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For the high-speed, high-maneuverability and stealthy target detection via modern radar in complicated electromagnetic environment, a novel radar signal processing approach called Space-Time-Frequency Focus-Before-Detection (STF-FBD) via multi-dimensional coherent integration is proposed. Based on space-timefrequency signal modeling for modern radar systems, the proposed method can effectively suppress the strong interference, such as clutter and active jamming, and overcome the problems of scaled effect of high-speed targets, aperture fill time, sparse frequency sub-band synthesis, across range units, across Doppler units and across beam units. The proposed methods improves radar signal processing performance on the steps like energy integration, target detection, parameter estimation, maneuver tracking, feature extraction and target recognition. It also outperforms the existing Track-Before-Detection (TBD) methods and establish a unified STF-FBD and STF-FBD-TBD radar signal processing frame work. The proposed method is suitable for high-speed, high-maneuverability and stealthy target, as well as for conventional targets. It is applicable for new-generation modern radar, as well as for conventional radars, and may find application to different field.

A new data association algorithm is proposed in this paper for heterogeneous sensors information fusion system, which consists of Radar and high-dynamic-range IR, based on joint usage of the nearest-neighbor (NN) and the topology similarity. The proposed algorithm can avoid the complicated system biases compensation based on topology information. Because the NN method is sensitive to the system bias while the existing topology-based algorithms need a large amount of computation, the proposed algorithm shows a lot of advantages, like high estimation accuracy and robustness as well as the improvement of success association rate. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed method. And the estimation accuracy can be as high as 90%.

A new algorithm is proposed for velocity estimation of moving targets in single antenna synthetic aperture radar (SAR). Based on the fact that different velocity vectors cause different geometrical figures of the two-dimensional (2-D) signature in the range-Doppler (RD) domain, this algorithm estimates the azimuth and range velocities by a 2-D search such that the range cell migration correction (RCMC) and the second range compression (SRC) are correctly performed. It is shown that, using the proposed algorithm, the Doppler ambiguity problem can lie avoided and satisfactory accurate velocity estimation can be obtained in high signal-to-clutter ratio (SCR) scenarios.

Based on a parametric model and some optimum methods, it has been previously proved that parametric velocity synthetic aperture radar (VSAR) may improve the performances of moving target detection and parameter estimation simultaneously. In this paper, multilook processing is studied for parametric VSAR. At first, statistical signal models are established for azimuth multilook processing (AMLP) and range multilook processing (RMLP), respectively. By combining the multiple AMLP sublook pixel vectors, it is shown that the clutter parameter estimation accuracy can be further improved via the maximum-likelihood estimation methods. Meanwhile, based on the adaptive implementation for the optimum processing of VSAR, RMLP can be used to improve slowly moving target detection performance via the noncoherent integration of multiple range sublooks. Furthermore, it is shown that the Doppler frequencies of moving targets vary linearly with different range sublooks due to the different carrier frequencies of sublooks. Therefore, based on a proposed novel two-step multilook diversity (TS-MLD) estimator for RMLP, the 'azimuth location ambiguity' of VSAR can be well resolved via least squares linear regression, without configuration change of the conventional VSAR. Also, the estimation accuracy of target's unambiguous Doppler frequency, as well as target's azimuth location, is derived for the proposed TS-MLD method. Finally, numerical experiments and scene simulations are provided to demonstrate the effectiveness of the proposed multilook-based methods. Index Terms--Azimuth location ambiguity, maximum likelihood (ML), moving target detection, multilook processing, two-step multilook diversity (TS-MLD) method, velocity synthetic aperture radar (VSAR).

