Multitarget detection/tracking for monostatic ground penetrating radar: application to pavement profiling

In monostatic ground penetrating radar (GPR) the interfaces profile can be estimated from echoes amplitude and time of delay (TOD) using a layer stripping inversion algorithm. The authors' aim is to establish a reliable processing sequence for layer stripping inversion by estimating echoes TOD that keeps into account the layers lateral continuity, and by tracking the corresponding interfaces. The authors propose first an algorithm for multitarget tracking and then they describe the application of detection/tracking to Ins pulse monostatic GPR. The system is used to estimate layer thicknesses of asphalt and concrete in pavement profiling. Detection/tracking shows a better recognition capability of the lateral continuity in near surface interfaces with respect to algorithms that employ only local detection of echoes.

A Bayesian estimation approach for enhancing speech signals which have been degraded by statistically independent additive noise is motivated and developed. In particular, minimum mean square error (MMSE) and maximum a posteriori (MAP) signal estimators are developed using hidden Markov models (HMMs) for the clean signal and the noise process. It is shown that the MMSE estimator comprises a weighted sum of conditional mean estimators for the composite states of the noisy signal, where the weights equal the posterior probabilities of the composite states given the noisy signal. The estimation of several spectral functionals of the clean signal such as the sample spectrum and the complex exponential of the phase is also considered. A gain-adapted MAP estimator is developed using the expectation-maximization algorithm. The theoretical performance of the MMSE estimator is discussed, and convergence of the MAP estimator is proved. Both the MMSE and MAP estimators are tested in enhancing speech signals degraded by white Gaussian noise at input signal-to-noise ratios of from 5 to 20 dB. >

In this paper, we propose a distributed source localization algorithm based on the Maximum A Posteriori (MAP) criterion, where the observations generated by each of the distributed sensors are quantized before being transmitted to a fusion node for localization. If the source signal energy is known, each quantized sensor reading corresponds to a region in which the source can be located. Aggregating the information obtained from multiple sensors corresponds to generating intersections between the regions. In our previous work we developed quantizer design techniques aimed at optimizing localization accuracy for a given aggregate rate. In this paper we develop localization algorithms based on estimating the likelihood of each of the intersection regions. This likelihood can incorporate uncertainty about the source signal energy as well as measurement noise. We show that the computational complexity of the algorithm can be significantly reduced by taking into account the correlation of the received quantized data. We also propose a technique, based on a weighted average of estimators, to address the case when the signal energy is unknown. Our simulation results show that our localization algorithm achieves good performance with reasonable complexity as compared with Minimum Mean Square Error (MMSE) estimation.

Multiple-input multiple-output (MIMO) systems can achieve high data-rate and high capacity transmission. In MIMO systems, the scheme that weight substreams based on minimum mean square error (MMSE) criterion at the transmitter using feedback channel state information (CSI) achieves good performance (denoted hereafter as MMSE precoder). However, if the optimum number of substreams is not selected based on CSI and methods for reducing the performance degradation due to feedback delay is not used, MMSE precoder cannot achieve the full performance possible. In this paper, we propose an MMSE precoder with mode selection for MIMO systems, which selects both the number of substreams and the modulation scheme for each substream to minimize the average BER at a fixed rate. We evaluate the BER of the proposed MMSE precoder with two methods for reducing performance degradation due to feedback delay, channel prediction and receive weight robust to feedback delay. We also evaluate the BER of the proposed MMSE precoder with the method that combines channel prediction with receive weight robust to feedback delay. Simulation results show that the BER of the proposed MMSE precoder is improved compared to that of the conventional MMSE precoder using the fixed number of substreams. We also show that the method that combines channel prediction with receive weight robust to feedback delay can achieve good BER even when the large feedback delay exists.

