Cross-correlation Characteristics Research Articles

Orbital-angular-momentum-dependent speckles for spatial mode sorting and demultiplexing

Characterizing the orbital-angular-momentum (OAM) modes is critically important for OAM-encoded data transfer. However, traditional characterizing approaches rely on either complex and precise experimental configurations or complicated prior information processing. In these scenarios, the correlation features of OAM-dependent speckles from the scattering effect have received little attention. Here, we investigate the cross-correlation characteristics of the OAM speckles resulting from a scattering medium and propose an appealing alternative for spatial mode sorting and demultiplexing based on the OAM-dependent speckles. We demonstrate theoretically and experimentally that the cross-correlation operation between two different OAM-dependent speckles can uniformly derive an annulus pattern, whose dimension is determined by the absolute topological charge difference value between the two corresponding OAM modes. Based on this mechanism, the original coherent OAM modes can be easily sorted by implementing the cross-correlation operation between incoherently measured OAM-dependent speckles. To fully capitalize on the orthogonal feature of the OAM-dependent speckles, we also construct OAM mode demultiplexing experiments using a ground glass diffuser, where both 8-bit grayscale and 24-bit RGB OAM-encoded data demultiplexing are successfully demonstrated with superior error rates. Results show that the OAM-dependent speckles, previously treated as a nuisance for practical applications, can be surprisingly utilized as a competitive candidate for OAM mode sorting and demultiplexing, thus opening new directions in optical communication and information processing.

Measurement-based fading characterization of multi-link UAV to mobile vehicle channel

To establish a stable, effective and reliable communication systems between unmanned aerial vehicle (UAV) and mobile vehicles, the propagation characteristics of air-ground (AG) wireless channels need to be better understand. In this paper, a narrowband multi-link channel measurement for UAV to mobile vehicles at 2.4 and 5.9 GHz is presented. Based on the measured data, some essential channel characteristics such as large-scale fading (LSF) including path loss, shadow fading (SF), and small-scale fading (SSF) including fading depth (FD), magnitude distribution and Rician distribution K-factor are analyzed. Then the cross-correlation characteristics of different channel parameters and receiving antenna numbers at 2.4 GHz and 5.9 GHz are systematically investigated. These researches provide some references to optimize antenna design and multi-link channel modeling of UAV to mobile vehicle communication systems in future intelligent transportation system (ITS).

Cluster-based sensor selection framework for acoustic emission source localization in concrete

The acoustic emission (AE) method can determine the location of damage initiation and progression in large-scale structures using an array of sensors. As the location accuracy depends on proper identification of time of arrival and wave velocity, the method is more successful in homogeneous and isotropic materials than in heterogeneous materials such as concrete. The heterogeneity causes dispersive and attenuative properties such that the source-sensor distance and angle control the AE signal characteristics influencing the source location accuracy. Generally, using more than the minimum number of sensors in the location algorithm results in more accurate source localization. If the AE signal of a sensor is significantly different from the other sensors, its time of arrival may not contribute beneficially to the source localization algorithm. On the contrary, this may increase errors in determining arrival time due to signal distortion caused by dispersion, low signal-to-noise ratios resulting from attenuation, or sensors being occupied with detecting nearby noise signals. In this paper, a new cluster-based sensor selection framework is developed for selecting the best sensor combinations before applying the source localization algorithm. The framework involves selecting the best combination of sensors to input into the source localization algorithm based on the cross-correlation characteristics of signal features for single AE events or cluster analyses for large datasets. Both approaches identify the signal similarity of sensors to be used in the location sensor group. As two-dimensional source localization requires a minimum of three sensors, the number of sensor outputs extracted from the sensor selection framework is bounded by three. The source localization accuracy is evaluated using the data collected impact excitation with the known source location and structural tests of a Basalt Fiber-Reinforced Polymer (BFRP) reinforced concrete slab. The impact experiments show that the new framework to select the best three-sensor combination increases the localization accuracy to 93%, unlike the results obtained by using all six sensors (80%) and the first three sensors in the hit sequence (75%). The framework is applied to the AE data recorded from actual damage initiation and progression in a simply supported BFRP-reinforced concrete slab. The cluster of events accumulates more densely at the mid-span, where the crack initiation is validated with crack sensors and images. It is demonstrated that selecting the sensors for two-dimensional source localization using similarity analyses improves the accuracy of the source location in concrete.

