Abstract: Alerts of intrusion detection system are numerous, complex, and difficult to analyze. Alert correlation of multi-step attack is one of the solutions to this problem. Intelligence planning is an important research area of artificial intelligence, and always used in fields problems. Intelligence planning is used to deal with multi-step attack recognition in this work. A multi-step attack planning domain description model is proposed, in order to describe the attack knowledge base, and based on knowledge representation. The model is described with PDDL (Planning domain definition language). Experiments with DARPA 2000 dataset showed the model proposed can recognize multi-step attacks effectively, and is available and practical.
Abstract: This paper propose a framework Knowledge advantage machine (KAM) to help in organizing individually discovered knowledge drawn from a narrowly bounded domain into a personal knowledge network based on personal request and tags. Ontologies, folksonomy and personomy are employed in KAM to constitute the useful repositories of knowledge. Ontologies offer a flexible and expressive layer of abstraction, very useful for capturing the semantics of information repositories, but they can not reflect the user's interest. The user in KAM can freely choose the words to tag the resources which are the reflection of the user's own interest. The set of tags and tagged knowledge of a user comprise the personomy. In a group the shared tags and knowledge are known as folksonomy. Our approach investigates how to map these tags in personomy and folksonomy to existing domain ontology in order to add accurate meanings. The user's behaviors are also used to re-rank the query results. So the user can find the useful knowledge quickly and accurately.
Abstract: To resolve the problem of assessing the effect of a network attack, this paper combines cross entropy with network character entropy method and proposes scheme to evaluate the malicious code attack effect. It captures the related indicators in real time and normalizes the data so as to evaluate it at the same level. It adopts cross entropy method to pretreat the indicators adaptively. We calculate the weight coefficient and exploit network character entropy method to evaluate the attack with accuracy according to the importance of the indicators in the evaluation system. Experimental results and corresponding comparisons reveal that the proposed method can quantitatively determine the exact effect of the malicious code attack.
Abstract: Periodicity of a moving body is one of important characteristics in activity monitoring. We present a method to estimate the trajectory of human gait in 3D space from a single camera by exploring the periodicity of the human movement. Geometric constraints are established to characterize the periodic motion, which are invariant under viewing geometry variations. According to our analysis, these geometric constraints reduce the overall computational complexity of evaluating periodicity. Meanwhile, with the help of these geometric constraints, we develop a novel method to reconstruct the 3D motion trajectory from a single camera. Experimental results demonstrate the accuracy and robustness of the algorithm.
Abstract: Traditional fuzzy clustering algorithms based on objective function is unable to determine the optimum number of clusters, sensitive to the initial cluster centers, and easily sunk into the issue of local optimum. A Fuzzy similarity-based clustering (FSBC) algorithm is proposed in this paper. This method consists three phases: first, the objective function is modified by integrating Fuzzy C-means (FCM) and Possibilistic C-means (PCM) method; second, using the density function from data for similarity-based clustering to automatically generate initial prototype without requesting users to specify; finally, the iteration process optimized by Particle swarm optimization (PSO) to obtain appropriate adjustment parameters that can provide better results, which avoids the local minimum problems of traditional methods. The experimental results on the synthetic data and UCI standard data sets show that the proposed algorithm has greater searching capability, less computational complexity, higher clustering precision.
Abstract: IPv6-enabled low-power wireless personal area network (6LoWPAN) is an important part of the Internet of Things which will drastically transform the way our society functions. Designing an efficient routing strategy is crucial for optimizing traffic over 6LoWPAN resources and extending the network lifetime. This paper proposes an energy balancing adaptive routing strategy named EBAR. Considering the dynamic traffic and constrained energy in 6LoWPAN, EBAR adaptively updates paths between different source-destination pairs and balances the energy in 6LoWPAN. Simulation results indicate that EBAR can promote network performance and balance the energy in the network.