In orthogonal frequency-division multiplexing (OFDM) systems, it is generally assumed that the channel response is static in an OFDM symbol period. However, the assumption does not hold in high-mobility environments. As a result, intercarrier interference (ICI) is induced and the system performance is degraded. A simple remedy for this problem is the application of the zero-forcing (ZF) and minimum mean square error (MMSE) equalizers. Unfortunately, the direct ZF method requires the inversion of an NxN ICI matrix, where N is the number of subcarriers. When N is large, the computational complexity can become prohibitively high. As for the direct MMSE method, in addition to an NxN matrix inverse, it requires an extra NxN matrix multiplication, making the required computational complexity higher compared to the direct ZF method. In this dissertation, we first propose a low-complexity ZF method to solve the problem in single-input-single-output (SISO) OFDM systems. The main idea is to explore the special structure inherent in the ICI matrix and to apply Newton's iteration for matrix inversion. With our formulation, fast Fourier transforms (FFTs) can be used in the iterative process, reducing the complexity from O(N^3) to O(Nlog_2 N) . Also, the required number of the iteration is typically one or two. We also analyze the convergence behavior of the proposed method and derive the theoretical output signal-to-interference-noise-ratio (SINR). For the MMSE method, we first reformulate the MMSE solution in a way that the extra matrix multiplication can be avoided. Similar to the ZF method, we then exploit the structure of the ICI matrix and apply Newton's iteration to reduce the complexity of the matrix inversion. For a multiple-input-multiple-output (MIMO) OFDM system, the required complexity of the ZF and MMSE methods becomes more intractable. We then manage to extend the proposed ZF and MMSE methods for SISO-OFDM systems to MIMO-OFDM systems. It turns out that the computational complexity can be reduced even more significantly. Simulation results show that the performance of the proposed methods is almost as good as that of the direct ZF and MMSE methods, while the required computational complexity is reduced dramatically. Finally, we explore the application of the proposed methods in mobility-induced ICI mitigation for OFDM multiple access (OFDMA) systems, and in carrier frequency offset (CFO) induced ICI mitigation for OFDMA uplink systems. As that in OFDM systems, the proposed methods can reduce the required computational complexity, effectively.

We generalize the multiuser (CDMA spread spectrum) communication systems to the fading environments. We first extend Verdu's (1986) conventional optimum receiver to Rayleigh-fading environments and then evaluate its performance. Having no knowledge of the received power at the receiving end, we therefore need an estimator to efficiently estimate the received signal strength of each user in fading environments. A linear minimum mean square error (LMMSE) unbiased estimator is proposed to attain this goal. By using the minimum mean square error (MMSE) Bayesian estimation, we further propose the LMMSE bit estimator for efficient demodulation. Its performance is close to the optimum multiuser receiver but with a much simpler polynomial complexity. To further reduce the complexity, we extend the LMMSE estimator to the sequential LMMSE estimator. In sequential estimation, we do not need to implement the matched filter banks and to perform the matrix inversion when estimating. In addition, it converges after approximately 2k iterations, where k is the number of users. With this fast convergence property and the simple structure, the sequential LMMSE estimator provides an attractive alternative to the implementation of a multiuser system.

In present days, there has been a race among the communication systems in different schemes and generations. Gradually the transmission rate of data is increasing tremendously and breaking the previous generation's record. Now people are thinking of transmitting data beyond imagination. Using multiple antennas both at transmitter and receiver side, MIMO wireless technology efficiently provides very high spectral efficiency, boost the data transmission rate, enhance link reliability and improve system coverage. While transmitting data over mobile radio channels at high bit rates, the impulse response of the channel can be extended over a lot of symbol periods, leading fading and Inter-symbol interference (lSI). Equalizers are used to minimize Fading and interference effects. The analysis of the paper is studying the performance of Minimum mean square error method in terms of BER for MIMO (2×2) and (3×3) wireless channels. Binary Phase Shift Keying (BPSK) modulation technique was used in Rayleigh flat fading channel. Simulating in MATLAB results show that Minimum mean square error (MMSE) receivers is good for noise free channel and is successful in removing ISI. Successive interference method with optimal ordering outperforms the MMSE however a few problems arise because of iterative nature of the algorithm.

For the code division multiple access (CDMA) downlink channel, the chip-level equalization has been considered in this paper. There is no interference after despreading if all spreading codes are orthogonal, as in IS-95. However, it cannot be true for a frequency-selective fading channel. In this case, the chip-level equalization can be applied to restore the orthogonality. We investigate the chip-level equalization using finite impulse response (FIR) equalizers for the mobile station with multiple receive antennas. A blind approach and the minimum mean square error (MMSE) approach with code-multiplexed pilot are considered. A generalized MMSE equalization, which combines the MMSE and blind approaches together, is also investigated. It is shown that the generalized MMSE equalizer can effectively increase the number of samples to track the variation of channel and thereby performs better when the coherence time is small. In addition, we derive closed-form solutions of the blind, MMSE, and generalized MMSE equalizers for given channels.

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation.

A turbo receiver combined with CFO compensation in frequency-domain for OFDMA uplink

In this paper, the minimum mean square error (MSE) convergence of the RBF equalizer is evaluated and compared with the linear equalizer based on the theoretical minimum MSE. The basic idea of comparing these two equalizers comes from the fact that the relationship between the hidden and output layers in the RBF equalizer is also linear. As extensive studies of this research, various channel models are selected, which include linearly separable channel, slightly distorted channel, and severely distorted channel models. The theoretical minimum MSE for both RBF and linear equalizers were computed, compared and the sensitivity of minimum MSE due to RBF center spreads was analyzed.

