Adaptive Doppler Estimation for Random PRI Staggering with Clutter Cancellation

In this paper, we introduce a channel parameter tracking method for adaptive OFDM MMSE channel estimation. Even though the MMSE estimation is one of the most accurate channel estimation methods, it requires several channel information including Doppler frequency, RMS (root mean squared) delay spread, and noise power. To implement the MMSE estimation, tracking of such parameters should be preceded. We first propose methods to track such parameters focusing on the estimation of Doppler frequency, and mention their performance and complexity. We then implement and simulate the adaptive MMSE estimation using these tracking schemes and analyze the estimator's performance in terms of channel tracking accuracy and BER performance. We conclude that the proposed channel parameter estimator tracks channel condition reliably in various channel conditions without significant increase in computational complexity and that BER performance of the adaptive MMSE estimation is better than that of a robust MMSE estimator by 5 dB in average.

Electronic warfare (Polish acronym WRE ‒ walka radioelektroniczna) is a set of militaryactions based on the use of the electromagnetic spectrum and it includes:— reception and identification of electromagnetic emissions,— reducing the effectiveness of the enemy’s electronic devices,— enabling effective use of the spectrum by own forces.The task of the WRE in peacetime is to obtain information about radio-electronic devices, especiallyradars. These tasks are performed by ELINT (Electronic Intelligence) and/or ESM (Electronic WarfareSupport Measures) devices. They operate passively in the 0.5÷18 GHz band, their detection is difficultand the information they provide allows to detect a threat to own forces and to take appropriatecountermeasures. Radar recognition is a complex process that involves the following stages. The firststage: gathering measurements and their grouping into the so-called packets and graphical display ofthe received pulse trains. The measurements are represented by the so-called measurement vectors(descriptors) containing signal parameters, including the TOA (Time of Arrival) pulse with nanosecondaccuracy. The second stage: associating pulse packets with logical sources in order to obtainrepresentative data strings. This stage is signal sorting and deinterleaving. The third stage: estimationof the WS signal vector based on the associated pulse packets. The final stage of data processing is thecomparison of the obtained signal descriptor parameters WS with Database (BD) radar patterns. Thereconnaissance result allows for the assessment of the threat resulting from the operation of this radar.Radar recognition requires precise determination of many parameters of its signal. For pulsed radars,important parameters are the Pulse Repetition Interval (PRI) and the type of PRI modulation. Radarsare characterised by a high complexity of PRI changes. This provides great utility and makes it possibleto distinguish even radar units and changes in their locations. This allows for the detection and fullidentification of objects that use radars (e.g. ships) with the given object completely out of sight; it alsoallows to predict intentions of the target. The basic inter-pulse PRI modulations include: stagger, dwelland switch, jitter or sliding. PRI changes in radar signals may be periodic with long cycles, difficultto identify in cluttered conditions. Unfortunately, the received pulse trains are usually distorted. Thismakes the WS estimation process difficult ‒ especially in automatic mode. PRI analysis has receivedmuch attention in the literature. The methods are computationally complex and have numerouslimitations. This article presents ASWC algorithm (Sequential Cycle Detection Algorithm) of PRIproprietary, with relatively low computational complexity. Examples of test results confirming its higheffectiveness in automatic PRI analysis performed under interference conditions were also presented.Keywords: radars, electronic warfare, radar recognition, ELINT, ESM, pulse descriptor, PRI

Estimating the failure rate of the log-logistic distribution by smooth adaptive and bias-correction methods

We consider the downlink of a multiple-input multiple-output broadcast channel where the transmitter uses per user unitary and rate control (PU2RC) to perform the user scheduling and beamforming. The main shortcoming of the PU2RC scheme is in networks where the number of users is less than or comparable with the codebook size. In these networks, PU2RC cannot properly exploit the available channel dimensions. To deal with this problem, they propose two different approaches to enhance the throughput performance of the system. In the first approach, they try to find and assign unscheduled users to the unused channel dimensions. This is done through a signal-to-interference plus noise ratio (SINR) estimation method in which they approximate the SINR of the users along new beamforming vectors. In this approach, they usually achieve the maximum multiplexing order of the system. In the second approach, they adjust the SINR feedbacks according to the number of scheduled users. When the number of scheduled users is less than the number of transmitting antennas, the feedback SINRs are in fact underestimated. In this case, they correct the SINRs and improve the average throughput. The simulation results show that both proposed methods can effectively improve the performance of the PU2RC scheme.

Adaptive spectrum estimation is based on a local stationarity assumption for the studied process, and uses methods of the stationary case with data windows of reduced length. But conventional least squares methods and parsimony principle (for example Akaike's criterion) preclude use of long AR models necessary for a good spectral resolution. We develop here a bayesian adaptive spectrum estimation method using long AR models and normal prior distributions expressing a smoothness priors on the solution. This is now a classical approach to spectrum estimation. The main originality of our approach lies in the order choice and in the computation of the solution which is performed by a fast Kalman filter of the Chandrasekhar type (B-CAR), with a reduced complexity of O(p) per recursion, p being the model order. The likelihood of the regularizing factor which weights the smoothness priors is maximized to obtain the best data-dependent priors and is computed recursively as a by-product of our fast Kalman filter, which facilitates the determination of the hyperparameters. The method performances are illustrated by examples of adaptive spectrum estimation for simulated signals and Doppler signals.

