In tracking applications target motion is usually best modeled in a simple fashion using Cartesian coordinates. Unfortunately, in most systems the target position measurements are provided in terms of range and azimuth (bearing) with respect to the sensor location. This situation requires either converting the measurements to a Cartesian frame of reference and working directly on converted measurements or using an extended Kalman filter (EKF) in mixed coordinates. An accurate means of tracking with debiased consistent converted measurements which accounts for the sensor inaccuracies over all practical geometries and accuracies is presented. This method is compared with the mixed coordinates EKF approach as well as a previous converted measurement approach which is an acceptable approximation only for moderate cross-range errors. The new approach is shown to be more accurate in terms of position and velocity errors and provides consistent estimates (i.e., compatible with the filter calculated covariances) for all practical situations. The combination of parameters (range, range accuracy, and azimuth accuracy) for which debiasing is needed is presented in explicit form. >

Tracking with debiased consistent converted measurements versus EKF

Short-range compact radar systems offer attractive modality for localization and tracking of human targets in indoor and outdoor environments for industrial and consumer applications. Micro-Doppler radar reflections from human targets can be sensed and used for human activity classification, which has applications in human–computer interaction and health assessment among others. Traditionally, the detected human targets’ location are tracked and its micro-Doppler spectrogram extracted for further activity classification of the human target. In this letter, we propose a novel integrated human localization and activity classification using unscented Kalman filter and demonstrate our results using a short-range 60-GHz frequency modulated continuous wave radar. The proposed solution is shown to result in an improved classification accuracy with the capability of providing uncertainty with associated classification probabilities and, thus, is a simple mechanism to achieve Bayesian classification.

Continuous Human Activity Classification With Unscented Kalman Filter Tracking Using FMCW Radar

An adaptive Gaussian sum algorithm for radar tracking

In a recent paper the authors introduced an all-Cartesian formulation of a jerk model for tracking highly maneuvering targets. Here a more complex but realistic case is considered, where target motion modeling and tracking are carried out in the 3-D Cartesian frame using measurements obtained in a spherical system. The transformation of the measurements into the Cartesian system results in nonlinear measurement equations. We solve the problem using an extended Kalman filter (EKF) approach, and also treat the earlier acceleration model similarly for comparison of results.

/pdf/mixed-coordinate-tracking-of-generalized-maneuvering-targets-4p4fe1xwu9.pdf

Mixed coordinate tracking of generalized maneuvering targets using acceleration and jerk models

In this paper, we propose a new radar tracking algorithm based on the Gaussian sum filter. We have developed a new systematic and efficient way to approximate a non-Gaussian and measurement-dependent function by a weighted sum of Gaussian density functions. We have derived the formula for updating the weights involved in the bank of Kalman-type filters and also suggested a way to alleviate the growing memory problem inherent in the Gaussian sum filter. Our method is compared with the extended Kalman filter (EKF) and the converted measurement Kalman filter (CMKF) and it is shown to be more accurate in term of position and velocity errors.

/pdf/an-adaptive-gaussian-sum-algorithm-for-radar-tracking-3o7n6uwqpa.pdf

A numerically well-conditioned, quasi-extended Kalman filter is proposed. The filter is numerically described. The simulation results presented show that the estimation performance of the quasi-extended filter is superior, for short distances, compared with the widely used linear tracking filters. In addition, the simplicity of the quasi-extended filter makes it very easy to implement. >

C.E. D'Attellis

Papers

Maneuvering target tracking using extended Kalman filter