligent interference decision-making task when resisting a networking radar system in a complex and changeable electromagnetic environment. In order to improve the performance of an existing interference decision-making system in the aspects of sample efficiency and strategy stability, the interference decision-making algorithm DAR-PPO comprises two major innovation modules: one module is a KTD-Clip module which constructs a feedback signal based on experience KL divergence and dynamically adjusts a clipping threshold value in strategy updating so as to control the strategy updating step length, so that the strategy updating step length is always in a stable and effective trustworthiness region; the adaptive capacity of the algorithm to environment change is obviously improved; the second module is an RA-IS module, effective reuse of historical interaction samples is achieved by constructing a trajectory playback pool and introducing clipping importance sampling, meanwhile, a weight entropy regularization mechanism is designed, strategy fluctuation caused by abnormal weights is restrained, and therefore the training sample utilization rate is remarkably increased, and the algorithm robustness is enhanced.

An exemplary embodiment of the invention relates to a radar system comprising a radar transmission unit (200), a radar reception unit (300), and a central unit (100) which is optically connected to the radar transmission unit (200) and the radar reception unit (300), wherein at least one of said units comprises a signal separator (500) configured to separate a first signal from a second signal, said first and second signal being comprised in a respective optical carrier signal that said at least one unit receives, the signal separator (500) outputting the first signal at a first electrical output port (P1) and the second signal at a second electrical output port (P2), wherein the signal separator (500) comprises a photodetector (510) configured to receive the respective optical carrier signal and generate a photocurrent (Ip), and wherein the signal separator (500) is configured to extract both the first signal and the second signal from said same photocurrent (Ip) in the electrical domain.