To achieve tight beam coverage and obtain the optimal target detection performance for a space-based early warning radar (SBEWR) system, the reasonable radar beam design is prerequisite. In this paper, an optimal beam position design algorithm in a SBEWR system is proposed, where the beam position overlap rate (BPOR) choice is reasonably designed. In the proposed algorithm, the maximum signal-to-clutter and noise ratio (SCNR) of the targets located at the beam position edge is chosen as the criterion for the BPOR optimization design. The BPOR designment result provides important guidance and references for beam filling designment and guarantee the effective target detection capability of a SBEWR system.

The space-based surveillance radar system has a higher field of view and can overcome interference from Earth's atmosphere and terrain occlusion, which has been widely applied in high-threat near-Earth asteroid (NEA) warning and defense applications. Due to the limited power aperture product of the space-based system and the far distance between the radar and asteroid targets, the target signal is extremely weak. Prolonging the coherent accumulation time can effectively improve the radar detection capability of small asteroid targets, but the complex effects of range migration (RM) and Doppler frequency migration (DFM) will degrade the target coherent accumulation performance. To effectively solve this problem, an improved Keystone transform (KT) matched filtering banks method based on particle swarm optimization algorithm is proposed. Compared with traditional methods, the proposed method can not only ensure that the asteroid target detection performance is close to the theoretical optimum, but also reduce the system computation complexity. Simulation results verify the effectiveness of the proposed algorithm.

In this letter, the clutter range dependence issue caused by crab angle is investigated in an airborne multi-channel radar system. A new method is proposed to accomplish the crab angle estimation and the non-stationary error compensation. Firstly, from the center-biased and spectrum-broadened characteristics of the clutter space-time spectrum in the case of non-neglected crab angle, a 1-D cost function based on space-time spectrum broadening degree is constructed to achieve the accurate crab angle estimation. Then, the clutter non-stationary errors can be effectively compensated in the post-Doppler domain, significantly improving the subsequent space time adaptive processing (STAP) performance. Simulation results are presented to validate the feasibility and effectiveness of the proposed method.

For synthetic aperture radar (SAR), multipolarization and multifrequency modes greatly enrich the acquired earth resource information and have been widely applied in remote-sensing fields. In this article, we compare the classification capabilities of multipolarization and dual-frequency SAR. To meet the objective of selecting consistent and complete polarimetric information, a unified classification framework is proposed. In the framework, covariance matrices are used directly as inputs instead of polarimetric indicators. Additionally, the Wishart mixture model (WMM) is utilized to characterize the statistical distribution of polarimetric SAR data. Besides, the data log-likelihood function is utilized to mitigate the influence of the initial values on the expectation-maximization (EM) algorithm. Then, among the combinations of four sample-to-subclass distances and two schemes for obtaining sample-to-class distances, the one with the best classification performance is selected as the default for this framework. In the experiments, the classification capabilities of full polarization (FP), compact polarization (CP), and dual-polarization (DP) modes are first compared through the proposed classification framework. Then, we compare the classification capabilities of dual-frequency SAR in FP, CP, and DP modes. For polarimetric SAR (PolSAR) system design, it is necessary to strike a balance between demand indexes (classification performance, coverage width, and so on) and cost (such as budget, weight, and so on). The comparison results provide a reference for the optimization of polarization modes and frequency bands of the existing multipolarization and dual-frequency SAR payloads and the design of future SAR systems.

Verifiable pattern matching enables users to obtain authenticated query results over outsourced data on an untrusted remote server. It is a fundamental problem in many security-critical big data applications, including big database search, human genome data search, text search, etc., especially when these applications are outsourced to third-party clouds. However, the state-of-the-art schemes do not yet support efficient data updates. In this work, we propose the first dynamic verifiable pattern matching scheme to support efficient data updates. The proposed scheme is built on two ideas: one is to embed unique randomness to decouple the character and its index in the outsourced data, enabling efficient data updates; the other is to reduce the verifiable pattern matching problem to a discrete set membership testing problem, which relies on the decoupling introduced in the first idea. Based on these two ideas, the proposed scheme first employs the suffix array index structure to search pattern matching queries. The scheme then authenticates the outsourced text using a newly designed authenticated data structure based on the RSA accumulator, which guarantees the verifiability of pattern matching query results. Data update is naturally supported using the RSA accumulator working on discrete sets. Based on the proposed design, we have prototyped a proof-of-concept for the proposed scheme and have conducted an extensive experimental evaluation. In addition to supporting efficient data update, our experimental results show that the proposed scheme incurs reduced verification cost in comparison with the baseline state-of-the-art scheme.

Verifiable pattern matching is the problem of finding a given pattern verifiably from the outsourced textual data, which is resident in an untrusted remote server. This problem has drawn much attention due to a large number of applications. The state-of-the-art method for this problem suffers from low efficiency. To enable fast verifiable pattern matching, we propose a novel scheme in this paper. Our scheme is based on an ordered set accumulator data structure and a newly developed verifiable suffix array structure, which only involves fast cryptographic hash computations. Our scheme also supports fast multiple-occurrence pattern matching. A striking feature of our proposed scheme is that our scheme works even with no secret keys, which ensures public verifiability. We conduct extensive experiments to evaluate the proposed scheme using Java. The results show that our scheme is orders of magnitude faster than the state-of-the-art work. Specifically, our scheme with public verifiability only costs a preprocessing time of 47 s (merely one-time off-line cost during outsourcing), a search time of 30 μs, a verification time of 149 μs, and a proof size of 2760 bytes for a verifiable pattern matching query with pattern length 200 on 10-million long textual data which consists of sequences of two-byte, Unicode characters in Java.

In this paper, we consider the design of a dynamic storage resource management framework for the grid. The framework is service-oriented and conforms to the Open Grid Service Infrastructure (OGSI) specifications. The higher-level framework design is inspired by the grid and the peer-to-peer network architecture, which have been proven to be very effective in distributed environments. The design guarantees a robust way of maintaining the storage resources as it does not rely on any supporting infrastructure. In our design, we included QoS performance metrics as part of our service interface to cater for high-performance grid applications. At the lower level, we consider the usage of Storage Management Initiative Specifications (SMI-S), which enhance the flexibility and management of heterogeneous storage systems/devices, of various vendors.