Velocity synthetic aperture radar (VSAR) is equipped with a linear array to receive the echoes from a radar illuminating area via multiple channels, each of which can reconstruct a reflectivity image for the same stationary scene. Based on analysis of pixel vector sampled among multi-images, VSAR may effectively suppress the strong ground clutter and improve moving target detection and location. In this paper, different Doppler-distributed properties are derived for the moving target and clutter, respectively. Then, we propose a novel parametric statistical model for VSAR by dividing the pixel vector into three components, namely, target, clutter, and noise. Furthermore, a method of adaptive implementation of optimal processing (AIOP-VSAR) is presented for moving target detection. It is shown that the optimum detection performance may be obtained via AIOP-VSAR, particularly for the slowly moving target in an inhomogeneous clutter environment. Also, the Cramer-Rao bounds (CRBs) are derived for the estimation of unknown model parameters, as well as the azimuth locations of moving targets, and the maximum-likelihood methods are proposed to reach these CRBs. Based on the proposed target detection and parameter estimation methods, we present a complete parametric flowchart for VSAR. It is demonstrated that the proposed flowchart may effectively mitigate the 'azimuth location ambiguity' of VSAR and has the super-resolution ability to resolve 'velocity layover' for multiple targets. Finally, some detailed numerical experiments and scene simulations are provided to show the effectiveness of the proposed methods. Index Terms--Adaptive implementation of optimal processing (AIOP), Cramer-Rao bounds (CRBs), maximum likelihood (ML), moving target detection, parameter estimation, velocity synthetic aperture radar (VSAR).

By using a linear antenna array, velocity synthetic aperture radar (VSAR) can detect, focus, and locate slowly moving targets well. However, it may mis-locate fast moving targets in the azimuth (cross-range) direction. In this correspondence, we propose a synthetic aperture radar (SAR) with a nonuniform linear antenna array and give a design of the antenna arrangement. It is shown that our proposed nonuniform linear antenna array SAR (NULA-SAR) can locate both slowly and fast moving targets correctly. An integrated NULA-SAR algorithm for moving target imaging is also presented, and it is verified by some simulations.

In this paper, we propose a bistatic linear array synthetic aperture radar (BLA-SAR) system for moving target detection, location, and imaging. In the BLA-SAR system, a geostationary satellite is used as a transmitter, and two airborne linear array radars are used as passive receivers, where the transmitted waveforms from the geostationary satellite may have two different carrier frequencies, two linear array antennas on two different airplanes may be equipped with different spacings, or two airplanes may fly with two different velocities. It is shown that, using the BLA-SAR, not only the stationary clutter can be suppressed but also locations of both slow and fast moving targets can be accurately estimated. Furthermore, an effective BLA-SAR algorithm of moving target imaging is also proposed. Lastly, some numerical experiments are given to demonstrate the effectiveness of the BLA-SAR. Index Terms--Bistatic synthetic aperture radar (SAR), Chinese remainder theorem (CRT), moving target.

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, 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.

Three types of distributed constant false-alarm rate (CFAR) detection based on local test statistic (LTS) are developed under the condition that all the samples in different sensors are independent and identically distributed (IID). They are called R (ratio), S (substract) and P (plus) types. For a two-sensor network and local order statistics CFAR (OS-CFAR) algorithm, we derive their closed-form expressions for false alarm and detection probabilities of detecting a Swerling II target in Gaussian noise. The numerical results show that [rho], the ratio between the noise power levels in two local sensors, has very strong effects on the performances of the S and P type schemes and the centralized detection. They can't hold to be CFAR when there isn't the precise prior knowledge of [rho]. Compared with the centralized detection, the distributed detection scheme is more robust for the effect of [rho]. Fortunately, the R type scheme is not affected by the [rho] at all. It implies that the local CFAR processing can immunize the distributed detection against the [rho].

Array errors are inherent in a realistic phased array radar system. The influence of array errors on the clutter degrees of freedom and the clutter subspace in an airborne phased array radar is analyzed. Based on the presented theoretic results, a method of short-time processing followed by coherent integration is proposed for clutter suppression in airborne phased array radars. It can approximate the two-dimensional optimal processor well even in the presence of array errors, clutter fluctuations and aircraft drift, with a considerable saving in computations.