Abstract: FPGA's configurability makes it difficult for FPGA's manufacturers to fully test it. In this paper, a full coverage test method for FPGA's Configurable logic blocks (CLBs) is proposed, through which all basic logics of FPGA's every CLB can be fully tested. Innovative test circuits are designed using FPGA's internal resources to build Iterative logic arrays (ILAs) for Look-up tables (LUTs), distributed random access memories, configurable registers and other logics. The programmable interconnects needed to connect CLBs in these test circuits are also repeatable, making the configuration process much easier and the test speed much faster. The test method is transplantable and independent of FPGA's array size, so it can be applied to the test of different FPGAs. Xilinx's Virtex FPGA is taken as an example to explain our method, where only 19 test configurations are needed to achieve 100% coverage for all CLBs. To evaluate the test method reliably and guide the process of test vectors generation, a fault simulator Turbofault is used to simulate FPGA's test coverage.
Abstract: In large-scale Wireless sensor networks (WSNs), the network status is complex and unpredictable, which brings great challenges to practical network design and management. Tracing the route path of each data packet in the network is an important way to observe network behaviors and understand network dynamics. However, tracing the full route path of each packet could be highly challenging, due to the hard resource constraint inWSNs. Our previous work proposes a hash-based path tracing mechanism, and leverages network connectivity and node locations to reduce the computational complexity. However, the node locations may be unavailable in some scenarios. In this work, we further propose a location-free enhancement to the hash-based path tracing mechanism, called P-Zone. P-Zone requires only network connectivity information to reduce the computational complexity. Theoretical analysis and practical simulations are conducted to evaluate the effectiveness and performance of our design. The results indicate that P-Zone can significantly reduce the computational complexity of the hashbased path tracing mechanism, while effectively tracing the full route path of each packet in the network in a real-time manner, and outperforms the state-of-the-art methods.
Abstract: A novel architecture of high precision, floating-point special Arithmetic function unit (SFU) for elementary transcendental functions is presented in this paper to provide area efficiency as well as high performance for programmable vertex shader. From the architecture point of view, the evaluation of quadratic approximation for special functions is performed by sharing the SIMD vector unit in shader architecture to minimize processing latency and to reduce area cost in SFU. An optimized minimax approach is proposed as well to obtain the finite-length and normalized quadratic coefficients for high precision. The experiment result shows that the proposed SFU can significantly reduce area cost and by adopting the proposed SFU, a vertex shader with Transport triggered architecture (TTA) can achieve 15.0% improvement on average in performance/area ratio for various shading benchmarks.
Abstract: Algebraic attacks on stream ciphers exploit annihilators of low degree. From another point of view, we concentrate on annihilators in fewer variables in this paper. This work consists of two parts. Firstly, the concept of singular annihilators is proposed and the basic theory is established. Secondly, we present two applications of singular annihilators. We propose a variant of the Filter states guessing attack (FSGA) introduced by Pasalic (2009). Our attack outperforms the FSGA in many cases. Moreover, we put forward a probabilistic algorithm, which can screen out a large number of Boolean functions with annihilators of low degree at a lower cost for the resistance against algebraic attacks.
Abstract: Low differential uniformity functions provide good resistance to differential attacks. The AES (Advanced encryption standard) uses a differentially 4 uniform function (the inverse function) as its S-box. We give a further study of the inverse function in this paper. It is observed that after exchanging two values of a low differential uniformity function, its differential property still keeps good. Especially, for the inverse function over F2n (n even), various possible differential uniformities are completely determined after its two values are exchanged. As a consequence, we get some highly nonlinear permutations with differential uniformity 4 which are not CCZequivalent (Carlet Charpin-Zinoviev equivalent) to the inverse function on F2n.
