JP7426167B1 - Modeling analysis method for array spectral sensing based on distributed satellite formation under the influence of perturbations - Google Patents
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Abstract
本発明は、主衛星がゲートウェイ局から関連のコマンドを受信した後、各付随衛星と共にローカル感知を行い、ランダムアンテナアレイ理論を利用して摂動下での衛星の平均ステアリングベクトルを取得するステップ１と、各付随衛星が、融合のために衛星間リンクを介して感知信号を主衛星に直接送信し、信号対干渉雑音比を最大化して最適化問題を確立することにより、アレイ衛星の重みベクトルを解き、到来波方向が正確に推定される場合と不一致の場合、最大信号対雑音比の正確な式と近似式をそれぞれ与えるステップ２と、分散型衛星編隊のシャドーライスチャネル下での正確な検出確率の理論式を用いて性能評価を行うステップ３とを含む、摂動の影響下での分散型衛星編隊に基づくアレイスペクトル感知のモデリング分析方法を開示する。本発明では、ビーム成形技術を利用してターゲット放射源の空間フィルタリングを実現し、最終的に強い信号と弱い信号が共存する中で、弱い信号に対する感知能力を効果的に向上させる。【選択図】なしThe present invention consists of step 1, in which after the main satellite receives the relevant commands from the gateway station, it performs local sensing with each satellite satellite and uses random antenna array theory to obtain the average steering vector of the satellite under perturbation. , each satellite satellite transmits its sensing signal directly to the main satellite via the intersatellite link for fusion, and the weight vector of the array satellite is determined by maximizing the signal-to-interference-noise ratio and establishing an optimization problem. step 2, which gives the exact and approximate expressions of the maximum signal-to-noise ratio, respectively, when the incoming wave direction is accurately estimated and when it is inconsistent, and the accurate detection under the shadow rice channel of the distributed satellite formation. A method for modeling and analysis of array spectral sensing based on a distributed satellite formation under the influence of perturbations is disclosed, including step 3 of performing a performance evaluation using a theoretical formula of probabilities. The present invention utilizes beam forming technology to realize spatial filtering of the target radiation source, ultimately effectively improving the sensing ability for weak signals in the coexistence of strong and weak signals. [Selection diagram] None
Description
本発明は、無線通信技術の分野に関し、特に、摂動の影響下での分散型衛星編隊に基づくアレイスペクトル感知のモデリング分析方法に関するものである。 The present invention relates to the field of wireless communication technology, and in particular to a modeling and analysis method for array spectral sensing based on distributed satellite formations under the influence of perturbations.
低軌道衛星はシームレスなグローバルカバレッジの特徴があり、衛星の本来の広いカバレッジの特徴により、そのビームカバレッジ範囲が広いため、一つのビームのメインローブの範囲内に多くのノードが存在する一方で、低軌道衛星はサイズと重量に制限があり、大口径の高利得アンテナを搭載することができず、単一の衛星は地上周波数機器に対する感知能力が比較的弱い。マルチ衛星協調に基づくスペクトル感知は、空間ダイバーシティを有効に利用し、感知性能を向上させることができる。衛星システムにおいては、分散型衛星編隊は協調方式の一つであり、複数の同軌道または隣接軌道の衛星を編隊飛行させることで、分散した衛星資源を統合し、衛星間高速相互接続、分散型自律協調、資源仮想化などの重要な技術により、サービス拡張機能を実現する。従来の衛星コンスタレーションと比較して、コストが安く、信頼性が高く、システムの再構築が可能であるなどの利点がある。現在、分散型編隊衛星は、三次元イメージング、気象学、ナビゲーションなどの分野で広く使用されており、公開されたプロジェクトまたは計画には、米国のＴｅｃｈｓａｔ－２１、大学のナノ衛星工学、欧州宇宙機関のＣｌｕｓｔｅｒＩＩ、フランスのＣａｒｔｗｈｅｅｌなどの項目が含まれ、ｓｔａｒ－ｌｉｎｋなどの現在注目されている大規模なコンステレーションも、軌道変更によりマルチ衛星編隊を実現できる。 