Electro-optical chaotic communication system based on additional chaotic phase-shift with time-delay signature concealment

We propose and demonstrate an electro-optical chaotic communication system with additional chaotic phase-shift based on the mutual coupling structure. Under the condition of complete concealment of time-delayed signatures (TDSs), the key space of the system is greatly enhanced and highly reliable chaotic communication is finally realized. The additional-phase introduced by the new branch is nonlinearly superimposed on the phase generated by the chaotic signal of the original branch, which improves the complexity and aperiodicity of chaos. For security performance, the system only needs a very small feedback gain (i.e., βi = 2.5) to conceal TDSs. Moreover, since the system has only one chaotic signal transmitted in the transmission link, it overcomes the exposure of TDSs caused by the cross-correlation characteristics between different links. At the same time, the system has strong synchronization robustness and can achieve high-quality communication when the parameters are well-matched. To sum up, the proposed system can effectively resist eavesdropping, which provides security for the physical layer of optical networks.

Response of cross-correlations between high PM2.5 and O3 with increasing time scales to the COVID-19: different trends in BTH and PRD

The air pollution in China currently is characterized by high fine particulate matter (PM2.5) and ozone (O3) concentrations. Compared with single high pollution events, such double high pollution (DHP) events (both PM2.5 and O3 are above the National Ambient Air Quality Standards (NAAQS)) pose a greater threat to public health and environment. In 2020, the outbreak of COVID-19 provided a special time window to further understand the cross-correlation between PM2.5 and O3. Based on this background, a novel detrended cross-correlation analysis (DCCA) based on maximum time series of variable time scales (VM-DCCA) method is established in this paper to compare the cross-correlation between high PM2.5 and O3 in Beijing-Tianjin-Heibei (BTH) and Pearl River Delta (PRD). At first, the results show that PM2.5 decreased while O3 increased in most cities due to the effect of COVID-19, and the increase in O3 is more significant in PRD than in BTH. Secondly, through DCCA, the results show that the PM2.5-O3 DCCA exponents α decrease by an average of 4.40% and 2.35% in BTH and PRD respectively during COVID-19 period compared with non-COVID-19 period. Further, through VM-DCCA, the results show that the PM2.5-O3 VM-DCCA exponents [Formula: see text] in PRD weaken rapidly with the increase of time scales, with decline range of about 23.53% and 22.90% during the non-COVID-19 period and COVID-19 period respectively at 28-h time scale. BTH is completely different. Without significant tendency, its [Formula: see text] is always higher than that in PRD at different time scales. Finally, we explain the above results with the self-organized criticality (SOC) theory. The impact of meteorological conditions and atmospheric oxidation capacity (AOC) variation during the COVID-19 period on SOC state are further discussed. The results show that the characteristics of cross-correlation between high PM2.5 and O3 are the manifestation of the SOC theory of atmospheric system. Relevant conclusions are important for the establishment of regionally targeted PM2.5-O3 DHP coordinated control strategies.

THE NEXUS BETWEEN TWITTER-BASED UNCERTAINTY AND CRYPTOCURRENCIES: A MULTIFRACTAL ANALYSIS

We take the novel Twitter-based economic uncertainty (TEU) to examine if it has cross-correlation characteristics with four major cryptocurrencies i.e. Bitcoin, Ethereum, Litecoin, and Ripple. To conduct a more thorough analysis, we apply multifractal detrended cross-correlation analysis (MFDCCA) on seasonal-trend decomposition using Loess (STL) decomposed series as well as without decomposed series on the daily data, ranging from 1 June 2011 to 30 June 2021. The findings of this study indicate that: (i) all pairs of TEU with cryptocurrencies are multifractal and have power-law behavior; (ii) the pairs of Ethereum and Bitcoin with TEU are found to be the most multifractal while Litecoin with TEU has the lowest multifractal characteristics; (iii) all STL decomposed series of cryptocurrency have persistent cross-correlation with TEU with the exception of Ethereum which has anti-persistent cross-correlation with TEU; (iv) all without decomposed series of cryptocurrencies show significant persistent cross-correlation characteristics with TEU; (v) the highest linkage is found for the pair of Bitcoin with TEU. Moreover, to reveal the dynamic characteristics in the cross-correlation of TEU with cryptocurrencies, the rolling window is employed for MFDCCA. These findings have important managerial and academic implications for policymakers, investors, and market participants.