Abstract: The 6LoWPAN protocol is used in delivering IPv6 packet over IEEE 802.15.4 based low power Wireless personal area network (WPAN). The Mesh-under routing (MUR) presented in the 6LoWPAN conducts routing in the adaptation layer. When delivering an IPv6 packet over a route consisting of multiple unreliable links, the probability that the IPv6 packet reaches the destination via MUR is very low. This drawback is remedied by the proposed Cost-aware and reliable MUR (CAR-MUR) scheme, which extends the MUR to incorporate a packet redelivery mechanism in the transport layer and is able to find the best number of trials in the transport layer, the best number of the retrials in the MAC layer, and the best number of fragments in each packet so that the total cost for packet delivery is minimized while guaranteeing all the packets to reach the destination with a preset probability. The numerical analysis shows that, compared to the MUR, the proposed CAR-MUR has better performance as it minimizes the packet delivery cost while guaranteeing that all the packets are delivered to the destination with an intended probability.
Abstract: In order to take full advantage of valuable information from all source domains and to avoid negative transfer resulted from irrelevant information, a kind of weighted multi-source TrAdaBoost algorithm is proposed. At first, some weak classifiers are respectively trained based on training sample sets constituted by both each source domain and the target domain. Then we assign a weight to each weak classifier according to its error on the target training set. In the third step, a candidate classifier is obtained based on the weighted sum of all weak classifiers. In the fourth step, sample weights of the source and target domains are updated according to the error of the candidate classifier on corresponding domains. At last, all weak classifiers are retrained based on the training samples with new updated weights. The above steps repeated until the number of maximum iterations is reached. Experimental results on bimonthly datasets show that, compared with TrAdaBoost and multi-source TrAdaBoost, the proposed algorithm has higher classification accuracy.
Abstract: Image localization using local features has attracted a lot of attention in recent mobile robots research. A novel, fast local invariant feature in affine transformation is proposed in this article, called AIFLC (Affine invariant feature based on local color). We adopt affine moment invariants to build affine invariant descriptors. Moreover, we use color gradient based center moment instead of original pixel values in order to enhance discriminative power and robustness of descriptor in photometric transformations. Simulation results show that the run time of AIFLC using optimal selection parameters is about 1/3 of classical SIFT algorithm. Using the standard evaluation images and the ones taken by mobile robots, we experimentally demonstrate that the AIFLC outperforms the state-of-art approaches such as SIFT and SURF in terms of image scaling, rotation, viewpoint changing, and blur transformations.
Abstract: Densely connected patterns in biological networks can help biologists to elucidate meaningful insights. How to detect dense subgraphs effectively and quickly has been an urgent challenge in recent years. In this paper, we proposed a local measure named the edge density coefficient, which could indicate whether an edge locates a dense subgraph or not. Simulation results showed that this measure could improve both the accuracy and speed in detecting dense subgraphs. Thus, the G-N algorithm can be extended to large biological networks by this local measure. Finally, we applied this algorithm to microarray data sets of Saccharomyces cerevisiae, and performed the gene ontology analysis of the result by the GOEAST.
Abstract: To deal with the insufficiency problem of Laplacian eigenmap (LE) method and Maximum margin criterion (MMC) method in feature extraction, a new dimensionality reduction method called Laplacian eigenmap based on Improved maximum margin criterion (LE/IMMC) is proposed with applications in gene expression data classification. The LE/IMMC intends to constrain similar data points as close to each other as possible and maximize the margin regions between different pattern classes simultaneously. The proposed LE/IMMC by introducing IMMC into the cost function of LE retains the characteristic of local neighborhood relationship of LE. Meanwhile, it emphasizes the discriminative information by incorporating IMMC, which can maximize the betweenclass scatter and minimize the within-class scatter. Gene expression data classification experiments on four public datasets demonstrate our method is effective for feature extraction.
Abstract: Time-varying bias estimation problem was studied for multi-target tracking systems with asynchronous sensors without knowing bias dynamics. We considered general situations, where the number of sensors was arbitrary as well as their sampling rates and initial sampling instants. A two-layer fusion structure was adopted. For each target, a pseudo-measurement of sensor bias was generated by fusing sensor measurements of this target. To make the pseudo-measurement decoupled from the target state, the fusion coefficient matrix was determined to be a basis for the left null space of an augmented observation matrix. The bias estimation algorithm was proposed based on the Strong tracking filter (STF) by fusing pseudomeasurements. The proposed algorithm makes use of all available sensor information, has strong tracking ability to abrupt changes, and avoids the matrix inversion for large dimensional matrices. Finally, the performance of the proposed algorithm is illustrated by the numerical simulation.