Low orbit satellites are characterized by seamless global coverage, and due to the inherent wide coverage characteristics of satellites, their beam coverage range is wide, while many nodes are within the range of the main lobe of one beam. Low-orbit satellites are limited in size and weight, cannot carry large-diameter, high-gain antennas, and single satellites have relatively weak sensing capabilities for ground-frequency instruments. Spectral sensing based on multi-satellite coordination can effectively utilize spatial diversity and improve sensing performance. In satellite systems, distributed satellite formation is one of the cooperative methods. By flying multiple satellites in the same orbit or adjacent orbits in formation, distributed satellite resources are integrated, high-speed interconnection between satellites, and distributed Achieve service expansion capabilities through key technologies such as autonomous coordination and resource virtualization. Compared to conventional satellite constellations, it has advantages such as lower cost, higher reliability, and system reconfiguration. Currently, distributed formation satellites are widely used in fields such as three-dimensional imaging, meteorology, and navigation, and published projects or plans include Techsat-21 in the United States, Nanosatellite Engineering at the University, European Space Agency This includes items such as Cluster II of France and Cartwheel of France, and large-scale constellations currently attracting attention such as Star-Link can also realize multi-satellite formations by changing their orbits.
しかし、同じビーム内に強い信号と弱い信号が共存することを考慮すると、ハード融合におけるエネルギー感知に基づく「ａｎｄ」、「ｏｒ」基準融合方式、ソフト融合における固有値比検定（ＥＲＤ）、一般化尤度比検定（ＧＬＲＴ）及びＲｏｙ'ｓ最大根検定（ＲＬＲＴ）などの従来のマルチ衛星協調感知だけでは、強い信号と弱い信号が共存する中で、弱い信号に対する感知は十分に完了することができず、特定の領域にある特定のターゲットに対する衛星の感知効果が低下する。 However, considering the coexistence of strong and weak signals in the same beam, energy sensing based "and", "or" criterion fusion schemes in hard fusion, eigenvalue ratio test (ERD) in soft fusion, generalized likelihood Conventional multi-satellite cooperative sensing such as Magnitude Ratio Test (GLRT) and Roy's Maximum Root Test (RLRT) alone cannot sufficiently complete the sensing of weak signals in the coexistence of strong and weak signals. First, the satellite's sensing effectiveness for specific targets in specific areas is reduced.
分散型衛星編隊における衛星位置情報は、決定性とランダム性が共存する特徴があり、決定性とは、軌道上の衛星の軌道パラメータが既知であり、理想的には分散した衛星間の相対位置関係が明確であることを意味し、ランダム性とは、実稼働中に衛星が様々な摂動要因、例えば、地球の非球形、大気、光圧、太陽と月の引力などの影響を受けることを意味し、その中で、地球の非球形による摂動が衛星編隊飛行に与える影響は最も重要であり、衛星の瞬間的な実際の位置がずれることで、分散した衛星間の相対位置関係にランダムな乱れが生じ、衛星の瞬間的な実際の位置が時間とともに変化する。 Satellite position information in a distributed satellite formation is characterized by a coexistence of determinism and randomness. Determinism means that the orbital parameters of the satellites in orbit are known, and ideally the relative positional relationship between the distributed satellites is known. Being clear, randomness means that during production the satellite is affected by various perturbing factors, such as the non-spherical shape of the Earth, the atmosphere, light pressure, the gravitational forces of the Sun and the Moon, etc. Among these, the influence of perturbations due to the non-spherical shape of the Earth on satellite formation flight is the most important, as the momentary actual position of the satellite shifts, causing random disturbances in the relative positional relationship between the dispersed satellites. occurs, and the instantaneous actual position of the satellite changes over time.