Covariance-Based Activity Detection With Orthogonal Pilot Sequences for Cell-Free Distributed Massive MIMO Systems

The grant-free random access (GFRA) has been the mainstream access scheme in the massive ultra-reliable low-latency communications (mURLLC). Device activity detection is the foremost issue in GFRA. Most of previous works assume unique non-orthogonal pilot sequence assignment in device activity detection which brings about severe inter-device interference and requires high-complexity access point (AP) coordination to eliminate interference. Taking advantage of the good cross-correlation characteristic of orthogonal pilot sequences and the sparsity in power domain of cell-free (CF) distributed massive multiple-input multiple-output (mMIMO) systems, this paper proposes a novel orthogonal pilot sequences based activity detection (OPSAD) algorithm to alleviate inter-device interference and thus improve device activity detection performance. In the proposed OPSAD algorithm, the min-max covariance interference (MMCI) pilot assignment algorithm considering AP selection and power control is introduced and a new interference metric is defined to efficiently capture the inter-device interference in this algorithm. Simulation results show that the proposed algorithm reduces computational complexity and achieves better performance than the benchmark algorithm utilizing non-orthogonal pilot sequences in practical mURLLC scenario.

Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features

Aiming to solve the problem of the distinction of scrambled linear block codes, a method for identifying the scrambling types of linear block codes by combining correlation features and convolution long short-term memory neural networks is proposed in this paper. First, the cross-correlation characteristics of the scrambling sequence symbols are deduced, the partial autocorrelation function is constructed, the superiority of the partial autocorrelation function is determined by derivation, and the two are combined as the input correlation characteristics. A shallow network combining a convolutional neural network and LSTM is constructed; finally, the linear block code scrambled dataset is input into the network model, and the training and recognition test of the network is completed. The simulation results show that, compared with the traditional algorithm based on a multi-fractal spectrum, the proposed method can identify a synchronous scrambler, and the recognition accuracy is higher under a high bit error rate. Moreover, the method is suitable for classification under noise. The proposed method lays a foundation for future improvements in scrambler parameter identification.

THE STUDY OF THE CROSS-CORRELATION PROPERTIES OF COMPLEX SIGNALS ENSEMBLES OBTAINED BY FILTERED FREQUENCY ELEMENTS PERMUTATIONS

Context. The relevance is to study the cross-correlation properties of the developed complex signals ensembles of large volume with a low level of multiple access interference, thereby increasing the efficiency of using a limited radio frequency range. The Object of Research is a method of bandpass filtering with permutations, which allows forming complex signals ensembles of large volume. Objective. The Objective is to determine the optimal cross-correlation properties for the formation of complex signals ensembles of large volume with a low level of multiple access interference. Method. The work has the study results of cross-correlation properties of complex signals ensembles obtained by applying the filtered elements permutation method. The formation of complex signals ensembles is based on pseudo-random sequences with improved cross-correlation properties in the time domain. Bandpass filtering is applied to such sequences, and the number of filter bands is determined based on the calculation of the frequency spectrum utilization coefficient. The filter band optimal width determination is based on a comparison of the maximum emissions of the side lobes values of the of cross-correlation function of signals from the elements number in the involved sequences. The signals obtained by frequency bands allocating are characterized by a difference in form in the minimal similarity condition. In order to reduce the multiple access interference impact, the frequency components transfer obtained by spectral filtering to the common frequency range is carried out. After that, the signals are transferred using the full search method. As a result, it was obtained all possible combinations of signal pairs permutations. The use of permutations in the complex signals ensemble formation can significantly increase the ensemble volume. The signals generated by frequency filtering, to which the transfer to the common frequency band and their subsequent permutation was applied, are subjected to correlation analysis based on the calculation of the maximum emissions values of the side lobes of the cross-correlation function. Comparative characteristic of cross-correlation properties of developed signals with known signals prove that signals generated based on pseudo-random sequences with improved cross-correlation properties have a much larger ensembles volume, are formed on the basis of simple algorithms that don’t require significant computing resources and have satisfactory cross-correlation characteristics. The use of bandpass filtering method with permutation allows the formation of large-volume ensembles whose signals differ inform, and the combination of different frequency bands reduces the vulnerability to multiple access interference. Results. Due to the software implementation of the bandpass filtering method with permutations, the comparison of crosscorrelation properties of nonlinear sequences, M-sequences, multiphase signals and developed signals based on sequences with improved cross-correlation properties was performed. In estimating the levels of maximum emissions of side lobes of the cross-correlation function, it was found that the generated signals obtained by bandpass filtering with permutations deteriorate cross-correlation characteristics by increasing the pulse duration proportional to the decrease in signal frequency band, but their value satisfies the minimal similarity condition, used in cognitive telecommunications systems. Conclusions. The study of the signals cross-correlation properties proves the effectiveness of the developed bandpass filtering with permutations method. The generated signals have cross-correlated characteristics no worse than ensembles based on known signals. At this level of maximum emissions of the side lobes of the cross-correlation function of the developed signals is 7–12% less than the known signals. Thus, the method of bandpass filtering with permutations can be used to increase the efficiency of radio frequency resource use of both existing and advanced cognitive telecommunication networks of wireless access based on systems with code division multiplexing.