Abstract: A novel 3D freehand tracking algorithm based on relevancy among local motion models is put forward. Firstly, a specification of the Cognitive and behavioral model (CBM) called PAMT is proposed. Secondly, we regard PAMT as a data structure upon which freehand tracking algorithm is designed, and we describe the PAMT in detail. Lastly, the experimental results are provided. The proposed algorithm is tested in a virtual assembly platform and two other application systems. The highlights of this paper are as follows: (1) A new cognitive and behavioral model, called PAMT, is presented; (2) The PAMT is explained with cognitive model; (3) Focus on describing ‘Attractor' in PAMT with the relevancy among local motion models; (4) Shows us how the PAMT is shaped and used to design the 3D freehand tracker. One of the advantages of PAMT and RLMM model is that it is easier to explore some of the complex correlations among the variables of the 3D hand model. Our experimental results show that, compared with the particle filter and the annealed particle filter, our algorithm effectively reduces dimensionality and can track 3D hand in real-time.
Abstract: An Affine projection algorithm with Direction error (AP-DE) is presented by redefining the iteration error. Under a measurement-noise-free condition, the iteration error is directly caused by the direction vector. A statistical analysis model is used to analyze the AP-DE algorithm. Deterministic recursive equations for the mean weight error and for the Mean-square error (MSE) in iteration direction are derived. We also analyze the stability of MSE in iteration direction and the optimal step-size for the AP-DE algorithm. Simulation results are provided to corroborate the analytical theory.
Abstract: Image quality assessment (IQA) is of fundamental importance for image compression applications. Traditional IQA measures used for Synthetic aperture radar (SAR) image compression do not consider the properties of Human visual system (HVS). Since human beings are the final users in most SAR image applications, the objective evaluation coordinate to human's perception is the most acceptable and practical IQA method. In this paper, we propose a novel objective approach based on image content partition and Neural network (NN) by introducing the HVS and SAR image characteristics. Experimental results demonstrate that the proposed metric correlates well with subjective quality of SAR image compression and outperforms those state-of-art objective models using Structural similarity index (SSIM), Singular value decomposition (SVD) and Visual information fidelity (VIF).
Abstract: Distance perception plays an important role in audio rendering. From listener's perspective, sound intensity and Direct-to-reverberant energy ratio (DRR) decrease while the distance between the listener and source increases. Though implementing changes in intensity is quite easy, providing realistic changes in directtoreverberant energy ratio is quite demanding. In this paper, reverberation has been introduced into the synthesized WFS virtual sound field, and it has been proved that the corresponding DRR decreases with increasing distance. Compared with implementing changes in sound intensity, subjective listening test has proved that the proposed method can provide more authentic distance perception in the reproduced sound field.
Abstract: A new algorithm, the RSEM (Recursive simultaneous equations model) algorithm, is presented for causal structure learning under the LSEM (Linear structural equations model). The algorithm effectively applies recursive simultaneous equations model to causal structure learning. This paper makes two specific contributions. Firstly, under the assumption that knowing the causal order of the variables, we show that recursive simultaneous equations model can be used for causal structure learning under the LSEM regardless of whether the datasets follow multivariate Gaussian distribution. Secondly, the performance of the RSEM algorithm is compared with the state-of-the-art algorithms on 7 networks. Simulation results show that the RSEM algorithm outperforms existing algorithms in terms of time performance, and has a quite high accuracy for thresholds 0.005 and 0.01.