従来技術における授権公告番号ＣＮ１１００３４８１３Ｂの中国特許では、分散衛星クラスタに基づくパターンを形成するための包括的な方法を開示し、目的関数として異なる電磁信号の送受信タスクの所望のアレイパターン関数を使用し、再構成可能な編隊衛星の隊形を実行可能領域とし、アレイ衛星の送受信信号の重み値を求め、編隊衛星クラスタがアレイを形成することで、衛星リンクの電磁波信号に対する送受信能力を向上させ、アレイ衛星間の相対位置関係が摂動によりランダムに変化するという欠点を克服する。このことから弱い信号に対する感知性能をいかに改善するかが特に重要であることが分かる。 A Chinese patent with Grant Publication No. CN110034813B in the prior art discloses a comprehensive method for forming a pattern based on distributed satellite clusters, using desired array pattern functions of different electromagnetic signal transmission and reception tasks as objective functions, By setting the reconfigurable formation satellite formation as a feasible region, determining the weight values of the transmitting and receiving signals of the array satellites, and forming the formation satellite cluster into an array, the transmission and reception capability of the satellite link for electromagnetic wave signals is improved, and the array satellite This overcomes the drawback that the relative positional relationship between the two randomly changes due to perturbation. This shows that it is particularly important to improve the sensing performance for weak signals.
上記の課題を解決するために、本発明は、摂動の影響下での分散型衛星編隊に基づくアレイスペクトル感知のモデリング分析方法を提供する。分散型衛星編隊アレイシステムモデルを確立することにより、感知ターゲットＴでの平均ステアリングベクトルを取得することができる。瞬時値を平均ビームパターンで近似し、平均信号対干渉雑音比を取得して感知性能を評価する。信号対干渉雑音比の最大化を最適化目的関数として確立することにより、マルチ衛星アレイスペクトル感知システムの性能が得られる。シャドーライスチャネルモデルの下での正確な検出確率により、弱い信号に対する性能向上を効果的に判断することができる。 To solve the above problems, the present invention provides a modeling analysis method for array spectral sensing based on distributed satellite formation under the influence of perturbations. By establishing a distributed satellite formation array system model, the average steering vector at the sensing target T can be obtained. The instantaneous value is approximated by the average beam pattern, and the average signal-to-interference-noise ratio is obtained to evaluate the sensing performance. The performance of the multi-satellite array spectral sensing system is obtained by establishing the maximization of the signal-to-interference-noise ratio as the optimization objective function. Accurate detection probability under the shadow Rice channel model can effectively judge the performance improvement for weak signals.
上記の目的を達成するために、本発明は、以下の技術的手段によって達成される。 In order to achieve the above object, the present invention is achieved by the following technical means.
本発明は、分散型アレイモデリング、信号対干渉雑音比の求解、及び感知性能評価の３つの部分を含む、摂動の影響下での分散型衛星編隊に基づくアレイスペクトル感知のモデリング分析方法であって、
具体的なステップとして、
分散型アレイモデリングであるステップ１では、ゲートウェイ局が感知タスクの開始または終了のコマンドを発行し、感知用の主衛星が、関連のコマンドを受信した後、各付随衛星と共にローカル感知を行い、ランダムアンテナアレイ理論を利用して摂動下での衛星の平均ステアリングベクトルを取得し、
信号対干渉雑音比の求解であるステップ２では、各付随衛星によって検出された信号を主衛星で融合し、信号対干渉雑音比を最大化して最適化問題を確立することにより、最適化されたビーム形成重みベクトルを取得し、到来波方向が正確に推定される場合と不一致の場合、最大信号対雑音比の正確な式と近似式をそれぞれ与え、
感知性能評価であるステップ３では、得られた最大信号対干渉雑音比に基づいて、シャドーライスフェージングモデルの下での分散型編隊衛星の正確な検出確率の閉形式表現を導出し、外乱と強い干渉信号による感知性能への影響を分析し、摂動の影響下での分散型衛星編隊のスペクトル感知性能の理論的分析を完了する、
摂動の影響下での分散型衛星編隊に基づくアレイスペクトル感知のモデリング分析方法である。
The present invention is a modeling analysis method for array spectral sensing based on a distributed satellite formation under the influence of perturbations, comprising three parts: distributed array modeling, solving the signal-to-interference-noise ratio, and sensing performance evaluation. ,
As a concrete step,
In step 1, which is distributed array modeling, the gateway station issues a command to start or end a sensing task, and the primary sensing satellite, after receiving the relevant command, performs local sensing with each satellite satellite and randomly Obtain the average steering vector of the satellite under perturbation using antenna array theory,
In step 2, which is the solution of the signal-to-interference-noise ratio, the signals detected by each satellite satellite are fused at the main satellite, and the optimized signal-to- interference-noise ratio is maximized to establish an optimization problem. Obtain the beamforming weight vector and give the exact and approximate expressions of the maximum signal-to-noise ratio when the incoming wave direction is accurately estimated and when it is inconsistent, respectively,
In step 3, which is the sensing performance evaluation, based on the obtained maximum signal-to-interference-noise ratio, we derive a closed-form expression of the accurate detection probability of a distributed formation satellite under the shadow rice fading model, and Analyze the impact of interference signals on the sensing performance and complete the theoretical analysis of the spectral sensing performance of distributed satellite formations under the influence of perturbations;
This is a modeling analysis method for array spectral sensing based on distributed satellite formation under the influence of perturbations.