Deep Multi-Similarity Hashing with semantic-aware preservation for multi-label image retrieval

Recently, many deep supervised hashing has been developed for multi-label image retrieval applications and has already achieved good effects. However, current methods quantify the similarities between image pairs by counting the number of shared labels roughly, totally ignoring the semantic relationships of image pairs. Besides, the sample imbalance of different classes could bias the training process toward majority categories, deteriorating the model performance. To solve the above-mentioned problems, we propose a novel deep supervised hashing framework, named Deep Multi-Similarity Hashing with semantic-aware preserving (DMSH), to generate binary codes with high-level semantic similarity. Specifically, to obtain distinguishing features, Convolutional Neural Networks (CNN) with correlation operators are introduced to capture discriminative semantic features of the input images, including auto-correlation characteristics and cross-correlation characteristics. By jointly exploiting label-level and semantic-level similarity, the semantic-aware similarity module is developed to quantify the high-level semantic similarity of image pairs, distinguishing the differences beyond categories. Furthermore, the multi-similarity loss is incorporated into deep hashing to collect informative pairs efficiently, which could alleviate the impact of imbalanced sample pairs during model training. Extensive comparison experiments on three benchmark datasets demonstrate that our DMSH achieves promising performance with respect to different evaluation metrics.

Pulsed-chaos MIMO radar based on a single flat-spectrum and Delta-like autocorrelation optical chaos source.

We propose and demonstrate a pulsed-chaos multiple-input-multiple-output (MIMO) radar system in this paper. In the proposed MIMO radar system, multi-channel pulsed chaotic signals are extracted from an optical seed chaos source with Delta-like autocorrelation and flat spectrum. The seed chaos source is generated by passing the chaotic output of an external-cavity semiconductor laser through a dispersive self-feedback phase-modulation loop and used for MIMO radar signal generation. The cross-correlation characteristics of MIMO radar signals, the maximum channel number of separable mixed echoes, as well as the performances of multi-target ranging and anti-interference in the proposed pulsed-chaos MIMO radar system are systematically investigated. The results indicate that multi-channel pulsed-chaos signals with Delta-like autocorrelation can be simultaneously generated from the seed chaos source, and excellent quasi-orthogonality of transmission radar signals can be guaranteed. Moreover, it is demonstrated that the proposed pulsed-chaos MIMO radar supports multi-target ranging with a centimeter-level resolution and can maintain satisfactory performance under low SNR scenarios with various interferences.

A Subspace Projection Approach for Clutter Mitigation in Holographic Subsurface Imaging

The holographic subsurface radar (HSR) is recognized as an effective remote sensing modality for the detection of shallowly buried objects with a high-resolution image in plain view. However, subsurface detection with HSR is prone to be impaired by clutter contamination, which often obscures the target response. In this letter, a novel clutter mitigation method combining singular value decomposition (SVD) and response cross correlation analysis is presented. The proposed method first applies SVD to decompose the radar data matrix to a number of singular components. Furthermore, the signal cross correlation characteristics are analyzed to demonstrate that the variance of left singular vectors is directly proportional to the target proportion in radar data. Then, target and clutter subspaces can be identified by maximizing the defined weighted target-to-clutter ratio (WTCR). Results of numerical simulation and laboratory experiments corroborate the effectiveness of the proposed method in reducing clutter while preserving the target image.

