We fuse a self-organizing hyperellipsoid clustering (HEC) Kohonen neural network with the Julier-Uhlmann-Kalman filter (JUKF) to perform map building and low-level position estimation. The HEC Kohonen uses the Mahalanobis distance to learn elongated shapes (typical of sonar data) and obtain a stochastic measurement of data-node association. The number of nodes is regulated by measuring how well a node model matches its associated data. The HEC Kohonen can handle high-dimensional problems and can be generalized to other pattern recognition problems. The JUKF compliments the HEC Kohonen in that it performs low-level (nonlinear) tracking more efficiently and more accurately than the extended Kalman filter. By estimating and propagating error covariances through system transformations, the JUKF eliminates the need to derive Jacobian matrices. The inclusion of stochastic information inherent to the HEC map renders the JUKF an excellent tool for our HEC-based map building, position estimation, motion planning and low-level tracking.