Joint Domain Localized Research Articles

A Single-Dataset-Based Pre-Processing Joint Domain Localized Algorithm for Clutter-Suppression in Shipborne High-Frequency Surface-Wave Radar.

Due to the motion of the platform, the spectrum of first-order sea clutter will widen and mask low-velocity targets such as ships in shipborne high-frequency surface-wave radar (HFSWR). Limited by the quantity of qualified training samples, the performance of the generally used clutter-suppression method, space–time adaptive processing (STAP) degrades in shipborne HFSWR. To deal with this problem, an innovative training sample acquisition method is proposed, in the area of joint domain localized (JDL) reduced-rank STAP. In this clutter-suppression method, based on a single range of cell data, the unscented transformation is introduced as a preprocessing step to obtain adequate homogeneous secondary data and roughly estimated clutter covariance matrix (CCM). The accurate CCM is calculated by integrating the approximate CCM of different range of cells. Compared with existing clutter-suppression algorithms for shipborne HFSWR, the proposed approach has a better signal-to-clutter-plus-noise ratio (SCNR) improvement tested by real data.

Improved application of space-time adaptive monopulse to joint domain localized STAP

ABSTRACTIn this paper, we focus on the efficient joint domain localized (JDL) space-time adaptive processing (STAP) algorithm, and present a modified JDL space-time adaptive monopulse (STAM) scheme to offer improved accuracy estimation of target parameters. The procedure iteratively modifies the JDL transform matrix according to the instantaneous estimation of the target angle-Doppler location using STAM. Thereby, it ensures the target steering vector exactly localizing at the center of the localized processing region (LPR). Furthermore, it also eliminates the mismatch error of the test steering vector in STAM application, which consequently results in the improved estimation of target direction and Doppler frequency. Simulation results demonstrate that when targets deviate from the center of test angle-Doppler bin, the proposed approach can significantly outperform the conventional JDL-STAM estimator, which suffers from angle-Doppler straddling loss and steering vector mismatch.

The Application of JDL to Suppress Sea Clutter for Shipborne HFSWR

This paper deals with the problem of sea clutter suppression for shipborne high frequency surface wave radar (HFSWR) based on the joint domain localized (JDL) adaptive processing algorithm. The performance of the novel method is compared with 2D FFT plus digital beamforming (FFT-DBF) and orthogonal weight in different azimuths. The results based on simulated and real data show that the novel method provides higher detection performance than others.

Reduced-Rank Adaptive Filtering Using Localized Processing for CDMA Systems

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> An integrated beamforming (spatial processing) and multiuser-detection (temporal processing) scheme is an effective approach to increase system capacity but is also impractical due to the high associated computational costs. To overcome this problem, researchers have developed reduced-rank approaches. Adding to this class of algorithms, this paper introduces a joint-domain adaptive algorithm, which processes spatial and temporal data within a localized region after transformation to a “beamspace.” This new joint-domain-localized (JDL) adaptive algorithm is developed for single-cell uplink code-division multiple-access (CDMA) systems with a receive-antenna array. Given a rank constraint on the JDL transformation, this paper develops the optimal choice of transformation matrix and justifies a simpler suboptimal choice. The JDL algorithm is shown to have relatively low computation load. We also introduce the JDL-Z algorithm that combines JDL processing with zero forcing for multicell uplink CDMA systems. At the cost of higher computational complexity, this new scheme provides better performance and a faster convergence rate than the JDL algorithm. However, the key contribution is a framework wherein adaptive processing can be followed by yet another stage of adaptive processing. Simulations are used to illustrate the efficacy of the two algorithms. </para>

Robust STAP detection in a dense signal airborne radar environment

This paper presents the performance of several space–time adaptive processing (STAP) detection methods in a dense signal environment. These include the normalized parametric adaptive matched filter (N-PAMF), the joint domain localized (JDL), and a variant of JDL referred to as the normalized JDL (NJDL). Issues considered here include robust detection with respect to signal contamination of training data and efficient estimation procedures with limited training data. The paper also introduces the innovations power sorting (IPS) pre-processing procedure for representative training data selection. Performance analyses are carried out with measured data from the Multichannel Airborne Radar Measurement (MCARM) program.

Joint Domain Localized Adaptive Processing Using Nonorthogonal Steering Vectors

The joint domain localized (JDL) processing adaptive algorithm significantly speeds up space-time adaptive processing by working within a localized processing region in the angle–Doppler domain. The two-dimensional data collected in space and time domains are transformed to the angle–Doppler domain using the spatial and temporal steering vectors. However, the algorithm depends on the assumption that the steering vectors form an orthogonal set. In practice, due to the mutual coupling between antenna elements, the spatial steering vectors are nonorthogonal. This paper presents a modification of the JDL algorithm to account for the nonorthogonality of the spatial steering vectors. The modification is extended to allow for the use of windows in transforming from the space-time to the angle–Doppler domain. Improvements in detection capability are demonstrated using examples from the MCARM database.