The Chinese ocean salinity satellite mission is currently undergoing development and rigorous testing. The satellite will be equipped with an L-band Y-shaped Synthetic Aperture Interferometric Radiometer (SAIR), which is similar to the highly regarded MIRAS. Leveraging lessons learned from the SMOS mission, significant emphasis has been placed on addressing Radio Frequency Interference (RFI). A pre-launch ground experiment has been conducted specifically for the SAIR payload of the Chinese ocean salinity satellite. This experiment aims to research and verify the efficacy of RFI processing algorithms. Notably, our observations indicate that the radiometer detects Gaussian thermal radiation noise, whereas RFIs consist of non-Gaussian signals, such as radar and wireless communication signals. This fundamental difference in signal types results in distinct system responses, leading to biases in RFI suppression, particularly for powerful RFI sources. To mitigate the suppression residuals of strong RFI, we have introduced an innovative adaptive channel response adjustment strategy. The core concept involves decomposing the covariance matrix to isolate the signal space dominated by intense RFI signals. By projecting the steering vector onto this signal space, we can accurately estimate the channel response of the RFI and make precise adjustments accordingly. Our experimental and simulation results are encouraging, demonstrating a marked improvement in RFI suppression effectiveness. This advancement is crucial for enhancing the accuracy and reliability of ocean salinity measurements from our satellite, ultimately contributing to a deeper understanding of our planet’s vital ocean ecosystems.

The Chinese Ocean Salinity Mission (COSM) was successfully launched on November 14, 2024, with the objective of observing global sea surface salinity (SSS). To fulfill this mission, two payloads were onboard, one of which is a key instrument called the microwave imager combined active and passive (MICAP). MICAP comprises L-/C-/K-band 1-D synthetic aperture interferometric radiometers (SAIRs) that share a parabolic reflector, as well as an L-band scatterometer. As evidenced by SMOS, accurately locating low- and mid-level radio frequency interference (RFI) signals for SAIR is challenging due to issues such as relatively low sensitivity and Gibbs oscillations in complex scenarios. Owing to the relatively small number of array elements in 1D-SAIRs and advanced data transmission technology, MICAP has achieved the capability to downlink short-duration array raw sampling data before array signal correlation, providing strong support for identifying low- and mid-level RFI signals in complex scenarios. In this article, a signal-level RFI localization method is proposed. First, the strong RFI signal is eliminated by leveraging the spatial point source characteristics of RFI. Then, low- and mid-level RFI detection and localization are achieved by extracting non-Gaussian RFI signals from Gaussian background signals through the calculation of kurtosis spectra. Simulations demonstrate that the detection rate and localization accuracy for low- and mid-level RFI signals can be significantly improved.

The mirrored aperture synthesis (MAS) was proposed to improve the spatial resolution in passive microwave remote sensing. In MAS, the elements in the (H matrix) are the sampled values of the h function. In this article, the h function considering practical factors is derived. These practical factors include the normalized antenna pattern, the reflector reflectivity, the reflector length, and the distance between the array and the scene. The normalized antenna pattern modulates the received signals. The reflector reflectivity affects amplitudes of the reflected signals. The reflector length determines whether the reflected signals can be received. The distance between the array and the scene influences the incident angles of the signals from the same point source in the scene. The distance also influences the path differences between signals. The formulas and related analysis are verified through the numerical simulations and experiments.

L-band Radio Frequency Interferences (RFIs) seriously contaminate remote sensing data. This poses a major challenge in processing microwave radiometric data and can substantially degrade the quality of the data. There is an urgent need to estimate RFI intensity in order to mitigate Gibbs errors caused by RFIs along the sidelobes. The traditional CLEAN algorithm is limited in its ability to account for the interactions among multiple RFIs, which results in biased intensity estimations. In this paper, the RFI brightness temperature model is modified to incorporate the interactions among multiple RFI sources. Building upon this modified model, we establish fundamental equation sets and propose a numerical solution method for calculating RFI intensity. Experiments and simulations based on Soil Moisture and Ocean Salinity (SMOS) data confirm that the method presented in this paper, which we have named MUSID, outperforms the traditional CLEAN algorithm.

