Conventional Matched Filter Research Articles

Analysis of Hybrid Spectrum Sensing for 5G and 6G Waveforms

More spectrum bands are needed as the number of wireless applications rises. The spectrum band, though, is now very difficult to adapt to new applications. Because of this, the spectrum is getting more crowded, which also affects quality of service (QoS). One of the most promising technologies to address the issue of spectrum scarcity is cognitive radio (CR). Spectrum sensing (SS) is thought to be essential to CR. It determines that when primary users (PUs) are not using the spectrum, the spectrum can be allocated to secondary users (SUs). In this paper, a novel 5G spectrum sensing technique was implemented using a hybrid matched filter (HMF) algorithm based on the fusion of two matched filters (MF). In addition, we compared the performance of the HMF and traditional MF in Rayleigh and Rician channels. It has been observed that the HMF performs more effectively than the conventional MF in both channels.

Neural-network-based direct waveform to symbol conversion for joint ISI and ICI cancellation in non-orthogonal multi-band CAP based UDWDM fiber-mmWave integration.

The six-generation mobile network (6G) based on millimeter-wave (mmWave) is expected to deliver more capacity and higher connection density compared with 5G. We demonstrate an ultra-dense wavelength division multiplexing (UDWDM) fiber-mmWave integration network based on non-orthogonal multiband carrier less amplitude and phase (NM-CAP) modulation to address the needs for dense access cells, high-spectral efficiency, and high data rate. We demonstrate a neural-network-based waveform to symbol converter (NNWSC), which can directly convert the received NM-CAP waveform into quadrature amplitude modulation (QAM) symbols to simultaneously handle the inter-symbol interference (ISI) and inter-channel interference (ICI), without the need for conventional matched filters and additional ISI and ICI equalizers. Experimental results show that this method is also effective for QAM constellations with probabilistic shaping. Since NNWSC simplifies the demodulation process of NM-CAP and avoids error accumulation caused by cascading filters and post-equalizers, NNWSC can reduce the computational complexity and provide good performance. Compared with the regular receiver with cascaded least mean square equalizer, matched filters, and ICI equalizer, NNWSC can reduce the computational complexity by 93%. The demonstrated spectrally efficient fiber-mmWave transmission is achieved at a total 414-Gbps net data rate with 24 PS-QAM NM-CAP sub-bands on 8 UDWDM channels with 25-GHz spacing.

Radar Pulse Compression with Optimized Weighting Window for SAR Receivers

This paper proposes a novel design of a software-defined matched filter (MF) for digital receivers of synthetic aperture radar (SAR). The block diagram of the proposed receiver is described in detail. The purpose of this filter is to produce a SAR pulse with higher compression ratio (CR) and lower side lobe level (SLL) than that produced by the conventional MF. The proposed design is based on the idea of time windowing of the SAR pulse to construct the transfer function of the receiver filter. The shape of the proposed time-domain window is optimized to achieve the filter design goals including the minimization of the SLL and the realization of the target value of the CR. The transmitted SAR pulse is, first, subjected to linear frequency modulation and then subjected to the optimized window. The width (time duration) of the proposed window is divided into equal time intervals. The proposed time-domain window is constructed as a sequential continuous piecewise linear segments. The instantaneous value of the time-domain window at the start of each time interval is optimized so as to achieve the optimization goals. The width of the time-domain window is shown to be proportional to the width of the compressed pulse after optimization. The number of the time intervals into which the time duration of the window is divided is shown to have a significant effect on the optimization results. The particle swarm optimization (PSO) technique is then applied to get the window shape that minimizes the SLL for a specific predetermined value of the pulse CR. It is shown that the iterations of the PSO are fastly convergent and that the applied algorithm is computationally efficient. Also, it is shown that the desired value of the pulse CR is achieved with accuracy of 100%. Moreover, the achieved SLLs are about − 65,mathrm{dB}, - 90,mathrm{dB}, - 114,mathrm{dB}, and - 133,mathrm{dB} for pulse CR of 5, 3, 2, and 1.5, respectively. Finally, for practical implementation of the introduced SAR pulse processing technique, the proposed optimized window is placed as a building block in a software-defined receiver of the SAR system.

