Abstract

In various applications of target tracking and sensor data fusion all available information related to the sensor systems used and the underlying scenario should be exploited for improving the tracking/fusion results. Besides the individual sensor measurements themselves, this in particular includes the use of more refined models for describing the sensor performance. By incorporating this type of background information into the processing chain, it is possible to exploit ‘negative’ sensor evidence. The notion of ‘negative’ sensor evidence covers the conclusions to be drawn from expected but actually missing sensor measurements for improving the position or velocity estimates of targets under track. Even a failed attempt to detect a target is a useful sensor output, which can be exploited by appropriate sensor models providing background information. The basic idea is illustrated by selected examples taken from more advanced tracking and sensor data fusion applications such as group target tracking, tracking with agile beam radar, ground moving target tracking, or tracking under jamming conditions.

Keywords: Negative information/evidence; Target tracking; Sensor resolution; Local search; Adaptive beam positioning; GMTI sensor fusion

Article Outline

1. Introduction

1.1. The notion of ‘negative’ evidence

1.2. Bayesian approach to target tracking

1.3. ‘Negative’ evidence and Bayes’ formalism

2. ‘Negative’ evidence in group tracking

2.1. Sensor resolution model

2.2. Impact of the sensor-to-target geometry

2.3. Update by exploiting ‘negative’ evidence

2.4. Verification with real radar data

3. ‘Negative’ evidence in ESA tracking

3.1. Radar pencil beam model

3.2. Search by exploiting ‘negative’ evidence

3.3. Discussion of a simulated example

4. ‘Negative’ evidence in GMTI tracking

4.1. GMTI detection model

4.2. GMTI-specific likelihood function

4.3. Update by exploiting ‘negative’ evidence

4.4. Fusion of ‘negative’ sensor evidence

4.4.1. Scenario

4.4.2. Discussion

5. ‘Negative’ evidence and jamming

6. Summary and conclusions

References