This letter pays attention to the mitigation of the Radio Frequency Interference (RFI) sources located at coastal regions for Synthetic Aperture Interferometric Radiometer (SAIR). To remove an RFI at the visibility level, it will be necessary to estimate the Brightness Temperature (BT) of the point source, which is currently done by computing the median of the surrounded pixels to get the background BT, and subtracting it from the original contaminated BT. However, this strategy is based on the assumption that the background BT distribution around the RFI is flat, and this is not reasonable for those RFIs from coastal cities, and could result in estimation deviation. A robust RFI mitigation method is proposed without introducing complex computing. Instead of focusing only on the local area, the basic idea of our method is to step into the wide area, and minimize the overall fluctuation of the pixels affected by the RFI in the BT image. The method tests based on SMOS visibility samples validate the correctness of the developed method.

Radio frequency interferences (RFIs) in the L-band heavily contaminate remote sensing data and bring many challenges to the product quality of synthesis aperture interferometric radiometers. Detecting and localizing RFIs is the urgent need for switching off these RFIs and conducting mitigation algorithms. The localization algorithm based on the brightness temperature (BT) image is limited by the resolution to separate closely spaced RFIs and achieve high localization accuracy in some cases. To achieve super-resolution, the MUSIC method using subspace decomposition technique is suggested. Nevertheless, when the BT image SNR is low, the power of RFI is insufficient to overwhelm the background scene, and the performance of the MUSIC algorithm is unsatisfactory. In this paper, a further improvement is proposed by combining BT image and subspace decomposition. In the BT image domain, background scene cancellation and RFI target enhancement are carried out for enhancing the SNR. In the frequency domain, the MUSIC algorithm is applied utilizing subspace-decomposition to achieve super-resolution localization. Experiments and simulations based on SMOS data validate that the method presented in this paper performs better than both the classical MUSIC algorithm and BT-based localization algorithm in low SNR cases.

Airborne passive interferometric microwave sensors (PIMSs) were proposed as a powerful complementary tool for ship detection, especially in low visibility and high sea clutter environments. At present, there is only one algorithm for ship detection by airborne PIMSs, and this algorithm is only based on the “cold” brightness temperature (TB) characteristic of metal ships with wakes in passive interferometric microwave images (PIMIs). Moreover, it has been found that ship wakes always exhibit a “hot” TB characteristic in PIMIs. In this article, an improved algorithm is innovatively proposed for ship detection by airborne PIMSs to improve detection performance, and it is simultaneously based on the “cold” and “hot” TB characteristics of metal ships with wakes. The microwave TB model of metal ships with wakes is established to analyze its TB characteristics. Given the TB model and its characteristics, an improved algorithm is introduced, which comprises five steps: searching targets, improving TB maps, clustering targets, estimating ship headings, and estimating ship trajectories. The improved algorithm can detect ships with a low false-alarm probability (FAR) and accurately estimate ship headings using only a single snapshot and the ship trajectory. The practicability and detection performance of the improved algorithm is verified by airborne experiments using an X-band PIMS. The experimental results demonstrate that the improved algorithm can accurately detect ships, estimate ship headings using only a single snapshot, and estimate ship trajectories. The impact of the algorithm parameters on the detection performance is analyzed and discussed. Moreover, the detection performance of the improved algorithm is analyzed and compared with the performance of the only existing algorithm, and the experimental results indicate that the improved algorithm has a better detection performance with a lower FAR value than the only existing algorithm, as expected.

In passive microwave remote sensing of the Earth, compared with real aperture radiometer, synthetic aperture radiometer (ASR) is a very powerful instrument with many advantages. However, the system design is more complex than the real aperture radiometer, and the hardware ideality is often not guaranteed. The nonideal characteristics of the system hardware will bring a variety of errors to the system, which will cause the Fourier transform relationship between the visibility function and the brightness temperature image to no longer be established, thus reducing the quality of microwave brightness temperature image reconstruction by traditional methods. In this article, a new microwave brightness temperature image reconstruction method for ASR by transformer is proposed. This method uses a specially designed transformer structure to extract the spectrum features in the visibility function. This method learns the mapping relationship between the visibility function and the original scene brightness temperature image through the supervised learning method, and learns as much as possible the spectrum information contained in the original scene brightness temperature image. Moreover, when there are missing baselines, this method will supplement the missing observation frequency information, so as to obtain better reconstructed image quality. With the above-mentioned advantages, this method can suppress the Gibbs oscillation, and greatly reduce the sidelobe. Compared with the existing reconstruction methods, whether missing baselines or not, the proposed image reconstruction method by transformer has advantages in image quality. We verify the performance of this brightness temperature image reconstruction method through simulation and experiment.

