Expected State Estimation Research Articles

Recursive state estimation for nonlinear coupling complex networks with time-varying topology and round-robin protocol

This paper is devoted to solving the recursive state estimation (RSE) issue for a class of complex networks (CNs) with Round-Robin (RR) protocol and switching nonlinearities (SNs). A random variable obeying the Bernoulli distribution with known statistical properties is introduced to describe the switching phenomenon between two nonlinear functions. A Gaussian noise and time-varying outer coupling strength are adopted to show the changeable network topology (CNT). The RR protocol is applied to regulate signal transmissions, which determines that the element in measurement output has access to the communication networks at each step. The purpose of this paper is to construct a recursive state estimator such that, for all SNs, time-varying topology and RR protocol, the expected state estimation performance is guaranteed. Specifically, based on two recursive matrix equations, the covariance upper bound (CUB) of state estimation error is obtained firstly and then minimized via designing estimator gain in a proper way. Moreover, a feasible criterion is given to guarantee that the trace of obtained CUB is bounded and a monotonicity relationship is established between state estimation error and time-varying outer coupling strength. Lastly, a simulation experiment is illustrated to verify the feasibility of the addressed estimation method.

Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes

Full autonomy for fixed-wing unmanned aerial vehicles (UAVs) requires the capability to autonomously detect potential landing sites in unknown and unstructured terrain, allowing for self-governed mission completion or handling of emergency situations. In this letter, we propose a perception system addressing this challenge by detecting landing sites based on their texture and geometric shape without using any prior knowledge about the environment. The proposed method considers hazards within the landing region such as terrain roughness and slope, surrounding obstacles that obscure the landing approach path, and the local wind field that is estimated by the on-board EKF. The latter enables applicability of the proposed method on small-scale autonomous planes without landing gear. A safe approach path is computed based on the UAV dynamics, expected state estimation and actuator uncertainty, and the on-board computed elevation map. The proposed framework has been successfully tested on photo-realistic synthetic datasets and in challenging real-world environments.

Stochastic link activation for distributed filtering under sensor power constraint

We consider the problem of link activation for distributed estimation with power constraint. To satisfy the requirement of power consumption, we propose a stochastic link activation scheme, where each sensor equipped with a distributed estimator sends data to its neighboring sensors according to different probabilities. First, we design the optimal estimator gain of each sensor to minimize the state estimation error covariance. Then, we find an upper bound of the expected state estimation error covariance and provide a sufficient condition to guarantee the stability of the proposed estimator. Finally, we formulate the link activation problem as an optimization problem, and convert it to a convex optimization.

Data-driven power control for state estimation: A Bayesian inference approach

We consider sensor transmission power control for state estimation, using a Bayesian inference approach. A sensor node sends its local state estimate to a remote estimator over an unreliable wireless communication channel with random data packet drops. As related to packet dropout rate, transmission power is chosen by the sensor based on the relative importance of the local state estimate. The proposed power controller is proved to preserve Gaussianity of local estimate innovation, which enables us to obtain a closed-form solution of the expected state estimation error covariance. Comparisons with alternative non-data-driven controllers demonstrate performance improvement using our approach.

Power allocation scheme for distributed filtering over wireless sensor networks

In this work, distributed filtering over wireless sensor networks with limited energy is considered, where each sensor sends its local state estimate to its adjacent sensors through an unreliable wireless channel, which introduces random data packet drops. The packet drop rate depends on the power allocated by the sensor under an energy constraint. Several offline power scheduling strategies are introduced to distribute the power of sensors. A sufficient condition is provided to guarantee the convergence of network estimation error covariance. Further, an online power scheduling strategy is proposed, where each sensor utilises its real-time information to distribute energy for communications. The filtering performance of different power scheduling strategies are compared to show the influence of power distributed scheme on the expected state estimation error covariance.

