Space-time adaptive radar performance in heterogeneous clutter

Traditional analysis of space-time adaptive radar generally assumes the ideal condition of statistically independent and identically distributed (IID) secondary data. To the contrary, measured data suggests realistic clutter environments appear heterogeneous and so the secondary data is no longer IID. Heterogeneity leads to mismatch between actual and estimated covariance matrices, thereby magnifying the loss between the adaptive implementation and optimum condition. Concerns regarding the impact of clutter heterogeneity on space-time adaptive processing (STAP) warrant further study. To this end, we propose space-time models of amplitude and spectral clutter heterogeneity, with operational airborne radar in mind, and then characterize expected STAP performance loss under such heterogeneous scenarios. Simulation results reveal loss in signal-to-interference plus noise ratio (SINR) ranging between a few tenths of a decibel to greater than 16 dB for specific cases     

Frequency stability in adaptive retrodirective arrays

The frequency stability of communications or remote sensing systems using phase conjugation as a means of retrodirective signaling is developed under the influence of constant rectilinear motion. The results indicate that for dynamic mobile platforms such as aircraft, helicopters, and ground vehicles the retrodirective properties of phase conjugating arrays are not guaranteed due to a possible instability of the frequency. It is shown that the frequency transmitted by each platform has a dependence on the open loop gains of the transceivers on each platform in the link. Fast and accurate reconstruction of the phase of the signal requires a high open loop gain, however, too high a gain will cause instability and eventual loss of lock. Design tradeoffs are discussed.     

Simplified covariance matrix measurement in adaptive arrays

A procedure to measure the covariance matrix of an antenna array directly from the array sum port is described. The procedure involves specifically chosen sets of antenna weights that will allow the matrix components to appear at the array output. A parallel method of measuring the covariance matrix is also described. The accuracy requirements of the power measurements are examined analytically and through simulation     

Application of Gram-Schmidt algorithm to fully adaptive arrays

The Gram-Schmidt orthogonalization (GSO) algorithm has excellent numerical performance and is readily applicable to systolic implementations such as in a field of adaptive cancellation systems. A modified GSO algorithm for a fully adaptive array is proposed and computer simulations show that the proposed algorithm gives superior performance. A systolic implementation of the proposed GSO algorithm for fully adaptive array is presented. A feedback mode GSO algorithm for use with analog weights is also presented and has been shown to have excellent performance in the presence of weight errors     

Multimode adaptive signal processing: a new approach to GMTI radar

We show how a single reflector antenna with a multimode feed horn can be used in a ground moving target indication (GMTI) radar. In particular, we demonstrate the simultaneous detection and estimation of angular location of a ground moving target via adaptive cancellation of ground clutter     

Improved clutter mitigation performance using knowledge-aided space-time adaptive processing

This paper presents a framework for incorporating knowledge sources directly in the space-time beamformer of airborne adaptive radars. The algorithm derivation follows the usual linearly-constrained minimum-variance (LCMV) space-time beamformer with additional constraints based on a model of the clutter covariance matrix that is computed using available knowledge about the operating environment. This technique has the desirable property of reducing sample support requirements by "blending" the information contained in the observed radar data and the a priori knowledge sources. Applications of the technique to both full degree of freedom (DoF) and reduced DoF beamformer algorithms are considered. The performance of the knowledge-aided beam forming techniques are demonstrated using high-fidelity simulated X-band radar data     

Implementing digital terrain data in knowledge-aided space-time adaptive processing

Many practical problems arise when implementing digital terrain data in airborne knowledge-aided (KA) space-time adaptive processing (STAP). This paper addresses these issues and presents solutions with numerical implementations. In particular, using digital land classification data and digital elevation data, techniques are developed for registering these data with radar return signals, correcting for Doppler and spatial misalignments, adjusting for antenna gain, characterizing clutter patches for secondary data selection, and ensuring independent secondary data samples. These techniques are applied to select secondary data for a single-bin post-Doppler STAP algorithm using multi-channel airborne radar measurement (MCARM) program data. Results with the KA approach are compared with those obtained using the standard sliding window method for choosing secondary data. These results illustrate the benefits of using terrain information, a priori data about the radar, and the importance of statistical independence when selecting secondary data for improving STAP performance     

