METHOD FOR DETECTING TARGETS USING SPACE-TIME ADAPTIVE PROCESSING AND SHARED KNOWLEDGE OF THE ENVIRONMENT

• US 20120062409A1
• Filed: 09/10/2010
• Published: 03/15/2012
• Est. Priority Date: 09/10/2010
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Sahinoglu, Zafer, Pun, Man-On, Wang, Pu
• Granted Patent: US 8,138,963 B1
• Time in Patent Office: Days
• US Class Current: 342/27
• CPC Class Codes: G01S 13/04 Systems determining presence of a target based on relative movement of target G01S13/56
• CPC Class Codes: G01S 7/292 Extracting wanted echo-signals

Abstract

A method detects a target in a radar signal using space-time adaptive processing. A test statistic is

$T=\frac{\underset{\alpha }{\max }\underset{\lambda }{\max }{\int }_R^{}f_1\left(x_0,x_1,\dots ,x_K\alpha ,\lambda ,R\right)p\left(R\right)R}{\underset{\lambda }{\max }{\int }_R^{}f_0\left(x_0,x_1,\dots ,x_K\lambda ,R\right)p\left(R\right)R},$

where x₀ is a test signal, x_k are K training signals, α is an unknown amplitude of a target signal within the test signal, λ is a scaling factor, R is a covariance matrix of the training signals, and a function max returns a maximum values. The test statistic is compared to a threshold to determine whether the target is present, or not.

Claims

1. A method for detecting a target in a radar signal using space-time adaptive processing, comprising the steps:

determining a test statistic

2. The method of claim 1, wherein a hypothesis testing problem is used as follows

H₀x₀=d₀,

x_k=d_k, k=1, . . . ,K,

H₁:

x₀=α

s+d₀,

x_k=d_k, k=1, . . . ,K,

where a hypothesis H₀ is that the target is not present in the test signal, a hypothesis H₁ is that the target is present in the test signal, and d₀ and d_k are noise terms for covariance matrices of the test signal and training signals, respectively.

3. The method of claim 1, wherein the covariance matrix R is random and has a probability density function p(R), which is a function of a covariance matrix prior probability matrix R.

4. The method of claim 1, further comprising:

replacing the unknown amplitude α

by a maximum likelihood estimate of the amplitude α

.