Lagrangian Point State Estimation with Optimized, Redundant Induction Coil Gauges

This research effort studies the use of redundant induction coil gauges to reduce state estimation uncertainties for moving Lagrangian points (LPs); e.g. discrete points, moving interfaces, projectiles, etc. The technique embeds a small, high-strength magnet at the LP and simultaneously tracks the magnet continuously with five (5) or more induction coils along a single axis of motion. A calibrated coil gauge model is presented as a function of LP position and velocity. The optimized LP state (position and velocity) estimate based upon redundant LP observations allows direct solution for LP velocity; requiring only one differentiation step to obtain acceleration. A specific experimental implementation (Particulate Materials Meso-scale Diagnostics system) is simulated to evaluate and minimize the expected state estimation errors. Induction coil signals with various levels of noise are simulated based upon a prescribed LP state variation with time. The state optimization algorithm attempts to recover the truth state values. Worst-case position estimation errors of ±0.3 mm and velocity estimation errors of ±0.46 m/s are determined for LPs travelling 0–1,000 m/s at realistic in-lab data noise levels.

Dynamic Quantization and Power Allocation for Multisensor Estimation of Hidden Markov Models

This paper investigates an optimal quantizer design problem for multisensor estimation of a hidden Markov model (HMMs) whose description depends on unknown parameters. The sensor measurements are simply binary quantized and transmitted to a remote fusion center over noisy flat fading wireless channels under an average sum transmit power constraint. The objective is to determine a set of optimal quantization thresholds and sensor transmit powers, called an optimal policy, which minimizes the long run average of a weighted combination of the expected state estimation error and sum transmit power. We analyze the problem by formulating an adaptive Markov decision process (MDP) problem. In this framework, adaptive optimal control policies are obtained using a nonstationary value iteration (NVI) scheme and are termed as NVI-adaptive policies. These NVI-adaptive policies are adapted to the HMM parameter estimates obtained via a strongly consistent maximum likelihood estimator. In particular, HMM parameter estimation is performed by a recursive expectation-maximization (EM) algorithm which computes estimates of the HMM parameters by maximizing a relative entropy information measure using the received quantized observations and the trajectory of the MDP. Under some regularity assumptions on the observation probability distributions and a geometric ergodicity condition on an extended Markov chain, the maximum-likelihood estimator is shown to be strongly consistent. It is shown that the NVI-adaptive policy based on this sequence of strongly consistent HMM parameter estimates is (asymptotically, under appropriate assumptions) average-optimal. Essentially, it minimizes the long run average cost of the weighted combination of the expected state estimation error and sum transmit power across the sensors for the HMM with true parameters in a time-asymptotic sense. The advantage of this scheme is that the policies are obtained recursively without the need to solve the Bellman equation at each time step, which can be computationally prohibitive. As is usual with value iteration schemes, practical implementation of the NVI-adaptive policy requires discretization of the state and action space, which results in some loss of optimality. Nevertheless, numerical results illustrate the asymptotic convergence properties of the parameter estimates and the asymptotically close to optimal performance of the adaptive MDP algorithm compared to the performance of an MDP based dynamic quantization and power allocation algorithm designed with perfect knowledge of the true parameters.

Estimation of Spatially Distributed Processes Using Mobile Spatially Distributed Sensor Network

The problem of estimating a spatially distributed process described by a partial differential equation (PDE), whose observations are contaminated by a zero mean Gaussian noise, is considered in this work. The basic premise of this work is that a set of mobile sensors achieve better estimation performance than a set of immobile sensors. To enhance the performance of the state estimator, a network of sensors that are capable of moving within the spatial domain is utilized. Specifically, such an estimation process is achieved by using a set of spatially distributed mobile sensors. The objective is to provide mobile sensor control policies that aim to improve the state estimate. The metric for such an estimate improvement is taken to be the expected state estimation error. Using different spatial norms, two guidance policies are proposed. The current approach capitalizes on the efficient filter gain design in order to avoid intense computational requirements resulting from the solution to filter Riccati equations. Simulation studies implementing and comparing the two proposed control policies are provided.