Indoor Object Sensing Using Radio-Frequency Identification With Inverse Methods

Indoor device-free object sensing can be utilized in many applications such as occupant-centered control of building utilities and assisted living. To satisfy the mathematical requirement of many observation points for arbitrary indoor layout and furnishing, radio-frequency identification (RFID) offers a low-cost solution with a plethora of maintenance-free passive tags. Both the received signal strength indicator and carrier phase from tag backscattering can be assembled to generate the voxel reflectivity distribution by the inverse method. We adopt the regularized truncated pseudo-inverse method, and devise the strategies for optimal selection of the critical parameters. Compared with conventional matched filtering, our method is more robust against random noises in the collected data. An experimental prototype was established to evaluate the system robustness and performance, and the dipole antennas were used to replace patch antennas to enhance the system signal-to-noise ratio (SNR). The regularized truncated pseudo-inverse method together with the improved system SNR has successfully shown higher locating accuracy and lower computational cost.

RMIST-Net: Joint Range Migration and Sparse Reconstruction Network for 3-D mmW Imaging

Compressed sensing (CS) demonstrates significant potential to improve image quality in 3-D millimeter-wave imaging compared with conventional matched filtering (MF). However, existing sparsity-driven 3-D imaging algorithms always suffer from large-scale storage, excessive computational cost, and nontrivial tuning of parameters due to the huge-dimensional matrix–vector multiplication in complicated iterative optimization steps. In this article, we present a novel range migration (RM) kernel-based iterative-shrinkage thresholding network, dubbed as RMIST-Net, by combining the traditional model-based CS method and data-driven deep learning method for near-field 3-D millimeter-wave (mmW) sparse imaging. First, the measurement matrices in ISTA optimization steps are replaced by RM kernels, by which matrix–vector multiplication is converted to the Hadamard product. Then, the modified ISTA optimization is unrolled into a deep hierarchical architecture, in which all parameters are learned automatically instead of manually tuned. Subsequently, 1000 pairs of oracle images with randomly distributed targets and their corresponding echoes are simulated to train the network. A well-trained RMIST-Net produces high-quality 3-D images from range-focused echoes. Finally, we experimentally prove that RMIST-Net is capable process <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$512 \times 512$ </tex-math></inline-formula> large-scale imaging tasks within 1 s. Besides, we compare RMIST-Net with other state-of-the-art methods in near-field 3-D imaging applications. Both simulations and real-measured experiments demonstrate that RMIST-Net produces impressive reconstruction performance while maintaining high computational speed compared with conventional and sparse imaging algorithms.

Under-ice target echo time delay estimation using [formula omitted]-norm based matched filter

In the ice-covered underwater environment of the Arctic area, impulsive noise is one of the most challenging problems for target echo time-delay estimation (TDE) and severely degrades the performance of some so-called traditional TDE algorithms. In this work, we propose a TDE approach called “ℓp-norm-based correlation,” which is explored for use in active sonar systems in an impulsive environment. The ℓp-norm-based optimization is presented here to improve the robustness, and we introduce the Toeplitz matrix to formulate the received signal. The different columns of the desired matrix represent the various time-delay replicas of the transmitted signal, and the global minimizer can be found by rewriting the optimization problem. Thus, all global minimizers are efficiently solved simultaneously. The proposed TDE technique can enhance time-delay resolution and provide reliable estimation results compared with conventional matched filtering and other competing approaches. Furthermore, we show that the performance of the resulting method can be verified by the results of numerical simulation and by comparison with the experimental data collected from the 11th Chinese National Arctic Research Expedition.