Optimizing Time-Series forecasting using stacked deep learning framework with enhanced adaptive moment estimation and error correction

The paper presents a review of methods for adaptive estimation of signals with unknown structures and parameters of models that determine the properties of both the signal itself and the errors of its measurement. The main approaches to solution of adaptive estimation problems are considered. One of them is aimed at designing algorithms with a learning sample, and the other one relies on the Bayesian approach and aims at solving the joint problem of signal estimation and identification of models used to describe the signal and measurement errors in real time. An adaptive estimation algorithm based on the Bayesian approach is described in detail. The problems in which the proposed algorithm is used are discussed. Its effectiveness is illustrated by the example of gravity anomaly estimation on a moving vehicle under model uncertainty of the signal and measurement errors.

The accuracy of quantum state estimation and dynamic signal tracking is crucial for the reliability and performance of quantum sensing instruments such as spin-exchange relaxation-free (SERF) co-magnetometers and gyroscopes. To improve the performance of the SERF gyroscope for future quantum navigation applications, an adaptive quantum estimation and closed-loop optimal control method based on fault detection and isolation (FDI) and an adaptive Kalman filter is proposed. Firstly, the system model of the SERF gyroscope is established, and a real-time angular velocity measurement method is provided. Then, the stochastic model of the SERF gyroscope is analyzed based on an adaptive quantum Kalman observer to track and compensate for real-time angular velocity error with the assistance of FDI. Finally, the real-time LQI (Linear-Quadratic-Integral) control method is adopted to improve the dynamic response and accuracy of the overall system. Simulations and experiments are conducted based on the SERF gyroscope platform. The results show that the system can achieve optimal tracking accuracy for polarization estimation and effectively isolate fault information. The dynamic response time has been improved by 58.2% under environmental disturbance, which provides a foundation for subsequent navigation applications.

This paper presents a new algorithm for estimating pulse repetition intervals (PRIs) of stagger PRI pulse trains using a 2 dimensional TDOA histogram. The PRI transform algorithm can remove harmonics of stable and jitter PRI pulse trains in a TDOA histogram. But, it is impossible for the algorithm to detect stagger PRI pulse trains because it regards frame PRIs of stagger Pulse trains as harmonics of stable PRI pulse trains. A new algorithm of this paper has an ability to distinguish stagger pulse trains from harmonics of other pulse trains and to estimate frame PRIs of detected pulse trains.

Rayleigh fading channel estimation using time multiplexed pilot symbols in WCDMA system is considered. The WCDMA uplink signal model is established. Considering that channel parameter estimation in the pilot symbols which are far away from or near to data symbols have different effects on channel parameter of data symbols, weighted least square method is proposed to estimate channel parameter of data symbols. A novel adaptive channel estimation method based on weighted least squares (AWLS) is presented in this paper to meet simultaneously the needs of high speed, low speed, medium speed and varying-speed and the weighed combination function is introduced to solve this problem. Weight factor is determined by channel changing rate or maximum Doppler frequency. The method of the channel changing rate estimation is proposed in this paper and it has many advantages, such as small calculation and robust characteristic. The simulation results reveal that AWLS can track and estimate channel parameter of data symbol and RAKE receiver with AWLS method can perform well under high speed, medium speed, low mobile speed and varying-speed.

Adaptive weight estimation and segmentation of beef carcass tissues from computed tomography images.

Electro-hydraulic systems are characterized by high complexity and nonlinearity, caused by the dynamic interactions between the fundamental quantities that determine their operation and control. The present study proposes a method for adaptive parameter estimation using an advanced method of least squares (A-LS) (Advanced Least Squares), applicable to nonlinear dynamical systems. The paper presents a mathematical model describing the dynamics of the electro-hydraulic system, as well as an approach for adaptive parameter estimation using Recursive Least Squares (RLS) in combination with the Stochastic Gradient Method (SGD). The main goal is to minimize errors between measurement results and predicted model values, ensuring high accuracy and stability under variable operating conditions. The results of the simulations show that the use of RLS for angular velocity estimation and SGD for adaptive load torque correction allows for effective noise suppression in measurements and precise tracking of system dynamics. The proposed approach provides a basis for optimizing electro-hydraulic systems through adaptive methods and can be useful for applications requiring high accuracy and reliability.

This paper presents a method for real time adaptive hybrid state estimation of the power system states using the SCADA as well as limited synchronous phasor measurements. The proposed method can track the power system dynamics by periodically updating the sensitivities to the unobservable buses during the intervals between successive outputs of state estimator with SCADA measurements. The performance of the proposed method is evaluated on a number of standard test systems for different sets of un-observability. Simulation result shows that the proposed method can track the electromechanical dynamics of the system during the large disturbances in the system, it also improves the accuracy of the estimates and enhances the numerical stability of the estimation.

Adaptive estimation for delayed neural networks with Markovian jumping parameters

Next-generation spaceborne Synthetic Aperture Radar (SAR) systems aim at very high resolution imaging over wide swaths in the order of hundreds of kilometers. One viable realization of such a concept is distributed SARs with along-track baselines operated under the Nyquist frequency. Undersampled received data stack needs to be reconstructed to obtain azimuth-ambiguity free data on a regular sampling grid. On the other hand, very high-resolution distributed SAR system requires very precise orbit control to avoid sampling instabilities in along-track and phase error due to the topographic sensitivity. Both time and Doppler domain reconstruction methods in the literature perform well in case of uniform and nonuniform sampling conditions. However, highly nonuniform sampling and coinciding sampling result in severe signal-to-noise ratio degradation or impossibility to recover the entire Doppler bandwidth. Depending on the frequency band, even a small cross-track baseline in the order of few meters may result in high phase errors over mountainous terrain. In this paper, we analysed the impact of the varying pulse repetition interval (PRI) on the azimuth sampling condition and proposed two time-domain polychromatic reconstruction methods suitable for multistatic SAR constellations with varying PRI.