Stationary Target Localization Research Articles

Cooperative positioning method of underwater autonomous vehicle formation based on extended Kalman filter

Autonomous Underwater Vehicles (UAVs) have shown significant potential application value in marine environmental surveillance, development, and utilization of resources. However, due to the complex underwater acoustic channel environment and the relative motion between AUVs, this may lead to heavy-tailed non-Gaussian process noise and measurement noise, leading to increased error and the Extended Kalman Filter (EKF) may fail. In prior studies, we propose a multi-AUV formation hierarchical target location algorithm based on the EKF, which can realize multi-AUV self-localization and stationary target localization. The AUV formation consists of one high-precision piloting AUV and several low-precision following AUVs. The following AUVs are divided into two levels, the reference AUVs and the AUVs to be tested. According to the designed positioning period, the reference AUV receives the position parameters from a high-precision piloting AUV and transmits its own position parameters to the AUV to be measured. Then use the EKF to complete the cooperative position correction of the AUV cluster. In this paper, we will study the influence of heavy-tailed noise on this method and study the EKF based on Huber estimation to improve the antijamming capability.

Optimal control and learning for cyber‐physical systems

Optimal control and learning for <scp>cyber‐physical</scp> systems

Stationary target localization and circumnavigation by a non‐holonomic differentially driven mobile robot: Algorithms and experiments

SummaryThis article addresses a surveillance problem where the goal is to achieve a circular motion around a target by a non‐holonomic differentially driven mobile robot. The available information for the mobile robot includes its own position, velocity with respect to the inertial frame, and the bearing angle of the target in its body frame. Since the target position is unavailable, an estimator is first proposed to localize it. Then a two‐step controller is given to drive the mobile robot to move onto a circular trajectory with a desired radius around it. It is shown that the proposed algorithm guarantees the convergence of the estimation error to a small neighborhood of zero and the motion of the robot to a small neighborhood of a designed radius. The performance of the proposed algorithm is first verified by simulations. Then, several experiments on a differentially driven mobile robot, Pioneer 3‐DX, are presented to further validate the proposed algorithm.

A Semidefinite Programming Approach for Doppler Frequency Shift Based Stationary Target Localization

A Semidefinite Programming Approach for Doppler Frequency Shift Based Stationary Target Localization

An Effective and Simple Solution for Stationary Target Localization Using Doppler Frequency Shift Measurements

An Effective and Simple Solution for Stationary Target Localization Using Doppler Frequency Shift Measurements

Wall Clutter Mitigation Using Discrete Prolate Spheroidal Sequences for Sparse Reconstruction of Indoor Stationary Scenes

Detection and localization of stationary targets behind walls is primarily challenged by the presence of the overwhelming electromagnetic signature of the front wall in the radar returns. In this paper, we use the discrete prolate spheroidal sequences to represent spatially extended stationary targets, including exterior walls. This permits the formation of a linear block sparse model relating the range profile and observation vectors. Effective wall clutter suppression can then be performed prior to sparse signal image reconstruction. We consider stepped-frequency radar with two cases of frequency measurement distributions over antenna positions. In the first case, the same subset of frequencies is used for each antenna in physical or synthetic aperture arrays, whereas the other case allows different sets of few frequency observations to be available at different antennas. Using experimental data, we demonstrate that the proposed scheme enables sparsity-based image reconstruction techniques to effectively detect and localize behind-the-wall stationary targets from reduced measurements.

Exploring the edges of visual space: The influence of visual boundaries on peripheral localization

Previous studies of localization of stationary targets in the peripheral visual field have found either underestimations (foveal biases) or overestimations (peripheral biases) of target eccentricity. In the present study, we help resolve this inconsistency by demonstrating the influence of visual boundaries on the type of localization bias. Using a Goldmann perimeter (an illuminated half-dome), we presented targets at different eccentricities across the visual field and asked participants to judge the target locations. In Experiments 1 and 2, participants reported target locations relative to their perceived visual field extent using either a manual or verbal response, with both response types producing a peripheral bias. This peripheral localization bias was a non-linear scaling of perceived location when the visual field was not bounded by external borders induced by facial features (i.e., the nose and brow), but location scaling was linear when visual boundaries were present. Experiment 3 added an external border (an aperture edge placed in the Goldmann perimeter) that resulted in a foveal bias and linear scaling. Our results show that boundaries that define a spatial region within the visual field determine both the direction of bias in localization errors for stationary objects and the scaling function of perceived location across visual space.

Exploiting geometry for improved hybrid AOA/TDOA-based localization

In this paper we examine the geometrically constrained optimization approach to localization with hybrid bearing (angle of arrival, AOA) and time difference of arrival (TDOA) sensors. In particular, we formulate a constraint on the measurement errors which is then used along with constraint-based optimization tools in order to estimate the maximum likelihood values of the errors given an appropriate cost function. In particular we focus on deriving a localization algorithm for stationary target localization in the so-called adverse localization geometries where the relative positioning of the sensors and the target do not readily permit accurate or convergent localization using traditional approaches. We illustrate this point via simulation and we compare our approach to a number of different techniques that are discussed in the literature.

Target tracking by time difference of arrival using recursive smoothing

The paper presents a simple recursive solution to passive tracking of maneuvering targets using time difference of arrival (TDOA) measurements. Firstly, an iterative Gauss–Newton algorithm is developed for stationary target localization based on a constrained weighted least-squares (CWLS) criterion. The advantages of the CWLS estimate are its inherent stability due to the absence of local minima at infinity and its capability to match the performance of the maximum-likelihood (ML) estimate. To track maneuvering targets, a computationally efficient recursive least-squares (RLS) algorithm is developed, which smoothes successive stationary target location estimates obtained from the ML or CWLS solution using a constant-acceleration motion model. In simulation studies, the proposed recursive tracking algorithm is compared with a Kalman tracking algorithm that estimates the target track directly from the TDOA measurements, and is shown to be capable of outperforming the Kalman tracker.