本発明のさらなる改良としては、ステップ（２）では、主衛星は、付随衛星と地上感知ターゲットの位置情報に基づいて、重みベクトルを決定し、到来波方向が正確に推定される場合、最大信号対雑音比の正確な式を用いて、信号対干渉雑音比を算出するための係数を決定し、それ以外の場合、主衛星は最大信号対雑音比の近似式を用いて、前記係数を決定する。 In a further improvement of the invention, in step (2), the main satellite determines the weight vector based on the position information of the satellite satellite and the ground-sensing target, and if the incoming wave direction is accurately estimated, the main satellite Determine the coefficients for calculating the signal-to-interference-noise ratio using an exact formula for the signal-to-noise ratio; otherwise, the primary satellite determines said coefficients using an approximate formula for the maximum signal-to-noise ratio. do.
本発明のさらなる改良としては、ステップ２で確立された制約付き最適化問題は、次のように表され、
As a further refinement of the invention, the constrained optimization problem established in step 2 can be expressed as:
本発明は、以下の有益な効果を有する。本発明では、ビーム成形技術を利用して、ターゲット放射源の空間フィルタリングを実現し、弱い信号に対する感知能力を向上させる。摂動の影響下での分散型衛星編隊の性能解析を行い、摂動による感知性能への影響を重点的に分析し、それぞれ感知ターゲットの到来波方向に誤差がない場合と不一致の場合の信号対雑音比の式について、シャドーライスチャネルモデルの下での正確な検出確率の式を導出し、弱い信号に対する感知能力を効果的に向上させる。 The present invention has the following beneficial effects. The present invention utilizes beam-forming techniques to achieve spatial filtering of the target radiation source and improve the sensing ability for weak signals. We analyze the performance of a distributed satellite formation under the influence of perturbations, focusing on the influence of perturbations on sensing performance, and compare signal-to-noise when there is no error in the arrival wave direction of the sensing target and when there is no error, respectively. For the ratio expression, an accurate detection probability expression under the shadow Rice channel model is derived, which effectively improves the sensing ability for weak signals.
以下、添付図面を参照して本発明の実施形態における技術的手段について明確かつ完全に説明する。明らかに、記載された実施形態は、本発明の一部の実施形態に過ぎず、全ての実施形態ではなく、本発明の実施形態に基づいて、当業者が創造的な労働を行うことなく得られるその他の実施形態は、いずれも本発明の保護の範囲に属する。 Hereinafter, technical means in embodiments of the present invention will be clearly and completely explained with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments, and those skilled in the art may be able to obtain the same without creative effort based on the embodiments of the present invention. Any other embodiments provided fall within the scope of protection of the present invention.
本発明は、摂動の影響下での分散型衛星編隊に基づくアレイスペクトル感知のモデリング分析方法であり、具体的なステップは、次のとおりである。 The present invention is a modeling analysis method for array spectrum sensing based on distributed satellite formation under the influence of perturbation, and the specific steps are as follows.
