Detection, Mode Selection, and Parameter Estimation in Distributed Radar Networks: Algorithms and Implementation Challenges

There has been an increasing investigation in distributed radar sensor network (RSN) and MIMO-RSN, including waveform design, antenna strategy and spacetime coding, etc. However, distributed-RSN and MIMO-RSN are more like two parallel paths other than one research field in current literature. In this paper, we address the comparison between distributed-RSN and MIMO-RSN for target detection. We first establish the wireless statistic channel model for them. Unlike the conventional models that assume the received signals are static plus a Gaussian random for simplicity and clarity, we believe it's more genuine to model the received signals as random variables due to the uncertainty of channels. Choosing selection combination (SC) as a combining method of distributed-RSN, we get the receiver operation characteristic (ROC) evaluation of these two types through simulations. A comparative analysis of the advantages and disadvantages of them shows that target detection performance of distributed-RSN is better than that of MIMO-RSN when signal to noise ratio (SNR) is small and the probability of false alarm (P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fa</inf> ) is lower. As both SNR and P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fa</inf> increase, the probability of target detection (P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> ) of MIMO-RSN exceeds that of distributed-RSN.

Detection and diagnosis of breast cancer from diffusion signals by diffusion-weighted imaging involves in estimation of quantitative metrics by signal attenuation models fitted to the signals. The process suffers from the implementation difficulty of the fitting algorithms and their sensitivity to noise. This study aims development of neural networks to facilitate the classification of the breast tissues from the signals. 37500 diffusion MR signals are synthetically generated for noise-free and noisy conditions by signal-to-noise ratio (SNR) for malignant, benign, and healthy breast tissues. Forty neural networks employing traditional long short-term memory (LSTM) or bidirectional long short-term memory (BiLSTM) blocks up to twenty are trained and tested for the signals using bootstrapping incorporated accuracy analysis. Specificity, sensitivity, and accuracy metrics are computed for the higher performance networks. For noise-free and noisy signals with SNR ≥ 80, networks may achieve excellent sensitivities, specificities, and accuracies (100% at all), but LSTM networks require fewer number of memory blocks. For noisy signals having SNRs ≤ 40, the networks may deliver high to very high sensitivities (74.8-98.3%), specificities (87.4-99.2%), and accuracies (83.2-98.9%) better for malignant and healthy tissues than benign tissue but BiLSTM ones perform slightly better. LTSM networks eliminate the need for any signal decay model while outputting remarkably good performances in the classification of diffusion signals. BiLSTM networks perform slightly better for very noisy conditions. Prospective studies are needed to justify the potential benefits in a clinical setup.

Recently, automatic modulation classification (AMC) has been studied by more and more researchers, and a host of methods based on deep learning have been proposed. Different from image data, signal data is a sequence that changes with time, and has temporal characteristics. Like the spatial feature, the temporal feature cannot be ignored. In this paper, we add a Long Short-Term Memory (LSTM) module to the convolutional neural network (CNN) to learn the time-related features of wireless signals. LSTM learns the dependency relationship between the current element and the elements before-after through the gating structure. Experimental results show that our proposed methods perform better than the model without LSTM. The proposed network Incept-LSTM has a classification accuracy of 97.5% at a high signal-to-noise ratio (SNR). Compared to the model without LSTM, the classification accuracy of our proposed model is improved by 0.1% to 5.7%. At the same time, we compared the classification accuracy under different SNRs to explore its impact on classification performance. We found the classification ability of LSTM is more prominent at the low SNRs.

A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters

The signal-to-noise ratio may be increased by the cross-range coherence integration so as to detect the moving target in low signal-to-noise ratio (SNR) condition. But, the radial velocity, acceleration and the change of acceleration due to the maneuvering motion of target may induce serious range migration and cross-range high-order phase terms leading to the unfocused cross-range image, the reduction of signal-noise ratio and the invalidation of target detection. Therefore, in order to solve these problems, this paper proposes a new method based on the adjacent correlation and scale transform methods for detection, parameters estimation and imaging of maneuvering targets in wide-band signal. This method can align the range and remove the cross-range high-order phase terms induced by the radial motion of target, enabling us to detect the target and estimate its moving parameters better. Finally, the simulated target is used to confirm that the method proposed by this paper can perfectly detect the maneuvering target in low signal-to-noise ratio condition, estimate its motion parameters and obtain an ISAR image of target.

