Joint Estimation Method Research Articles

Lifting Wavelet-Assisted EM Joint Estimation and Detection in Cooperative Spectrum Sensing.

Spectrum sensing in Cognitive radio (CR) is a way to improve spectrum utilization by detecting spectral holes to achieve a dynamic allocation of spectrum resources. As it is often difficult to obtain accurate wireless environment information in real-world scenarios, the detection performance is limited. Signal-to-noise ratio (SNR), noise variance, and channel prior occupancy rate are critical parameters in wireless spectrum sensing. However, obtaining these parameter values in advance is challenging in practical scenarios. A lifting wavelet-assisted Expectation-Maximization (EM) joint estimation and detection method is proposed to estimate multiple parameters and achieve full-blind detection, which uses lifting wavelet in noise variance estimation to improve detection probability and convergence speed. Moreover, a stream learning strategy is used in estimating SNR and channel prior occupancy rate to fit the scenario where the SU has mobility. The simulation results demonstrate that the proposed method can achieve comparable detection performance to the semi-blind EM method.

An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

State-of-charge (SoC) estimation is one of the core functions of battery energy management systems. An accurate SoC estimation can guarantee the safe and reliable operation of the batteries system. In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm in multiple time scales for 18,650 ternary lithium-ion batteries. Based on the slowly varying characteristics of lithium-ion batteries’ parameters and the quickly varying characteristics of the SoC parameter, firstly, the AEKF algorithm was used to online identify the parameters of the model of batteries with a macroscopic time scale. Secondly, the identified parameters were applied to the AUKF algorithm for SoC estimation of lithium-ion batteries with a microscopic time scale. Finally, the comparative simulation experiments were implemented, and the experimental results show the proposed joint algorithm has higher accuracy, adaptivity, robustness, and self-correction capability compared with the conventional algorithm.

A novel joint multi-target detection and tracking approach based on Bayes joint decision and estimation

This paper proposes a novel joint decision and estimation (JDE) solution for the multi-target detection and tracking (MDT) problem. MDT aims to jointly detect the number of targets and estimate their states, which is essentially a JDE problem since detection and tracking are highly coupled. Thus, a joint solution which can utilize the coupling is preferable. However, the existing JDE approach has either poor performance or excessive design parameters without considering the MDT problem realities, i.e., the losses that different decisions may lead to. Therefore, we propose a compact conditional JDE (CCJDE)-based MDT method with less design parameters but superior performance. Specifically, we propose a CCJDE-based MDT risk which unifies the detection and tracking risks in a compact way. Then, we derive the joint detection and tracking solution accounting for their couplings, where the joint probabilistic data association filter is adopted due to its advantageous performance and the adaptability to the JDE framework. Then, an efficient CCJDE-MDT algorithm is developed. Besides, some parameter designing guidelines are presented by considering the MDT realities. Simulation results verify the effectiveness of the proposed CCJDE-MDT method, which outperforms the traditional decision-then-estimation in joint performance and also beats the existing recursive joint decision and estimation(RJDE) method in many cases.

State of Charge and Temperature Joint Estimation Based on Ultrasonic Reflection Waves for Lithium-Ion Battery Applications

Accurate estimation of the state of charge (SOC) and temperature of batteries is essential to ensure the safety of energy storage systems. However, it is very difficult to obtain multiple states of the battery with fewer sensors. In this paper, a joint estimation method for a lithium iron phosphate battery’s SOC and temperature based on ultrasonic reflection waves is proposed. A piezoelectric transducer is affixed to the surface of the battery for ultrasonic–electric transduction. Ultrasonic signals are excited at the transducer, transmitted through the battery, and transmitted back to the transducer by reaching the underside of the battery. Feature indicator extraction intervals of the battery state are determined by sliding–window matching correlation analysis. Virtual samples are used to expand the data after feature extraction. Finally, a backpropagation (BP) neural network model is applied to the multistate joint estimation of a battery in a wide temperature range. According to the experimental results, the root mean square error (RMSE) of the lithium-ion battery’s SOC and temperature estimation results is 7.42% and 0.40 °C, respectively. The method is nondestructive and easy to apply in battery management systems. Combined with the detection of gas production inside the battery, this method can improve the safety of the battery system.