IRCI‐free multiple subband MIMO SAR

An inter-range cell interference (IRCI)-free multiple subband multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) algorithm is proposed to obtain a high-resolution wide swath (HRWS) imaging. The received signals of the proposed configuration are formulated as multiple-input single-output (MISO) system identification problems using the principle of the displaced phase centre in a way that all the subband waveforms are processed simultaneously without the need to separate them at the receiver. This would maximise the bandwidth utilisation efficiency. The channel impulse response in the range dimension is identified using a frequency domain system identification--based estimation algorithm, which is free from IRCI, instead of using conventional matched filters. A pulse repetition frequency lower than the Doppler bandwidth is used to obtain the HRWS imaging, and the resulted azimuth ambiguity is removed using a set of spatial filters applied on the received signals formulated as separate MISO system identification problems. Finally, both simulated and constructed raw data are used to validate the efficiency of the proposed algorithm.

Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning

One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers in real LIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals in real LIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 [Abbott et al. (2019) [4]]. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves.

Beam-time delay domain deconvolved scheme for high-resolution active localization of underwater targets

Matched filter (MF) and conventional beamforming (CBF) are widely used in active sonar; the performance of the former (temporal resolution) is limited by the signal bandwidth, and that of the latter (angular resolution) is limited by the array aperture. Previous work has shown that angular resolution can be significantly improved by deconvolving the CBF outputs. In this paper, deconvolution is extended to the time domain by deconvolving the MF outputs, and a high-resolution two-dimensional deconvolution method is proposed to simultaneously improve the temporal and angular resolution. Numerical simulations and experimental tank data show that angular resolutions are improved 26 times, and temporal resolutions are improved 10 times compared with the conventional MF and CBF methods. Reverberations are much suppressed to allow target echoes to be detected from the received time series data.

Three-Dimensional High-Resolution MIMO Radar Imaging via OFDM Modulation and Unitary ESPRIT

Imaging and recognition of targets with complex maneuvers bring a new challenge to conventional radar applications. In this paper, the three-dimensional (3D) high-resolution image is attained in real-time by a Multiple-Input-Multiple-Output (MIMO) radar system with single Orthogonal-Frequency-Division-Multiplexing (OFDM) pulse. First, to build the orthogonal transmit waveform set for MIMO transmission, we utilize complex orthogonal designs (CODs) for OFDM subcarrier modulation. Based on the OFDM modulation, a preprocessing method is developed for transmit waveform separation without conventional matched filtering. The result array manifold is the Kronecker product of the steering vectors of subcarrier/transmit antenna/receive antenna uniform linear arrays (ULAs). Then, the high-resolution image of target is attained by the Multidimensional Unitary Estimation of Signal Parameters via Rotational Invariant Techniques (MD-UESPRIT) algorithm. The proposed imaging procedures include the multidimensional spatial smoothing, the unitary transform via backward-forward averaging, and the joint eigenvalue decomposition (JEVD) algorithm for automatically paired coordinates estimation. Simulation tests compare the reconstruction results with the conventional methods and analyze the estimation precision relative to signal-to-noise ratio (SNR), system parameters, and errors.

Locally Optimal Detection of Signals in Underwater Acoustic Noise with Student’s t-distribution

Signal detection is imperative in underwater signal processing and digital communication, and based on a knowledge of noise statistics, optimum signal detection in underwater acoustic noise (UWAN) can be more effectively realised. The hypothesis of normal (Gaussian) noise allows the use of matched filter (MF) detectors; accordingly, a locally optimal detector (LO) is designed in this study to improve detection probability (PD) based on the knowledge of noise probability density function. The underwater noise used for validation is real data collected from the sea using broadband hydrophones at the beach of Desaru on the eastern seashore of Johor, Malaysia. The performance of the LO detector is then compared with a conventional MF detector and these are evaluated according to their PD values. For a time-varying signal, a false alarm probability specified as 0.01, and a PD value of 90%, the energy-to-noise ratios (ENR) of the LO are better than those of the MF by 4.2 dB and for fixed frequency signals, the LO is better than the MF by 5.2 dB.

InSAR imaging based on L1 regularisation joint reconstruction via complex approximated message passing

This Letter presents an interferometric synthetic aperture radar (InSAR) imaging method based on L1 regularisation joint reconstruction model via complex approximated message passing (CAMP). The proposed method enables to ensure the preservation of background information via CAMP and interferometric phase difference information among multiple channels via L1 regularisation joint reconstruction model. Compared with conventional matched filtering results, the proposed method can improve the performance of interferometric phase noise reduction and the continuity of interference fringes, which has broad application prospect in InSAR imaging. Experiments carried out on real data confirmed the effectiveness of the proposed method with improved performance.

