The basic concept of threshold detection was discussed in Chapter 3. Chapter 7 described common statistical models for the target echo power, including both probability distributions and pulse-to-pulse decorrelation models. Chapters 3 and 8 discussed coherent and noncoherent integration of data to improve the signal-to-noise ratio (SNR). In this chapter, these topics are brought together to provide a more detailed look at the optimal detection of fluctuating targets in noise. The analysis shows how the idea of threshold detection arises. The strategy for determining threshold levels and predicting detection and false alarm performance is demonstrated, and specific results are developed for the common Swerling target models. Also discussed are Albersheim's equation (first mentioned in Chapter 3) and Shnidman's equation, both very simple but useful analytical tools for estimating detection performance.

Chapter Contents:

• 15.1 Introduction
• 15.2 Detection Strategies for Multiple Measurements
• 15.3 Introduction to Optimal Detection
• 15.4 Statistical Models for Noise and Target RCS in Radar
• 15.5 Threshold Detection of Radar Signals
• 15.6 Further Reading
• 15.7 References
• 15.8 Problems

Inspec keywords: statistical analysis; radar detection

Other keywords: noncoherent integration; target echo power; threshold levels; probability distributions; Shnidman equation; Albersheim equation; detection prediction; common Swerling target models; threshold detection; false alarm performance; signal-to-noise ratio; coherent integration; radar targets; pulse-to-pulse decorrelation models; common statistical models; SNR

Subjects: Other topics in statistics; Signal detection; Radar equipment, systems and applications