Collaborative evaluation of SoC, SoP and SoH of lithium-ion battery in an electric bus through improved remora optimization algorithm and dual adaptive Kalman filtering algorithm

Many countries are switched to electric vehicles (EVs) for public transportation, which increases the adoption of electric buses. Batteries are the key component in battery-operated electric vehicles and must be monitored for the system to operate efficiently. This work has proposed a joint estimation method to examine the battery states. The lithium-ion battery (LiB) is initially designed through a first-order RC equivalent (FO-RC) circuit. To optimize the modelling parameters of a battery, an improved remora optimization algorithm (IROA) is proposed in this work. After detecting the optimum values for the parameters, the SoC is evaluated by a dual adaptive Kalman filtering algorithm (DAKF). Then the SoH is estimated based on the predicted SoC of a battery, whereas the SoP is evaluated by considering current and voltage constraints during battery operation. After that, the battery's remaining useful life (RUL) is examined based on the estimated SoC. The proposed work is implemented on the MATLAB platform, and the results will be validated under varying operating conditions. The comparative analysis shows that the IROA provides optimum parameters near the actual parameters' actual values, thereby improving the prediction accuracy of battery states.

Joint estimation of DOA and coupling matrix based on linear prediction for small spacing antenna array

A method for joint estimation of DOA and coupling matrix based on linear prediction is proposed for low resolution of mutual coupling calibration method in the case of small spacing antenna array. This method augments array aperture virtually according to linear prediction principle and calculates the coupling matrix by this virtual array. Then, this coupling matrix is used as the initialization matrix of joint estimation algorithm for coupling calibration. Simulation results confirm the effectiveness of the proposed method and provide the theoretical foundation for engineering realization. This method could also be applied in the field of tactical anti-jamming communication.

3-D Hybrid RSS-AoA Passive Source Localization With Unknown Path Loss Exponent

The use of Received Signal Strength (RSS) mea- surements for passive localization is a simple and versatile method in Wireless Sensor Networks (WSNs). Another commonly used method is based on Angle of Arrival (AoA) measure- ment. Combining these two methods (RSS-AoA) can enhance the reliability of localization. In this letter, we investigate the localization problem (using joint RSS and AoA measurements) when the Path Loss Exponent (PLE) is unknown and only some of the nodes are anchors with known locations. When PLE is unknown, localization performance is affected. Therefore, we propose a joint estimation method for source location and PLE using an iterative Weighted Least Square (WLS) estimation. Simulation results show that our proposed method outperforms other competing algorithms in terms of localization accuracy.

Multistate joint estimation of ultracapacitor based on trans-scale dual extended Kalman filter

Accurate ultracapacitor state of charge (SOC) estimation directly affects safety, durability and reliability of hybrid energy storage system. For that, this paper presents a multistate joint estimation method of ultracapacitor based on trans-scale dual extended Kalman filter to predict SOC and model parameters in real-time. Firstly, an ultracapacitor model is established by using experimental data. Then, the model is combined with two Kalman filters, one of which is used to estimate SOC, and another one is applied to achieve trans-scale update the model parameters. Finally, the effectiveness of the method is verified in the urban dynamometer driving schedule at different temperatures. The validation results show that the mean absolute errors of voltage, SOC and capacity estimation are within 0.49 mV, 0.63% and 0.04 Ah, respectively. Moreover, compared with the other two traditional SOC estimation methods, whose results show that the proposed algorithm has higher estimation accuracy and more stable convergence ability.

Joint Estimation of Ground Displacement and Atmospheric Model Parameters in Ground-Based Radar

Atmospheric delay is the primary error in ground-based synthetic aperture radar (GBSAR). The existing compensation methods include the external meteorological data correction method, the polynomial fitting method, and the persistent scatterers SAR interferometry (PSInSAR) calibration method. Combining the polynomial fitting and the persistent scatterers targets is the most popular method of GBSAR atmospheric delay compensation. However, the displacement component of the coherent target is always ignored in the atmospheric delay compensation, which is unpractical. A joint estimation method of ground displacement and atmospheric model parameters is developed in this paper. The displacement component is determined by the spatial and temporal features of the objects. The atmospheric delay component is regarded as a systematic error represented by a quadratic polynomial related to distance. The result is resolved by the least-square method. Compared to the existing method, the root-mean-square error (RMSE) of the proposed method had a significant improvement in the validation experiment. In the real in situ experiment, the time series obtained by the GBSAR had a similar trend to that acquired by the Global Positioning System (GPS) receiver. It is indicated that the proposed method can lead to a better deformation estimation by taking the deformation component into account in the atmospheric compensation.