Currently, target detection usually requires long-time coherent integration and suffers the Doppler shift, due to the velocity of maneuvering targets. This paper has proposed a complete scheme, applying the fractional Fourier transform into the estimation of range and velocity parameters of targets. The radar signal processing technique is developed to measure the range and velocity of moving targets simultaneously in a single frequency-stepped burst without the long-time integration and avert the negative effect of Doppler shift.

This paper proposes an algorithm for targets detection and parameters estimation, based on intra-pulse Doppler compensation. Firstly, Keystone transform is employed to correct range migration. Then, according to parameters of radar transmitting signal and sample frequency, pulse compression coefficients which can remove the effect of intra-pulse Doppler is produced, and pulse compression is finished with the pulse compression coefficients. Finally, fractional Fourier transform is used to eliminate Doppler spread and integrate energy. This algorithm eliminates the effect of intra-pulse Doppler and guarantees the successes of targets detection and parameters estimation in lower SNR. Computer simulation demonstrates the validity of the proposed algorithm.

When it comes to studies on smart receiver designs, using machine learning and deep learning techniques for the development of automatic modulation classifiers as well as demodulators which require little to no information about the transmitted signal or the channel state is an area of interest. Through this study, we have proposed a combined classifier-demodulator system that is entirely deep learning-based and one that is focused on higher-order quadrature amplitude modulation (QAM) schemes such as 64QAM and 256QAM that can be used in next-generation mobile technologies. The system was developed by training a bidirectional long-short-term memory (BiLSTM) and long-short-term memory (LSTM) network for the classifier and demodulator, respectively, using randomly generated data, demodulated using binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), 16QAM, 64QAM, and 256QAM transmitted through a simulated additive white Gaussian noise (AWGN) channel of varying signal to noise ratio (SNR) levels. The classifier was then tested for its prediction accuracy while the demodulator models were tested against traditional mathematical models while calculating the effective capacity. The results showcased that the classifier worked extremely well for the QAM schemes across all SNR levels and less so with the PSK models. Considering the demodulator models’ performance, all schemes except the 256QAM demodulator were able to reach a zero or near zero bit error rate (BER) level within minimum acceptable SNR ranges.

In performing surveillance using a sensor network, data association and registration are two essential processes which associate data from different sensors and align them in a common coordinate system. While these two processes are usually addressed separately, they actually affect each other. That is, registration requires correctly associated data, and data with sensor biases will result in wrong association. We present a novel joint sensor association, registration, and fusion approach for multisensor surveillance. In order to perform registration and association together, the expectation-maximization (EM) algorithm is incorporated with the Kalman filter (KF) to give simultaneous state and parameter estimates. Computer simulations are carried out to evaluate the performances of the proposed joint association, registration, and fusion method based on EM-KF.

Target detection is a core function of integrated sensing and communication (ISAC) systems. The traditional likelihood ratio test (LRT) target detection algorithm performs inadequately under low signal-to-noise ratio (SNR) conditions, and the performance of mainstream orthogonal frequency division multiplexing (OFDM) waveforms declines sharply in high-speed scenarios. To address these issues, an information-theory-based orthogonal time frequency space (OTFS)-ISAC target detection processing framework is proposed. This framework adopts the OTFS waveform as its fundamental signal. The target detection is implemented through a relative entropy test (RET) comparing echo signals against target presence/absence hypotheses. Furthermore, to enhance the system's target detection capability, the iterative OTFS-ISAC waveform design (I-OTFS-WD) method which maximizes the relative entropy is proposed. This method utilizes the minorization-maximization (MM) algorithm framework and semidefinite relaxation (SDR) technique to transform the non-convex optimization problem into an iterative convex optimization problem for resolution. The simulation results demonstrate that, under sufficient sample conditions, the RET algorithm achieves a 9.12-fold performance improvement over LRT in low-SNR scenarios; additionally, the optimized waveform reduces the sample requirements of the RET algorithm by 40%, further enhancing the target detection capability of the OTFS-ISAC system.