Hybrid Low Complex near Optimal Detector for Spatial Modulation

In our previous work maximum throughput in multi stream MIMO is analyzed by overcoming the inter antenna interference. To mitigate the Inter antenna interference spatial modulation can be used. Spatial Modulation(SM) aided MIMO systems are the emerging MIMO systems which are low complex and energy efficient. These systems additionally use spatial dimensions for transmitting information. In this paper a low complex detector based on matched filter is proposed for spatial modulation to achieve near maximum likelihood performance while avoiding exhaustive ML search since MF based detector exhibits a considerable reduced complexity since activated transmitting antenna and modulated amplitude phase modulation constellation are estimated separately. Simulation results show the performance of the proposed method with optimal ML detector, MRC and conventional matched filter methods.

Multiuser Detection For DS-CDMA Systems Using Honeybees Mating Optimization Algorithm

The future wireless mobile communication systems will be required to support high-speed transmission rate and high quality of service. Direct sequence code division multiple access (DS-CDMA) is an important scheme for high-rate wireless communication. The capacity of DS-CDMA can be impaired by two problems; near-far effect and multiple-access interference (MAI). The use of conventional-matched filter detector for multiple users in DS-CDMA fails to combat any of these problems. The performance degradation caused by MAI can be overcome using multiuser detection (MUD). The use of maximum likelihood (ML) sequence estimation detector provides excellent results, but involves high computational complexity. In this paper, we propose a new meta-heuristic approach for MUD using honeybees mating optimization (HBMO) algorithm to detect the user bits based on the ML decision rule for DS-CDMA systems in additive white-Gaussian noise and flat Rayleigh fading channels. In order to improve the solutions generated by the HBMO, a second meta-heuristic method simulated annealing is used. By computer simulations, the bit error rate performance and the complexity curves show that the proposed HBMO-SA MUD is capable of outperforming the other conventional detectors and genetic algorithm detector.

Development and performance evaluation of generalised Doppler compensated adaptive pulse compression algorithm

The adaptive pulse compression (APC) algorithm is superior to conventional normalised matched filter (NMF) and least square mismatched filter techniques. However, its performance degrades for non-stationary targets in noisy condition as the algorithm has not taken into consideration the Doppler shift effect. On the other hand, the recently reported Doppler compensated APC (DC-APC) technique performs well only for non-stationary objects. Thus, there is a need to develop an algorithm that would work efficiently both for stationary and non-stationary conditions. Keeping this in view, a generalised APC (G-APC) algorithm is developed in which the effect of target Doppler is incorporated in the received signal model. The efficacy of the proposed algorithm has been evaluated through five different cases. The results of the simulation study demonstrate comparable and superior performance of the proposed G-APC over several pulse compression models based on NMF, APC and DC-APC in all cases.

Evaluation of complex broadband biomimetic waveforms for active sonar

There is an expanding requirement to reduce the impact of man-made sound, including active sonar transmissions, on marine mammals in the defence, offshore, and other sectors. One way this might be achieved in sonar applications is to use signals derived from natural sounds such as the vocalizations of the animals themselves. It might be expected that such sounds would appear more familiar, thus reducing possible abnormal behavioral impacts. This paper reviews the use of such waveforms and presents the results from a trial designed to compare the detection capabilities of a variety of broadband signals, both conventional and ‘novel’, with a medium frequency active sonar. Two biomimetic signals were tested, one based on sperm whale echolocation clicks and the other on pilot whale whistles. Preliminary analysis suggests the detection performance of these signals using conventional matched filters is comparable with linear FM chirps with a similar bandwidth, but may be improved with detection techniques commonly used for marine mammal vocalizations, such as spectrogram correlation. The paper will conclude with an assessment of the potential impacts of such signals on marine life.