MULTIFRACTAL CROSS-CORRELATION ANALYSIS BETWEEN CARBON SPOT AND FUTURES MARKETS CONSIDERING ASYMMETRIC CONDUCTION EFFECT

The cross-correlation between carbon spot and futures markets reflects the risk conduction mechanism between the two markets. Deeply depicting and analyzing this risk conduction mechanism is of great significance for investors to carry out risk management strategies. Considering the nonlinear and asymmetric characteristics of cross-correlation between carbon spot and futures markets, this paper applies multifractal cross-correlation analysis method to investigate the cross-correlation between carbon spot and futures markets. Firstly, through Empirical Mode Decomposition (EMD)-Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) detection, it is found that there is an obvious cross-correlation between carbon spot and futures markets, and the cross-correlation has multifractal characteristic. Secondly, by using EMD-time delay-DCCA method, we find the conduction direction between carbon spot and futures markets is bidirectional, and the futures market has a greater impact on the spot market in the short term. Thirdly, through using EMD-MF-ADCCA method, we find the cross-correlation between the two markets is asymmetric, and the cross-correlation between the two markets is more significant when carbon market is in a downward trend than in an upward trend. Fourthly, through constructing EMD-time delay-ADCCA model, we find that there is a two-way asymmetric conduction effect between carbon spot and futures markets when the lag period is short, and when the carbon market is in a downward trend, the conduction effect between the two markets is stronger. However, with the extension of time lag, the conduction effect of the two markets no longer presents obvious asymmetric characteristics.

Multifractal Fluctuation Analysis of Correlations between Agricultural Futures Markets in China and the US Based on MF-X-DFA and MF-DPXA Methods

The fluctuation characteristics of the correlations between China and the US agricultural futures markets have attracted extensive attention from academic circles and government departments. As the main factor that affects the agricultural futures price, the impact of international crude oil futures price on the correlations of the Sino-US agricultural futures markets is also worth discussing. Therefore, this paper adopts the multifractal detrended cross-correlation analysis (MF-X-DFA) and multifractal detrended partial cross-correlation analysis (MF-DPXA) to explore the fluctuation characteristics of cross-correlations for China and the US agricultural futures markets before and after removing the West Texas Intermediate (WTI) crude oil futures price as well as the impact on the cross-correlations. The results show that the fluctuation characteristics of the cross-correlations and partial cross-correlations between the corresponding varieties of China and the US agricultural futures markets as well as among the varieties within the markets are multifractal. The cross-correlation behaviors and the cross-market risks are all affected to varying degrees by the West Texas Intermediate (WTI) crude oil futures. The West Texas Intermediate (WTI) crude oil futures weaken the cross-market risk of the Sino-US soybean futures, while strengthening the cross-market risk of the Sino-US corn and wheat futures. In addition, the impact of the West Texas Intermediate (WTI) crude oil futures on the cross-market risks among China agricultural futures is greater than those among the US corresponding agricultural futures.

Information Security of New Media Art Platform of Distributed System Based on Blockchain Technology

New media art is different from ready-made art, installation art, body art, and land art. New media art is a new art subject category with “optical” media and electronic media as the basic language. And, because of the continuous development of computer networks, distributed database management systems are becoming more and more popular. This article aims to study the information security control problem in the distributed new media system. Through comprehensive access control, reliable support, and many-to-many random mutual encryption, it solves the security requirements of supporting mobile computing in a distributed network environment, communication, and hierarchical grouping. Group key management and other issues have studied some key security technologies for building secure distributed information systems. This article proposes covering the behavior-based access control model ABAC (operation-based access control model), the architecture of the new Trusted Platform Module, the architecture of multithreaded crypto chips, and their service methods, as well as distributed information systems and key management solutions. Approximately obeying the chi-square distribution with 2 degrees of freedom, when the significance level is taken as 5%, the chi-square value for the skin is 5.99. Different initial values are selected for the chaotic sequence, 200 groups of chaotic sequences with a length of 2000 are selected for sequence inspection, and the pass rate is 97.5%. It can be seen that the autocorrelation and cross-correlation characteristics of the improved spatiotemporal chaotic sequence are still relatively ideal, so the usability of the platform is relatively high.

Millimeter-wave channel simulation algorithm with consistency based on spatial recursion

Based on the Bird’s Nest 28 GHz channel measurement data, this paper proposes a spatially consistent channel large-scale parameter generation algorithm based on spatial recursion, and compares it with the traditional WINNER II algorithm. This algorithm uses multi-dimensional normal distribution and specific recursion algorithm and generates the channel large-scale parameters of each simulation point sequentially, to realize the auto-correlation and cross-correlation characteristics of the parameters. The results show that, on the premise of achieving spatial consistency, the simulation results of the spatial recursive parameter generation algorithm are highly consistent with the measured data, the simulation accuracy of this algorithm is better than the WINNER II algorithm. Compared with the WINNER II algorithm, the spatial recursive parameter generation algorithm does not need to generate the parameter cross-correlation characteristics separately, which simplifies the simulation steps. The spatial recursive parameter generation algorithm provides a way for the research of related algorithms. This new idea is of great significance for millimetre-wave time-varying channel simulation.