A novel variational inference method for joint estimation of target and reflectors in household multipath environment

Compared to the situation in unobstructed scenarios, RF sensing information obtained in the household environment has significant multipath components. Multiple reflections of electromagnetic waves in the household caused multipath components which bring ambiguity to the linear programming association algorithm. In this paper, we propose a structured variational inference algorithm. By using multipath components wisely, the algorithm improved sensing accuracy and can jointly estimate reflector and target states. Considering the multi-frame association problem, a belief propagation algorithm is used to approximately estimate the discrete association variables. And then, mean-field variation is used to approximate the hierarchical measurement model. With simulation experiments, the proposed algorithm can achieve better estimation accuracy than traditional methods.

Research on Multi-Time Scale SOP Estimation of Lithium–Ion Battery Based on H∞ Filter

Battery state of power (SOP) estimation is an important parameter index for electric vehicles to improve battery utilization efficiency and maximize battery safety. Most of the current studies on the SOP estimation of lithium–ion batteries consider only a single constraint and rarely pay attention to the estimation of battery state on different time scales, which can reduce the accuracy of SOP estimation and even cause safety problems. In view of this, this paper proposes a multi-time scale and multi-constraint SOP estimation method for lithium–ion batteries based on H∞ filtering. Firstly, a second-order RC equivalent circuit model is established with a ternary lithium–ion monolithic battery as the research object, and parameter identification is performed by using the recursive least squares method with a forgetting factor. Secondly, the H∞ filtering algorithm is applied to estimate the state of charge (SOC), and then the joint multi-time scale multi-constrained SOC-SOP estimation is performed. Finally, the joint estimation algorithm is validated under UDDS conditions. The mean absolute value relative error (MARE) of SOC estimation is 1.17%, and the MARE of SOP estimation at different time scales is less than 1.6%. The results indicate the high accuracy and strong robustness of the joint estimation method.

Research on Underdetermined DOA Estimation Method with Unknown Number of Sources Based on Improved CNN.

This paper proposes a joint estimation method for source number and DOA based on an improved convolutional neural network for unknown source number and undetermined DOA estimation. By analyzing the signal model, the paper designs a convolutional neural network model based on the existence of a mapping relationship between the covariance matrix and both the source number and DOA estimation. The model, which discards the pooling layer to avoid data loss and introduces the dropout method to improve generalization, takes the signal covariance matrix as input and the two branches of source number estimation and DOA estimation as outputs, and achieves the unfixed number of DOA estimation by filling in invalid values. Simulation experiments and analysis of the results show that the algorithm can effectively achieve the joint estimation of source number and DOA. Under the conditions of high SNR and a large snapshot number, both the proposed algorithm and the traditional algorithm have high estimation accuracy, while under the conditions of low SNR and a small snapshot, the algorithm is better than the traditional algorithm, and under the underdetermined conditions, where the traditional algorithm often fails, the algorithm can still achieve the joint estimation.

Research on the angular velocity estimation method based on the state augmentation method

In the maneuvering target tracking research due to the unpredictability of the target’s maneuvering, it makes it difficult to establish an accurate target motion model to reflect the real motion trajectory of the target. Based on the determination of the parametric maneuver model and the radar measurement conditions of the target, the joint estimation method of the motion state and the maneuver parameters is designed to obtain the optimal estimation of the motion state and the maneuver parameters based on the optimal evaluation criteria. Currently, in the descriptive category of the state space model, the state augmentation method is obtained to solve the joint estimation of the motion state and maneuver parameters based on the estimation criterion [1]. Therefore, the angular velocity estimation method of the state augmentation method is used to consider the estimated value of the target angular velocity and the estimated variance comprehensively to improve the convergence speed of target tracking, minimize the error between the filtered angular velocity and the real angular velocity of the maneuvering target, and finally achieve the purpose of improving the target tracking accuracy.