Range estimation is the core of many positioning systems such as radar, and Wireless Local Positioning Systems (WLPS). The estimation of range is achieved by estimating Time-of-Arrival (TOA). TOA represents the signal propagation delay between a transmitter and a receiver. Thus, error in TOA estimation causes degradation in range estimation performance. In wireless environments, noise, multipath, and limited bandwidth reduce TOA estimation performance. TOA estimation algorithms that are designed for wireless environments aim to improve the TOA estimation performance by mitigating the effect of closely spaced paths in practical (positive) signal-to-noise ratio (SNR) regions. Limited bandwidth avoids the discrimination of closely spaced paths. This reduces TOA estimation performance. TOA estimation methods are evaluated as a function of SNR, bandwidth, and the number of reflections in multipath wireless environments, as well as their complexity. In this research, a TOA estimation technique based on Blind signal Separation (BSS) is proposed. This frequency domain method estimates TOA in wireless multipath environments for a given signal bandwidth. The structure of the proposed technique is presented and its complexity and performance are theoretically evaluated. It is depicted that the proposed method is not sensitive to SNR, number of reflections, and bandwidth. In general, as bandwidth increases, TOA estimation performance improves. However, spectrum is the most valuable resource in wireless systems and usually a large portion of spectrum to support high performance TOA estimation is not available. In addition, the radio frequency (RF) components of wideband systems suffer from high cost and complexity. Thus, a novel, multiband positioning structure is proposed. The proposed technique uses the available (non-contiguous) bands to support high performance TOA estimation. This system incorporates the capabilities of cognitive radio (CR) systems to sense the available spectrum (also called white spaces) and to incorporate white spaces for high-performance localization. First, contiguous bands that are divided into several non-equal, narrow sub-bands that possess the same SNR are concatenated to attain an accuracy corresponding to the equivalent full band. Two radio architectures are proposed and investigated: the signal is transmitted over available spectrum either simultaneously (parallel concatenation) or sequentially (serial concatenation). Low complexity radio designs that handle the concatenation process sequentially and in parallel are introduced. Different TOA estimation algorithms that are applicable to multiband scenarios are studied and their performance is theoretically evaluated and compared to simulations. Next, the results are extended to non-contiguous, non-equal sub-bands with the same SNR. These are more realistic

High-accuracy angle of arrival (AoA) and angle of departure (AoD) estimation is critical for cell search, stable communications and positioning in millimeter wave (mmWave) cellular systems. Moreover, the design of low-complexity AoA/AoD estimation algorithms is also of major importance in the deployment of practical systems to enable a fast and resource-efficient computation of beamforming weights. Parametric mmWave channel estimation allows to describe the channel matrix as a combination of direction-dependent signal paths, exploiting the sparse characteristics of mmWave channels. In this context, a fast Transformed Spatial Domain Channel Estimation (TSDCE) algorithm was recently proposed to perform parametric channel estimation with low complexity, which in turn results in a full characterization of the transmitting and receiving angles for dominant signal paths. In this paper, we analyze the AoA/AoD estimation capability and accuracy of the TSDCE algorithm in detail. We find that the TSDCE algorithm has a significant performance advantage with respect to the traditional approach, which is based on frequency domain processing, in complexity-constrained environments, especially at high signal-to-noise ratios.