Space-time adaptive processing (STAP) has been widely discussed for airborne radar systems to improve the system performance of detecting targets. This is especially for airborne early warning (AEW) radar, which should find the long-range and small radar cross section (RCS) targets such as the stealth aircrafts and missiles. However, in existing airborne radar literature, STAP is mainly considered for clutter and jamming rejection in side-looking airborne radar (SLAR) applications. There have been fewer discussions on airborne radar with non-side-ways looking array radar (non-SLAR). The STAP of non-SLAR such as forward looking array radar is also very important and can not be avoided for airborne radar to detect targets in all directions. The STAP of the non-SLAR is studied here. A scheme has been proposed, which is processed by the way of STAP combined with multiple staggered medium pulse repetition frequencies (PRFs). We further study the selection of PRFs in order to make the scheme more available for non-SLAR radar. We analyze two typical non-SLAR cases, i.e., inclined-sideways looking array and forward looking array. We examine this scheme by comparing the performances of three processing systems under the criteria of range-velocity blind zone minimization. Computer simulation results show the multiple-PRFs STAP scheme is feasible for non-SLAR and can be applied to phased-array AEW radar systems.

Many adaptive detectors regulate the detection threshold adaptively through an estimate of the clutter power level, which is formed by reference cell samples. This paper proves by the use of the invariant test that a large class of adaptive detectors possesses the constant false-alarm rate (CFAR) property when they satisfy certain weak conditions. The proof reveals the mechanism of this class of CFAR detectors and provides a general method of proving the CFAR property.

Due to high-speed, highly maneuvering, and weak targets as well as strong clutter and jamming, it is known that the environment of modern radar becomes increasingly challenging [1?7]. To deal with the challenges, focus-before-detection (FBD) [8?15] has been introduced in the first article [1] of this two-article series. As some of the typical FBD methods, Radon-Fourier transform (RFT) and generalized RFT (GRFT) originally proposed in [8, 9] have briefly introduced in [1] for coherent integration of a long time on target (TOT) to focus the energy of a target in parameter space with arbitrary parametric motion. That is, the motion is modeled with a finite number of translational and rotational motion parameters, such as velocity, acceleration, and also jerk. The FBD methods can overcome the effects of across range cells (ARC), across Doppler cells (ADC), and even across beamwidths (ABW) in a long TOT. As a result, by combining accurate environment sensing from echoes and effective resource management for optimization, the proposed FBD-based methods can effectively improve target detection and parameter estimation without changing system parameters in a complicated environment. [ABSTRACT FROM PUBLISHER]

During the World War II (WWII), radar was invented as an all-day, all-weather, long-range sensor. Over the past 75 years or so, radar has acted as the clairvoyance and clairaudience of humans and has had wide applications [1]?[19] in both defense and civilian fields, e.g., surveillance, reconnaissance, fire control, border monitoring, collision avoidance, and traffic control. Along with radar development, radar signal processing (RSP) always plays an important role. In 1943, North [4] pointed out that the matched filter should be the foundation of radar to detect targets from echoes generated by the known transmitting waveform. In 1950, Woodward and Davies [5] introduced the Bayesian statistics theory into the RSP field and proposed the constant false alarm ratio (CFAR) rule for target detection. Based on matched filtering and the CFAR rule, the embryonic framework of classic RSP has been formed and used thus far. In 1953, Woodward [6] further proposed an ambiguity function as a useful tool to evaluate the range and Doppler discrimination abilities of a transmitting waveform, which paves the foundation for radar waveform design. Furthermore, to deal with the tradeoff between large transmitting energy and high range resolution, the early unmodulated rectangular waveform has been replaced by more waveforms with a large time-bandwidth product (TBP), such as linear frequency modulation (LFM) and phase-coded waveforms. These waveforms with large TBP have been widely used to realize the range compression via matched filter. [ABSTRACT FROM PUBLISHER]