State Estimation Models of Lithium-Ion Batteries for Battery Management System: Status, Challenges, and Future Trends

The state estimation technology of lithium-ion batteries is one of the core functions elements of the battery management system (BMS), and it is an academic hotspot related to the functionality and safety of the battery for electric vehicles. This paper comprehensively reviews the research status, technical challenges, and development trends of state estimation of lithium-ion batteries. First, the key issues and technical challenges of battery state estimation are summarized from three aspects of characteristics, models, and algorithms, and the technical challenges in state estimation are deeply analyzed. Second, four typical battery states (state of health, state of charge, state of energy, and state of power) and their joint estimation methods are reviewed, and feasible estimation frameworks are proposed, respectively. Finally, the development trends of state estimation are prospected. Advanced technologies such as artificial intelligence and cloud networking have further reshaped battery state estimation, bringing new methods to estimate the state of the battery under complex and extreme operating conditions. The research results provide a valuable reference for battery state estimation in the next-generation battery management system.

Joint Estimation of Metal Density and Attenuation Maps with Pencil Beam XFET.

X-ray fluorescence emission tomography (XFET) is an emerging imaging modality that images the spatial distribution of metal without requiring biochemical modification or radioactivity. This work investigates the joint estimation of metal and attenuation maps with a pencil-beam XFET system that allows for direct metal measurement in the absence of attenuation. Using singular value decomposition on a simplified imaging model, we show that reconstructing metal and attenuation voxels far from the detector is an ill-conditioned problem. Using simulated data, we develop and compare two image reconstruction methods for joint estimation. The first method alternates between updating the attenuation map with a separable paraboloidal surrogates algorithm and updating the metal map with a closed-form solution. The second method performs simultaneous joint estimation with conjugate gradients based on a linearized imaging model. The alternating approach outperforms the linearized approach for iron and gold numerical phantom reconstructions. Reconstructing an (8 cm)3 object containing gold concentrations of 5 mg/cm3 and an unknown beam attenuation map using the alternating approach yields an accurate gold map (NRMSE = 0.19) and attenuation map (NRMSE = 0.14). This simulation demonstrates an accurate joint reconstruction of metal and attenuation maps, from emission data, without previous knowledge of any attenuation map.

Integrated Pressure Estimation and Control for Electro-hydraulic Brake Systems of Electric Vehicles Considering Actuator Characteristics and Vehicle Longitudinal Dynamics

In the automotive field, electro-hydraulic brake systems (EHB) has been developed to take place of the vacuum booster, having the advantage of faster pressure built-up and continuously pressure regulation. Pressure control, one of the most crucial issues to be solved for EHB, is influenced by the system nonlinearities (e.g., pressure-position, friction). Recently, there are a series of studies that focus on this issue. However, the pressure control based on pressure estimation is rarely investigated in the previous literatures. In order to achieve cost-effective and highly integrated, the pressure is regulated without any add-in sensors, making it very difficult to achieve a satisfactory performance. A pressure control system based on pressure joint estimation is developed in this article according to a requirement-oriented model. The pressure joint estimation is designed considering actuator characteristics and vehicle dynamics. The wheel speed feedback is used for modification via a proportional-integral observer. A pressure control combining feedforward, torque-adaptive friction compensation with feedback controller is employed to improve the pressure-tracking performance. The constraint of feedback gain of controller is derived based on the small-gain theorem to remain the input–output stability of the overall control system. Superior to the estimation method based on actuator characteristics, the joint estimation method improves the accuracy by more than 10%. The pressure-tracking performance and robustness of system are validated by vehicle experiments under the typical common braking situations.