The theme of this study is derivative nuclear magnetic resonance (dNMR) spectroscopy. This versatile methodology of peering into the molecular structure of general matter is common to e.g. analytical chemistry and medical diagnostics. Theoretically, the potential of dNMR is huge and the art is putting it into practice. The implementation of dNMR (be it in vitro or in vivo) is wholly dependent on the manner in which the encoded time signals are analyzed. These acquired data contain the entire information which is, however, opaque in the original time domain. Their frequency-dependent dual representation, a spectrum, can be transparent, provided that the appropriate signal processors are used. In signal processing, there are shape and parameter estimators. The former processors are qualitative as they predict only the forms of the lineshape profiles of spectra. The latter processors are quantitative because they can give the peak parameters (positions, widths, heights, phases). Both estimators can produce total shape spectra or envelopes. Additionally, parameter estimators can yield the component spectra, based on the reconstructed peak quantifiers. In principle, only parameter estimators can solve the quantification problem (harmonic inversion) to determine the structure of the time signal and, hence, the quantitative content of the investigated matter. The derivative fast Fourier transform (dFFT) and the derivative fast Padé transform (dFPT) are the two obvious candidates to employ for dNMR spectroscopy. To make fair comparisons between the dFFT and dFPT, the latter should also be applied as a shape estimator. This is what is done in the present study, using the time signals encoded from a patient with brain tumor (glioma) using a 1.5T clinical scanner. Moreover, within the dFPT itself, the shape estimations are compared to the parameter estimations. The goal of these testings is to see whether, for in vivo dNMR spectroscopy, shape estimations by the dFPT could quantify (without fitting), similarly to parameter estimations. We check this key point in two successive steps. First, we compare the envelopes from the shape and parameter estimations in the dFPT. The second comparison is between the envelopes and components from the shape and parameter estimations, respectively, in the dFPT. This plan for benchmarking shape estimations by the dFPT is challenging both on the level of data acquisition and data analysis. The data acquisition reported here provides encoded time signals of short length, only 512 as compared to 2048, which is customarily employed. Moreover, the encoding echo time was long (272 ms) at which most of resonances assigned to metabolites with shorter spin-spin relaxations are likely to be obliterated from the frequency spectra. Yet, in face of such seemingly insurmountable obstacles, we are looking into the possibility to extract diagnostically relevant information, having particularly in focus the resonances for recognized cancer biomarkers, notably lactate, choline and phosphocholine. Further, we want to see how many of the remaining resonances in the spectra could accurately be identified with clinical reliability as some of them could also be diagnostically relevant. From the mathematical stance, we are here shaking the sharp border between shape and parameter estimators. That border stood around for a long time within nonderivative estimations. However, derivative shape estimations have a chance to tear the border down. Recently, shape estimations by the dFPT have been shown to lead such a trend as this processor could quantify using the time signals encoded from a phantom (a test sample of known content). Further, the present task encounters a number of additional challenges, including a low signal-to-noise ratio (SNR) and, of course, the unknown content of the scanned tissue. Nevertheless, we are determined to find out whether the nonparametric dFPT can deliver the unique quantification-equipped shape estimation and, thus, live up to the expectation of derivative processing: a long-sought simultaneous improvement of resolution and SNR. In every facet of in vivo dNMR, we found that shape estimations by the dFPT has successfully passed the outlined most stringent tests. It begins with transforming itself to a parameter estimator (already with the 3rd and 4th derivatives). It ends with reconstructing some 54 well-isolated resonances. These include the peaks assigned to recognized cancer biomarkers. In particular, a clear separation of choline from phosphocholine is evidenced for the first time by reliance upon the dFPT with its shape estimations alone.

In this paper an uncooled infrared camera with embedded target detection and tracking capability is presented. The camera is built upon four main electronic cards. Sensor card is designed to read and digitize the analog output signal generated by the infrared (IR) imaging sensor. FPGA card carries out the control process of the data transfer between sensor card and other peripherals. Target detection/tracking task is on the row image data provided by the FPGA card is carried out on the DSP card. Frame thresholding and differencing methods are used for target detection. Kalman filter is utilized for target tracking. According to the experimental results, camera is capable of performing target detection and tracking tasks in real time.

This study introduces an innovative framework called Wavelet-Based Bidirectional Long Short-Term Memory (W-BiLSTM) designed to enhance spectral and energy efficiency in millimeter-wave (mmWave) large scale multiple-input multiple-output (LS-MIMO) systems.The approach combines wavelet transforms for efficient signal decomposition and noise reduction with BiLSTM networks to extract robust features and optimize hybrid beamforming.By integrating these techniques, the W-BiLSTM framework improves channel estimation precision and beamforming performance, tackling key issues related to energy usage and spectral efficiency in advanced wireless communication systems.Comprehensive simulations reveal that the W-BiLSTM framework outperforms existing methods such as Dual-Deep-Network (DDN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Autoencoders.Notably, it achieves spectral efficiency improvements of up to 13.5 bps/Hz and energy efficiency gains of up to 50 bits/Joule, alongside a substantial reduction in bit error rate (BER) across various signalto-noise ratios (SNRs).These findings underscore the framework's capability to address the demanding needs of 5G and future wireless technologies, offering a pathway toward more efficient and dependable communication systems.To improve reproducibility and benchmarking, the suggested framework is also assessed using the publicly accessible DeepMIMO dataset (Scenario O1), demonstrating its resilience under standardized mmWave conditions.