Non-Antipodal Signaling Based Robust Detection for Cooperative Spectrum Sensing in MIMO Cognitive Radio Networks

In this letter, we present novel detection schemes for non-antipodal signaling based cooperative spectrum sensing in multiple-input multiple-output (MIMO) cognitive radio (CR) networks, which are robust against the uncertainty in channel estimates. We consider a scenario in which the secondary users (SU) cooperate by reporting the sensed data to the fusion center for soft combining towards primary user (PU) detection. We formulate this problem employing the optimal linear discriminant and model the uncertainties in the channel state information (CSI) as ellipsoidal uncertainty sets. It is then demonstrated that this problem of PU detection with uncertainty in the channel estimates for cooperative spectrum sensing in a CR system can be formulated as second order cone program (SOCP). Further, we extend this paradigm to the associated relaxed robust detector (RRD) and multicriterion robust detector (MRD) that maximally separate the hypothesis ellipsoids in low signal-to-noise power (SNR) and deep fade channel conditions. We present a closed form solution for the proposed robust detector for the above MIMO cooperative spectrum sensing scenario. Simulation results demonstrate a significant improvement in the detection performance of the proposed uncertainty aware robust detection schemes in comparison to the conventional uncertainty agnostic matched filter detector for cooperative MIMO PU detection.

Reduced-Dimension Multiuser Detection

We present a reduced-dimension multiuser detector (RD-MUD) structure for synchronous systems that significantly decreases the number of required correlation branches at the receiver front end, while still achieving performance similar to that of the conventional matched-filter (MF) bank. RD-MUD exploits the fact that, in some wireless systems, the number of active users may be small relative to the total number of users in the system. Hence, the ideas of analog compressed sensing may be used to reduce the number of correlators. The correlating signals used by each correlator are chosen as an appropriate linear combination of the users' spreading waveforms. We derive the probability of symbol error when using two methods for recovery of active users and their transmitted symbols: the reduced-dimension decorrelating (RDD) detector, which combines subspace projection and thresholding to determine active users and sign detection for data recovery, and the reduced-dimension decision-feedback (RDDF) detector, which combines decision-feedback matching pursuit for active user detection and sign detection for data recovery. We derive probability of error bounds for both detectors, and show that the number of correlators needed to achieve a small probability of symbol error is on the order of the logarithm of the number of users in the system. The theoretical performance results are validated via numerical simulations.

A SEGMENTED MATCHED FILTER FOR PN CODE ACQUISITION

This paper presents a segmented matched filter (SMF) for PN code acquisition in direct sequence spread spectrum systems. While conventional matched filters provide fast acquisition in the presence of high co-user interference, they are unable to handle the problem of Doppler shift. This problem is alleviated by filter segmentation with non-coherent summation. The paper focused on validating the segmented matched filter codephase synchronization method. Simulations were carried out to verify the functionality of the SMF from a statistical point of view. The simulated performance of the SMF was compared with that of the traditional synchronization techniques and was found to show an improvement over the traditional methods when Doppler shift was present.

Time-Reversal Ground-Penetrating Radar: Range Estimation With Cramér–Rao Lower Bounds

In this paper, first, a new range-estimation technique using time reversal (TR) for ground-penetrating-radar (GPR) applications is presented. The estimator is referred to as the TR/GPR range estimator. The motivation for this paper comes from the need of accurately estimating the location of underground objects such as landmines or unexploded ordinance for safe clearance. Second, the Cramer-Rao lower bound (CRLB) for the performance of the TR/GPR range estimator is derived and compared with the CRLB for the conventional matched filter (MF). The CRLB analysis shows that the TR/GPR range estimator has the potential to achieve higher accuracy in estimating the location of the target than that of the conventional MF estimator. Third, the proposed TR/GPR estimator is tested using finite-difference time-domain simulations, where the surface-based reflection GPR is modeled using an electromagnetic transverse-magnetic (TM) mode formulation. In our simulations, the TR/GPR estimator outperforms the conventional MF approach by up to 5-dB reduction in mean square error at signal-to-noise ratios ranging from -20 to 20 dB for dry-soil environments.