The demand for high-precision sea surface temperature (SST) has been growing rapidly in recent years because SST is one of the key parameters to describe the thermal state of the sea surface. This article analyzes the differences between microwave remote sensing and infrared remote sensing for SST, including the spatial resolution difference and the penetration depth difference. In order to improve the accuracy of retrieved SST, this article proposes a joint inversion algorithm for SST from combining microwave brightness temperature (BT) and infrared BT, which has also taken the influence of wind speed and atmosphere into consideration. Experiments confirm that SST data obtained from the joint inversion algorithm are more accurate than those obtained from the existing inversion algorithms.

Finding out and turning off the radio-frequency interference (RFI) sources as many as possible is an important work for synthetic aperture interferometric radiometer (SAIR). While the impact of strong RFIs on reconstructed brightness temperature (BT) image is clearly observable, the presence of low- or moderate-level RFIs is often hard to be aware of, leaving the RFI detection and localization a difficult problem. In this article, an improved RFI detection method is proposed by making use of the point-source ripple to give prominence to the RFIs presented in the SAIR imagery. The method tests based on Soil Moisture and Ocean Salinity satellite (SMOS) visibility samples and the performance evaluations by numerical simulations validate the correctness and effectiveness of the developed method.

The presence of unauthorized radio frequency interference (RFI) sources seriously affects the retrieval of brightness temperature of a synthetic aperture interferometric radiometer (SAIR). Postobservation RFI geolocation is important for improving the RFI situation hereafter and mitigating the impact of the identified RFIs. Previous works have demonstrated the potential of using high-/super-resolution direction-of-arrival (DOA) estimation techniques to achieve accurate localization of RFI sources in SAIR imagery. In this article, a multiple snapshot difference method is proposed to further reduce the RFI source geolocation bias. The covariance matrices of the consecutive snapshots with power changed RFIs are picked out and subtracted to cancel out the background scene. A MUSIC-based DOA algorithm is developed. Numerical simulations validate the theoretical correctness and the practical effectiveness of the proposed method.

Physical layer key agreement techniques derive a symmetric cryptographic key from the wireless fading channel between two wireless devices by exploiting channel randomness and reciprocity. Current efforts have focused mainly on the security issue and protocol design of the techniques under passive attack and jamming attack. In this paper, we raise the subject of manipulative attack. The attacker wants the keying devices to actually agree on some valid, but manipulated bits. We discuss the feasibility and different means to launch manipulative attack. Aiming at a practical countermeasure, we further propose PHY-UIR (PHYsical layer key agreement with User Introduced Randomness). Both keying devices generate and use random voltage levels X = {x[1], x[2] ... x[N]} and Y = {y[1], y[2] ... y[N]} to exchange probing frames over reciprocal random fading channels H = {h[1], h[2] ... h[N]}. At receiving end, they multiply the received channel measurements XH and YH with the random sequences Y and X to compose a common keying sequence XYH. With this solution, the attacker can no longer manipulate the keying sequences established at legitimate sides and infer the subsequent key. We analyze the security strength of PHY-UIR and conduct extensive simulations to evaluate it. We also perform proof-of-concept experiments by using software defined radios in a real-world environment. Both simulations and experiments demonstrate the effectiveness of PHY-UIR.

In two-dimensional mirrored aperture synthesis (2-D MAS), the rank of the transformation matrix can affect the accuracy of the solved cosine visibilities; therefore, it has an impact on the accuracy of the reconstructed brightness temperature image. In this article, the influence of the rank deficient on the accuracy of the reconstructed brightness temperature image of 2-D MAS is discussed. An analysis of the rank-deficient error is performed by computing its impact on the radiometric accuracy for a reference scene. Two correction methods based on multiple measurements are proposed to correct the rank-deficient error. The simulations and experiments are carried out to demonstrate the effectiveness of the proposed methods.

With the rapid and continuous growth of various types of wireless devices in IoT, securing the communications among heterogeneous devices becomes an emerging issue. A physical layer security scheme, called “friendly jamming”, has drawn great attention recently owing to its ability to protect the confidentiality of the communication as well as to enable message authentication and access control for those already employed, unencrypted, weakly encrypted, or resource constrained devices. We notice that in a large number of cases in which friendly jamming are preferable, the transmitting signals to be protected have varying spectrum utilization at symbol level. In this paper, we rebuild secrecy capacity models and re-evaluate the jamming efficiency by taking this micro time scale non-stationary characteristic into consideration. Our reassessments reveal that jamming efficiency is greatly overestimated in the existing literature. The second part of our work further proposes a waveform design on jamming signal as a means to enhance the jamming efficiency. The basic idea is to consider both time and frequency domain structure of the transmitting signal when designing the jamming signal, making both time and frequency bandwidth largely match to each other. We discuss the implementation details for jamming common QAM and PSK modulated signals. Both simulations and proof-of-concept experiments validate the theoretical correctness of our reassessment and practical effectiveness of our method.