分散型アレイモデリングでは、ゲートウェイ局は感知タスクの開始または終了のコマンドを発行し、感知用の主衛星は、関連のコマンドを受信した後、各付随衛星と共にローカル感知を行い、ランダムアンテナアレイ理論を利用して摂動下での衛星の平均ステアリングベクトルを取得する。 In distributed array modeling, the gateway station issues the command to start or end the sensing task, and the main satellite for sensing, after receiving the relevant commands, performs local sensing with each satellite satellite, using the random antenna array theory. to obtain the average steering vector of the satellite under perturbation.
ステップ１では、分散型衛星編隊は、ゲートウェイ局が発行した感知コマンドに従って、単一衛星のスペクトル感知を行い、各付随衛星は、衛星間リンクを介して感知信号を主衛星に直接送信し、主衛星で信号レベル融合を実行し、ステップ２に進む。 In step 1, the distributed satellite fleet performs spectrum sensing of a single satellite according to the sensing command issued by the gateway station, and each satellite satellite directly transmits the sensing signal to the main satellite via the intersatellite link, and the satellite Perform signal level fusion on the satellite and proceed to step 2.
ステップ２では、主衛星は、付随衛星と地上感知ターゲットの位置情報に基づいて、重みベクトルを決定し、地上感知ターゲットが正確に既知である場合、最大信号対雑音比の正確な式を用いて、信号対干渉雑音比を算出するための係数を決定し、それ以外の場合、ステップ３に進む。 In step 2, the primary satellite determines the weight vector based on the position information of the satellite satellite and the ground-sensing target, and if the ground-sensing target is precisely known, the primary satellite determines the weight vector using the exact formula for the maximum signal-to-noise ratio. , determine the coefficients for calculating the signal-to-interference-noise ratio, otherwise proceed to step 3.
ステップ３では、主衛星は、最大信号対雑音比の近似式を用いて、信号対干渉雑音比を算出するための係数を決定する。 In step 3, the main satellite uses the maximum signal-to-noise ratio approximation formula to determine coefficients for calculating the signal-to-interference-noise ratio.
信号対干渉雑音比の求解では、各低軌道編隊衛星は、重み付け融合のために感知信号ｓｉ（ｔ）を主衛星に送信し、信号対干渉雑音比の最大化を目指して最適化関数を確立する。
In solving the signal-to-interference-noise ratio, each low-orbit formation satellite transmits its sensed signal s i (t) to the main satellite for weighted fusion, and uses an optimization function to maximize the signal-to-interference-noise ratio. Establish.
本発明は、分散型編隊衛星における衛星の相対位置の特性について、摂動による分散型衛星編隊に基づく感知性能への影響を調査し、感知ターゲットの到来波方向に誤差がない場合と不一致の場合について、シャドーライスフェージング下でのスペクトル感知の正確な検出確率をそれぞれ評価し、図２に示すように、到来波方向が正確に推定される場合、従来の方法と比較して、本発明で提案された方法は、誤警報確率が与えられた場合に最も高い正確な検出確率を得ることができ、正確な検出確率が与えられた場合に最も低い誤警報確率を得ることができ、その結果は図２に示される。また、図３に示すように、さらに摂動半径による正確な検出確率への影響を評価し、与えられた誤警報確率が０．０１、摂動半径が５０ｍ、到来波方向に不一致がある（推定誤差が５％以下）場合、本発明の提案方法は９５％の正常な検出確率を得ることができ、これは正確に推定される場合の正確な検出確率に近いものである。その結果、本発明の提案方法が、ターゲット放射源に対して空間フィルタリングを行うことにより、最終的に強い信号と弱い信号が共存する中で、弱い信号に対する感知能力を効果的に向上させることを証明している。 The present invention investigates the influence of perturbation on the sensing performance based on the distributed satellite formation regarding the characteristics of the relative position of the satellite in the distributed formation satellite, and investigates the cases where there is no error in the direction of the arrival wave of the sensing target and the case where there is no discrepancy. , respectively evaluate the accurate detection probability of spectrum sensing under shadow Rice fading, and as shown in Fig. 2, when the incoming wave direction is accurately estimated, compared with the conventional method, the proposed method in the present invention The proposed method can obtain the highest accurate detection probability given the false alarm probability, and the lowest false alarm probability given the accurate detection probability, and the results are shown in Fig. 2. In addition, as shown in Figure 3, we further evaluated the influence of the perturbation radius on the accurate detection probability, and found that the given false alarm probability is 0.01, the perturbation radius is 50 m, and there is a mismatch in the direction of the arriving wave (estimation error is less than 5%), the proposed method of the present invention can obtain a normal detection probability of 95%, which is close to the accurate detection probability when accurately estimated. As a result, the proposed method of the present invention can effectively improve the sensing ability for weak signals in the coexistence of strong and weak signals by performing spatial filtering on the target radiation source. It's proven.