Abstract: Node localization is important in WSNs (Wireless sensor networks) applications. We present a new cruise positioning algorithm, where a single beacon node cruises an entire network, broadcasting its position periodically with fixed signal strength. Meawhile, an unknown node obtains the location and the corresponding distance of the beacon node. Two possible position coordinate sets for the unknown node can then be calculated, and the coordinate mean of the point set with a small variance is an unbiased estimator of the unknown node's coordinate. The positioning algorithm's computation is simple, positioning accuracy is not limited by a physical distance measuring tool, and there are no restrictions on the path of motion, which makes it more realistic. An algorithm simulation is presented to compare the original and cruise location algorithm. Numerical results show that the cruise localization algorithm can achieve good positioning accuracy.
Abstract: The secure sum protocol is a useful basic protocol of Secure multiparty computation (SMC). And it has numerous applications. However traditional secure sum protocol can not guarantee the fairness. In addition, most previous protocols can not resist the collusion-attack. This paper proposes a collusion-free rational secure sum protocol in which we combine game theory with the multiparty secure sum protocol. In the setting of rational secure sum protocol, the gain of following the protocol is more than the gain of deviating, and no player of the coalition parties can do better, even if the whole coalition parties cheat. Analysis shows that the protocol can resist the collusion attack with at most n?2 players, and rational players have to abide by the protocols. Unlike previous secure sum algorithms, this paper aims at obtaining complete fairness even though without a majority of honest parties.
Abstract: To emphasize the decisions of all users, and the total number of users sharing the same technique, we propose a novel Average cost sharing (ACS) pricing mechanism to study the game between Network coding (NC) and routing flows sharing a single link when users are price anticipating. We characterize the worst-case efficiency bounds of the game compared with the optimal (i.e., the Price of anarchy (PoA)), which can be as low as 4/9 when the number of routing users becomes sufficiently large. NC cannot improve the PoA significantly under ACS. However, to achieve a more efficient use of limited resources, this approach indicates the sharing users have a greater tendency to choose NC. However, the users will follow the majority users' choice of data transmission technique.
Abstract: Underwater acoustic sensor networks (UWASN) is a key technology for the ocean exploitation. Routing schemes specially designed for it are urgently needed. In this paper, we consider energy aware and energy balance routing schemes and aim to prolong network lifetime. By introducing the concept of “virtual path”, we propose a routing scheme named virtual path based routing, which gets more global information, especially the energy information, in a local and distributed way. Simulations show that our scheme can balance the energy consumption and prolong the network lifetime with no loss of the network performance.
Abstract: ZigBee network is a kind of flexible wireless network technology for control and monitoring applications and new techniques of security measures are essential for high-survivability network. Based on the effectiveness of AODVjr (Ad hoc on-demand distance vector junior) routing protocol in ZigBee networks, in this paper, we proposed a new security-enhanced key distribution scheme for AODVjr routing protocol in ZigBee networks. The key distribution was scheme implemented by combining the parameter exchange of Diffie-Hellman algorithm into the handshake protocol for node's joining a ZigBee network. Especially, the major improvement of Diffie-Hellman algorithm is to mix the parameters of key exchange with XOR operation so as to defend against typical man-in-the-middle attacks. Meanwhile, we analyzed the effect of XOR operation on key parameters by deducing the related theorems. Through the security analysis, the key distribution scheme demonstrates stronger security. We can verify that the AODVjr routing protocol with security enhancement has larger flexible application in ZigBee networks.
Abstract: Collaborative in-network processing operations in Wireless multimedia sensor networks (WMSNs) often require effective synchronization control. Extensive researches in the traditional networks mainly focus on the synchronization control with the buffer management in the receiver. However, for WMSNs, the chaotic transport channel and low bandwidth introduce serious jitter. Jitter degrades the timing relationship among packets in a single media stream and between packets from different media streams and, hence, creates multimedia synchronization problems. Moreover, too much jitter will also degrade the performance of the streaming buffer. By only employing the buffer management scheme in the receiver, we can hardly satisfy the synchronization requirement of the in-network processing. In this study, we propose an active jitter detection mechanism for the synchronization control inWMSNs. This mechanism will improve the quality of service in multimedia networking by discarding the jitter-corrupted packets immediately and balancing the delay and jitter actively. We implement the proposed scheme in the practical WMSNs platform. The experiment results show that our scheme can reduce the average packet jitter effectively and improve the synchronization controlling performance significantly.