Chinese ocean salinity satellite is a mission designed for global ocean salinity monitoring. Two main payloads equipped onboard the satellite are the Interferometric Microwave Radiometer (IMR) and the Microwave Imager Combined Active and Passive (MICAP) which both have L-band radiometer and adopt aperture synthesis technology. A simulation is designed according to the payloads' configurations and consists of three parts: the brightness temperature generator, the radiometer system and the parameters retrieval. Preliminary results, which contain IMR and MICAP's brightness temperature resolution, spatial resolution, and parameter retrieval accuracy, are presented.

Radio frequency interferences (RFIs) are present in the data collected by a synthetic aperture interferometric radiometer (SAIR), and these would seriously affect the retrieval of brightness temperature (BT), if not properly handled. Due to the truncated Fourier characteristic, RFIs create ripples that extend over a large set of pixels of the whole image, which is difficult to cancel out their impacts at the BT domain. The current strategy relies on removing the unwanted sources at the visibility level (Fourier domain) with a set of synthetic visibilities of the RFI sources for a given snapshot. However, a major problem is that, as unauthorized emissions, the energies of RFIs are typically unknown and the estimation to the sources' temperature could be inaccurate without credible prior information. In this article, a subspace algorithm is proposed to handle the RFI mitigation problem by exploiting RFI's point-source characteristic, where the visibilities related to the RFIs are excluded from that of the BT scene through an orthogonal design without the need of knowing the sources' power. Both the numerical simulations and the proof-of-concept experiment validate the theoretical correctness and the practical effectiveness of our proposed method.

Near field communication (NFC) is widely used today in many useful applications, such as contactless payment, identification, and file exchange. Due to the limitations on computation, power, and cost of NFC devices, NFC systems often lack encryption or are weakly encrypted, leaving them exposed to security attacks. One solution for this problem is to install strong cryptographic protocols on NFC devices. However, it involves upgrading and revoking deployed NFC devices, which is costly and impractical. Moreover, encryption algorithms are usually considered expensive for resource constrained NFC devices in terms of computation overhead and energy consumption. Aiming at a solution to tackle the security threat without revoking or changing the insecure NFC devices, this paper investigates whether the recent advance of physical layer security can be applied as a means to secure NFC. A detailed analysis is performed to reveal two unique challenges brought by NFC's data transmission mechanism. A practical solution, SecNFC, through special waveform design at the initiator is proposed. Extensive simulations and concept-proof experiments are conducted to evaluate the performance of our solution. Both simulation and experimental results show that SecNFC can efficiently prevent NFC from eavesdropping with a slight and tolerable decoding performance degradation at the initiator.

Aperture synthesis with high spatial resolution would seriously increase the hardware requirements and system complexity. To reduce the system complexity, Rotating mirrored aperture synthesis (RMAS) that combines the antenna array rotation with mirrored aperture synthesis was proposed to reduce the number of antennas. In this paper, the sieving method is presented to optimize the array configuration for RMAS. The initial simulation results demonstrate the validity of the proposed method and the optimized array of RMAS can reconstruct the brightness temperature of the observed scene with fewer antennas.

For SMOS data, significant errors exist in coastal areas because of the contamination by the nearby land. In this paper, the analysis of SMOS brightness temperature (TB) data in coastal area of South China Sea (SCS) reveals a decreasing trend with the increase of the distance to coast. A SMOS TB measurement model according to geophysical parameters and SMOS/MIRAS antenna array is established. The simulation shows that TB is large within 40 km since land is much warmer than ocean and is observed by the mainlobe of the antenna array. Then the simulation result of land contamination is used for correction of SMOS measured TB data in coastal area of SCS. Then the corrected TB is applied for sea surface salinity (SSS) retrieval, which reduces SSS outliers and improves the accuracy of SSS in coastal area of SCS.

The calibration of aperture synthesis radiometers (ASRs) with large arrays by internal noise injection suffers from high increased mass and volume and is not capable to deal with the out-of-plane distortion of antenna arrays. In this letter, a hybrid calibration method is proposed, which combines the redundant baselines and reduced noise injection. The hybrid method needs only a small noise injection network and can deal with the out-of-plane distortion of antenna arrays. The performance of the hybrid method depends on the noise injection element distribution, which is analyzed and given based on minimizing the root mean square of all residual amplitude and phase errors. Simulation results demonstrate its performance. The proposed hybrid method is particularly helpful to the calibration of ASRs with large arrays in geostationary orbit.

