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SAFE-IMM: Robust and Lightweight Radar-Based Object Tracking on Mobile Platforms

Dnyandeep Mandaokar, Bernhard Rinner

TL;DR

SAFE-IMM is proposed, a lightweight IMM variant for tracking on mobile and resource-limited platforms with a safe covariance-aware gate that permits WTA only when the implied jump from the mixture to the winner is provably bounded.

Abstract

Tracking maneuvering targets requires estimators that are both responsive and robust. Interacting Multiple Model (IMM) filters are a standard tracking approach, but fusing models via Gaussian mixtures can lag during maneuvers. Recent winnertakes-all (WTA) approaches react quickly but may produce discontinuities. We propose SAFE-IMM, a lightweight IMM variant for tracking on mobile and resource-limited platforms with a safe covariance-aware gate that permits WTA only when the implied jump from the mixture to the winner is provably bounded. In simulations and on nuScenes front-radar data, SAFE-IMM achieves high accuracy at real-time rates, reducing ID switches while maintaining competitive performance. The method is simple to integrate, numerically stable, and clutter-robust, offering a practical balance between responsiveness and smoothness.

SAFE-IMM: Robust and Lightweight Radar-Based Object Tracking on Mobile Platforms

TL;DR

SAFE-IMM is proposed, a lightweight IMM variant for tracking on mobile and resource-limited platforms with a safe covariance-aware gate that permits WTA only when the implied jump from the mixture to the winner is provably bounded.

Abstract

Tracking maneuvering targets requires estimators that are both responsive and robust. Interacting Multiple Model (IMM) filters are a standard tracking approach, but fusing models via Gaussian mixtures can lag during maneuvers. Recent winnertakes-all (WTA) approaches react quickly but may produce discontinuities. We propose SAFE-IMM, a lightweight IMM variant for tracking on mobile and resource-limited platforms with a safe covariance-aware gate that permits WTA only when the implied jump from the mixture to the winner is provably bounded. In simulations and on nuScenes front-radar data, SAFE-IMM achieves high accuracy at real-time rates, reducing ID switches while maintaining competitive performance. The method is simple to integrate, numerically stable, and clutter-robust, offering a practical balance between responsiveness and smoothness.

Paper Structure

This paper contains 10 sections, 8 equations, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Methodology
  3. Conventional IMM
  4. SAFE-IMM
  5. Hardening Strategies
  6. Experiments
  7. Simulation Setup
  8. Simulation Result
  9. Evaluation with NuScenes Dataset
  10. Conclusion

Figures (3)

  • Figure 1: Illustration of the SAFE WTA rule with covariance geometry. The panels demonstrate when WTA is permitted (left), rejected (center), or permitted with a close rival (right).
  • Figure 2: Ground truth vs. simulated tracks of targets T1-T3 under profiles 1 (high position noise) and 2 (high velocity noise).
  • Figure 3: Model probabilities (CV:1, CA:2) and WTA decisions for T2. The orange spike shows WTA fire, and the blue spike shows the winner change.