上記は、本発明の好ましい実施形態に過ぎず、当業者にとって、本発明の原理から逸脱することなく、いくつかの改良及び修正を行うことができ、これらの改良及び修正も本発明の保護の範囲と見なされるべきである。 The above are only preferred embodiments of the present invention, and those skilled in the art can make some improvements and modifications without departing from the principles of the invention, and these improvements and modifications also fall under the protection of the present invention. should be considered as a range.
Claims (5)
具体的なステップとして、
分散型アレイモデリングであるステップ（１）では、ゲートウェイ局は感知タスクの開始または終了のコマンドを発行し、感知用の主衛星は、関連のコマンドを受信した後、各付随衛星と共にローカル感知を行い、ランダムアンテナアレイ理論を利用して摂動下での衛星の平均ステアリングベクトルを取得し、
信号対干渉雑音比の求解であるステップ（２）では、各付随衛星によって検出された信号を主衛星で融合し、信号対干渉雑音比を最大化して最適化問題を確立することにより、最適化されたビーム形成重みベクトルを取得し、到来波方向が正確に推定される場合と不一致の場合、最大信号対雑音比の正確な式と近似式をそれぞれ与え、
感知性能評価であるステップ（３）では、得られた最大信号対干渉雑音比に基づいて、シャドーライスフェージングモデルの下での分散型編隊衛星の正確な検出確率の閉形式表現を導出し、外乱と強い干渉信号による感知性能への影響を分析し、摂動の影響下での分散型衛星編隊のスペクトル感知性能の理論的分析を完了し、
前記ステップ（２）では、確立された信号対干渉雑音比を最大化する最適化問題は、次のように表され、
A modeling analysis method for array spectral sensing based on a distributed satellite formation under the influence of perturbations, comprising three parts: distributed array modeling, solving for signal-to-interference-noise ratio, and sensing performance evaluation, comprising:
As a concrete step,
In step (1), which is distributed array modeling, the gateway station issues a command to start or end a sensing task, and the main sensing satellite performs local sensing with each satellite satellite after receiving the relevant command. , utilize random antenna array theory to obtain the average steering vector of the satellite under perturbation,
In step (2), which is the solution of the signal-to-interference-noise ratio, the signals detected by each satellite satellite are fused at the main satellite, and the optimization problem is established by maximizing the signal-to-interference-noise ratio. obtain the calculated beamforming weight vector and give the exact and approximate expressions for the maximum signal-to-noise ratio when the direction of the arriving wave is accurately estimated and when it is inconsistent, respectively.
In step (3), which is the sensing performance evaluation, based on the obtained maximum signal-to-interference-noise ratio, we derive a closed-form expression of the accurate detection probability of a distributed formation satellite under the shadow rice fading model, and and the influence of strong interference signals on the sensing performance, and completed the theoretical analysis of the spectral sensing performance of distributed satellite formations under the influence of perturbations .
In said step (2), the optimization problem of maximizing the established signal-to-interference-noise ratio is expressed as:
In step (3), the closed-form expression of the accurate detection probability of a distributed satellite formation under independent and identically distributed shadow Rice fading is as follows:
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