As a new measure of similarity, the correntropy can be used as an objective function for many applications. In this letter, we study Bayesian estimation under maximum correntropy (MC) criterion. We show that the MC estimation is, in essence, a smoothed maximum a posteriori (MAP) estimation, including the MAP and the minimum mean square error (MMSE) estimation as the extreme cases. We also prove that under a certain condition, when the kernel size in correntropy is larger than some value, the MC estimation will have a unique optimal solution lying in a strictly concave region of the smoothed posterior distribution.

Vector quantisation for joint source‐channel (JSC) coding over a finite‐state Markov channel (FSMC) is studied. In particular, minimum mean square error (MMSE) decoding of a vector quantised source in the absence of channel state information (CSI) is considered. Based on hidden Markov modelling of the channel output, two decoding strategies are proposed. The first one is a soft‐decoder which estimates the source reconstruction vectors directly from a sequence of channel output samples. The second one is a hard‐decoder based on joint maximum a posteriori (MAP) probability estimation of channel symbols and channel states. An iterative procedure for designing JSC optimised vector quantisers (VQs) is also proposed. Finally, the design of VQs for wireless channels using a finite‐state model is examined. Experimental results are provided for a Gauss‐Markov source and flat‐fading wireless channels. A comparison with an idealised tandem source‐channel coding system is also provided to demonstrate the advantage of the proposed JSC coding approach. Copyright © 2007 John Wiley & Sons, Ltd.

The Performance of various estimators, such as minimum mean square error (MMSE) is strongly dependent on correctness of the proposed model for original data distribution. Therefore, the selection of a proper model for distribution of wavelet coefficients is important in wavelet based image denoising. This paper presents a new image denoising algorithm based on the modeling of wavelet coefficients in each Subband with multivariate radial exponential probability density function (pdf) with approximated MAP estimation for statistical parameter (local variance) using exponential priori. Generally these multivariate extensions do not result in a closed form expression, and the solution requires numerical solutions as in [1]. However, we drive a closed form MMSE shrinkage functions for a Radial-Exponential random vector in Gaussian noise. Experimental results show that our proposed method, MMSE_TriShrink_Radial, outperforms several exiting methods in terms of peak signal-to-noise ratio (PSNR).

проведено исследованию методов оценки состояния канала связи (CSI) в ортогональном частотно-разделённом мультиплексировании (OFDM), применяемом в современных стандартах беспроводной связи, таких как LTE, Wi-Fi и 5G. Рассматриваются традиционные методы оценки канала, такие как Least Squares (LS) и Minimum Mean Square Error (MMSE), отмечаются их недостатки в сложных условиях, связанных с многолучевым распространением сигнала и высокой мобильностью абонентов. Рассмотрено перспективное направление оценки канала связи — глубокое обучение (DL). Доказано, что применение нейронных сетей позволяет существенно повысить точность оценки канала. Приводятся примеры исследований, использующих свёрточные нейронные сети (CNN) и другие архитектуры нейронных сетей для улучшения качества оценки CSI. Отмечено, что ряд аспектов применения нейронных сетей требует дальнейшего изучения, включая оптимизацию моделей под изменяющиеся условия среды и снижение вычислительной сложности. Предлагаются новые подходы для повышения эффективности оценки канала, среди которых использование рекуррентных нейронных сетей (RNN) и методов сжатия моделей (квантование и прунинг). Результаты экспериментов продемонстрировали потенциал использования RNN для эффективной оценки канала связи и восстановления сигналов в OFDM-системах. Моделирование подтвердило преимущество рекуррентных нейронных сетей над CNN в плане потребления вычислительных ресурсов, при сохранении сопоставимой производительности. Описана важность внедрения глубоких нейронных сетей для совершенствования технологии OFDM и предложены направления дальнейших исследований и разработок we conducted a study of methods for assessing the state of a communication channel (CSI) in orthogonal frequency-divided multiplexing (OFDM), used in modern wireless communication standards such as LTE, Wi-Fi and 5G. We considered traditional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE), and their disadvantages are noted in difficult conditions associated with multipath signal propagation and high subscriber mobility. We considered a promising area of communication channel assessment, deep learning (DL). We proved that the use of neural networks can significantly improve the accuracy of channel estimation. We give examples of studies using convolutional neural networks (CNNs) and other neural network architectures to improve the quality of CSI assessment. We note that a number of aspects of the use of neural networks require further study, including optimizing models for changing environmental conditions and reducing computational complexity. We proposed new approaches to improve the efficiency of channel estimation, including the use of recurrent neural networks (RNNs) and model compression methods (quantization and pruning). The simulation confirmed the advantage of recurrent neural networks over CNNs in terms of computing resource consumption, while maintaining comparable performance. The experimental results demonstrated the potential of using RNN for effective assessment of communication quality and signal recovery in OFDM systems. We proposed the importance of the introduction of deep neural networks for the improvement of OFDM technology is described and the directions of further research and development