Abstract: This paper mainly focuses on the security of Zodiac against integral cryptanalysis. Firstly, a systematic method is given to extend an integral distinguisher of Feistel ciphers with PS or SP round functions into a higher order integral distinguisher. Secondly, this method is applied to Zodiac, and a full-round (16-round) integral distinguisher is given. Taking the properties of the linear transformation into consideration, it is showed that extending an integral distinguisher into a higher order one can be reduced into decomposition of linear spaces into direct sums. At last, some key-recovery attacks against full round Zodiac are applied using distinguishers with 15-/13round, respectively.
Abstract: This paper presents a Distributed compressive video sensing scheme with Adaptive measurements (DCVS-AM). In this approach, the key frame in each Group of pictures (GOP) is coded by Compressive sensing (CS) with a fixed measurement rate; whereas other frames in the same GOP are compressed by an adaptive random projection in two stages, yielding the Adaptive compressive sensing (ACS) frames. The first stage uses a small and fixed measurement rate and recovers a coarse version. In the second stage, each coarse-version ACS-frame together with its proceeding and following key frames will go through a joint analysis at the decoder side and the analysis result Structural similarity (SSIM) that is based on a motion-guided interpolation and calculated in a multilevel discrete wavelet transform domain is sent back to the encoder side to facilitate a re-sampling of the ACS-frame with an adaptive measurement rate. Experimental results show that our proposed DCVS-AM consistently outperforms the state-of-the-art DCVS with a fixed measurement.
Abstract: This paper proposes an opportunistic data aggregation algorithm to support the data collection in low-duty-cycle Wireless sensor networks (WSNs) with unreliable links. In this algorithm, each sensor selectively waits for some certain time to maximize the number of packets that it can aggregate from its downstream nodes, then transmits the aggregated result to an adaptively selected upstream node following an optimal forwarding sequence. Simulation results show that the algorithm can significantly increase the data aggregation efficiency, and reduce energy consumption and message overhead.
Abstract: In order to more exactly describe the errorfloor phenomenon in the iterative decoding of Low-density parity-check (LDPC) codes, a modified concept of the stable trapping set is introduced. Based on this new concept, an improved belief propagation algorithm with setbreaking mechanism is proposed to lower the error-floors of LDPC codes. Message ranking of the bit nodes in the stable trapping sets will be greatly lowered than that of other bit nodes in the iterative decoding process. By using this characteristic to label the bit nodes in the set, the corresponding initial log likelihood ratios will be flipped to break the stable trapping set and restart to decode. Simulation results verify the validity of the proposed algorithm.
Abstract: A Layered dynamic scheduling (LDS) for Belief-propagation (BP) decoding of LDPC codes over GF(q) is presented, which is derived from the dynamic scheduling for the BP decoding of binary LDPC codes. In order to restrain the LDS from cycling in certain checknodes, a life-index for each check-node is adopted and the optimal value of the life-index is analyzed. Furthermore, in consideration of hardware implementation and decoding latency, a strategy, which allows many more checknodes to be updated in parallel, is introduced. Simulations show that the LDS with life-index speeds up the convergence rate and greatly improves the performance of the BP decoding at medium to high signal-to-noise ratio value, and the algorithm employing the LDS with life-index and the new strategy offers good trade-off between the performance and the decoding latency.