With the prevalence of mobile computing, lots of wireless devices need to establish secure communication on the fly without pre-shared secrets. Device pairing is critical for bootstrapping secure communication between two previously unassociated devices over the wireless channel. Using auxiliary out-of-band channels involving visual, acoustic, tactile, or vibrational sensors has been proposed as a feasible option to facilitate device pairing. However, these methods usually require users to perform additional tasks, such as copying, comparing, and shaking. It is preferable to have a natural and intuitive pairing method with minimal user tasks. In this paper, we introduce a new method, called MagPairing, for pairing smartphones in close proximity by exploiting correlated magnetometer readings. In MagPairing, users only need to naturally tap the smartphones together for a few seconds without performing any additional operations in authentication and key establishment. Our method exploits the fact that smartphones are equipped with tiny magnets. Highly correlated magnetic field patterns are produced when two smartphones are close to each other. We design MagPairing protocol and implement it on Android smartphones. We conduct extensive simulations and real-world experiments to evaluate MagPairing. Experiments verify that the captured sensor data on which MagPairing is based has high entropy and sufficient length, and is nondisclosure to attackers more than few centimeters away. Usability tests on various kinds of smartphones by totally untrained users show that the whole pairing process needs only 4.5 s on average with more than 90% success rate.

Near Field Communication (NFC) is a promising short distance radio communication technology for many useful applications. Although its communication range is short, NFC alone does not guarantee secure communication and is subject to security attacks, such as an eavesdropping attack. Generating a shared key and using symmetric key cryptography to secure the communication between NFC devices is a feasible solution to prevent various attacks. However, conventional Diffie-Hellman key agreement protocol is not preferable for resource constrained NFC devices due to its extensive computational overhead and energy consumption. In this paper, we propose a practical, fast and energy-efficient key agreement scheme, which uses random bits transmission with waveform shaking, for NFC devices by exploiting its off-the-shelf full-duplex capability. In the proposed method, two devices send random bits to each other simultaneously without strict synchronization or perfect match of amplitude and phase. On the contrary, the method randomly introduces synchronization offset and mismatch of amplitude and phase for each bit transmission in order to prevent a passive attacker from determining the generated key. A shared bit can be established when two devices send different bits. We conduct theoretical analysis on the correctness and security strength of the method, and extensive simulations to evaluate its effectiveness. We build a testbed based on USRP software defined radio and conduct proof-of-concept experiments to evaluate the method in a real-world environment. It shows that the proposed method achieves a high key generation rate of about 26 kbps and is immune to eavesdropping attack even when the attacker is within several centimeters from the legitimate devices. The proposed method is a practical, fast, energy-efficient, and secure key agreement scheme for resource-constrained NFC devices.

Near Field Communication (NFC) is a promising short distance radio communication technology for many useful applications. Although its communication range is short, NFC alone does not guarantee secure communication and is subject to security attacks, such as eavesdropping attack. Generating a shared key and using symmetric key cryptography to secure the communication between NFC devices is a feasible solution to prevent various attacks. However, conventional Diffie-Hellman key agreement protocol is not preferable for resource constrained NFC devices due to its extensive computational overhead and energy consumption. In this paper, we propose a practical, fast and energy-efficient key agreement scheme, called RIWA (Random bIts transmission with Waveform shAking), for NFC devices by exploiting its full-duplex capability. In RIWA, two devices send random bits to each other simultaneously without strict synchronization or perfect match of amplitude and phase. On the contrary, RIWA randomly introduces synchronization offset and mismatch of amplitude and phase for each bit transmission in order to prevent a passive attacker from determining the generated key. A shared bit can be established when two devices send different bits. We conduct theoretical analysis on the correctness and security strength of RIWA, and extensive simulations to evaluate its effectiveness. We build a testbed based on USRP software defined radio and conduct proof-of-concept experiments to evaluate RIWA in a real-world environment. It shows that RIWA achieves a high key generation rate about 26kbps and is immune to eavesdropping attack even when the attacker is within several centimeters away from the legitimate devices. RIWA is a practical, fast, energy-efficient, and secure key agreement scheme for resource-constrained NFC devices.