Spurious North Tropical Atlantic precursors to El Ni\xf1o

The El Niño-Southern Oscillation (ENSO), the primary driver of year-to-year global climate variability, is known to influence the North Tropical Atlantic (NTA) sea surface temperature (SST), especially during boreal spring season. Focusing on statistical lead-lag relationships, previous studies have proposed that interannual NTA SST variability can also feed back on ENSO in a predictable manner. However, these studies did not properly account for ENSO’s autocorrelation and the fact that the SST in the Atlantic and Pacific, as well as their interaction are seasonally modulated. This can lead to misinterpretations of causality and the spurious identification of Atlantic precursors for ENSO. Revisiting this issue under consideration of seasonality, time-varying ENSO frequency, and greenhouse warming, we demonstrate that the cross-correlation characteristics between NTA SST and ENSO, are consistent with a one-way Pacific to Atlantic forcing, even though the interpretation of lead-lag relationships may suggest otherwise.

Performance of Improved-DCSK system over land mobile satellite channel under effect of time-reversed chaotic sequences

Among non-coherent chaos-based communication schemes, improved-differential chaos-shift keying (I-DCSK) with the use of time-reversed chaotic sequences shows good achievement in bit-error-rate (BER) performance and spectral efficiency. To date, BER performance of the I-DCSK system has been investigated over various transmission channels in the presence of Gaussian noise and Rayleigh fading. In this paper, we studied for the first time the performance of the I-DCSK system over a land mobile satellite channel. Discrete-time models of the transmitter and receiver over a two-state Lutz channel typically describing satellite environments are studied. We point out that there are differences in cross-correlation characteristics between the time-reversed and conventional chaotic sequences. From this aspect, BER performance of the studied system is analyzed in scenario of taking into account the cross-correlation effect of time-reversed chaotic sequences. BER expressions are verified by Monte Carlo simulations. Performance comparison between I-DCSK and DCSK-based schemes over the studied channel is carried out. Obtained results prove that the I-DCSK system is able to be used as a physical-layer modulation method using chaos for land mobile satellite applications.

A GPS signal acquisition algorithm for the high orbit space

In the high orbit space, the visibility of GPS main lobe signal is poor due to the earth's occlusion and the increase in signal transmission distance. Therefore, the side lobe signals are essential for navigation mission, which puts forward higher requirements for the sensitivity of the receiver. In addition, the cross-correlation interference (CCI) caused by the large power difference between the main and side lobe signals is serious and results in the false acquisition of the side lobe signals. To improve the acquisition sensitivity and stability of the receiver, we designed a GPS signal acquisition algorithm for the high orbit space. The characteristics of high orbit GPS signal are analyzed according to the propagation process of GPS signal link. The influence of CCI on the acquisition performance of the side lobe signals is explored. Based on the parallel code phase search method, the coherent accumulation and incoherent accumulation are combined to improve the acquisition sensitivity. At the same time, according to the different probability distribution characteristics of autocorrelation peak and cross-correlation peaks in the correlator output, CCI is detected by means of hypothesis testing. The strong signals are reconstructed by using the amplitudes, code phases and Doppler shifts provided by the tracking loop. The CCIs are estimated by correlating the reconstructed strong signals with the local duplicated signals and eliminated from the correlation results of the side lobe signals to obtain the correct acquisition results. Simulation results show that the proposed algorithm can eliminate the CCIs in the high orbit space and improve the acquisition sensitivity of the receiver. Moreover, the CCIs under different Doppler shift differences of the main and side lobe signals can be effectively eliminated.

Dual-Use Signal Design for Radar and Communication via Joint Orthogonal Signal and Phase Modulation

This paper proposes a new information modulation resorting to orthogonal signal and its phase for dual-function radar communication (DFRC) systems. Focusing on the standardized linear frequency modulation (LFM) signal by additional phase, a bank of signals enjoying satisfactory autocorrelation and cross-correlation characteristics, are generated. Then, these signals map the different information as well as their phases are also modulated to increase the communication bit rate, thus yielding a series of dual-use signals. Finally, the radar detection and communication performance of dual-use signals are also provided through numerical simulation and half-physical platform verification, confirming the effectiveness of the designed signals compared with the existing design strategy.