Abstract: Choosing the optimum diagnostic nodes is helpful to increase the accuracy and the efficiency of circuits fault diagnosis. Grey entropy relation algorithm is proposed to choose the optimum diagnostic nodes of analogue circuits in this paper. Analogue circuits are regarded as grey system. Grey relation analysis and grey entropy analysis are combined to grey entropy relation algorithm. Grey entropy relation algorithm is used to quantify the relationship between the diagnostic nodes and fault components. The relevance between the diagnostic nodes and fault components can be evaluated by grey entropy relation degrees. According to the rank order of grey entropy relation degrees, the optimum diagnostic nodes of analogue circuits can be selected objectively and accurately. An example of fault diagnosis is presented to verify the validity of the optimum diagnostic nodes.
Abstract: Through theoretical analysis and experiments, we discovered the quantized phase step technique existing in two cyclical movements, and the variation law of phase difference between two different frequency signals. This discovery is a major breakthrough in the traditional phase processing for researching frequency signals or cyclical movements that widely exist in nature. It has realized direct phase comparison between any signals without frequency normalization. Experimental results show that the resolution with femtosecond (fs) can be easily achieved in frequency measurement, frequency standard comparison and control on the basis of the quantized phase step characteristics. This important discovery can be widely used in navigation positioning, space technique, communication, radar, astronomy, atomic frequency standard and so on.
Abstract: An on-line system delay calibration method based on dynamic cancellation for generalized high-precision Tracking, telemetry and command (TT&C) channel simulator is proposed. This method manages to estimate the time-varying system delay in real time through the coupling signal of input signal and simulated output signal, and then modifies the simulated parameters by the estimated value. With this method, it effectively avoids the effect of time-varying system delay caused by temperature drift, aging of components and other factors on high precision simulation. In this paper, the dynamic cancellation technology, which is the kernel and foundation of this method, is presented to eliminate the simulated motion law between the input signal and output signal. The time delay estimation method based on cross correlation and area barycenter arithmetic is introduced to estimate the time-varying system delay. The simulation results show the validity and high-precision performance.
Abstract: RF Power amplifiers (PA) are critical components in Time division-Synchronous code division multiple access (TD-SCDMA) systems, and PA nonlinearity is one of the main concerns in RF power amplifier designs. This paper presents experimental verification of the spectrum modeling of a RF power amplifier in TD-SCDMA system based on our previous work. The results verify the theoretical spectrum model we derived closely fits the experimental measurements.
Abstract: Tracking an unknown and time-varying number of maneuvering targets is a challenging problem in the presence of noise, clutter, uncertainties in target maneuvers, data association, and detection. To account for this problem, a multi-model extension of the Cardinalized probability hypothesis density (CPHD) filter is proposed in this paper. Additionally, a particle implementation and a Gaussian mixture implementation of the proposed extension are given for generic models and linear Gaussian models, respectively. The effectiveness of the extension is illustrated through Monte Carlo simulation.
Abstract: In this paper, we present a novel ship detection approach for Polarimetric synthetic aperture radar (PolSAR) images based on a Foreground/background separation (FBS) framework, which exploits the statistical dissimilarity of PolSAR data to separate desired targets from the background clutter. Since the FBS framework takes the spatial relations between pixels into consideration and the separation process exploits the inherent characteristics of PolSAR data, the proposed detector is stable to speckle. And the detection process is filter-free, thus it can preserve the edge and shape information of the extracted targets. Experimental results and comparisons with the standard polarimetric detector show that the proposed method is promising. Factors that affect the performance of the proposed detector are also analyzed and discussed in this paper.
Abstract: Compressed sensing (CS) provides great potential to reduce radar sampling rate while improve the imaging performance. In this paper, the application of CS to ISAR imaging of high-speed space targets is introduced. Firstly, based on the analysis of the echo model of highspeed targets, we elucidate that the dechirped high-speed target echo is of sparsity in fractional Fourier domain. Then the Analog-to-information conversion (AIC) is used to take compressive measurements, following which the radar image can be recovered via nonlinear optimization. In particular, considering the non-cooperative characteristic of targets, an optimization search algorithm based on sparsity of the reconstructed range profiles is presented, so as to find the optimal transform order of fractional Fourier transform. Experiment results from both simulated data and measured data show the validity and superiority of the proposed imaging method.