Reiterative median cascaded canceler for robust adaptive array processing

A new robust adaptive processor based on reiterative application of the median cascaded canceler (MCC) is presented and called the reiterative median cascaded canceler (RMCC). It is shown that the RMCC processor is a robust replacement for the sample matrix inversion (SMI) adaptive processor and for its equivalent implementations. The MCC, though a robust adaptive processor, has a convergence rate that is dependent on the rank of the input interference-plus-noise covariance matrix for a given number of adaptive degrees of freedom (DOF), N. In contrast, the RMCC, using identical training data as the MCC, exhibits the highly desirable combination of: 1) convergence-robustness to outliers/targets in adaptive weight training data, like the MCC, and 2) fast convergence performance that is independent of the input interference-plus-noise covariance matrix, unlike the MCC. For a number of representative examples, the RMCC is shown to converge using ~ 2.8N samples for any interference rank value as compared with ~ 2N samples for the SMI algorithm. However, the SMI algorithm requires considerably more samples to converge in the presence of outliers/targets, whereas the RMCC does not. Both simulated data as well as measured airborne radar data from the multichannel airborne radar measurements (MCARM) space-time adaptive processing (STAP) database are used to illustrate performance improvements over SMI methods.     

Robust space-time adaptive processing for airborne radar in nonhomogeneous clutter environments

Space-time adaptive processing (STAP) holds tremendous potential for the new generation airborne surveillance radar, in which the phased array antennas and pulse Doppler processing mode are adopted. A new STAP approach using the multiple-beam and multiple Doppler channels is presented here for airborne phased array radar. The approach with space-time multiple-beam (STMB) architecture is robust to array errors and has very low system degrees of freedom (DOFs). Hence, it has low sample support requirement and it is very suitable for the practical planar phased array radar under nonhomogeneous clutter environments. Meanwhile, a new nonhomogeneous detector (NHD) based on the correlation dimension (CD) is also proposed here, which is used as an effective method to screen tracing data prior to detection processing. It can further improve the performance of the STAP approach in the severely nonhomogeneous clutter environments. Therefore, a scheme that incorporates the correlation dimension nonhomogeneity detector (CD-NHD) with the STMB is recommended, which we term CD-NHD-STMB. The experimental simulation results indicate that: 1) the STMB processor is robust to array element error and has high performance under nonhomogeneous clutter environments; 2) the CD-NHD is also effective on the nonhomogeneous clutter. As a result, the CD-NHD-STMB scheme is robust to array element error and nonhomogeneous clutter, and therefore available for airborne phased array radar applications.     

Spaceflight testing of solar cells and concentrating arrays

The following topics are dealt with: new solar cell performance developments; calibration related developments; solar array concentrator; solar cell efficiency; nighttime power generation     

Sequences and arrays with perfect periodic correlation

Properties and methods for synthesizing sequences with perfect periodic autocorrelation functions and good energy efficiency are discussed. The construction is extended to two-dimensional perfect arrays. The construction methods used are based mainly on a search in the frequency domain and on a multiplication theorem for periodic sequences and arrays     

CFAR behavior of adaptive detectors: an experimental analysis

We conduct an experimental analysis for assessing the constant false alarm rate (CFAR) behavior of four coherent adaptive radar detectors in the presence of experimentally measured clutter data. To this end we exploit several data files containing both land, lake, and mixed land and sea clutter, collected by two radar systems (the MIT Lincoln Laboratory Phase-One radar and the McMaster IPIX radar) at different polarizations, range resolutions, and frequency bands. The results show that all the receivers, in the presence of real data, don't respect their nominal probability of false alarm (P/sub fa/), namely they exhibit a false alarm rate higher than the value preassigned at the design stage. Nevertheless one of them, the recursive persymmetric adaptive normalized matched filter (RP-ANMF) is very robust, in the sense that it presents an acceptable displacement from the nominal P/sub fa/, in correspondence of all the analyzed scenarios.     

