We report two decentralized multi-agent cooperative localization algorithms in which, to reduce the communication cost, inter-agent state estimate correlations are not maintained but accounted for implicitly. In our first algorithm, to guarantee filter consistency, we account for unknown inter-agent correlations via an upper bound on the joint covariance matrix of the agents. In the second method, we use an optimization framework to estimate the unknown inter-agent cross-covariance matrix. In our algorithms, each agent localizes itself in a global coordinate frame using a local filter driven by local dead reckoning and occasional absolute measurement updates, and opportunistically corrects its pose estimate whenever it can obtain relative measurements with respect to other mobile agents. To process any relative measurement, only the agent taken the measurement and the agent the measurement is taken from need to communicate with each other. Consequently, our algorithms are decentralized algorithms that do not impose restrictive network-wide connectivity condition. Moreover, we make no assumptions about the type of agents or relative measurements. We demonstrate our algorithms in simulation and a robotic experiment.Joint CL, which treats the team of mobile agents as one system and processes the inter-agent measurements to update the state estimate of all the agents, delivers the best localization accuracy. This is because the prior correlations allow agents other than the two involved in a relative measurement also benefit from relative measurement update (see [6] for further discussions). However, decentralized implementation of a joint CL in its naive form requires all-to-all or all-to-a-fusion-center communication at each timestep. To reduce the communication cost, [6]-[8] use decomposition techniques to fully decouple The authors are with the Mechanical and Aerospace Eng.
We present an effective bias compensation method to process none-line-of-sight (NLoS) and long-distance lineof-sight (LD-LoS) Ultra-Wideband (UWB) range measurement signals used to aid a pedestrian inertial navigation system (INS). The common UWB bias compensation techniques use machine learning methods to identify and remove the bias in the measurements. These techniques are computationally expensive and require extensive prior data. Here, we propose to use an algorithmic compensation technique that accounts for the bias by estimating it using the Schmidt Kalman filter. Next, we exploit the positivity of the error in the UWB range measurements to propose a novel constrained sigma point based correction filtering that can be used atop the Schmidt Kalman filter for further improvement in the positioning accuracy of the UWB aided pedestrian inertial navigation. Experiments demonstrate the effectiveness of our methods.
In this paper, a multi-feature detector based on isolation forest (iForest) algorithm is developed to detect floating small targets in sea clutter. The conventional multi-feature detector can only process three features or less. The proposed detector aims to break the limitation of feature dimensions' number of the existed feature-based detectors and to improve the detection performance. It transforms the detection of floating small target into an anomaly detection problem in a high-dimensional feature space, breaking the limitation of the number of features. Firstly, a modified isolation forest is constructed from multiple features extracted from sea clutter. Meanwhile, the relative Doppler coefficient of variation (RCV) is proposed and added into the feature library. Then, taking the average path length as detection statistic, the detection threshold is obtained by Monte-Carlo technique at the given false alarm probability (PFA). Finally, the final decision is made by comparing the path length calculated from the cell under test (CUT) of radar returns with the detection threshold. Detection performances are evaluated based on twenty measured IPIX radar data sets. The experiment results show that the multi-feature detector based on isolation forest can obtain a significant performance improvement and has lower computation cost compared with the existed detectors.
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