Secret key generation by extracting the shared randomness in wireless fading channel is a promising way to ensure wireless communication security. Previous works only consider key generation in static networks, but real-world key establishments are usually dynamic. In this work, for the first time we investigate the pairwise key generation in dynamic wireless networks with a center node (eg. access point (AP)) and random arrival users. We establish the key generation model for this kind of networks. We propose a method based on discrete Markov chain to calculate the average time a user will spend on waiting and completing the key generation (average key generation delay, AKGD). Our method can tackle both serial and parallel key generation scheduling under various conditions. We conduct extensive simulations to show the effectiveness of our model and method. The analytical and simulation results match to each other.

Traditionally, the system errors of synthetic aperture interferometric radiometers (SAIRs) are analyzed and classified by the causes they are generated, which is easy for understanding but complicated for calibration. This paper presents a new error model based on the visibility function to analyze the system errors of SAIRs. In this model the system errors are classified by their effects on the ideal visibility function, which is simple for calibration. Firstly, the error model based on the visibility function is proposed, in which the errors according to their effects on the ideal visibility function are grouped in: 1) the orientation-independent multiplicative error, 2) the orientation-dependent multiplicative error, and 3) the additive error. Then, the two overall calibration methods respectively using external reference source and external reference scene are proposed. Finally, the preliminary calibration experiment results on the 8mm-band HUST-SAIR prototype show the validity of the error model and the overall calibration methods. The study in this paper provides a means to reduce the complexity of calibration routine of SAIRs and to improve the imaging performance of SAIRs.

The calibration of aperture synthesis radiometers (ASRs) by the standard method of noise injection would seriously increase hardware requirements and system complexity. To reduce the network of noise injection, redundant space calibration (RSC) method has been considered. However the number of error phases is larger than the number of independent equations. In this paper, an external calibration method with 120-degree antenna array rotation for geostationary ASR with staggered Y-shaped array is proposed. More redundant baselines are produced by rotation. The equations by rotation and by RSC are combined for solving the phase errors. Basic procedure on this method and preliminary simulation are introduced. As another advantage, if twice 120-degree rotation is taken, one of the antenna arms can be removed from staggered Y-shaped arrays with the missing baselines supplied by the rotations. In this way, the number of antenna elements can be reduced by 1/3.

Regularization methods are very efficient in reconstructing the radiometric brightness temperature maps form interferometric measurements. The performance of these methods depends on selecting an optimal regularization parameter, which is typically defined by using the cross-validation or the L-curve. In this paper, we present a novel brightness temperature reconstruction method, which can automatically learn the optimal parameter by Bayesian learning without reducing the performance of the brightness temperature image. To support the theory, numerical simulations are presented and analyzed with emphasis on stability and error analysis.

The on-board calibration of aperture synthesis radiometers (ASRs) with large arrays by the standard method of noise injection would seriously increase hardware requirements and system complexity. To avoid the use of noise injection, an external phase calibration method with antenna array rotation is proposed in this letter, which uses the observation scene as reference without needing for an external calibrator. The relationship of the visibility samples before and after the rotation is established and used to solve for the instrumental phase errors. The proposed method has been proved to be valid by simulations as well as some preliminary experimental results. It is applicable to geostationary ASRs with rotating array such as the geostationary atmospheric sounder.

This letter deals with small target detection in passive millimeter wave (PMMW) imaging. Specifically, it focuses on a general detection scheme, where, first, the background is suppressed through a background prediction algorithm, and then the detection is accomplished. A precise prediction of the background is essential to a successful outcome. In practical applications, background estimation problem is more suitable to be considered as a nonlinear regression problem. Kernel methods are effective to solve the nonlinear problem. To improve the accuracy of the background prediction with kernel methods, we utilize robust loss function, to tolerate the noise outliers, and regularization methods, to avoid overfitting of the data. Experiments are conducted on PMMW images collected by a synthetic aperture imaging radiometer. The results demonstrate the effectiveness of the proposed algorithm.

Mirrored interferometric aperture synthesis (IAS) (MIAS) is a novel interferometry which has the potential to reduce system complexity compared with that of conventional IAS. Array configuration design plays a major role in imaging. As the concept of MIAS was proposed just a few years ago, the problem of array configuration design has not been solved yet. In this letter, the principles of array configuration design of 1-D MIAS are proposed, and the corresponding optimization model is established and refined. The optimal array configurations are presented through simulated annealing method and show that 1-D MIAS can achieve almost twice as much of spatial resolution as 1-D IAS with the same array size.