Study on the estimation of Euler angles for Macau Science Satellite-1

The Euler angle estimation is a calibration method for vector data measured by the magnetometer on a satellite. It is used to find the relative rotation between the coordinate system of the magnetometer and the satellite (usually determined by Star Imagers). Before launch of the low-orbit, low-inclination Macau Science Satellite-1 (known as MSS-1), we simulated the estimation of Euler angles by using the magnetic measurements of the in-orbit Swarm and China Seismo-Electromagnetic Satellite (noted as CSES), with various data combinations. In this study, 11 data sets were designed to analyze the estimation results for the MSS-1 orbit by using a joint estimation method of the geomagnetic field model parameters and Euler angles. For the model results, we found that all the spatial power spectral lines showed behavior consistent with that of the CHAOS-7.8 model at low degrees (corresponding to large-scale magnetic signals). The spectra of models without global data coverage deviated much more (by a maximum of ~10⁴ nT²) from those of the CHAOS-7.8 model at higher degrees. For models with global data coverage and with various data combinations, the spectral lines were distributed similarly. Moreover, the models with accordant power spectral distributions demonstrated different Euler angle estimations. As more vector data at higher latitudes were included, the estimated Euler angles varied monotonically in all three directions. The models with vector data in the same latitude range showed similar Euler angle results, regardless of whether the poleward scalar data were included. The largest value difference was found between the models using vector data within ±40° latitudes and those using vector data within ±60° latitudes, which reached to ~28″. Therefore, we concluded that the inversion of the spherical harmonic Gauss coefficients in our tests was mainly affected by the spatial coverage range of the data, whereas the estimation of Euler angles largely depended on the latitude range where the vector data could be obtained. These results can be used for future in-flight data testing. We expect the estimation of Euler angles to improve as other methods are adopted.

Interference suppression algorithm for wireless communication network based on joint estimation

When the wireless communication network is interfered, the communication effect will be affected. In order to improve the interference signal processing effect and the identification accuracy of the interference signal, a wireless communication network interference suppression algorithm based on joint estimation is proposed. Using the deep learning method to identify the interference signal, obtain the effective interference signal of wireless communication network, improve the accuracy of interference signal identification, and track and parameter modulation the identified signal; The node model of wireless communication network is established, and the joint estimation method is used to suppress the interference signal for the nodes in the model. The interference suppression of wireless communication network is realized through the state estimation of single tone interference and narrowband interference. The experimental results show that the proposed algorithm has a high accuracy of interference signal recognition, the highest value reaches 98%, and the wireless communication data packet loss rate is low, the highest value is only 0.37, which verifies its interference suppression effect.

Joint estimation of imaging plane and three‐dimensional structure based on inverse synthetic aperture radar image sequences

It is difficult to estimate the effective rotation velocity for non-triaxial stabilized space targets, which leads to deviations in the estimation of imaging plane vectors and azimuth resolution. This makes it hard to reconstruct the three-dimensional (3D) of space targets based on inverse synthetic aperture radar (ISAR) image sequence. To solve this problem, a joint estimation method of imaging plane vector and 3D structure based on ISAR image sequences is proposed. First, a compact form of imaging plane vector is defined. Then, the 3D structure of a target is characterized by a fully connected deep network. The volume rendering for ISAR images is redesigned with the analysis of projection formula for ISAR imaging. The network is trained using the discrepancy between the modulus of rendered and observed ISAR images in a self-supervised manner without 3D supervision. And the 3D structure can be obtained by inquiring all points in the 3D space. Therefore, it can optimize imaging planes and produce more complete and accurate results for complex space targets. Adequate simulation experiments verify its superiority.

Joint Estimation Method with Multi-Innovation Unscented Kalman Filter Based on Fractional-Order Model for State of Charge and State of Health Estimation

This study simulates the polarization effect during the process of battery charging and discharging, and investigates the characteristics of the process. A fractional-order model (FOM) is established and the parameters of the FOM are identified with the adaptive genetic algorithm. As Kalman filter estimation causes error accumulation over time, using the fractional-order multi-innovation unscented Kalman filter (FOMIUKF) is a better choice for state of charge (SOC) estimation. A comparative study shows that the FOMIUKF has higher accuracy. A multiple timescales-based joint estimation algorithm of SOC and state of health is established to improve SOC estimation precision and reduce the amount of computation. The FOMIUKF algorithm is used for SOC estimation, while the UKF algorithm is used for SOH estimation. The joint estimation algorithm is then compared and analyzed alongside other Kalman filter algorithms under different dynamic operating conditions. Experimental results show that the joint estimation algorithm possesses high estimation accuracy with a mean absolute error of under 1% and a root mean square error of 1.35%.