Optimal and adaptive reduced-rank STAP

This paper is concerned with issues and techniques associated with the development of both optimal and adaptive (data dependent) reduced-rank signal processing architectures. Adaptive algorithms for 1D beamforming, 2D space-time adaptive processing (STAP), and 3D STAP for joint hot and cold clutter mitigation are surveyed. The following concepts are then introduced for the first time (other than workshop and conference records) and evaluated in a signal-dependent versus signal independent context: (1) the adaptive processing “region-of-convergence” as a function of sample support and rank, (2) a new variant of the cross-spectral metric (CSM) that retains dominant mode estimation in the direct-form processor (DFP) structure, and (3) the robustness of the proposed methods to the subspace “leakage” problem arising in many real-world applications. A comprehensive performance comparison is conducted both analytically and via Monte Carlo simulation which clearly demonstrates the superior theoretical compression performance of signal-dependent rank-reduction, its broader region-of-convergence, and its inherent robustness to subspace leakage     

On cross-ambiguity properties of Welch-Costas arrays

We discuss cross-ambiguity properties of a specific family of Costas arrays called Welch-Costas (W-C) arrays. These properties are of interest in multiuser radar and sonar system, especially since Costas arrays are known to possess ideal auto-ambiguity functions. The theory of W-C arrays is reviewed. It is then proved that only pairs of W-C arrays can have at most two hits in their cross-ambiguity function (best possible case). The maximum number of hits in the cross-ambiguity functions of a family of W-C arrays is shown to be a function of the number of W-C arrays in the family. The upper bound on the number of hits in the cross ambiguity functions for a family of W-C arrays is also derived. Specific examples of how reducing the number of W-C arrays improves the cross-ambiguity properties are given for various types of prime numbers     

Resolution and synthetic aperture characterization of sparse radar arrays

The concept of radar satellite constellations, or clusters, for synthetic aperture radar (SAR), moving target indicator (MTI), and other radar modes has been proposed and is currently under research. These constellations form an array that is sparsely populated and irregularly spaced; therefore, traditional matched filtering is inadequate for dealing with the constellation's radiation pattern. To aid in the design, analysis, and signal processing of radar satellite constellations and sparse arrays in general, the characterization of the resolution and ambiguity functions of such systems is investigated. We project the radar's received phase history versus five sensor parameters: time, frequency, and three-dimensional position, into a phase history in terms of two eigensensors that can be interpreted as the dimensions of a two-dimensional synthetic aperture. Then, the synthetic aperture expression is used to derive resolution and the ambiguity function. Simulations are presented to verify the theory.     

Phase quantized SAR signal processing: theory and experiments

A complete theory of synthetic aperture radar (SAR) processing from phase-only data is presented, for both analogue and coded raw signals and reference functions. Appropriate processing architectures are presented as well as quality indexes to judge performance of the selected coding. The analysis encompasses both images and interferometric fringes for interferometry SAR (IFSAR) application. Theoretical results are validated by a large number of experiments, comparing the performance of the suggested coding levels, and their combinations between raw and reference functions. Experiments are extended until the comparison of final digital elevation models     

Phase-only LMS and perturbation adaptive algorithms

The author defines a phase-only gradient-based adaptive algorithm analogous to the least mean square (LMS) algorithm. Two phase-only perturbation algorithms are then defined. It is shown that the gradient for the perturbation algorithms can be obtained by cross-correlating binary perturbation sequences, that are applied to the adaptive phases, with the resulting instantaneous output power or voltage. It is also shown that a single set of phase-only adaptive weights can be used to simultaneously null interference in multiple output beams. Simulation results are presented for all of the new algorithms. The phase-only perturbation techniques eliminate the need for element level receivers and support low cost retrofitting of adaptive nulling on phased arrays by using conventional beamsteering circuits to apply the adaptive weights     

Costas arrays with small number of cross-coincidences

A simple method is described for obtaining two or more Costas arrays with a maximum of one mutual bit or cross-coincidence, assuming that the vertical components of their relative shifts are appropriately bounded. The result can be useful for designing multiple Costas waveforms with small cross-ambiguities, if the potential Doppler shifts are restricted to a small portion of the waveform bandwidth